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The safety requirements in vehicles continuously increase due to more automated functions using electronic components. Besides the reliability of the components themselves, a reliable power supply is crucial for a safe overall system. Different architectures for a safe power supply consider the lead battery as a backup solution for safety-critical applications. Various ageing mechanisms influence the performance of the battery and have an impact on its reliability. In order to qualify the battery with its specific failure modes for use in safety-critical applications, it is necessary to prove this reliability by failure rates. Previous investigations determine the fixed failure rates of lead batteries using data from teardown analyses to identify the battery failure modes but did not include the lifetime of these batteries examined. Alternatively, lifetime values of battery replacements in workshops without knowing the reason for failure were used to determine the overall time-dependent failure rate. This study presents a method for determining reliability models of lead batteries by investigating individual failure modes. Since batteries are subject to ageing, the analysis of lifetime values of different failure modes results in time-dependent failure rates of different magnitudes. The failure rates of the individual failure modes develop with different shapes over time, which allows their ageing behaviour to be evaluated.

In this paper, a novel Asymptotic Probability Estimation (APE) method is proposed to estimate the probability of correlated rare failure events for complex integrated systems containing a large number of replicated cells. The key idea is to approximate the failure rate of the entire system by solving a set of nonlinear equations derived from a general analytical model. An error refinement method based on Look-up Table (LUT) is further developed to improve numerical stability and, hence, reduce estimation error. Furthermore, a statistical algorithm based on re-sampling is developed to accurately estimate the confidence interval of APE. Our numerical experiments demonstrate that compared to the state-of-the-art method, APE can reduce the estimation error by up to 30× without increasing the computational cost.

This paper presents reliability, availability, and maintainability (RAM) analysis framework for evaluating the performance of a circulation system of water (WCS) used in a coal-fired power plant (CFPP). The performance of WCS is evaluated using a reliability block diagram (RBD), fault tree analysis (FTA), and Markov birth–death probabilistic approach. In this work, the system under study consists of five subsystems connected in series and parallel configuration namely condensate extraction pump (CEP), low-pressure feed water heater (LPH), deaerator (DR), boiler feed pump (BFP), high-pressure feed water heater (HPH). The reliability block diagram (RBD) and fault tree approach (FTA) have been employed for the performance evaluation of WCS. The Markov probabilistic approach based simulation model is developed. The transition diagram of the proposed model represented several states with full working capacity, reduced capacity, and failed state. The ranking of critical equipment is decided on the basis of criticality level of equipment. The study results revealed that the boiler feed pump affects the system availability at most, while the failure of deaerator affects it least. The availability of the system is optimized using the particle swarm optimization method. The optimized availability parameter (TBF, TTR) based modified maintenance strategy is recommended to enhance the availability of the plant system. The optimized failure rate and repair rate parameters of the subsystem are used to suggest a suitable maintenance strategy for the water circulation system of the thermal power plant. The proposed RAM framework helps the decision-makers to plan the maintenance activity as per the criticality level of subsystems and allocate the resources accordingly.

In Software-Defined Networks (SDN), one of the critical research challenges for large-scale deployment of SDN is controller placement problem (CPP). However, link failures are critical security issues in networks and greatly impact SDN’s robustness. The CPP for link failures is still challenging today. To this end, we study the CPP for multi-link failures (CPP-MLF). Firstly, we investigate the real link data of today’s networks to analyze the characteristics of link failures and formalize the link failure rate. Next, we formulate minimizing the number of controllers and worst-case delay as the optimization objectives of CPP-MLF and develop a heuristic algorithm based on improved NSGA-II to solve CPP-MLF efficiently. Specifically, we improve the non-dominated set and crowding distance for the NSGA-II by dynamic competition mechanism and even distribution operator, respectively, thereby quickly obtaining the Pareto-optimal solutions of CPP-MLF. Afterwards, we introduce controller load variance to evaluate them to make a satisfactory decision on controller placement. We conduct simulations with real network topologies, and the results show that the proposed heuristic algorithm performs well on the number of controllers, worst-case delay and robustness, while producing acceptable runtime overheads.

Distribution utilities aim to operate and plan their network in a secure and economical way. The prime focus of this work is to assist utilities by developing a new integrated approach which considers the impacts of system reliability in distribution system planning (DSP). This approach merges different problems together and solves them in a two-stage process, as follows: 1. cable routing and optimal location and number of switching devices (circuit breakers and reclosers); 2. optimal location and number of tie switches. Moreover, the possibility of installing different cable options, with different prices and capacities, is included. The optimization algorithm is designed using mixed-integer programming (MIP). The developed algorithm analytically evaluates relationships between different components in the system and dynamically updates reliability indices, failure rate and restoration time, of every node in the system. This approach has been tested on two distribution systems. Despite the complexity and the exhaustiveness of the problem, MIP converges and provides the optimal solution for every studied scenario. The results show that an integrated approach enables utilities to obtain more comprehensive solutions. Moreover, by understanding the impact of parameter variation enables utilities to categorize their priorities in the decision making process and optimally invest in distribution network with respect to reliability. QC 20210607

Dynamic seals are one of the critical components of Absorber Rod Drive Mechanism of Fast Breeder Reactors, requiring separate experimental development. Their significance can't be overemphasized considering that the availability and re-usability of Control Rod Drive Mechanisms of Fast Breeder Test Reactor is governed by the failure rate of dynamic seals (bellows). For prototype and subsequent Fast Breeder Reactors in India, choice of the dynamic seal is changed to an in-house designed & developed labyrinth type V-ring seal. The present work is related to the numerical investigations carried out to gain insights into the sealing mechanism and the thermal behaviour of these seals. The results indicate that the seal geometry is very important for obtaining optimum performance. By changing the geometry of the present seal, performance enhancement by more than 50% in important indices is obtained. Further, the results point out that caution shall be exercised when the seal material & its operating temperature are changed. Also, the numerical model developed in this study will be useful for developing more robust dynamic seals in future.

