No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Applying fuzzy cognitive-maps knowledge-representation to failure modes effects analysis
Abstract
A failure mode and effects analysis (FMEA) seeks to determine how
a system will behave in the event of a device failure. It involves the
integration of several expert tasks to select components for analysis,
determine failure modes, predict failure effects, propose corrective
actions, etc. During an FMEA, numerical values are often not available
or applicable and qualitative thresholds and linguistic terms such as
high, slightly high, low, etc., are usually more relevant to the design
than numerical expressions. Fuzzy set theory and fuzzy cognitive maps
provide a basis for automating much of the reasoning required to carry
out an FMEA on a system. They offer a suitable technique to allow
symbolic reasoning in the FMEA instead of numerical methods, thus
providing human like interpretations of the system model under analysis,
and they allow for the integration of multiple expert opinions. This
paper describes how fuzzy cognitive maps can be used to describe a
system, its missions, failure modes, their causes and effects. The maps
can then be evaluated using both numerical and graphical methods to
determine the effects of a failure and the consistency of design
decisions
... In the following, some of the applications of this method are presented. Pelaez and Bowles in two similar studies explained how FCM can be useful in describing the relationships between components of a complex system and analyzing failure modes and their Effects (Pelaez & Bowles, 1995;Peláez & Bowles, 1996). Lee, Lee, Lee, and Lim (2013) proposed an agent-based FCM for industrial marketing planning. ...
... In this study, a score based on the multi-stage FCM method is used to solve these problems. Also, by evaluating two studies in the common area of FCM and FMEA which were reviewed in Section 2.3 (Pelaez & Bowles, 1995;Peláez & Bowles, 1996), it can be say that in these two studies, only the linguistic variables of FMEA technique has changed to quantity variables using FCM method and SOD factors have not been used. Also, the difference between this study and reviewed studies is that in addition to showing causal relationships between identified failures based on the FMEA using multi-stage FCM, the prioritization of failures has been done, and, it is possible to consider the connections among stages with a multi-stage view. ...
Since managers and staff have not understood the actual consequences of risks in the food industry well, risk management methods practically are limited to identification of the type of risks. In addition, created changes in the business environment have led to change in the attitude of risk management to a process-oriented and systematic view. Because managers cannot decide based on the output of risk management process to implement improvement projects and allocate resources to them. This study has been tried to exactly identify and prioritize potential failures of the production process by using an approach based on the multi-stage Fuzzy Cognitive Map (FCM) method and Process Failure Mode and Effects Analysis (PFMEA) technique with the help of the cross-functional team. In this approach, failures are prioritized according to the amount of impact of each failure on other failures, as well as the amount of three factors as severity, occurrence, and detection (outputs of PFMEA). This approach considers process-oriented view in manufacturing system through internal-stage and external-stage relationships between production process failures and covers disadvantages of traditional Risk Priority Number (RPN) score such as disregarding internal relationships between failures. Hence, prioritization of potential failures based on the score which includes RPN determinant factors and causal relationships between failures is performed using the multi-stage FCM and learning algorithm based on extended Delta rule. The results of the proposed approach’s implementation in an active company in the food industry show that prioritization of failures is closer to reality and presents more full prioritization in comparison with approaches such as traditional RPN. The real case study in the food industry has been used to show the ability of the proposed approach.
... Muchos fenómenos del mundo real pueden ser complicados si se los quiere representar por medio de un modelo matemático. En ocasiones para poder comprender la distribución de los principales conceptos y las relaciones que los vinculan, resulta de utilidad incorporar mapas cognitivos (Carlsson, 1996;Kosko, 1986;Kosko, 1997;Peláez & Bowles, 1995). Cuando se requiera analizar la evolución de uno o varios conceptos intervinientes en el mapa al sufrir éste variaciones o cambios, resulta de interés estudiar el proceso dinámico que permita interpretar el estado al que evolucionó el sistema y las consecuencias que el estado inicial provocó. ...
... De acuerdo con la ecuación 2, el siguiente paso consiste en aplicarle la función f a este valor obtenido. En este trabajo se adoptará una función salto unitario para el cálculo iterativo y la función identidad para seguir la evolución de los conceptos (Carlsson et al., 2006;Peláez et al., 1995). ...
Situations in which human beings develop their daily tasks are extremely complex and dynamic. In each field, the analysis of the system`s variables under consideration can be simplified if the system is conceived as a set of concepts, where a change in each of them will cause changes in the remainder. In the analysis of a particular problem, the representation of concepts in the form of a map helps to synthesize information detecting the main concepts that are linked to the problem. In these cases, Fuzzy Cognitive Maps (FCM) are able to synthesize much of this information. Also with this technique is possible to follow the evolution of the concepts to a state of equilibrium and therefore allow us to study the dynamics that takes the passage from one state to another given the situation under review. This paper describes the construction and analysis of the Fuzzy Cognitive Maps technique through an economy application example. The aim is to present a methodology supported by the FCM to analyze the evolution and impact on the system causing by the change in the value of one or more concepts involved.
... Edge values can be positive or negative depending on the nature and direction of effect. C i e ij C J Researchers have used FCMs for many tasks in several different domains [17, 20, 23, 24, 27]. In this research effort we have found a novel use of FCMs for decision support in the domain of network security and intrusion detection. ...
... Researchers have used FCMs for many tasks in several different domains [17,20,23,24,27]. In this research effort we have found a novel use of FCMs for decision support in the domain of network security and intrusion detection. ...
Most modern intrusion detection systems employ multiple intrusion sensors to maximize their trustworthiness. The overall security view of the multi-sensor intrusion detection system can serve as an aid to appraise the trustworthiness in the system. This paper presents our research effort in that direction by describing a decision engine for an intelligent intrusion detection system (IIDS) that fuses information from different intrusion detection sensors using an artificial intelligence technique. The decision engine uses fuzzy cognitive maps (FCMs) and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process. In this paper, we report on the workings of the decision engine that has been successfully embedded into the IIDS architecture being built at the Center for Computer Security Research (CCSR), Mississippi State University.
