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New diagnosis result summary page created by the Diagnostic Advisor when C 2 H 2 level reaches Watch List As monitoring continues, the FW-PHM's Diagnostic Advisor polls the Oracle Database at regular intervals to generate a series of diagnoses as shown in Figure 7. For the example presented in this paper, the Diagnostic Advisor polled the Oracle Database every minute. Observe that the updated Diagnosis Result Page presents the diagnosis update history and updated possible diagnosis with current query values. With time, Possible Diagnosis has been updated and now Paper Insulation Degradation: Thermal is identified as the most likely diagnosis based on the current C 2 H 2 , CO, and CO 2 /CO ratio. Also, observe that the Diagnostic Advisor has updated the Troubleshooting Advice, suggesting the inclusion of top insulating oil temperature information if possible (i.e., Time at Excess Temperature) to further refine the diagnosis.
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This paper presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented,...
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... Diagnostic Advisor of the FW-PHM Suite, via the information communication pathway, reads the updated information from the Oracle Database. At this point, the Diagnostic Advisor immediately recognizes the change in the C 2 H 2 concentration level and creates a new diagnosis, as seen in Figure 6 In this case, Paper Insulation Degradation: Electrical is identified as the most likely diagnosis based on the current C 2 H 2 concentration level (as expected). The pattern score is a percentage indicating the relative likelihood of the fault based on the current information. ...
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... Also, over time, the developer's knowledge of the software system(s) is enriched by continuously developing databases of release details. Such knowledge, gathered over time, can enhance the accuracy of future diagnosis and prognosis [38]. ...
Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data, none of which can predict an RUL for software. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update and upgrade, module changes, system reengineering, rejuvenation, maintenance scheduling, budgeting, and total abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed has been validated by comparing actual data, with the results that were generated by predictive models. Statistical validation (regression validation, and k-fold cross validation) has also been carried out. A case study, based on publicly available data for the Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make system management decisions.
... Also, over time, the developer's knowledge of the software system(s) is enriched by continuously developing databases of release details. Such knowledge, gathered over time, can enhance the accuracy of future diagnosis and prognosis [38]. ...
Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). PHM has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for application to software.
While software does not decay over time, it can degrade over release cycles. Software degradation is a common problem faced by legacy systems. Today, software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models that are built upon historical data – none of which can predict an RUL for software.
This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update/upgrade, module changes, rejuvenation, maintenance scheduling and abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time).
The model developed in this paper has been validated by comparing actual data, generated using test beds, where predicted results are generated by predictive models. Statistical validation (regression validation) has also been carried out.
A case study, based on publicly available data for the Bugzilla application is presented. Controlled test beds for multiple Bugzilla releases were prepared to formulate standard staging environments to populate relevant data. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make decisions on software version updates, module changes, rejuvenation, maintenance schedules and/or total abandonment.
... Also, over time, the developer's knowledge of the software system(s) is enriched by continuously developing databases of release details. Such knowledge, gathered over time, can enhance the accuracy of future diagnosis and prognosis [38]. ...
Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data – none of which can predict an RUL for software. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update/upgrade, module changes, system re-engineering, rejuvenation, maintenance scheduling, budgeting, and total abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed has been validated by comparing actual data, with the results that were generated by predictive models. Statistical validation (regression validation, and k-fold cross validation) has also been carried out. A case study, based on publicly available data for the Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make system management decisions.
... This technical brief expands on the work previously published in (Agarwal, Lybeck, Pham, Rusaw, & Bickford, 2015), which demonstrated the Chendong model and the Institute of Electrical and Electronics Engineers (IEEE) thermal life consumption model on plant data, with simulated drift to represent primary winding insulation degradation, as part of the Electric Power Research Institute's Fleet-wide Prognostic and Health Management Suite software (Electric Power Research Institute (EPRI), 2012). This research expands on that work by enabling broader usage, incorporating (IEEE, 2019) to enable prognostic models, and then testing the models on actual plant data. ...