Pricing decisions are often made when market information is still poor. In turn, existing theoretical models often reason about the response of optimal prices to changing market characteristics without exploiting all available information about the demand distribution. Our aim is to develop a theory for the optimization and systematic comparison of prices between different instances of the same market under various forms of knowledge about the corresponding demand distributions. We revisit the classic problem of monopoly pricing under demand uncertainty in a vertical market with an upstream supplier and multiple forms of downstream competition between arbitrary symmetric retailers. In all cases, demand uncertainty falls to the supplier who acts first and sets a uniform price before the retailers observe the realized demand and place their orders. Our main methodological contribution is that we express the price elasticity of expected demand in terms of the mean residual demand (MRD) function of the demand distribution. This leads to a closed form characterization of the points of unitary elasticity that maximize the supplier's profits and the derivation of a mild unimodality condition for the supplier's objective function that generalizes the widely used increasing generalized failure rate (IGFR) condition. A direct implication is that optimal prices between different markets can be ordered if the markets can be stochastically ordered according to their MRD functions or equivalently, their elasticities. Using the above, we develop a systematic framework to compare optimal prices between different market instances via the rich theory of stochastic orders. This leads to comparative statics that challenge previously established economic insights about the effects of market size, demand transformations and demand variability on monopolistic prices., Special Issue: Business Analytics and Operations Research

The effectiveness of an on-street bus facility depends upon the general traffic that shares the lane used by buses. The Transit Capacity and Quality of Service Manual (TCQSM) methodology is used to estimate facility bus capacity based upon critical stop operation. Hisham et al. (2019) provided an improved understanding of performance of an on-street, mid-block, off-line bus stop by relating bus stop capacity to the adjacent lane traffic flow rate. The effectiveness of an on-street bus facility depends on the general traffic that shares the lane used by buses. The Transit Capacity and Quality of Service Manual (TCQSM) methodology was used to estimate facility bus capacity based on critical stop operation. Hisham et al. provided an improved understanding of performance of an on-street, mid-block, off-line bus stop by relating bus stop capacity to the adjacent lane traffic flow rate. Using the TCQSM methodology, bus stop capacity was determined by adopting an estimated operation margin that relates to a design bus stop failure rate. However, failure rate is theoretically ambiguous and difficult to quantify in practice, particularly under high volume traffic conditions. In contrast, degree of saturation is a direct measure of operating conditions experienced by buses using the stop, and by the adjacent lane general traffic, so it directly affects approach delay and queuing. The aim of this study was to better understand performance at on-street, mid-block, off-line bus stops by considering degrees of saturation of loading areas and the adjacent lane rather than design failure rate according to the TCQSM methodology, and to ensure that waiting times upstream of bus stops are kept to acceptable levels by determining bus stop maximum working capacity.

The railway is one of the most prominent models of transportation across the globe and it carries a large number of people, thus requiring high reliability, maintainability and safety. The reliability of railways mostly depends on an effective signalling system, making it one of the critical parts of railway operation. A signalling system is part of a large array of systems with interconnected components and subcomponents. Therefore, there is a need to make the signalling system more reliable and optimised with enhanced fault detection. Proper inspection and maintenance are required to make the signalling system reliable and safe. In this study, different inspection modelling techniques are applied to find the reliability of the signalling system. The signalling system has been divided into subsystems (signal unit, track unit, point-and-point machine) considering their importance and their effects on the failure rate of the entire signalling system. Inspection modelling of each subsystem has been conducted to provide the basis for the entire signalling system. A case study has been investigated to validate the model developed in one of the busiest tracks in eastern India. The obtained data thus are used to analyse the inspection pattern of signalling subsystems. Special attention to maintenance for inspection activities and logistics support has been taken into consideration, which is required to improve the reliability and maintainability of signalling subsystems and systems to make the railway signalling system sustainable in the long run.

In this paper, the reliability allocation and index of overall cost of rotary ultrasonic vibration-assisted EDM machine tool were studied. The failure rate of each module of the rotary ultrasonic vibration-assisted EDM machine tool can be accurately predicted by the maximum entropy ordered weighted average algorithm so that the corrected maintenance cost can be predicted. And effective control of preventive maintenance costs can be achieved by selecting the best maintenance times under the premise of reliability. The cost of corrective maintenance can be reduced by improving modules with a high failure rate, so as to realize the purpose of cost optimization under the premise of determining the maintenance time and reliability. The reliability function of the rotary ultrasonic vibration-assisted EDM machine tool is established according to the series-parallel relationship of each module. The results showed that the reliability of rotary ultrasonic vibration-assisted EDM machine tool is stabler at the non-fixed maintenance period according to the comparison between the non-fixed and the fixed maintenance period.

The exponential model is the simplest among all lifetime distribution models and it possesses a constant failure rate. Here we propose a new class of lifetime distribution having decreasing failure...