... Muchos fenómenos del mundo real pueden ser complicados si se los quiere representar por medio de un modelo matemático. En ocasiones para poder comprender la distribución de los principales conceptos y las relaciones que los vinculan, resulta de utilidad incorporar mapas cognitivos (Carlsson, 1996;Kosko, 1986;Kosko, 1997;Peláez & Bowles, 1995). Cuando se requiera analizar la evolución de uno o varios conceptos intervinientes en el mapa al sufrir éste variaciones o cambios, resulta de interés estudiar el proceso dinámico que permita interpretar el estado al que evolucionó el sistema y las consecuencias que el estado inicial provocó. ...
... De acuerdo con la ecuación 2, el siguiente paso consiste en aplicarle la función f a este valor obtenido. En este trabajo se adoptará una función salto unitario para el cálculo iterativo y la función identidad para seguir la evolución de los conceptos (Carlsson et al., 2006;Peláez et al., 1995). ...
Situations in which human beings develop their daily tasks are extremely complex and dynamic. In each field, the analysis of the system's variables under consideration can be simplified if the system is conceived as a set of concepts, where a change in each of them will cause changes in the remainder. In the analysis of a particular problem, the representation of concepts in the form of a map helps to synthesize information detecting the main concepts that are linked to the problem. In these cases, Fuzzy Cognitive Maps (FCM) are able to synthesize much of this information. Also with this technique is possible to follow the evolution of the concepts to a state of equilibrium and therefore allow us to study the dynamics that takes the passage from one state to another given the situation under review. This paper describes the construction and analysis of the Fuzzy Cognitive Maps technique through an economy application example. The aim is to present a methodology supported by the FCM to analyze the evolution and impact on the system causing by the change in the value of one or more concepts involved.
... The primary purpose of FMEA was to prevent errors, but its modifications can be found in the literature for relatively diverse purposes (Madz ık and Kormanec, 2018). Fuzzy-oriented solutions such as fuzzy linguistic modeling (Sharma et al., 2005), fuzzy analytic hierarchy process (Kutlu and Ekmekçioglu, 2012), fuzzy cognitive maps (Pelaez and Bowles, 1995) and fuzzy data envelopment analysis (Garcia et al., 2005) can be mentioned. FMEA was also used to knowledge modeling (Teoh and Case, 2004), scenario-based, or cost-based solutions (Rhee and Ishii, 2003) or Bayes belief network (Lee, 2001). ...
Purpose-The purpose of this study is to present and explain a new customer segmentation approach inspired by failure mode and effect analysis (FMEA) which can help classify customers into more accurate segments. Design/methodology/approach-The present study offers a look at the three most commonly used approaches to assessing customer loyalty: net promoter score, loyalty ladder and loyalty matrix. A survey on the quality of restaurant services compares the results of categorizing customers according to these three most frequently used approaches. Findings-A new way of categorizing customers through loyalty priority number (LPN) is proposed. LPN was designed as a major segmentation criterion consisting of customer loyalty rate, frequency of purchase of products or services and value of purchases. Using the proposed approach allows to categorize customers into four more comprehensive groups: random, bronze, silver and gold-according to their loyalty and value to the organization. Practical implications-Survey will bring a more accurate way of categorizing customers even in those sectors where transaction data are not available. More accurate customer categorization will enable organizations to use targeting tools more effectively and improve product positioning. Originality/value-The most commonly used categorization approaches such as net promoter score, loyalty ladder or loyalty matrix offer relatively general information about customer groups. The present study combines the benefits of these approaches with the principles of FMEA. The case study not only made it possible to offer a view of the real application of the proposed approach but also made it possible to make a uniform comparison of the accuracy of customer categorization.
... automate: * FMEA, * XMEA, * generation, * prediction, * process, * mode, * OF …, * simulation, design *, process *, * OF …, in order to * automatic: * generation, * module, * extraction, * validation, * way, * update, * learning automating: by * simulate: simulate + system, * in ..., * and analyze, * or test, * how ... (1997), Montgomery et al. (1996), Ormsby et al. (1991), Pelaez and Bowles (1995), Palumbo (1994), Park et al. (2009), Pillay and Wang (2003, Price andTaylor (1998), Price (1996), Price et al. (1995) Bertolini et al. (2006), Bell et al. (1992), Chang and Wen (2010) Kmenta and Ishii (2000), Kmenta and Ishii (1998), Ku et al. (2008), Kutlu and Ekmekcioglu (2012), Liu et al. (2010), Liu et al. (2014), Liu et al. (2011), Lopez et al. (2010, Mader et al. (2013), Mathew et al. (2012), Montgomery and Marko (1997), Montgomery et al. (1996), Pillay and Wang (2003), Regazzoni and Russo (2011), Rhee and Ishii (2003), Sharma and Sharma (2010), Sharma et al. (2008), Ming Tan (2003, Teng and Ho (1996), Al-Humaidi and Tan (2012) (1995), Regazzoni andRusso (2011), Sharma et al. (2008), Suddle (2009), Teng andHo (1996), Vahdani et al. (2015), Wang et al. (2009), Wirth et al. (1996, Zafiropoulos and Dyalinas (2005), Zhou et al. (2012) 45 +5% , Kara-Zaitri et al. (1991), Kmenta et al. (1999), Kmenta and Ishii (2000), Kmenta and Ishii (2004), Kmenta and Ishii (1998), Ku et al. (2008), Chaturvedhy (2011), Laaroussi et al. (2007), Regazzoni and Russo (2011), Sharma et al. (2008), Yadav et al. (2006) Kmenta and Ishii (2000), Rhee and Ishii (2002), Rhee and Ishii (2003) (1995) ...
This paper proposes a semi-automatic methodology to assist the user in creating surveys about FMEA and Risk Analysis, based on a customized use of the tools for semantic analysis and in particular a home-developed syntactic parser called Kompat Cognitive. The core of this work has been the analysis of the specific FMEA-related jargon and its common modalities of description within scientific papers and patents in order to systematize the linguistic analysis of the reference documents within the proposed step-divided procedure. The main goals of the methodology are to assist not skilled in the art users about FMEA during the analysis of generic and specific features, by considering large moles of contributions in restricted amounts of time. The methodology has then been tested on the same pool of 286 documents, divided between 177 and 109 patents, manually analyzed in our previous survey, in order to replicate part of its classifications through the proposed new modality. In this way we evaluated the abilities of the methodology both to automatically suggesting the main features of interest and to classify the documents according to them.