As integral components of any power plant, transformers sup-ply the generated electricity to the grid. However, the trans-former’s cellulose-based paper insulation and the mineral oilin which it is immersed break down over time under stan-dard operating conditions—or more rapidly due to potentialfaults within the system. This technical brief exhibits a col-lection of diagnostic and prognostic techniques that utilitiescan adopted in lieu of labor-intense periodic preventive main-tenance routines. Furthermore, prognostic models have beenincorporated using the latest version of the Institute of Elec-trical and Electronics Engineers (IEEE) standard (IEEE StdC57.104TM-2019) for dissolved gas analysis (DGA), thusexpanding it to include estimation of the time to maintenance.Overall, four different methodologies are explained, each ofwhich aid in determining a transformer’s state of health. Thesemethodologies include the Chendong model, the IEEE C57.91-2011 thermal life consumption model, a diagnostic model forDGA, and a prognostic model for DGA that uses an autore-gressive integrated moving average (ARIMA) model. An ad-ditional improvement for estimating missing system parame-ters from monitoring data (i.e., a tool for parameter estimationutilizing Powell’s method) is presented, enabling the IEEEthermal life consumption model to benefit not only the col-laborating power plant, but also the power industry at large.
... Even when online monitoring is done, there is a need to estimate life of the equipment and system accurately [57]. Additionally, the use of Preventive Maintenance (PM) presents a cost burden to the industry [58]. PHM is thus employed to complement these limitations and to reduce the cost of overall safety activities [57,58]. ...
... Additionally, the use of Preventive Maintenance (PM) presents a cost burden to the industry [58]. PHM is thus employed to complement these limitations and to reduce the cost of overall safety activities [57,58]. Efforts have been put to ensure offline and online diagnostic and prognostic capability in sections of the plant with various degrees of implementation: reactor structure, non-reactor structure, mechanical components, electrical power system, power electronics system, micro-electronic system, process instrumentation and nuclear instrumentation [57]. ...
A review on reliability engineering applications in 4 industrial domains namely electronic, software, nuclear and aerospace from the 2000′s to the present day is compiled. The progress in industrial maintenance activities and Human Reliability Analysis (HRA) linked to these domains are explored. Then, the mathematical aspect of reliability evaluation, in particular multi-state system (MSS), which characterizes the system complexity in these domains are presented. Trends progression are obtained through literature of respective areas as well as available technical information and industrial circulations. Through this review, trend similarities between the mentioned domains and the challenges in reliability science implementation are uncovered. The methods employed in respective industry are explained, with each strength and weakness analysed, together with application examples. This work reveals the role of Prognostic and Health Management (PHM), HRA and analytical methodologies such as MSS and Probabilistic Risk Assessment (PRA) in managing reliability of complex industrial systems. Finally, it stresses on the importance of synergy between these frameworks to ensure a complete reliability assessment in the industry.
... Vivek [13] et al. applied Arrhenius model followed by cumulative calculation in per period with the actual life consumption of the step-up transformer in estimating the RUL. Camci et al. [15] constructed the implementation of hierarchical HMMs as dynamic Bayesian networks for health-state and remaining useful life estimation in drilling processes. ...
Remaining useful life (RUL) estimation is the core and basic of system Prognostic and Health Management (PHM) and also a challenging for complex systems. It is necessary to use performance indicators that are closely related to system status for analysis, due to the Multi-indicator different characterization change of system degradation, different detectability and the degree of correlation caused by system coupling. As the system status degradation shows certain scientific laws in macro, their would be certain random relationship between the system status degradation and RUL. To address these problems, the concept of imperfect condition monitoring followed by the concept of key performance indicators in order to reduce the blindness of analysis object selection. The condition degradation probability index is proposed to represent the status degradation degree of the system, whose future trend is fitted by Markov matrix obtained by the improved algorithm as a implementation of mapping condition monitoring data to CDPI. Finally, the system RUL estimation method at time t combined the hidden semi-Markov model with improved forward variable is given to realize the mapping CDPI to RUL. Experiments are carried out to validate the key concepts of the developed methods, and results suggest the effectiveness.