A new flexible extension of the Inverse Weibull distribution is proposed and studied. The new density can be “right skewed”, “left skewed” and “unimodal” with various shapes, the new failure rate function can be a “decreasing” or “upside down” or “increasing” shaped. Some of its statistical properties are derived. The importance of the new distribution is shown via three applications to real data sets. The new distribution is better fit than many important competitive models based on three real data sets.

The failure evaluation of electric energy metering equipment is essential for the equipment design and accurate measurement of electric energy, especially in extreme environmental stress. However, actual failure assessment is often affected by the environmental noise and insufficient interpretability. To address this problem, this article first proposes an improved k-nearest neighbor (IkNN) to identify potential outliers. In addition, an optimized distance function is used to obtain the score for each outlier. Next, a probability analysis method, namely, the weighted fusion Bayesian (WFB), is proposed to fuse multiple extreme environmental stresses and failure rate using the proposed nonlinear fusion function. Combining the WFB and the IkNN, examples from three extreme environmental regions show that the proposed evaluation framework has a higher assessment performance and less uncertainty. Compared with the classical prediction methods, our framework has profound outlier detection and failure prediction performance ever under the condition of small samples. More importantly, the parameters of this model are interpretable compared to some conventional approaches.

In this paper, we introduce a new three-parameter distribution defined on the unit interval. The density function of the distribution exhibits different kinds of shapes such as decreasing, increasing, left skewed, right skewed, and approximately symmetric. The failure rate function shows increasing, bathtub, and modified upside-down bathtub shapes. Six different frequentist estimation procedures were proposed for estimating the parameters of the distribution and their performance assessed via Monte Carlo simulations. Applications of the distribution were illustrated by analyzing two datasets and its fit compared to that of other distributions defined on the unit interval. Finally, we developed a regression model for a response variable that follows the new distribution.

This paper proposes a multi-objective integrated optimization model of production scheduling and machine maintenance to find the optimal production sequence and preventive maintenance (PM) decisions. This model considers the setup time, the learning-forgetting effects and the multi-degradation effects. The setup time and the learning-forgetting effects are associated with jobs' similarities and PM decisions. The multi-degradation effects including machine deterioration, failure rate and quality characteristic loss determine the stochastic nature of the objectives. A hybrid maintenance strategy combining imperfect PM and minimal repair (MR) is adopted to effectively reduce the failure frequency and improve the processing quality. Then, the multi-objective solution is simplified by normalizing cost, time and utilization. The local search and the elitism strategy are conducted to avoid the solutions falling into local optimum and losing the best chromosome during evolutions. Finally, a case study of automobile engine manufacturing shows that our proposed model can reduce the total maintenance costs by 27%, shorten the total processing time by 3%, as well as improve the machine utilization by 3%.

PurposeThe paper aims to explore unavailability of dormant systems that are under both preventive and corrective maintenance. Preventive maintenance is considered as a failure based maintenance model, where full renew is realized at the occurrence of every nth failure. It proposes the imperfect corrective maintenance model, where each restoration process deteriorates the system lifetime, probability distribution of which is gradually changed via increasing failure rate.Design/methodology/approachBasic reliability mathematics necessary for unavailability quantification of a system which undergoes a real aging process with maintenance has been derived proceeding from renewal theory. New renewal cycle was defined to cover the real aging process and the expectation of its length was determined. All events resulting in the failure of studied system were explored to determine their probabilities. An integral equation where the unavailability function characterizing studied system is its solution was derived.FindingsPreventive maintenance is closely connected with the occurrence of the nth failure, which starts its renew. The number n can be considered as a parameter which significantly influences the unavailability course. The paper shows that the real aging process characterized by imperfect repairs can significantly increase the unavailability courses in contrast with theoretical aging. This is true for both monitored and dormant systems.Originality/valueAlthough mathematical methods used in this article were inspired and influenced by the work of reference (van der Weide and Pandey, 2015), derivation of final formulas for unavailability quantification considering the new renewal cycle is original. Idea of the real aging process is new as well. This paper fulfils an identified need to manage the maintenance of realistically aging systems.

The subsea all-electric Christmas tree (XT) is a key equipment in subsea production systems. Once it fails, the marine environment will be seriously polluted. Therefore, strict reliability analysis and measures to improve reliability must be performed before a subsea all-electric XT is launched; such measures are crucial to subsea safe production. A fault-tolerant control system was developed in this paper to improve the reliability of XT. A dual-factor degradation model for electrical control system components was proposed to improve the evaluation accuracy, and the reliability of the control system was analyzed based on the Markov model. The influences of the common cause failure and the failure rate in key components on the reliability and availability of the control system were studied. The impacts of mean time to repair and incomplete repair strategy on the availability of the control system were also investigated. Research results show the key factors that affect system reliability, and a specific method to improve the reliability and availability of the control system was given. This reliability analysis method for the control system could be applied to general all-electric subsea control systems to guide their safe production.

The revolution in software development is increasing with the use of advanced techniques and tools. Software development organizations are considering standards, methods, approaches, and tools to support them in maintaining the effectiveness of software. An effective implementation of software process improvement (SPI) is important in order to gain effectiveness in the software industry. Disasters are happening in software projects and industry of software, which hinder the success of software and ultimately lead the software to failure. Peoples of organizations are demanding to devise methodological approaches for reducing the failure rate of software, but it perceived slight success. The proposed study’s aim is to offer multicriteria decision making for continuous Internet of software industry and to report the literature to early decision making associated to software developments. The study has devised a comprehensive overview of the existing methodological approaches, tools, and mechanisms applied in software improvement for the software industry and is based on multicriteria decision making. The research has analyzed the available literature from different perspectives and then reported it. The results of the study will help researchers to devise new solutions for effective software process improvement, and the this study will consider as a support of evidence.