... Fuzzy Cognitive Map (FCM) is modeled as a single layer network, which the nodes of relationships between concepts are presented by the fuzzy weights [23]. Concepts are defined as the modeled attributes which consist of key factors, input, output, variable, events, actions, goals, and trends within the system [6,34] and present the behavior of the system [6]. ...
... Las herramientas difusas permiten dar respuestas en donde la subjetividad e interpretación no se puede representar por modelos convencionales. Las redes juegan un papel importante en los procesos y relaciones sociales actualmente; y las características cognitivas también lo hacen de manera significativa y estos con el manejo de relaciones y conceptos; vinculados generalmente por mapas cognitivos (Kosco, 1986(Kosco, , 1997Carlsson, 1996;Peláez, Bowles, 1995). Las intensidades representadas de manera lingüística describen las relaciones entre conceptos en los mapas cognitivos difusos (MCD) y su correspondiente sentido, tanto positivo como negativo en los arcos que conectan a los nodos, permitiendo la simulación del fenómeno con iteraciones consecutivas resultando plenamente predictivo. ...
... Fuzzy logic is a relatively well-developed branch of FMEA research. The FMEA literature includes many fuzzy-logic-based modifications including weighted geometric mean , fuzzy linguistic modelling (Sharma et al., 2005), fuzzy analytic hierarchy process (Kutlu & Ekmekçioglu, 2012), fuzzy cognitive maps (Pelaez & Bowles, 1995) and fuzzy data envelopment analysis (Garcia, Schirru, & Frutuoso E^Melo, 2005). The mathematical apparatus of grey system theory has also been applied to FMEA. ...
The present study introduces revisions designed to develop the integration of the Kano model and Failure Mode and Effect Analysis (FMEA). Previous research in this area has frequently reported problems in the methodology for implementing FMEA, problems with interpreting its results and problems with the viewpoint for assessing potential errors. The present study offers an exploratory presentation of a more precise method for determining categories of requirements using requirement curves plotted on a graph. It also modifies the existing method for determining the Kano ‘k’ parameter for use in calculating the risk priority number (RPN). It also proposes an alternative method for assessing preventive actions based on the effort-to-effect ratio. The method was tested using a case study which confirmed that it calculates RPNs with greater precision.
... Current study follows Rodriguez-Repiso et al. (2007)'s FCM framework in causality relationships, which gives easier solution for composing and evaluating fuzzy cognitive strategy maps (FCSM). For making our FCM framework in current study, we define concepts as nodes; we use C i for concept i (for i =1, 2,…, 23; we have 23 objects from four TBSC perspectives) (Pelaez and Bowles, 1995;Tsadiras, 2008). ...
The main purpose of this study is to find out the causality relationships between the strategic management of technology (SMOT) objects in the selected Iranian high technology companies, based on the balanced scorecard (BSC) and fuzzy logic approaches. Evaluations of critical technological indicators in the selected high technology-based companies illustrated they have used different cognitive procedures in their strategic management of technology studies, which have previously been discussed throughout the SMOT literature. Technology strategy maps try to make convergences between the objects of SMOT using benefits of the technology balanced scorecard (TBSC) in the high technology environment. Technology strategy maps empower high technology companies in both technology and business areas, based on the four perspectives of proposed TBSC. The first step in our evaluations is based on-field studies questionnaires responded to by 150 personnel from different industries. The next step is based on the empirical collected data from 24 high technology companies; causal and effect relationship analysis between each of these objects was calculated and mapped using the fuzzy cognitive map (FCM). Obtained fuzzy cognitive strategy map (FCSM) simply explains the causality relationships between the objects of the SMOT, which were not well understood in the traditional technology strategy maps.
... Integrated, this latter, to the ILS inference engine [5][6][7][8][9][10]. The affective-motivational cognitive structure is built according to the OCC theory and the implementation of this structure is realized through FCM [11][12][13][14][15]. ...
... Gotoh et al. [188] plant control 1991 Styblinski et al. [189] analysis of electrical circuits 1991 Taber [190] disease diagnosis 1992 Kosko [191] political affairs 1993 Dickerson et al. [192] modeling of virtual worlds 1994 Dickerson et al. [193] modeling of virtual worlds 1995 Pelaezet al. [194] analysis of failure modes effects 1996 Ndousseet al. [195] fault management in distributed network environment 1997 ...
Through our four years experiments on students' Scrum based agile software
development (ASD) process, we have gained deep understanding into the human
factors of agile methodology. We designed an agile project management tool -
the HASE collaboration development platform to support more than 400 students
self-organized into 80 teams to practice ASD. In this thesis, Based on our
experiments, simulations and analysis, we contributed a series of solutions and
insights in this researches, including 1) a Goal Net based method to enhance
goal and requirement management for ASD process, 2) a novel Simple Multi-Agent
Real-Time (SMART) approach to enhance intelligent task allocation for ASD
process, 3) a Fuzzy Cognitive Maps (FCMs) based method to enhance emotion and
morale management for ASD process, 4) the first large scale in-depth empirical
insights on human factors in ASD process which have not yet been well studied
by existing research, and 5) the first to identify ASD process as a
human-computation system that exploit human efforts to perform tasks that
computers are not good at solving. On the other hand, computers can assist
human decision making in the ASD process.
... They are also characterized by flexibility of system design and control, comprehensible structure and operation, adaptability to a given domain, and capability of abstract representation and fuzzy reasoning [14]. The FCM models were developed and used in numerous areas of applications such as electrical engineering, medicine, political science, international relations, military science, history, supervisory systems, etc. Examples of specific applications include medical diagnosis [3], analysis of electrical circuits [22], analysis of failure modes effects [18], fault management in distributed network environment [15], modelling and analysis of business performance indicators [7], modelling of supervisors [23], modelling of software development project [24, 25], modelling of plant control [4], modelling of political affairs in South Africa [8] and modelling of virtual worlds [2]. The diversity and number of applications clearly show popularity of this modelling technique, justifying further research to enhance it. ...
