The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Importance Measure-Based Maintenance Strategy Considering Maintenance Costs

Maintenance is an important way to ensure the best performance of repairable systems. This paper considers how to reduce system maintenance cost while ensuring consistent system performance. Due to budget constraints, preventive maintenance (PM) can be done on only some of the system components. Also, different selections of components to be maintained can have markedly different effects on system performance. On the basis of the above issues, this paper proposes an importance-based maintenance priority (IBMP) model to guide the selection of PM components. Then the model is extended to find the degree of correlation between two components to be maintained and a joint importance-based maintenance priority (JIBMP) model to guide the selection of opportunistic maintenance (OM) components is proposed. Also, optimization strategies under various conditions are proposed. Finally, a case of 2H2E architecture is used to demonstrate the proposed method. The results show that generators in the 2E layout have the highest maintenance priority, which further explains the difference in the importance of each component in PM

Examining the impact of critical attributes on hard drive failure times: Multi-state models for left-truncated and right-censored semi-competing risks data

\ua9 2023 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons Ltd. The ability to predict failures in hard disk drives (HDDs) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss, improve service reliability, and reduce data center downtime. Most HDDs are equipped with a threshold-based monitoring system named self-monitoring, analysis and reporting technology (SMART). The system collects several performance metrics, called SMART attributes, and detects anomalies that may indicate incipient failures. SMART works as a nascent failure detection method and does not estimate the HDDs\u27 remaining useful life. We define critical attributes and critical states for hard drives using SMART attributes and fit multi-state models to the resulting semi-competing risks data. The multi-state models provide a coherent and novel way to model the failure time of a hard drive and allow us to examine the impact of critical attributes on the failure time of a hard drive. We derive dynamic predictions of conditional survival probabilities, which are adaptive to the state of the drive. Using a dataset of HDDs equipped with SMART, we find that drives are more likely to fail after entering critical states. We evaluate the predictive accuracy of the proposed models with a case study of HDDs equipped with SMART, using the time-dependent area under the receiver operating characteristic curve (AUC) and the expected prediction error (PE). The results suggest that accounting for changes in the critical attributes improves the accuracy of dynamic predictions

Data-driven extraction and analysis of repairable fault trees from time series data

Fault tree analysis is a probability-based technique for estimating the risk of an undesired top event, typically a system failure. Traditionally, building a fault tree requires involvement of knowledgeable experts from different fields, relevant for the system under study. Nowadays’ systems, however, integrate numerous Internet of Things (IoT) devices and are able to generate large amounts of data that can be utilized to extract fault trees that reflect the true fault-related behavior of the corresponding systems. This is especially relevant as systems typically change their behaviors during their lifetimes, rendering initial fault trees obsolete. For this reason, we are interested in extracting fault trees from data that is generated from systems during their lifetimes. We present DDFTAnb algorithm for learning fault trees of systems using time series data from observed faults, enhanced with Naïve Bayes classifiers for estimating the future fault-related behavior of the system for unobserved combinations of basic events, where the state of the top event is unknown. Our proposed algorithm extracts repairable fault trees from multinomial time series data, classifies the top event for the unseen combinations of basic events, and then uses proxel-based simulation to estimate the system’s reliability. We, furthermore, assess the sensitivity of our algorithm to different percentages of data availabilities. Results indicate DDFTAnb’s high performance for low levels of data availability, however, when there are sufficient or high amounts of data, there is no need for classifying the top event

Reliability Measurement for Multistate Manufacturing Systems with Failure Interaction

Reliability is one of the important factors for manufacturing system. Most researches assume that the failure is independent and the components only have two states, which will lead to inaccurate results. In this paper, a reliability model is proposed considering both failure interaction and multi-state property of the manufacturing system. Starting with a two-component system, a function of state probability under the impact of failure interaction is established after the analysis of failure interaction. Then the multi-component system is decomposed into several subsystems and the failure interaction coefficient is estimated in each subsystem with a Copula function and the Grey model method. Finally, the reliability model is realized with the performance generating function which is derived with the UGF technique and failure interaction coefficients. An example of a cylinder engine manufacturing system is studied, and the result is closer to the practical data

EVMDD-based analysis and diagnosis methods of multi-state systems with multi-state components

A multi-state system with multi-state components is a model of systems, where performance, capacity, or reliability levels of the systems are represented as states. It usually has more than two states, and thus can be considered as a multi-valued function, called a structure function. Since many structure functions are monotone increasing, their multi-state systems can be represented compactly by edge-valued multi-valued decision diagrams (EVMDDs). This paper presents an analysis method of multi-state systems with multi-state components using EVMDDs. Experimental results show that, by using EVMDDs, structure functions can be represented more compactly than existing methods using ordinary MDDs. Further, EVMDDs yield comparable computation time for system analysis. This paper also proposes a new diagnosis method using EVMDDs, and shows that the proposed method can infer the most probable causes for system failures more efficiently than conventional methods based on Bayesian networks.Japan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology (MEXT)Hiroshima City UniversityGrant-in Aid No. 2500050 (MEXT)Grant no. 0206 (HCU)Grant in Aid for Scientific Research (JSPS

Reliability of Redundant M-Out-Of-N Architectures With Dependent Components: A Comprehensible Approach With Monte Carlo Simulation

Redundant architectures can improve the reliability of complex systems. However, component dependencies can affect the architecture and negate the benefit of redundancy. In this paper, we develop three component dependency models and analyze the reliability of different M-out-of-N configurations using Monte Carlo simulation. The first model assumes a linear component dependency. The second and third models consider common cause failures, in the latter for all components and in the second for random groups of components. As expected, the results show that interdependency degrades the reliability of parallel 1ooN systems while improving it for serial NooN systems. Interestingly, 2oo3 systems produce intermediate results that show an improvement in reliability for certain indicators and a deterioration for some others, depending on the type of dependency models. The results show nonlinear properties of MooN systems with dependent components, which suggest careful handling in applications. An online simulation platform based on Monte Carlo Simulation enables product designers to use the models efficiently and achieve tailored result

ARPHA: an FDIR architecture for Autonomous Spacecrafts based on Dynamic Probabilistic Graphical Models

This paper introduces a formal architecture for on-board diagnosis, prognosis and recovery called ARPHA. ARPHA is designed as part of the ESA/ESTEC study called VERIFIM (Veri\ufb01cation of Failure Impact by Model checking). The goal is to allow the design of an innovative on-board FDIR process for autonomous systems, able to deal with uncertain system/environment interactions, uncertain dynamic system evolution, partial observability and detection of recovery actions taking into account imminent failures. We show how the model needed by ARPHA can be built through a standard fault analysis phase, \ufb01nally producing an extended version of a fault tree called EDFT; we discuss how EDFT can be adopted as a formal language to represent the needed FDIR knowledge, that can be compiled into a corresponding Dynamic Decision Network to be used for the analysis. We also discuss the software architecture we are implementing following this approach, where on-board FDIR can be implemented by exploiting on-line inference based on the junction tree approach typical of probabilisticgraphical models