The use of advanced communication technologies such as Internet of Things (IoT) in the domain of Ambient Assisted Living (AAL) tends to promote the quality of living for elderly staying independently. However, the state of the art IoT based solutions for AAL systems have not fully expressed the importance of building social connections between smart devices. This paper attempts to study the significance of deploying socially enabled IoT systems in AAL environment by proposing a robust Social IoT based AAL system for elderly named FriendCare-AAL. In addition, it presents a schematic approach to establish a partnership among smart devices and introduces the concept of responsibility offloading between devices. The proposed system is capable of providing assistance to the elderly staying in smart home environment. In case of emergency, the system automatically generates alerts intimating about the situation to the concerned entities. To experimentally evaluate the system’s performance, a smart home AAL environment for an elderly person is simulated using human activity simulator namely ‘Home Sensor Simulator’ and person’s routine dataset is generated. Further, two machine learning models; Naive Bayes (NB) and Random Forest (RF) are employed to analyze the data in order to predict the well being of the elderly person. The performance of the two classifiers is assessed using metrics such as sensitivity, specificity, detection rate and accuracy. Experimental results revealed that RF classifier outperforms NB classifier in terms of overall accuracy, detection rate and balanced accuracy. The overall accuracy is observed to be 89.2% for RF and 83.9% for NB classifier. Furthermore, a performance comparison of the proposed model is performed with two baseline approaches. A system prototype is also developed using Node-Red simulation tool to determine the performance of the proposed system in real-world and failure-prone environments. It turns out that the system performs well in critical situations with a tolerable response time of less than 1.2 s for a high failure rate of upto 50%.

In the present paper, we use the concept of virtual age to propose a periodic switching policy for assessing the reliability of a two-unit cold standby system. This approach is suitable for units that recover with discontinuous activity and get younger. To restore the units and bring back the state of the system to a desirable condition, the active unit and cold standby unit are periodically replaced with each other at some given time interval. The survival function of the system under the periodic switching policy is derived in terms of the baseline time-to-failure distribution. The results indicate that, when the units have an increasing failure rate, the new switching policy improves the performance of the units which, in turn, increases the reliability of the system. Since the switching is not always failure-free, we derive also the survival function of the system in the case of imperfect switching. By considering the cost of switching and the system profit, we investigate the optimal length of the time interval for switching the active unit with the standby unit such that the mean profit of the system is maximized. To examine the theoretical results, we present some illustrative examples. Finally, we use a Monte-Carlo simulation to evaluate the system reliability.

The high failure rate of semiconductor devices and capacitors, matched with their high requirement in the architecture of a multilevel inverter (MLI) topology, results in low reliability of the inverter. A low device count, preserving the output power upon fault occurrence and the ability to tolerate both open- and short-switch failures on all fault locations, handling both single- and multiple-switch failures, and achieving natural voltage balancing of the capacitors, are the main challenges for the existing solutions to the fault-tolerant topologies. Thus, a highly resilient fault-tolerant topology, based on conventional inverters, is proposed in this article. The proposed solution accomplishes all the aforementioned challenges without deteriorating the efficiency of the inverter during faulty conditions. The robustness and effectiveness of the proposed topology are verified by the obtained experimental results. Multiple cases of switch failure are considered to cover all fault locations and types. Comparative analysis with the recently proposed fault-tolerant topologies on MLIs shows the superiority of the proposed topology over the rest.

In practice, the assumption of failure independence between components is seldom valid, especially for those complex systems with complicated failure mechanism. Users can decide whether to purchase extended warranty (EW) at the end of basic warranty, and there are many factors that influence this decision, such as product reliability and EW price. In order to solve the problem of EW pricing for multi-component systems with failure interaction reasonably, considering the failure interaction characteristics between components of the multi-component systems, under the condition of type II failure interaction, this paper constructed a dependent failure rate model and developed a EW cost model of two-component systems. Thus, after optimizing the preventive maintenance (PM) strategy, this paper obtained the optimal PM interval when EW cost is the lowest, which is a win-win strategy to reduce the EW price for manufacturers and users under the premise of ensuring the manufacturer's profit demand. Finally, the validity of the model was verified by a numerical example and sensitivity analysis for important parameters was presented.

Recent works on seismic hazard have introduced the concept of time-dependent seismic hazard and different models have been proposed to predict the inter-arrival time between consecutive events. Currently, the reliability assessment of structures and relevant design rules proposed by the codes are based on the Poisson recursive model, for which the frequency of the occurrence of seismic events does not change over time. This paper presents results on the impact of Time-Dependent Seismic Hazard on structural design, by evaluating the strength required by the structure (seismic capacity) for different time intervals elapsing from the last event. “Seismic capacity” is understood here as the capacity required to provide a fixed reliability level, measured by the failure rate. Two different seismic scenarios have been investigated and results concerning different site-to-source distance, capacity dispersion of the structure and different recurrence properties of the time-dependent source have been discussed. Finally, the impact of recursive properties of time-dependent model is analysed and discussed. The results obtained from the analyses highlight a significant influence of time-dependent hazard properties on the structural capacity required to attain a target reliability, and give evidence to the different roles played by the parameters considered in the analysis. Within the set of the considered case studies, the ratios between seismic capacities evaluated by the time-dependent and non-time-dependent model span the range [0,1.32] in the first scenario, where inter-arrival time varies from 0 to two times the mean return period. The second scenario involves multiple sources and observed ratios were in the range [0.84,1.23], extreme values are relevant to inter-arrival times equal to 139 y and 371 y, respectively.