Many real world intelligence systems are too complex to be modeled and controlled by traditional mathematical mechanism models. The large amount of data from the systems and their environment is important information which objectively and truthfully reflects the nature of complex dynamic systems. Currently the advanced digital technology has made the digitized data easy to capture and cheap to store. However, raw digital data is rarely useful in practice and the capability to extract information from raw data is extremely important for description and understanding dynamic phenomenon of the data source. The description and control of complex dynamic systems without traditional mathematical mechanism models require new methods that can utilize the existing knowledge, human experience and data. In this paper, applying fuzzy set logic and Fuzzy Cognitive Map (FCM), we propose an innovative data based description and control of complex dynamic systems, which is totally different from the tradition mechanism mathematical model based approaches. In the FCM framework, all necessary information for description and control of the systems is directly extracted from the raw data and human experiences. In other words, as long as a few rough simple human experiences and sufficient data are given, the proposed FCMs can describe and control a complex dynamic system for which the traditional mathematical mechanism models can not be established. As an illustrative example, the simulation results of truck back-upper control problem verify the effectiveness, validity and advantageous characteristics of the learning techniques and control ability of the proposed methodology.
... The construction of an FCM requires human experience in the form of inputs and knowledge on the system under consideration [5]. FCM has been used in various applications like in the control related themes FCMs have been used to model and support plant control [12], to represent Failure Models and Effects Analysis for a system model [13]- [15] and to model the supervisor of control systems [16]. Fuzzy Cognitive Maps have been used for planning and making decisions in the field of international relations and political developments [6] and for analyzing graph theoretic behaviour [7], been proposed as a generic system for decision analysis [8] and for distributed cooperative agents [9]. ...
The systems and software development industry is characterized by a paradigm of project failure. There are various factors that can lead to project failure .One such factor is gold plating. Gold plating can cause various problems in the projects like increase in project complexity, use of more resources, and increase in time and budget of the project and many more. If effects of gold plating are not estimated it will lead to the failure of the project. So, in order to estimate the effects of gold plating we have designed a tool based on fuzzy cognitive maps (FCM). With the help of this tool developer can know the effects of doing gold plating on the project and based on the output of the tool the developer can make subsequent decisions regarding the project.
... In FCM's, it is able to represent all types of concepts and express the arcs (edges) connecting these concepts in terms of symbols or numeric values. Over the last ten years, fuzzy cognitive maps have been applied to represent knowledge and artificial inference, such as geographic information systems [18][19][20], fault detection [14,15], policy analysis [17], etc. Although many developments have been achieved recently, progress in the detailed investigation of basic behavior of inference patterns and the analysis has been little. ...
Fuzzy cognitive maps are approaches to knowledge representation and inference that are essential to any intelligent dynamical system. However, the current techniques for constructing fuzzy cognitive maps are inadequate and infeasible in practice. In this paper, we propose the fuzzy cognitive maps based on AFS fuzzy logic, by which we can design and analyze the fuzzy cognitive maps for large-scale intelligent systems. It makes the systematic and theoretical approaches possible. The most important things are that the fuzzy cognitive maps based on AFS fuzzy logic can be transacted by computers easily.
... Over the last years, a variety of FCMs have been used for capturing -representing knowledge and intelligent information in engineering applications, for instance, GIS (Liu & Satur, 1999) and fault detection (e.g. Ndouse & Okuda, 1996;Pelaez & Bowles, 1995). FCMs have been used in modeling the supervision of distributed systems Stylios, Georgopoulos, & Groumpos, 1997). ...
... Over the last years, a variety of FCMs have been used for capturing -representing knowledge and intelligent information in engineering applications, for instance, GIS (Liu & Satur, 1999) and fault detection (e.g. Ndouse & Okuda, 1996;Pelaez & Bowles, 1995). FCMs have been used in modeling the supervision of distributed systems (Stylios, Georgopoulos, & Groumpos, 1997). ...
This paper presents the application of a fuzzy cognitive map (FCM) based theoretical framework and its associated modeling and simulation tool to strategy maps (SMs). Existing limitations of SMs are presented in a literature survey. The need for scenario based SMs with inherited ability to change scenarios dynamically as well as the missing element of time are highlighted and discussed upon. FCMs are presented as an alternative to overcome these shortfalls with the introduction of fuzziness in their weights and the robust calculation mechanism. An FCM tool is presented that allows simulation of SMs as well as interconnection of nodes (performance measures) in different SMs which enables the creation of SM hierarchies. An augmented FCM calculation mechanism that allows this type of interlinking is also presented. The resulting methodology and tool are applied to two Banks and the results of these case studies are presented.
Real-world problems are commonly composed by interrelated components in many complex ways. They are usually dynamic, that is, they change with time through a series of interactions among related components. Classical decision-making techniques cannot support these kinds of challenges. For that reason, this paper focused on the extension of neuro-fuzzy techniques as Fuzzy Cognitive Maps with Grey Systems Theory.KeywordsSoft computingGrey Systems TheoryFuzzy Cognitive MapsFuzzy Grey Cognitive Maps
The determination of the strategic cognitive factors that intervene in the Second Order Change (SOC) are fundamental to achieve the transformation of organizational systems [6]. This work proposes a methodology based on the Fuzzy Cognitive Maps (FCM) model and the Forgotten Effects (FE) model, for the formalization and identification of these factors that intervene in the inference matrix. The inclusion of FE allows reducing inference errors in the matrix. The objective being to give certainty to the inference matrix in its state of reliability when applying FE, with the trade-off that this model only handles positive elements in the analysis matrix. This condition requires, first, to separate the elements into two matrices, one containing positive elements and the other, negative ones. The negative-element matrix is then temporarily transformed into a state of positive elements so that both matrices can operate the fuzzification-inferential-defuzzification process in the FE model [34, 16]. Once the matrices have been defuzzified, the matrix of temporarily positive elements returns to its original (negative) state and, finally, both matrices are added to obtain an adjusted inference matrix so it can be applied to the FCM model. A case study is presented where the main objective is to find the cognitive factors that intervene in the behavior of four companies’ staff, starting the procedure with two factors highly correlated: Stress Tolerance and Impulse Control. The main results represent the metrics of the absolute Hamming distances, laying the foundations for the formalization of the cognitive factors that hinder a SOC.KeywordsForgotten effectsFuzzy cognitive mapsSecond order change
This literature work includes the phenomenon of the socioeconomic situation of the residents of Ciudad Victoria, Tamaulipas, Mexico, who have solar panels as a savings mechanism for their homes and as a tool that contributes to their well-being. The research focuses on the economic flow of residents in relation to energy expenditure and surplus. The approach to the problem is due to the lack of an economic and social measurement system in relation to the energy expenditure of the housewith solar panels in extreme weather in the city of Ciudad Victoria, Tamaulipas, Mexico. Therefore, the research question was established: How much energy savings does the use of solar panels represent in the families of Victoria, Tamaulipas? For the elaboration of the hypothesis, the Cruz-Ardilla theory (2013), was necessary, which maintains that energy plays an important role in society since it allows access to technological and social advances in resources that provide greater comfort; In this same tenor, Díaz (2015), ensures that technological strategies for sustainability have an economic purpose transforming the territory and; as a last assumption according to Elías and Bordas (2012), which indicates that 44% of primary energy worldwide becomes useless. Thus, the hypothesis is: Energy savings with the use of solar panels in the homes of families in Ciudad Victoria, Tamaulipas, Mexico is equivalent to 40 percent.