... The last function inputs are the six outputs of the second function used to perform the fuzzy logic controller for HI. The PHM introduced here is an online PHM with the data-driven model, which is considered a black-box approach to PHM, as system models or systems specific knowledge is not required to start the prognostics [14,15]. ...
Prognostic health management (PHM) plays an important role in electric systems, especially for power transformer. This paper focuses on two of five processes of PHM, which are sensing and analysis the parameter of the oil-immersed power transformer. The sensing process covers the selection of the sensors. In addition, the analysis process includes the health index (HI) evaluation and the prediction of the remaining useful life (RUL). The data flow in the PHM system started from the transformer’s sensors to the merging unit then to the engineering workstation through fiber optic cable. These data will be the inputs for the proposed fuzzy logic controller to evaluate the transformer’s overall health and indicated the HI. Furthermore, this data will be used to predict the RUL by the proposed equation that is based on the degree of polymerization of the insulation paper in the transformer oil. The PHM will be applied for the existing unit auxiliary transformer system in the advanced power reactor (APR1400) nuclear power plant, as a case study. The benefits of using this system are to improve reliability, increase the lifetime, predict the RUL of the transformer. In addition, it will reduce system monitoring errors, maintenance work, cost, and lessen unscheduled maintenance.
... Equipping nonsmart plant components with smart diagnostics and prognostics is being explored in ongoing efforts. 6,7 Other maintenance prediction methods rely on smart plant state evaluation. This area of research has also been advancing. ...
As part of the ongoing efforts at the U.S. Department of Energy’s Light Water Reactor Sustainability Program, Idaho National Laboratory is conducting several research projects in collaboration with the nuclear industry to improve the reliability, safety, and economics of the nuclear power industry, especially as the nuclear power plants extend their operating licenses to 80 years. One of these projects is the automated work package (AWP) project. An AWP is an electronic intelligent and interactive work package. It uses plant condition, resources status, and user progress to adaptively drive the work process in a manner that increases efficiency while reducing human error. To achieve this mission, the AWP acquires information from various systems of a nuclear power plant and incorporates several advanced instrumentation and control technologies along with modern human factors techniques.
With the current rapid technological advancement, it is possible to envision several available or soon-to-be-available capabilities that can play a significant role in improving the work package process. As a pilot project, the AWP project develops a prototype of an expanding set of capabilities and evaluates them in an industrial environment. While some of the proposed capabilities are based on using technological advances in other applications, others are conceptual; thus, they require significant research and development to be applicable in an AWP. The scope of this paper is to introduce a set of envisioned capabilities, their need for the industry, and the industry difficulties they resolve.
... Usage: Identical (Lapira, 2012) | Stationary | Changing | Individual (Byttner et al., 2011) Load/ Stress: Identical | Stationary | Changing (Agarwal et al., 2012) ...
... Structure: Unstructured | Semi-structured | Structured (Agarwal et al., 2012) Values: Continuous | Discrete | Textual (Saxena, Wu, & Vachtsevanos, 2005) Dimension ...
... Single | Multiple (Al-Dahidi et al., 2016) Stationarity: Stationary | Non-Stationary (Liu, Djurdjanovic, Ni, Casoetto, & Lee, 2007) Types: Raw Signals (Monnin, Abichou et al., 2011) | Process Information (Agarwal et al., 2012; Monnin, Abichou et al., 2011) Data Properties Generation: Simulation (Byttner et al., 2011) | Experiment | Real (Byttner et al., 2011) Run-to-Failure: Incomplete (Al-Dahidi et al., 2016) | Complete (Al-Dahidi et al., 2016; Bagheri et al., 2015; Leone et al., 2017) Acquisition Time: Cycle (Guepie & Lecoeuche, 2015) | Time varying (Guepie & Lecoeuche, 2015)Data TransmissionTransmission: Real-time(Fang, Hongfu, & Shuhong, 2010) | Online(Agarwal et al., 2012;Fang et al., 2010) | Offline(Agarwal et al., 2012;Byttner et al., 2011) ...