The aim was to determine the reliability indicators of a power supply system using an artificial neural network model. A model for calculating technical reliability was developed using the following methods: an algorithm for calculating reliability indicators of power supply systems, the method of failure rate of a power supply system and a forecasting model using artificial neural networks. It was established that a power supply system is formed by an open radial power supply circuit. The failure rate of the power supply subsystem was determined by calculating the failure rate of i-th element of the subsystem. As a result of calculating the probability of failure-free operation of the subsystem for various conditions (5 time intervals), it was found that with an increase in the operating time from 100 to 500 h, a linear increase in the rate of system failures occurs from 0.0051 to 0.0073 1/h. A comparison of the obtained mean-to-failure values of the main and the same backup subsystem in the unloaded mode with an absolutely reliable switch (269.62 h) with the main and the same backup subsystem in the loaded mode (202.21 h) was carried out. The results differ by 67.41 h, which indicates a higher degree of reliability of the first method. The software package Prognoz_INS_2020 was developed. An acceptable accuracy of no more than 2.17% was obtained by comparing the results of the conventional calculation of the failure rate of power supply systems and using the Prognoz_INS_2020 software package. This indicates the efficiency of the proposed software package in reliability calculations at operating energy enterprises. The proposed methods for assessing technical reliability both using the conventional model and a model based on an artificial neural network made it possible to assess the state of power supply systems, which helps to prevent dangerous emergencies.

Purpose This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail. Design/methodology/approach First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency. Findings In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis. Originality/value The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

In the context of reliability theory, Eginhard J. Muth introduced in 1977 a continuous probability distribution that has been overlooked in the statistical literature. This paper is devoted to that...

This article introduces a novel strategy for determining the optimal control parameters of particle swarm optimization (PSO) for the shortest convergence time and lowest failure rate of photovoltai...

The pipes as one of the main and important components of a water distribution network break during operation due to various factors. Developing models for pipes failure rate prediction can be one of the most important tools for managers and stakeholders during optimal operation of the water distribution network. In this study, the statistical and soft models such as Linear Regression, Generalized Linear Regression, Support Vector Machine, Feed Forward Neural Network (FFNN), Radial-Based Function Neural Network (RBFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were studied in order to predict the pipes failure rate based on the characteristics of Gorgan city water distribution network including diameter, length, age, installation depth, and number of failures of each pipe. In order to determine the optimal values of the parameters of each model, appropriate error indices including correlation coefficient (R), Mean Square Error (MSE), and Correlation Mean Square Error Ratio (CMSER) for training and test data were calculated, and the values of the parameters related to the model with the highest value of the CMSER index were considered as the model optimal values. Furthermore, in the validation stage, the values of R and MSE error indices for each of the above models were considered as a criterion for selecting the most appropriate model for predicting pipe failure rate. The findings show that among the soft and statistical models investigated, ANFIS with MSE of 0.071 and R of 0.92 can predict the failure rate of the studied network pipes more efficiently and more accurately than other models. Yet, despite the superiority of this model over other models, this model cannot accurately predict the failure rate of the studied network pipes due to its relatively high MSE value. Therefore, a new approach was developed based on the hybridization of trained models to provide a more efficient model for a more accurate prediction of the pipe failure rates of water distribution network. In this approach, the values of the network pipe failure rate predicted by each of the soft and statistical models are considered as independent input variables, and the observational failure rate values are considered as the dependent output variable of the ANFIS model. A comparison between the values of non-hybrid model validation data indices and the results of the proposed hybrid prediction model reveals that the use of the developed hybrid model increased the R error value from 8.1% (compared to the ANFIS model) to 260% (compared to the RBFNN model). It also decreased the MSE error value from 37% (compared to the FFNN model) to 58% (compared to the RBFNN model). Moreover, the hybrid model, compared to the superior non-hybrid ANFIS model, decreased MSE error rates by 45%. The findings show that the proposed model can significantly raise the accuracy of predicting the failure rate of pipes, compared to other existing models.

PurposeIn some environments, the failure rate of a system depends not only on time but also on the system condition, such as vibrational level, efficiency and the number of random shocks, each of which causes failure. In this situation, systems can keep working, though they fail gradually. So, the purpose of this paper is modeling multi-state system reliability analysis in capacitor bank under fatal and nonfatal shocks by a simulation approach.Design/methodology/approachIn some situations, there may be several levels of failure where the system performance diminishes gradually. However, if the level of failure is beyond a certain threshold, the system may stop working. Transition from one faulty stage to the next can lead the system to more rapid degradation. Thus, in failure analysis, the authors need to consider the transition rate from these stages in order to model the failure process.FindingsThis study aims to perform multi-state system reliability analysis in energy storage facilities of SAIPA Corporation. This is performed to extract a predictive model for failure behavior as well as to analyze the effect of shocks on deterioration. The results indicate that the reliability of the system improved by 6%.Originality/valueThe results of this study can provide more confidence for critical system designers who are engaged on the proper system performance beyond economic design.