In this chapter, the authors propose a model for a risk assessment tool directed towards and tailored specifically for e-government projects. The authors’ goal is to cover the particular threats pertinent to the e-government project context and provide an interface between the broader philosophy of IT governance frameworks and the technical risk assessment methodologies, thus aiding in the successful and secure implementation and operation of e-government infrastructures. The model incorporates a wide range of applicable risk areas, grouped into eleven levels, as well as seven accompanying dimensions, assembled into a checklist-like matrix, along with an application algorithm and associated indices, which an evaluator can use to calculate risk for one or for multiple interacting projects.
Malathion is an organophosphorus insecticide and pesticide commonly used in crops and residential applications. The negative effects of Malathion on human health and ecosystems are of great concern. In this work, a mathematical model pivot on Fuzzy Cognitive Map (FCM) is used to analyse the causes and hazardous effects of Malathion to the environmental components (air, water and soil). Based on expert’s opinion the possible factors that cause damage to health and ecosystems due to Malathion is identified, which serve as the input to the FCM. The FCM mathematically establishes the causal relation between these factors. The mathematical simulation is done by Python Programming. This approach can be used to study the interdependencies between the adverse effects of any pesticide in human health and environment due to prolonged exposure.
Failure Modes and Effects Analysis (FMEA) has been applied in a large series of cases from different sectors, such as automotive, electronics, construction and services, and has become a standard procedure in many companies for quality control and for the design of new products. FMEA has also a great following in the scientific community as testified by the vast multitude of related documents from scientific and patent literature; to date, more than 3,600 papers in Scopus Database (DB) and 146 patents in Espacenet DB come up by just searching for FMEA without synonyms, with a trend of constant growth over the years. The chapter proposes a semi-automatic semantic analysis about documents related to FMEA modifications and the subsequent manual review for reassuming each of them through a simple sentence made by a causal chain including the declaration of the goals, the followed strategies, and integrations with methods/tools.
In Complex Large Scale Systems there is an oncoming need for more autonomous and intelligent systems, new methodologies from discipline research areas have been proposed. A general formulation for the Overall Control Problem of Complex Systems is presented. Then, the use of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling Supervisory Complex Systems, using is investigated. The description and the construction of Fuzzy Cognitive Map is examined and a model for the supervisor is proposed.
In this paper the application of Fuzzy Cognitive Map (FCM) in controlling a process problem and its use in modelling Supervisory Manufacturing Systems is presented. The description, construction and the mathematical model of Fuzzy Cognitive Map are examined. Then, a chemical process is modelled with FCM and its behaviour is simulated and the application of Fuzzy Cognitive Maps in the mode ling of the Supervisor of Manufacturing Systems is discussed.
The problem of modelling and control of complex systems is addressed. The interrelationship of systems-intelligence and control is analysed and ways to develop Intelligent Control Systems (ICS) are discussed. One useful approach, the Fuzzy Cognitive Maps (FCM), is briefly presented. Issues and challenges that the systems society is confronting with are discussed. Thus researchers and developers who are exploring ways to develop Intelligent Control Systems must give careful attention to the distinctive challenges and unique opportunities of the strong interrelationship and interdependence of the three fundamental fields of science; computers-communications-control.
This paper examines the usefulness of Fuzzy Cognitive Maps in modeling complex systems and specifically their use in modeling supervisory manufacturing systems and handling with information from an abstract point of view. The use of Fuzzy Cognitive Maps for developing behavioral model for a complex system is discussed. Fuzzy Cognitive Maps applicability in modeling complex systems is presented and a hierarchical structure is proposed to model and control a complex system. Complex system is decomposed in subsystems and for each one a FCM model is developed. A hierarchical structure is proposed where Fuzzy Cognitive Map models the supervisor of the system and develops an abstract conceptual model of the complex system.
In this study, a new static analysis approach is proposed for enhanced Fuzzy Cognitive Maps (FCMs), which have non-singleton fuzzy numbers in casual relation strength representation. Cognitive Maps (CMs) are proposed as a type of directed graph that offers a means to model interrelationships or causalities among concepts, and have a clear way to visually represent them. They graphically describe a system in terms of concepts, and causal beliefs, and are powerful graphical tools to represent knowledge of the experts. Fuzzy cognitive maps, which are weighted cognitive maps, are proposed also as graphical modeling technique that follows a reasoning approach similar to processes of human reasoning and human decision-making. In FCMs, the casual relations and its strengths are assigned in a unit interval with a sign. The assigned casual strengths in conventional FCMs are singleton fuzzy (crisp) numbers, and only allow to interpret the effects linguistically but do not represent the uncertainty or ambiguity in causality. In this paper, a new analysis is presented for finding the indirect effects and total effects between the concepts of enhanced FCMs that are represented with non-singleton fuzzy numbers, especially for triangular or trapezoidal fuzzy numbers. Firstly, the mathematical approach about fuzzy numbers and the proposed analysis is presented, then secondly an experimental study on modelling ERP maintenance risks via FCM is presented. The results of the proposed causal effect analysis are discussed for this model and the outcomes are compared with a conventional FCM model where the casual strengths are singleton fuzzy numbers. The results of the experiment show the benefit of using triangular fuzzy numbers when a group of experts are involved in modelling. The uncertainty and varieties between the experts’ knowledge are easily captured and the casual effect between the concepts are successfully shown with the presented static analysis.