Current prognostics and health management approaches are often not able to meet expectations due to their limited ability to accurately detect abnormal machine conditions, identify failures and estimate the remaining useful life. This is in many cases attributed to the lack of real data and knowledge about the component or machine under consideration. Instead, experimental data is often used for algorithm training, which is not able to reflect the complexity of real-world systems. To improve prognostics and health management approaches condition data from fleets of machines rather than single units can be taken into consideration. Therefor machine conditions are assessed against situations encountered by machines in the same fleet and knowledge is transferred to allow algorithms to intelligently learn and improve their capabilities.Several approaches have recently been presented in the literature, which make use of the fleet knowledge for condition-based maintenance. These approaches are designed for specific fleet compositions and characteristics. Therefore, in order to incorporate fleet knowledge into diagnostic and prognostic approaches the fleet under consideration and resulting requirements have to be analyzed. With this information, it is possible to determine whether fleet-based approaches are applicable in general to the specific case as well as facilitate the selection of a suitable fleet-based approach. Three types of fleets are distinguished in the literature, namely identical, homogeneous and heterogeneous fleets. This distinction makes reference to the structural dimension of fleets. For fleet-based approaches, however additional dimensions should be taken into account. These include among others the operating condition in the fleet (e.g. identical, different, or dynamically changing) and the type of available data (e.g. sensor reading, context data, textual description). This paper aims at identifying and analyzing different dimensions and respective characteristics of fleets to be considered in the context of prognostics and health management. The results are synthesized in a classification structure to support the categorization of fleets.
... PHM analyzes past failure data to devise ways to assess the system health based on current monitoring data. It can consequently allow for informed condition-based maintenance and extend the system lifetime or prevent failure, thus limiting cost of maintenance and allowing for a safer, more predictable system (Sun, Zeng, Kang, & Pecht, 2012), (Agarwal, Lybeck, Pham, Rusaw, & Bickford, 2015). More and more complex systems already make extensive use of PHM systems, across various industries such as automotive, aeronautics or nuclear (Coble, Ramuhalli, Bond, Hines, & Upadhyaya, 2015), but widespread industry application is still lagging behind (López, Márquez, Fernández, & Bolaños, 2014). ...
Prognostics and Health Management (PHM) systems are usually only considered and set up in the late stage of design or even during the system’s lifetime, after the major design decision have been made. However, considering the PHM system’s impact on the system failure probabilities can benefit the system design early on and subsequently reduce costs. The identification of failure paths in the early phases of engineering design can guide the designer toward a safer, more reliable and cost-efficient design. Several functional failure modeling methods have been developed recently. One of their advantages is to allow for risk assessment in the early stages of the design. Risk and reliability functional failure analysis methods currently developed do not explicitly model the PHM equipment used to identify and prevent potential system failures. This paper proposes a framework to optimize prognostic systems selection and positioning during the early stages of a complex system design. A Bayesian network, incorporating the PHM systems, is used to analyze the functional model and failure propagation. The algorithm developed within the proposed framework returns the optimized placement of PHM hardware in the complex system, allowing the designer to evaluate the need for system improvement. A design tool was developed to automatically apply the proposed method. A generic pressurized water nuclear reactor primary coolant loop system is used to present a case study illustrating the proposed framework. The results obtained for this particular case study demonstrate the promise of the method introduced in this paper. The case study notably exhibits how the proposed framework can be used to support engineering design teams in making better informed decisions early in the design phase.