Incident trend analysis has been important in the past for understanding how a system has been performing over time. The system can refer to a particular equipment, facility or organization and its performance can be monitored in terms of numbers or rates of failures, incidents or events over time. A good trend analysis leads to better projections into the future, enabling a more accurate prediction of future incidents or failures. In most cases however, it is generally assumed that incident or failure rates remain constant over time and the same value of the rate is used in all estimations. This study uses data of past offshore fire incidents in the Gulf of Mexico to predict future incidents and shows that such an assumption can fail to provide accurate predictions. The data is normalized to account for the year-to-year variation in operation and shows how using a nonhomogeneous Poisson process (NHPP) assumption, where failure rate is a function of time, enables a better understanding of performance, and can be used to predict future incidents more accurately. This will help regulatory bodies to understand whether operation in the Gulf of Mexico has been improving or not and to take proactive measures before the next fire incident occurs.

Value of the lost load (VOLL) is a useful quantity in economical evaluations of power systems. The VOLL depends on multiple factors such as type of customer, regional economic conditions and demographics, time and duration of outage, and other specific traits of power interruption. The reviewed literatures show that there are still issues which could be considered in estimation of the VOLL, especially for industrial customers. One of the most interesting issues is inclusion of reliability of an industrial plant in estimation of the VOLL. This means that how reliability data of the plant components (failure rates, repair time, etc.) can be incorporated in the studies to provide a probabilistic model for the VOLL and its related quantities. This paper incorporates failure probability in the VOLL estimation of an industrial plant. For this purpose, firstly an estimate for failure rate of the plant components is obtained and secondly, this estimate is applied in failure probability function which is to be incorporated in the VOLL. Results provide a probabilistic model for the VOLL.

In order to improve the reliability of the production line and reduce its maintenance costs, this paper establishes preventive maintenance (PM) models of the equipment, which are classified into different levels based on mixed failure rate. The differential and targeted maintaining strategies are formulated as well. First, a method for dividing important equipment and secondary equipment of a production line based on grey interval Analytic Hierarchy Process- (AHP-) Entropy method is proposed. Next, the dynamic service age regression factor and the dynamic failure rate increasing factor are combined to establish a mixed failure rate model. Finally, for the important equipment and secondary equipment, two PM models are established with the constraint of reliability, the objective of maximum availability, and minimum maintenance cost rate to determine the optimal number of PM and cycles. Combined with an example of production line equipment, the optimal maintenance strategies for important equipment and secondary equipment are obtained. The result verified the feasibility of the model, which effectively improved the reliability of the production line equipment and reduced maintenance costs.

In the reliability literature, maintenance efficiency is usually dealt with as a fixed value. Since repairable systems are subject to different degrees and types of repair, it is more convenient to regard a random variable for maintenance efficiency. This paper is devoted to the statistical study of a general hybrid model for repairable systems working under imperfect maintenance. For both failure improvement and virtual age reduction of the system, maintenance efficiency is assumed to be random, with an exponential distribution as a probability density function. The likelihood function of this model is provided, and the estimation of the model parameters is computed by considering the maximization likelihood procedure. Obtained results were tested and applied to simulated and real data sets. To construct confidence intervals, the bias-corrected accelerated bootstrap method has been used.

As an renewable and clean energy of the world, wind energy has gained more and more attention and its fault diagnosis becomes more and more important. The gearbox, as the kernel component of the wind turbine system, it’s robust conditions have a great influence on the whole wind turbines system. Wind turbine gearbox has complex structure, which is usually composed of solar planetary gearbox and cylindrical gearbox. In the process of operation, various kinds of faults easily occur, resulting in serious losses. Once the wind turbine gearbox is not functioning as smoothly as it could be, it may result in large economic losses for the company and owner. At the same time, the failure rate of wind turbine gearbox has always been high because of complicated mechanic structure and special motion. Therefore, the tasks of reducing the downtime and increasing the productivity of wind turbine gearbox are urgent. This paper reviewed some research results of faults diagnosis on wind turbines gearbox, such as time-frequency analysis method, vibration based methods, nondestructive testing methods, etc. Meanwhile, this paper finds out some key problems and the channel of the resolution of the issue in order to supply some information for the further research of wind turbines gearbox.

Various types of renewable energy systems can make vulnerable the reliability of the system because of initiating noteworthy degrees of uncertainty and availability despite of their good effects on the system operation cost and global concerns. The difficulty can be removed with using special renewable energy sources with have complimentary in natures. This paper presents the reliability assessment of wind and solar energy based power system at different levels. The optimization problem is to increase the reliability of the system with both solar and wind energy systems. First, the system is developed by taking the effect of uncertain nature of wind and solar energy sources, the probability of the top event and failure rate of different components. The model predictive method is used to take the different future value of the daily data. For this investigation, total 11 cases are taken and reliability indices are considered for every case. Data of each basic event of solar-wind energy system components are taken from the old research. Further, the gamma model is used to obtain the system model and different reliability indices are evaluated. The top event probability for case 1, case 2, case 3, case 4, case 5, case 6, case 7, case 8, case 9, case 10 are 0.0234, 0.0245, 0.0256, 0.0263, 0.0267, 0.0268, 0.273, 0.0278, 0.0286 and 0.0288 respectively. The helpfulness of the proposed technique is provided through different evaluation data.

In this paper a method is proposed to obtain explainability of the random forest model. Two Auto-Regressive Integrated Moving Average (ARIMA) models form the basis for this approach. The ARIMA models are used in a way similar to how local surrogate models are typically applied. The explanation of random forest’s prediction is derived from the numerical differences of the parameters of the ARIMA models. To demonstrate the feasibility of this idea, an experiment that implements this approach is conducted. The data used for this are similar to an accumulated bathtub curve representing failure rates in a production process. The results of the experiment show that the approach is able to identify a linear trend in some parts of the data, and therefore locally provide an explanation for the functional form of the underlying failure rate.