Reliability engineering is becoming a multidisciplinary science. In earlier days, reliability engineering was considered as equal to applied probability theory and statistics. Nowadays, the reliability research area has been clearly subdivided into smaller entities. The research topics may be divided by the methodology applied; mathematics-based approaches have a long history, especially in reliability analysis of large systems, while physics-based approaches are being introduced, especially in component level studies. New concepts in mathematics are swiftly being introduced to reliability engineering. These include, for example, fuzzy logic [1] and Petri Nets [2]. Physical reliability science has benefited from the increasing computing power that has enabled accurate modeling of complex structures [3–5].
For the last 15 years, in an attempt to successfully face the competition, organisations have been carrying out quality management initiatives such as Total Quality Management (TQM) and Business Process Re-engineering (BPR). A key aspect of quality management is business performance assessment and process re-design. Traditionally, the overall performance of an organisation has been associated with its financial performance. In the last decade however, there has been an increasing criticism of the traditional financial management driven approaches to business performance control (Olve et al. 1999). Financial measures show the effects of actions and decisions based on past market conditions that may in the mean time have changed. Therefore financial measurements fail to provide adequate guidance for long term planning because they may be based on assumptions and figures which are no longer valid. It is now recognised that running a company can hardly be reduced to optimising monetary profits. In fact, the need for more comprehensive evaluation of business performance has led to the development of new techniques such as the Balanced Scorecard (BS) (Kaplan and Norton, 1992,1993, 1996). Since 1992, BS has been widely applied in business management. The BS concept is based on three dimensions in time: yesterday, today and tomorrow; actions taken today for tomorrow may have no noticeable financial effects until the day after tomorrow (Olve et al. 1999). The company focus therefore must be broadened so that it keeps a continuous watch on non-financial performance indicators in addition to the financial ones.
This paper presents the application of a Fuzzy Cognitive Map (FCM) based theoretical framework and its associated modeling and simulation tool to StrategyMaps (SMs). Existing limitations of SMs are presented in a literature survey. The need for scenario based SMs with inherited ability to change scenarios dynamically as well as the missing element of time are highlighted and discussed upon. FCMs are presented as an alternative to overcome these shortfalls with the introduction of fuzziness in their weights and the robust calculation mechanism. An FCM tool is presented that allows simulation of SMs as well as interconnection of nodes (performance measures) in different SMs which enable the creation of SM hierarchies. An augmented FCM calculation mechanism that allows this type of interlinking is also presented. The resulting methodology and tool are applied to two Banks and the results of these case studies are presented.
In this dissertation we propose a novel risk assessment (RA) methodology and a corresponding implementation tool, which are directed specifically towards electronic governance (EG) initiatives, while providing the possibility of adaptation and implementation in other RA scopes. The methodology aspires to differ from the conventional ones, in its field of implementation, on the way it is applied, on the way it forms its results and
in the methods it proposes to mitigate risk on the projects and systems it is implemented.
We describe its background and the motives and experiences that led us to develop it. We analyze the methodology and its implementation procedure, as well as the way results are extracted. The implementation tool incorporates a broad library of levels, areas and dimensions of risk, as well as countermeasures and critical success factors. We provide ways of calculating the probabilities and the impact of the risks, the cost of the
countermeasures and their coverage of risk, as well as a series of indices used to express inferences about risk, total coverage, margin of coverage and countermeasure cost. The methodology provisions for risk and project dependencies, employing Bayesian Belief
Networks and three dimensional matrices. In order to demonstrate its usage and the usefulness of its results, it is implemented in two public key infrastructure (PKI) projects, one that has already been implemented and one that is proposed for implementation.
The proposed methodology aspires to:
a) Be a quick, easy and effective RA methodology and tool, specialized in its field,
b) To better target the security and privacy goals in e-government projects, since a
contextualized tool promotes improved formulation and facilitation of accurate security-related decisions,
c) To form a connection between technical ICT RA methodologies and Information Technology Governance (ITG) frameworks,
d) To increase security and privacy awareness by promoting the active involvement of a larger variety of non-technical personnel,
e) To facilitate the application of baseline security and privacy policies,
f) To integrate long term and diverse experience and research in public administration project structures and procedures, so as to be an effective aid in project success and
g) To have logic and processes that can be adapted and implemented to other RA fields.
The novelty of its approach lies in:
a) Its integration of a large number of unconventional, non-technical, but common EG-related risk factors, from areas such as the society, the end-users, the public administration personnel, politics, legal and regulatory frameworks, even psychology, in an easy to use iterative RA process.
b) The expression of its results using practical, comparison-friendly and succinct risk indices.
c) Its diverse approach to EG systems and projects RA. Following a dissimilar philosophy than conventional RA methodologies and tools (which focus mainly on technical issues and processes), it specifically incorporates areas of risk particularly important in EG, which constitute the most common causes for failure. It attempts to provide an interface between the broader managerial philosophy of COBIT, ISO/IEC 27002 and ITIL and the technical methodologies, by adding
and integrating dimensions, upon which the attention of key EG stakeholders can be drawn and respective actions or measures can be undertaken.
d) Its promotion of self-check and self-evaluation of the RA process, beyond the limits of technical tools and into the realm of information technology governance (ITG) frameworks and effective EG practices.
e) In its great flexibility. The evaluators can choose (and add/subtract) from the elements provided those that they wish, without inhibiting the methodology’s ability to extract results. Naturally, the more comprehensive they are and the better they cover the case study, the more trustworthy the results are. However, the evaluators can choose the risks they evaluate, the results in case of their fulfillment, the factors that may cause them, the vulnerabilities that may be affected by them and the coverage of the countermeasures. As a result, the proposed methodology possesses the flexibility to be used as a template in virtually any kind of system, in any area of RA.
