Table 1 - uploaded by Teodora Sanislav
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Cyber-Physical Systems (CPSs) represent a fusion between embedded systems and distributed systems, which implies among others the development of new formalisms for dependability assurance within this type of systems. In this context, the present paper introduces a dependability assurance methodology in order to create a dependability model (DAM) of...
Citations
... The functional analysis allows a synthetic description of the systems operating modes it and identifies the functions to be accomplished. It establishes systematically and comprehensively the functional relationships inside and outside of this system [5,6,23]. In other words, the functional analysis consists to research and characterize the offered functions by a system to fulfill the user's needs [7,9]. ...
The work presented in this article is a contribution to the implementation of an approach dedicated to the behavioral analysis of industrial systems starting from the design Maintenance engineering techniques inspire the suggested approach, and it aims at deducing and classifying the industrial systems' probable failure modes. The latter modes can alter the systems proper functioning. Our suggested approach is a combination of three complementary tools. The SysML language is applied to express customers' needs and requirements, such as future systems' functions and operating conditions. Besides, the FMECA method analyzes systems' potential dysfunction and the recommendation of appropriate maintenance actions. Finally, the K-means method classifies failure modes to get detailed mode criticality instead of calculating this latter according to ancient methods. The result will objectively make it possible to develop systems with reliable and maintainable components. It also helps to recommend optimal maintenance strategies according to the equipment evolution state. The approach is carried out through two application cases. The first is a practical and straightforward system used to check the methods feasibility, and the second is a more elaborated one, used to observe the effectiveness of the approach.
... The next OD1 steps are to consider reuse and enumerate terms. In this regard, ontological models developed by other researchers should be considered to determine their adaptability to the current research proposal such as those proposed in Nuñez and Borsato (2018), Sanislav and Miclea (2015). Moreover, this phase involves the enumeration of all terms pertinent to the area of the ontology being developed. ...
Asset management is concerned with the management practices, technologies and tools necessary to maximize the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximized too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modeling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, 2-fold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications
... In literature, very few works studied availability and security quantification of IoT infrastructures, especially only some works considered the integration of cloud/fog/edge computing paradigms in the evaluation of IoT infrastructure. The work [7] presented a comprehensive systematic literature review on the dependability of fog computing. The work highlighted the existing consideration of dependability metrics (availability, reliability) of fog computing systems, but the safety and security metrics and the trade-offs between dependability and security metrics have not been investigated in a complete manner. ...
Modeling a complete Internet of Things (IoT) infrastructure is crucial to assess its availabilityand security characteristics. However, modern IoT infrastructures often consist of a complex andheterogeneous architecture and thus taking into account both architecture and operative details ofthe IoT infrastructure in a monolithic model is a challenge for system practitioners and developers.In that regard, we propose a hierarchical modeling framework for the availability and securityquantification of IoT infrastructures in this paper. The modeling methodology is based on ahierarchical model of three levels including (i) reliability block diagram (RBD) at the top levelto capture the overall architecture of the IoT infrastructure, (ii) fault tree (FT) at the middle level toelaborate system architectures of the member systems in the IoT infrastructure, and (iii) continuoustime Markov chain (CTMC) at the bottom level to capture detailed operative states and transitionsof the bottom subsystems in the IoT infrastructure. We consider a specific case-study of IoT smartfactory infrastructure to demonstrate the feasibility of the modeling framework. The IoT smartfactory infrastructure is composed of integrated cloud, fog, and edge computing paradigms. Acomplete hierarchical model of RBD, FT, and CTMC is developed. A variety of availability andsecurity measures are computed and analyzed. The investigation of the case-study’s analysis resultsshows that more frequent failures in cloud cause more severe decreases of overall availability, whilefaster recovery of edge enhances the availability of the IoT smart factory infrastructure. On theother hand, the analysis results of the case-study also reveal that cloud servers’ virtual machinemonitor (VMM) and virtual machine (VM), and fog server’s operating system (OS) are the mostvulnerable components to cyber-security attack intensity. The proposed modeling and analysisframework coupled with further investigation on the analysis results in this study help develop andoperate the IoT infrastructure in order to gain the highest values of availability and security measuresand to provide development guidelines in decision-making processes in practice.
... This work presents an ontology applied only to a specific mechanical equipment, but assists in the structuring classes for effects and failure modes related to the hierarchy of a mechanical equipment, as well as motivating the use of constraints by means of rules in the SWRL language. Also, some suggested concepts in this work were reused, such as an ontology model for dependability in CPS [95], in which a taxonomy is presented to classify an ontology using FMEA techniques. This work builds queries using the SPARQL language. ...
Trends in Prognostics Health Management (PHM) have been introduced into mechanical items of manufacturing systems to predict Remaining Useful Life (RUL). PHM as an estimate of the RUL allows Condition-based Maintenance (CBM) before a functional failure occurs, avoiding corrective maintenance that generates unnecessary costs on production lines. An important factor for the implementation of PHM is the correct data collection for monitoring a machine's health, in order to evaluate its reliability. Data collection, besides providing information about the state of degradation of the machine, also assists in the analysis of failures for intelligent interventions. Thus, the present work proposes the construction of an ontological model for future applications such as expert system in the support in the correct decision-making, besides assisting in the implementation of the PHM in several manufacturing scenarios, to be used in the future by web semantics tools focused on intelligent manufacturing, standardizing its concepts, terms, and the form of collection and processing of data. The methodological approach Design Science Research (DSR) is used to guide the development of this study. The model construction is achieved using the ontology development 101 procedure. The main result is the creation of the ontological model called OntoProg, which presents: a generic ontology addressing by international standards, capable of being used in several types of mechanical machines, of different types of manufacturing, the possibility of storing the knowledge contained in events of real activities that allow through consultations in SPARQL for decision-making which enable timely interventions of maintenance in the equipment of a real industry. The limitation of the work is that said model can be implemented only by specialists who have knowledge in ontology.
... Since the main purpose of a qualitative analysis is to identify failure modes in mechanical items, it provides the effects on the system as a whole, the causes of failure, and the determination of repair/recovery strategies. In this sense, FMEA is a qualitative technique that stands out [46]. ...
... analysing the known causes and predict possible effects, especially FMEA techniques and/or FMECA [46]. Thereby, PHM needs techniques to analyse the system, providing early detection and isolation of potential faults by monitoring techniques in its components and thus estimating the progression of a failure [7]. ...
Recent advances in smart manufacturing open up opportunities in industrial support, specifically in maintenance and physical asset management. This trend allows data collected from machines in operation to interact with cyberspace computers through a communication network, thus forming the concept of cyber-physical systems (CPS). Besides, rapid advances in information and communications technologies provide approaches for analysing data, in an increasingly rapid, autonomously, ubiquitous and in real time way, providing information that assists humans in making better decisions. In this sense, Prognostics and Health Management (PHM) of machines, is indicated as a promising application of Smart Manufacturing in the CPS context, demanding the standardization of concepts, terms, and a formal implementation of data collection and treatment. For this purpose, the Design Science Research (DSR) methodology is used in this paper, encompassing international standards, the unified 5-level architecture, ontology, and dependability for failure analysis in mechanical components. In addition, the creation of an ontology using the OWL 2 language was guided by the ‘Ontology Development 101’ approach. A pilot test was carried out using a centrifugal pump to demonstrate the applicability of the ontology. Thus, the ontology is evaluated in Protégé, which allow queries with SPARQL language to provide future decision-making for condition-based maintenance in real processes.
