Some Challenges and Opportunities in Reliability Engineering

Zio, Enrico

doi:10.1109/tr.2016.2591504

Cited by 175 publications

(95 citation statements)

References 136 publications

(136 reference statements)

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“…In this case, one year of SF 6 gas density data was calculated from measured gas pressure and ambient temperature. The density of SF 6 gas is used to interrupt current flow in a circuit breaker [42]. When this density drops to a predefined lockout level (i.e., 8192 mbar) it is no longer able to operate.…”

Section: Asset-level Prognostics Models and Parametrizationmentioning

confidence: 99%

“…System-level prognostics estimation is an open problem in the PHM field [6]. The system RUL prediction is more than a simple combination of individual component failures [27], [67] because there are stochastic and temporal dependencies between assets that need to be taken into account.…”

Section: A Accuracy Of the System-level Prognostics Assessmentmentioning

confidence: 99%

“…In this paper, we argue that the increasing capabilities for condition monitoring and the availability of operational data in many engineering fields [4]- [6] create opportunities for changing this situation by forming a more accurate picture of the health of the system as it evolves during operation. Data from multiple sensors and monitoring systems can improve dependability prediction and inform maintenance planning.…”

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confidence: 99%

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Improved Dynamic Dependability Assessment Through Integration With Prognostics

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This version is available at https://strathprints.strath.ac.uk/60422/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognosticsupdated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset interdependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations.

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Section: Asset-level Prognostics Models and Parametrizationmentioning

confidence: 99%

Section: A Accuracy Of the System-level Prognostics Assessmentmentioning

confidence: 99%

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confidence: 99%

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Improved Dynamic Dependability Assessment Through Integration With Prognostics

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“…However, such consequences can be strongly mitigated if those events are -to some extent -known in advance (PateCornell, 2002;Paté-Cornell, 2012). The importance of knowledge as a means for avoiding surprise and unexpected events is testified by the large number of works emerging on the subject in recent years (Terje Aven, 2015Terje Aven & Krohn, 2014;Cavalcante, Oriá, Sornette, Ott, & Gauthier, 2013;Sornette, 2009;Sornette, Maillart, & Kröger, 2013;Turati et al, 2016a;Zio, 2016b).…”

Section: Introductionmentioning

confidence: 99%

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Turati

Pedroni

Zio

2017

Reliability Engineering & System Safety

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. Simulation-based exploration of highdimensional system models for identifying unexpected events. Reliability Engineering and System Safety.http://dx.doi.org/10.1016/j.ress.2017.04.004Simulation-based exploration of high-dimensional system models for identifying unexpected events AbstractMathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system's design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging.In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information.The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling).

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“…Multi-State Physics Modeling (MSPM) is a semiMarkov modeling approach that can be used in componentor/and system-level reliability assessment, for accounting for the effects of both the stochastic degradation process and the uncertain environmental and operational parameters Lin et al, 2015;Zio, 2016). MSPM offers the possibility of embedding the physical degradation process into the assessment (Wang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A sensitivity analysis for the adequacy assessment of a multi-state physics modeling approach for reliability analysis

Wang¹,

Maio²,

Zio

2016

Risk, Reliability and Safety: Innovating Theory and Practice

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In this work, a moment-independent Sensitivity Analysis (SA) based on Hellinger distance and Kullback-Leibler divergence is proposed to identify the component of a system most affecting its reliability. This result is used to adequately allocate modeling efforts on the most important component that, therefore, deserves a component-level Multi-State Physics Modeling (MSPM) to be integrated into a system-level model, to estimate the system failure probability. A Reactor Protection System (RPS) of a Nuclear Power Plant (NPP) is considered as case study, wherein the embedded Resistance Temperature Detector (RTD) is identified as the most important component. Results show the benefits of using MSPM to model the RTD with respect to a Markov Chain Model (MCM), because the former gives due account to the aging-and environmental-dependent phenomena that affect the transition rates of the system-level degradation model. In conclusion, the proposed RPS-MSPM can be seen as a trade-off model leveraging accuracy and computational demand.

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Some Challenges and Opportunities in Reliability Engineering

Cited by 175 publications

References 136 publications

Improved Dynamic Dependability Assessment Through Integration With Prognostics

Improved Dynamic Dependability Assessment Through Integration With Prognostics

Simulation-based exploration of high-dimensional system models for identifying unexpected events

A sensitivity analysis for the adequacy assessment of a multi-state physics modeling approach for reliability analysis

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