Wind energy plays a significant role in the renewable energy scenario of the planet. The wind turbine systems have multipart components which are repairable. The failure characteristics of the onshore wind turbines rely on the terrain and climatic conditions. Although mountain passes and hill areas are highly suitable for wind turbine (WT) installations, there is a substantial uncertainty in the wind pattern because of the frequent changes in the direction and velocity of wind. It creates a rapid failure in different components of the wind turbine. It is an amazement that the failure rate has rapidly increased in normal operating period of the wind turbines that placed exactly in the mountain pass and hilly area due to the recurrent uncertainty in the wind. Hence there is an urgent need for improving the reliability in wind turbine. For diminishing the lifecycle cost of a wind turbine and failure, it is significant to evaluate the reliability for all individual components in all respects. This paper deals with sensitivity analysis for major components of wind turbine system and its sub systems such as rotor system, gear box, brake system, generator, hydraulic system and yaw system. The Reliability analysis was carried out successfully based on Weibull analysis and Sensitivity Analysis on constant speed and constant pitch wind turbines with a capacity of 250 kW over a span of 19 years for at Muppandal main pass, Tamil Nadu, India.

The transmission synchronizer is an important variable speed synchronization mechanism of a vehicle. Since accurate and feasible working load data at the design stage of the synchronizer are lacking, the design loads are taken to be static rather than actual working ones, resulting in a high failure rate of the synchronizer in service, which seriously affects the reliability of the whole transmission. The virtual prototype model of a transmission synchronizer is presented. The working load-effected fatigue life of a synchronizer pin is calculated with MSC.Patran software. The failure mechanism of the synchronizer pin is analyzed to predict its fatigue life under complicated working conditions. It provides an important theoretical basis for the optimum design of synchronizer mechanism parameters and reliable transmission design, which is of great practical significance.

The in-wheel electric vehicle with distributed drive units has better stability and flexibility than traditional centralized drives, but may encounter a higher failure rate due to additional actuators and sensors, especially that the faults of the wheel-side position sensor make motor torque out of control. To overcome this problem, a fault-tolerant control strategy with a multi-states switching method is proposed. The strategy judges the sensor failure by verifying redundant speed information, realizes sensorless control schemes by flux-observer based algorithm in high-speed range and I-F control algorithm in low-speed range with low acoustic noise, and applies adaptive transition process between different control schemes. To pursuit high stability, the signal-to-noise analysis for fault judgment due to sensorless estimation accuracy is discussed. Meanwhile, the principle of I-F resonant oscillations during the transition process is initially deduced in detail, and the conclusion of stability condition is obtained. Finally, the influence of system parameters on resonance performance is analyzed by simulation, and the effectiveness and reliability of the proposed strategy for the risk-controlling process are verified by experiments.

This paper studies the single server repairable queueing system with variable service rate and failure rate. The rule of variation of the service rate is that the service rate changes when the number of customers in the system reaches a certain value. The server may fail at any time, and the failure rates of idle periods and busy periods are different. The system has one reliable repairman to repair the failure server. The steady-state joint distribution of the customers number and server states is obtained by the matrix geometric solution method, the steady-state availability of the server and significant queue performances measures are evaluated. Two special cases are analyzed, and some numerical experiments are given for the illustrations of the parameters effect.

In recent years, erasure codes have become the de facto standard for data protection in large scale distributed cloud storage systems at the cost of an affordable storage overhead. However, traditional erasure coding schemes, such as Reed-Solomon codes, suffer from high reconstruction cost and I/Os. The recent past has seen a plethora of efforts to optimize the tradeoff between the reconstruction cost, I/Os and storage overhead. Quiet different from all prior studies, in this paper, our erasure coding technique makes the first attempt to take advantage of the unequal failure rates across the disks/nodes to optimize the system reliability and reconstruction performance. Specifically, our proposed technique, the Unequal Failure Protection based Local Reconstruction Code (UFP-LRC) divides the data blocks into several unequal-sized groups with local parities, assigning the data blocks stored on more failure-prone disks/nodes into the smaller-sized group, so as to provide unequal failure protection for each group. In this way, by exploiting the nonuniform local parity degrees, the proposed UFP-LRC enables the data blocks that are stored on more failure-prone disks/nodes to tolerate a greater number of failures while suffering from less repair cost than others, leading to a substantial improvement of the overall reliability and repair performance for cloud storage systems. We perform numerical analysis and build a prototype storage system to verify our approach. The analytical results show that the UFP-LRC technique gradually outperforms LRC along the increase of failure rate ratio. Also, extensive experiments show that, when compared to LRC, UFP-LRC is able to achieve a 10 to 15 percent improvement in throughput, and an 8 to 12 percent reduction in decoding latency, while retaining a comparable overall reliability.