The critical success factors (CSFs) of the methodology itself and its ability to extract
useful results are:
a) The inclusion in the evaluation of the all the important for the project risk factors, even beyond, if necessary, the ones proposed in the methodology, according to the judgment of the evaluators.
b) The inclusion of all the essential, for the purposes of the project, CSFs and
c) The selection of effectual, attainable and cost-effective countermeasures that do
not operate against the functionality and friendliness of the system.
The main weakness of the methodology, in its current form, is that its performance
and effectiveness rests upon the determination, insight and experience of the professionals who will use it (which is true for all RA methodologies and tools anyway), complementary to other more established toolkits. This because, while it comprises a complete
tool with a rich library of data, it is not currently implemented in software, so as to
autonomously guide and assist in a systematic evaluation, determine a detailed security
approach for assets needing protection and suggest the security policies to apply.
As further development, we intent to implement the methodology as a software toolkit, with a knowledge base for the risks, CSFs and countermeasures, tools for dependency graph construction, probabilities calculation and reports, as well as an interface with other well-known toolkits.
As a subject of further research, we suggest the formation of the methodology into a template, for application in other areas of RA. Adjusting the risk areas, the CSFs and the countermeasures, the application algorithm and the risk indices can be fitted appropriately, so as to consist a useful tool in other fields, technological and non-technological, such as biological systems, ecosystems, social structures etc.
Fuzzy cognitive map (FCM) [5] was a modification of the cognitive map of Axelrod [1]. FCMs can be used in knowledge representation and inference which are essential to any intelligent system. FCM encodes rules in its networked structure in which all concepts are causally connected. Rules are fired based on a given set of initial conditions and the structure of the FCM. The resulting map pattern represents the causal inference of the FCM. In FCMs, we are able to represent all concepts and arcs (edges) connecting the concepts by symbols or numerical values. Moreover, in such a framework it is possible to handle different types of uncertainties effectively and to combine readily several FCMs into a single FCM that takes the knowledge from different experts into consideration [6]. FCM provides a mechanism for handling causality between events/objects in a more natural fashion. Indeed, FCM is a flexible and realistic representation scheme for dealing with knowledge. This scheme is potentially useful in the development of human-centered systems that require soft-knowledge in the sense that system concepts, their relationships, and the meta-system knowledge can be represented only to a certain degree. In addition, subtle (spatial and temporal) variations in the knowledge base can often result in completely different outcomes or decisions [25]. Many recently developed systems and successful applications have shown that fuzzy cognitive maps represent a promising paradigm for the development of functional intelligent systems [10, 15, 19, 20].
Human element forms an inevitable part of maintenance activity and gets affected by a variety of interacting factors, ranging from environmental, organizational, job factors, and so on to personal characteristics, which bring in inherent variability in its reliability. Assessment of impact of these factors is, therefore, critical for human reliability estimation in maintenance. In every probabilistic risk, safety or maintenance analysis, human reliability does act as an effective aspect to assess implications of various aspects of the human performance. But the main constraint with various human reliability analysis methods is in judging the important human performance influencing factors. Because of high degree of uncertainty and variability that characterizes the plant maintenance environment, it is proposed to use the soft computing technique of fuzzy cognitive maps in exploring the importance of performance shaping factors in maintenance scenario. For this purpose, the maintenance environment is modeled in terms of factors affecting human reliability using cognitive maps. The causal relationships among these factors are explored and simulations performed to quantify its effect on the human reliability. The applicability of the methodology is demonstrated through an example. Copyright © 2013 John Wiley & Sons, Ltd.
Fuzzy Cognitive Map (FCM) is a powerful and flexible framework for knowledge representation and causal inference. However, in most real applications, it is difficult to design and analyze FCMs due to their structural complexity. Simplification, merging, and division are the important operations on the structure of FCMs. In this paper we present approaches to simplifying FCMs. These approaches show how to clean up a FCM, how to divide a complex FCM into basic FCMs, and how to extract the eigen structure of these basic FCMs. Two improved methods for merging FCMs from different human experts are also proposed in this paper. We discuss difficulties in merging FCMs and present possible solutions.
Failure modes and effects analysis (FMEA) is a step-by-step approach for identifying all possible failures in a design, a manufacturing or assembly process, or a product or service. The purpose of the FMEA is to take actions to eliminate or reduce failures, starting with the highest-priority ones. This article presents a basis for prioritizing the healthcare problems using FMEA with linguistic variables and fuzzy if–then rules. Severity rating of healthcare problems is handled under the categories of catastrophic, major, moderate, and minor events. MATLAB 2007 is used to obtain the results of fuzzy if–then rules for prioritizing healthcare failures. A numerical example is given for four possible healthcare problems to be prioritized, which are wrong patient, delay in therapy, wrong analgesic selection, and wrong drug.
Fractional Bessel processes are defined and considering the processes associated with fractional Bessel processes XH(t)=∫0t sign(BH(s))dBH(t),1/2<H<1 ...
In this paper, we present an adaptive control framework, which study combines control theory with fuzzy cognitive map theory. The proposed framework utilizes the feature and the inference mechanism of FCM. The causal relationship of variables is constructed by online learning, the values of control variables are given by the inference of FCM model, and the control variables are used to adjust the controlled variables in actual process and carries out the control of multi-input and multi-out. Finally, an illustrative example is provided, its results suggest that the method is capable of FCM model.
Fuzzy cognitive maps are a qualitative tool that can capture the extensive cause/effect relationships that an expert believes exist within a complicated system such as an electronic circuit. In addition to predictive capabilities when inputs are applied and propagated through the model, the topology of the map itself can be used in diagnosing failures by identifying causes for nodes of interest. But simply tagging a node as a cause of another is inadequate because this identification does not indicate whether this node is a sole cause or whether other nodes must also be present. The identification of combinations of nodes is key to the determination of a strategy for locating the system problem. An algorithm is given in this paper that uses a modified version of the reachability matrix to identify multi-node combinations that can cause a node of interest.
A learning methodology is proposed for automatically constructing fuzzy cognitive map. In the proposed method, the evolutionary mechanism of cellular automata is used to learn the connection matrix of FCM. One-dimension cellular automata are used to code weight parameters, the cellular states are chosen within the range [0, 1] to form a cell space. In order to guide the optimization direction effectively and accelerate the speed of convergence, a mutation operator is added in the algorithm. Finally, an illustrative example is provided, and its results suggest that the method is capable of automatically generating FCM model.
