1,721,793 research outputs found
Corrigendum to “Reliability assessment of generic geared wind turbines by GTST-MLD model and Monte Carlo simulation” (Renewable Energy (2015) 83 (222–233), (S0960148115003158), (10.1016/j.renene.2015.04.035))
No full textThe authors regret that the Order of Authors in this article published in November 2015 is incorrect. Thus, the objective of this Corrigendum is to re-establish the originally agreed Order of Authors, as described below. Order of Authors from published Article: Yan-Fu Li, PhD; Sebastien Valla; Enrico Zio, PhD. Corrected Order of Authors to implement with this Corrigendum: Sebastien Valla, Yan-Fu Li, PhD; Enrico Zio, PhD. The Corresponding author to contact for these changes are the Primary Author, Sebastien Valla (email below). The authors would like to apologise for any inconvenience caused
Industry 5.0: Do risk assessment and risk management need to update? And if yes, how?
No full textIn relation to the new vision of Industry 5.0 released in January 2022 by the European Commission for pushing the human centricity of any transformative effort in present and future industrial developments, we pose the daunting question of whether the existing and practiced approaches of risk assessment and risk management are fit to follow and contribute to such vision or require updating. Some reflections are provided and, even more, points of discussion are raised. We argue that the human-centric and environment-concerned vision of Industry 5.0 extends the boundaries of the risk landscape, with resilience standing out as a core value to drive industrial decisions on system reliability and operation safety. We argue that a change is, then, needed in the perspectives of risk assessment and risk management, that calls for the introduction of specific metrics to measure vulnerabilities and the performance of preventive, mitigative, and recovery solutions in relation to the objectives of human wellbeing and environment protection
Assessing reliability reputation of products based on online customer reviews
No full textThe traditional concept of functional reliability refers to the ability of an item to perform a required function without failure, under stated conditions for a stated period of time. Numerical indexes of functional reliability are estimated from laboratory test data or field failure data. Theses indexes are not always available to the customers. On the other hand, reliability reputation, which concerns the opinions of the general public regarding the functional performance of a given product, is dominant in affecting the purchase willingness of the customers. In this paper, we develop a new method for evaluating the reliability reputation of a given product. Numerical metrics are first defined to measure reliability reputation. Online customer's reviews are collected and used as data for reliability reputation assessment. Text mining algorithms are used to extract information regarding the reliability of the product and the maintenance service of the company. Finally, an integrated approach is proposed to evaluate the reliability reputation. A case study is conducted to demonstrate the validity of the developed methods
Estimation of the value of prognostic information for condition-based and predictive maintenance
No full textFor components subject to degradation, cost-efficient maintenance is necessary. Periodic or continuous collection of information, reducing uncertainty on the component's state of health, generally leads to a better-informed and, thus, more efficient maintenance. Processing condition monitoring data to estimate the current and future health states of the component, can prove valuable. In this paper, it is proposed to quantify the Value of Information (VoI) that may be obtained from state estimation and prediction procedures, with known precision, applied for condition-based and predictive maintenance. VoI is computed numerically using gamma process paths and on the basis of the optimization of the parameters of different maintenance strategies
Optimization of an aperiodic sequential inspection and condition-based maintenance policy driven by value of information
Full text linkThe issue of the optimal planning of inspection and maintenance actions for a randomly deteriorating system constitutes a difficult sequential decision-making problem in which the objective is generally to achieve minimal life-cycle cost. For mathematical tractability, most approaches rely either on the consideration of specific maintenance strategies, e.g. Periodic Inspection and Replacement (PIR), whose defining parameters are optimized, or on time-and-space-state discretization using Markov Decision Process (MDP) models and resolution through policy iteration. In both cases, optimality may be hard to guarantee. In this paper, the decision-theoretic concept of Value of Information (VoI) is used as a metric to guide resource prioritization in time, that is, to schedule inspections in a piecewise optimal manner. An aperiodic sequential inspection policy is proposed, where the determination of the next best time for inspection, or replacement, is based on the current condition and on the computed expected gain from possible inspections, i.e. on a VoI metric. This policy can be implemented when the current condition is known from imperfect inspection or processing of condition-monitoring data. Also, more generally, a discussion is proposed on the use of VoI as a guide for information collection in life-cycle management
Digital twins in safety analysis, risk assessment and emergency management
No full textDigital twins (DTs) represent an emerging technology that is currently leveraging the monitoring of complex systems, the implementation of autonomous control systems, and assistance during accidents and emergencies in real time. However, aspects such as safety, cybersecurity and reliability of DTs are still open issues that have not been comprehensively addressed. These aspects can offer new insights to evaluate the risk and return obtained from the implementation of DTs. This paper presents a systematic literature review of DTs focused on their use in safety analysis, risk assessment and emergency management. The aim of this work is twofold: (i) to point at the latest advancements in this technology by presenting a catalog of expected functions and twinning enabling technologies in the application domains of interest; and (ii) to point at the limitations and pending challenges on the implementation of DTs for safety analysis, risk assessment and emergency management
Ecological network analysis and optimization of resilience and efficiency for electric power systems design
Full text linkThe simultaneous increase in natural disasters and human dependence on critical infrastructures for essential services such as water, electricity, etc., places ever-increasing demands on the reliable, safe, resilient design and operation of these infrastructures, with a trade-off between continuity of supply (safety and resilience) and quality of supply (reliability and efficiency) at limited cost. With this in mind, a new methodology for the analysis of electric power systems inspired by natural ecosystems is proposed here and applied to representative systems from literature. Information theory is used to quantify the results of the ecological network analysis (ENA) performed. The analysis shows that electric power systems are more efficient than reliable and vulnerable to disasters. A flow matrix is constructed from the available IEEE systems data, quantified and analyzed using information theory, and finally validated by contingency analysis and SCOPF analysis. The original network configurations are compared to random generated topologies. Comparisons are also made with ENA-inspired configurations. The latter show significantly fewer violations in each contingency scenario compared to the original configurations, further supporting the use of ENA to balance power system efficiency and resilience. Thus, ENA can be used to develop power systems with balanced efficiency and resilience
Sensitivity analysis of the model of a nuclear passive system by means of Subset Simulation
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On-line Estimation of Degradation State Under Random Change of Mode
No full textDue to changes of the surrounding environment, the dynamic of one degradation process may change at random time and it follows different modes before and after the change point. For solving on-line degradation state estimation problems subject to random change of mode, a novel state estimation method is proposed in this paper based on the degradation models and related monitored data. The proposed method employs sequential probability ratio test based on log-likelihood ratio to detect the unknown change time of degradation mode, and particle filtering to estimate the degradation states given observations and also to evaluate the decision functions of the sequential probability ratio test. Two case studies referring to a pneumatic valve considering single and multiple change times of degradation mode are presented to illustrate the accuracy and effectiveness of the proposed method
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