Generalized integrated importance measure for system performance evaluation: application to a propeller plane system

The integrated importance measure (IIM) evaluates the rate of system performance change due to a component changing from one state to another. The IIM simply considers the scenarios where the transition rate of a component from one state to another is constant. This may contradict the assumption of the degradation, based on which system performance is degrading and therefore the transition rate may be increasing over time. The Weibull distribution describes the life of a component, which has been used in many different engineering applications to model complex data sets. This paper extends the IIM to a new importance measure that considers the scenarios where the transition rate of a component degrading from one state to another is a time-dependent function under the Weibull distribution. It considers the conditional probability distribution of a component sojourning at a state is the Weibull distribution, given the next state that component will jump to. The research on the new importance measure can identify the most important component during three different time periods of the system lifetime, which is corresponding to the characteristics of Weibull distributions. For illustration, the paper then derives some probabilistic properties and applies the extended importance measure to a real-world example (i.e., a propeller plane system)

Importance-informed reliability engineering

This book provides university students and practitioners with a collection of importance measures to design systems with high reliability, maintain them with high availability, and restore them in case of failures. Optimal reliability design, properly system maintenance and resilience management are vital for retaining a high level of system availability. Reliability importance measures, which are used to identify the weakest components from different perspectives, can be used to achieve this goal. The book has seven parts. Chapter 1 introduces the basic concepts. Chapter 2 focuses on importance measures for the system design phase and introduces how the system reliability can be improved with importance measures. Chapters 3 and 4 provide importance measures-related methods for scheduling maintenance policies under different scenarios. Chapter 5 provides importance measures for networks. Chapter 6 proposes importance measures for resilience management. The last chapter, or Chapter 7, illustrates the importance measures with case studies adopted from four types of systems: mechanical systems, energy systems, transport networks, and supply chain networks

Reliability analysis and resilience measure of complex systems in shock events

The working environment of complex systems is complex and variable, and their performance is often affected by various shock events during the service phase. In this paper, first, considering that the system performance will be affected by shocks again in the process of maintenance, the reliability changes and fault process of complex systems are discussed. Second, the performance change processes of complex systems are analyzed under multiple shocks and maintenance. Then, based on performance loss and recovery, this paper analyzes the reliability and resilience of complex systems under the intersecting process of multiple shocks and maintenance. Considering the direct and indirect losses caused by shocks, as well as maintenance costs, the changes in total costs are analyzed. Finally, the practicability of the proposed model is checked by using a specific welding robot system

Reliability Evaluation and Prediction Method with Small Samples

How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities

Component Maintenance Strategies and Risk Analysis for Random Shock Effects Considering Maintenance Costs

Maintenance can improve a system’s reliability in a long operation period or when a component has failed. The reliability modeling method that uses the stochastic process degradation model to describe the system degradation process has been widely used. However, the existing reliability models established using stochastic processes only consider the internal degradation process, and do not fully consider the impact of external random shocks on their reliability modeling. Furthermore, the existing theory of importance does not consider the actual factors of maintenance cost. In this paper, based on the reliability modeling of random processes, the degradation rate under the influence of random shocks is introduced into the time scale function to solve the impact of random shocks on product reliability, and two cost importance measures are proposed to guide the maintenance selection of the components under limited resources in the system.Finally, a subsystem of an aircraft hydraulic system is analyzed to verify the proposed method’s performance

Importance-based Resilience Assessment and Optimization of Unmanned Ship Swarm System

Based on the unmanned ship swarm system, a resilience model for unmanned ship swarms is proposed by comprehensively considering the preventive indicators, robustness indicators, recoverability indicators, and reconfigurability indicators of the swarm system. Firstly, preventive and robust indicators are proposed based on the characteristics of the unmanned ship swarm system, and the improvement of system performance efficiency by redundant unmanned ships is established as a recoverability indicator. Then, reconfigurable indicators are proposed based on importance, and the resilience indicator of the unmanned ship swarm is determined. Finally, a numerical example is used to model and simulate the performance change and capricious process of the unmanned ship swarm. Most of the research on the resilience assessment model of unmanned ship swarms considered too single indicators. The model of the unmanned ship swarm under attack is constructed, and the superiority of the resilience optimization strategy proposed in this paper is verified

Phased-Mission Reliability and Importance Measure Analysis for Linear and Circular UAV Swarms

The phased-mission reliability of unmanned aerial vehicle (UAV) swarm refers to its capability to successfully complete the missions of each phase under specified conditions for a specified period. In order to study the reliability of phased-mission in UAV swarm, this paper firstly studies the reliability of a single UAV under fault coverage. Then, considering the mission characteristics of UAV swarm, the consecutive k-out-of-n system is studied to model and predict the reliability of UAV swarm phase mission. Some importance measures are introduced to analyze the influence of UAV in different positions on the reliability of the whole system. Finally, numerical examples of linear and circular UAV swarms are given to demonstrate and verify the correctness of the model. The reliability modeling established in this paper can predict the phased-mission reliability of UAV swarm scientifically

Reliability analysis and recovery measure of an urban water network

Urban water networks are important infrastructures for cities. However, urban water networks are vulnerable to natural disasters, causing interruptions in water. A timely analysis of the reliability of urban water networks to natural disasters can reduce the impact of natural disasters. In this paper, from the perspective of network reliability, the reliability analysis method of urban water networks under disaster is proposed. First, a reliability model is established with the flow rate of nodes in the water network as the index. Second, the user's demand is considered, as well as the impact of water pressure on water use. Therefore, a node failure model considering node water pressure and flow rate is established. The performance degradation of the urban water network is analyzed by analyzing the cascading failure process of the network. Third, the recovery process of the urban water network is analyzed, and the changes in the reliability of the urban water network before and after the disaster are analyzed to assess the ability of the urban water network to resist the disaster. Finally, an urban water network consisting of 28 nodes, 42 edges and 4 reservoirs is used to verify the effectiveness of the proposed method

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

Opportunistic Maintenance Strategy of a Heave Compensation System for Expected Performance Degradation

In the marine industry, heave compensation systems are applied to marine equipment to compensate for the adverse effects of waves and the hydraulic system is usually used as the power system of heave compensation systems. This article introduces importance theory to the opportunistic maintenance (OM) strategy to provide guidance for the maintenance of heave compensation systems. The working principle of a semi-active heave compensation system and the specific working states of its hydraulic components are also first explained. Opportunistic maintenance is applied to the semi-active heave compensation system. Moreover, the joint integrated importance measure (JIIM) between different components at different moments is analyzed and used as the basis for the selection of components on which to perform PM, with the ultimate goal of delaying the degradation of the expected performance of the system. Finally, compared with conditional marginal reliability importance (CMRI)-based OM, the effectiveness of JIIM-based OM is verified by the Monte Carlo method