As a powerful paradigm for knowledge represen- tation and causal inference, Fuzzy Cognitive Map (FCM) has gradually emerged as a powerful modeling and simulation mech- anism applicable to numerous research and application fields. However, conventional FCM theory greatly depends on the ex- perts' knowledge. The excessive subjective factors involved in the determination of FCM weights restrict accuracy and reliability of inference results generated by FCMs. A promising approach to reducing or even eliminating the subjective intervention is the development of learning algorithm for FCMs, namely FCM learning. This paper proposes a new learning algorithm for FCMs which is based on the application of multi-objective particle swarm optimization. The novel approach integrates the FCM learning with the inference mechanism of FCMs. In order to validate the proposed FCM learning algorithm, we explore it to model the mental and physical behaviors of an emotional agent in a virtual world. The simulation results show that the novel method not only implements inference process and FCM learning in parallel, but also overcomes some deficiencies of other learning algorithms, therefore, improves the efficiency and robustness of FCMs. Copyright c ∞ 2008 Yang's Scientific Research Institute, LLC. All rights reserved.
Bridge engineers and bridge inspectors recognize that deterioration of one element influences the deterioration of another. The effect of this phenomenon may be critical if it increases the deterioration rate of the affected elements. The phenomenon is known as element interaction. The consequences of element interaction can help to explain why elements of the same type have varying deterioration rates. No practical model exists for modeling element interaction. This paper presents the use of a fault tree for qualitative and quantitative evaluation of element interaction phenomena. The fault-tree results can be used to better assess the deterioration rate and improve deterioration prediction of an element. An example of the fault-tree model for accelerated deterioration of concrete bridge decks is presented. The fault-tree model is not a deterioration model, but can be used to focus on mitigating accelerated element deterioration by certain maintenance or repair actions. Systematic implementation of the fault-tree approach presented herein must be preceded by field verification.
System failure engineering may encompass reliability, safety, security, maintainability, risk, and quality control. In this paper we pay a unified view, i.e., a failure-oriented view to system failure engineering. The notion of failure can be represented in terms of fuzzy sets and widely interpreted. We then discuss various application aspects of fuzzy methodology in system failure engineering, which include fuzzy methodology in fault diagnosis, in probist systems, in structural reliability, in software reliability, in human reliability, in safety engineering, in security engineering, in risk engineering, and in quality control, as well as profust, posbist, and posfust reliability theories. Engineering case studies and mathematical problems raised by applications of fuzzy methodology in system failure engineering are also addressed in this paper.
A broad range of well-structured problems—embracing forms of diagnosis, catalog selection, and skeletal planning—are solved in ‘expert systems’ by the methods of heuristic classification. These programs have a characteristic inference structure that systematically relates data to a pre-enumerated set of solutions by abstraction, heuristic association, and refinement. In contrast with previous descriptions of classification reasoning, particularly in psychology, this analysis emphasizes the role of a heuristic in routine problem solving as a non-hierarchical, direct association between concepts. In contrast with other descriptions of expert systems, this analysis specifies the knowledge needed to solve a problem, independent of its representation in a particular computer language. The heuristic classification problem-solving model provides a useful framework for characterizing kinds of problems, for designing representation tools, and for understanding non-classification (constructive) problem-solving methods.
Computer-aided failure modes and effects analysis (FMEA) is a goal of design, manufacturing, and reliability engineers to improve the quality and reliability of systems; however, commercial implementations have utilized little or no artificial intelligence (AI) techniques in their design. Computer-aided FMEA is viewed from an AI perspective. First, the types of knowledges exploited by FMEA experts are discussed, and model-based methods that are applicable to intelligent automation are addressed to provide a foundation for building expert systems for design and reliability analysis. Next, existing prototype systems and techniques are characterized, according to their knowledge and reasoning strategies, to familiarize the reader with recent developments. In addition, a blackboard model of an expert system for FMEA is proposed that functionally decomposes the knowledge associated with each subfunction of the FMEA process into a set of knowledge sources.
The Failure Mode and Effects Analysis (FMEA) is an industry accepted method to identify improvement strategies and derive measures for their utilization. Principally FMEAs are applied to the analysis of design procedures or manufacturing processes. In this article, a methodical FMEA-approach is described for the analysis of planning processes and practical experiences are presented.
Fuzzy cognitive maps (FCMs) are fuzzy-graph structures for representing causal reasoning. Their fuzziness allows hazy degrees of causality between hazy causal objects (concepts). Their graph structure allows systematic causal propagation, in particular forward and backward chaining, and it allows knowledge bases to be grown by connecting different FCMs. FCMs are especially applicable to soft knowledge domains and several example FCMs are given. Causality is represented as a fuzzy relation on causal concepts. A fuzzy causal algebra for governing causal propagation on FCMs is developed. FCM matrix representation and matrix operations are presented in the Appendix.
This paper describes a new technique, based on fuzzy logic, for prioritizing failures for corrective actions in a Failure Mode, Effects and Criticality Analysis (FMECA). As in a traditional criticality analysis, the assessment is based on the severity, frequency of occurrence, and detectability of an item failure. However, these parameters are here represented as members of a fuzzy set, combined by matching them against rules in a rule base, evaluated with min-max inferencing, and then defuzzified to assess the riskiness of the failure. This approach resolves some of the problems in traditional methods of evaluation and it has several advantages compared to strictly numerical methods: 1) it allows the analyst to evaluate the risk associated with item failure modes directly using the linguistic terms that are employed in making the criticality assessment; 2) ambiguous, qualitative, or imprecise information, as well as quantitative data, can be used in the assessment and they are handled in a consistent manner; and 3) it gives a more flexible structure for combining the severity, occurrence, and detectability parameters.Two fuzzy logic based approaches for assessing criticality are presented. The first is based on the numerical rankings used in a conventional Risk Priority Number (RPN) calculation and uses crisp inputs gathered from the user or extracted from a reliability analysis. The second, which can be used early in the design process when less detailed information is available, allows fuzzy inputs and also illustrates the direct use of the linguistic rankings defined for the RPN calculations.
Knowledge Organization For Failure Modes and Effects Analysis (FMEA) Expert System
- D J Russomano