Manufacturing activities dominate the energy consumption and greenhouse emissions of the industrial sector. With the increasing concerns of greenhouse gas (GHG) emissions and climate change in recent years, the significance of the performance in terms of sustainability of manufacturing has been gradually recognized by both academia and industry. Various researches have been implemented to analyze, model, and reduce the energy consumption of manufacturing activities toward sustainable manufacturing. In a typical manufacturing system with multiple machines and buffers, the state of a certain machine is not only determined by the machine itself, but also the states of the adjacent machines and buffers. Therefore, machines may be in idle states due to nonincoming part from the upstream section of the manufacturing system or noncapacity to hold the delivered part to the downstream section of the manufacturing system. Those idle machines consume energy without production if there is no appropriate energy control strategy. In this article, we focus on the reduction of the energy waste for those idle machines in a typical multi-machine and multi-buffer manufacturing system. A Gaussian mixture model (GMM) integrated neural network is proposed to predict the duration of the idle periods for the idle machines, during which optimal energy control action can be identified and implemented under the constraint of production throughput of the manufacturing system. A manufacturing system simulator is built to provide the training dataset including the information, such as production throughput, energy consumption, buffer content, and failure rate, to the proposed neural network. A numerical case study for a five-machine-and-four-buffer manufacturing system is conducted to validate the effectiveness of the proposed prediction model in terms of the energy waste reduction for the idle machines. Note to Practitioners —This article proposes a prediction model to forecast the idle duration of the manufacturing machines in a typical multi-machine and multi-buffer manufacturing system. With this predicted result, two concerns in energy control for the idle machine, i.e., throughput protection and energy consumption reduction, can be more accurately modeled in decision-making procedure. Optimal energy control actions under the constraints of throughput maintaining and energy saving can be identified and implemented considering different warmup energy consumption and warmup time of the machines to reduce the energy waste for those machines in idle states without any production and thus, improve the energy efficiency of the entire manufacturing system.

Test suite reduction is an effective way to reduce the cost of regression testing by identifying and removing redundant test cases from the original test suite. In this paper, we propose a novel cluster test suite reduction using an evolutionary multi-objective optimization algorithm. Specifically, we use a K-means algorithm to group similar test cases to the same cluster. Then the evolutionary algorithm is used to remove redundant test cases based on the clustering results, and optimization objects are represented as the coverage-related criteria, fault-related criteria and cost-related criteria. The experimental results involving eight subject programs show that the proposed method can outperform the other three state-of-the-arts with respect to both fault detection (4.61% -9.44%) and reduction ratio (4.10% -10.64%). Meanwhile, the experiments also prove that our method has a better performance of missing failure rate (0.049% -0.132%) and code coverage rate (3.34% -6.10%). Besides, the proposed method costs are found to be comparable to the other techniques.

The timely restoration of water treatment devices is a prerequisite for sustainable water supply to consumers. Difficulties in estimating the moments of such recovery are associated with variability in the failure rate of mechanical systems. In this regard, two statistical approaches are proposed in this paper for scheduling the frequency of such recovery: estimating the constant frequency of maintenance for different groups of identical devices or systems (standard maintenance) or evaluating this frequency from the actual performance of individual devices (state maintenance). It is shown what approach should be used depending on the goal: to prevent devices from operation with expired service life or to make better use of the resource capabilities of these devices. It is also shown that the actual service life may significantly differ from its average value for groups of similar systems due to the influence of variability in the composition of source water and the characteristics of working units. Some limitations and recommendations on the practical implementation of forecasting the moments of unacceptably high losses in the performance-controlling parameter of water treatment devices and systems are considered. The novelty of this study is that it is based on the provisions of reliability theory, which provides a substantiated approach to the formation of a maintenance regime: the matter that becomes more urgent when designing water treatment technologies and equipment operation. The transition to these provisions, even in “advanced” areas of technology such as aerospace industry, encounters a number of difficulties, which the authors have managed to overcome in the case of water use problems to recommend the relevant specialists to pay attention to the potential efficiency of this approach and its use in the practice of operating water treatment devices and systems.

The major functions of the subsea Christmas tree include production management, wellhead pressure control, chemical reagent injection, and oil/gas production safety control. It is necessary to investigate the reliability and safety of the tree for safe and stable operation of subsea production systems. A fault tree and a dynamic Bayesian model are used to assess reliability and safety and to consider the time slice-based characteristics of the reliability. Using the control variable method, impact analysis is performed to quantify the impact of each failure rate on the system and sort the impacts of failure modes on reliability and safety. Finally, failure prediction is performed. The results show that the annulus loop has the lowest reliability and is the most likely to fail. The failure rate of the tubing hanger has the biggest impact on system reliability, while the failure rate of the choke valve has the biggest impact on system safety. When the tree fails, the failure probabilities of the tubing hanger and the valve group of the production loop are relatively high. On the basis of the above results, corresponding control measures are proposed that can significantly reduce the failure risk of the tree.

In the era of industrial big data, the demand for high-speed data transmission is higher and higher. The traditional Modbus protocol uses fixed data frame length transmission, which can not make full use of link resources, resulting in very low transmission rate. In the early stage, in order to solve the problem of traditional ModBus transmission protocol, an algorithm was proposed to adjust the frame length by calculating the optimal frame length through the frame error rate. However, because the frame length of each data frame is adjusted in this algorithm, the link stability becomes worse and the efficiency is reduced. In order to solve this problem, this paper proposes an improved adaptive frame length adjustment algorithm, which adjusts the frame length according to the retransmission failure rate per unit time, and gives different adjustment schemes according to different situations, which improves the link stability. Finally, this paper takes the servo drive data acquisition system as the carrier, and verifies the effectiveness of the algorithm through experiments.

In order to reduce the failure rate of Integrated Modular Avionics (IMA) partition software, due to the reliability block diagram (RBD) method, fault tree analysis (FTA) method and GO method cannot describe the state transition process of partition software, according to the ARINC 653 standard and the actual running status of the partition software, this paper determines the state machine and conversion delay of the partition software, and establishes the stochastic Petri nets (SPN) reliability quantitative model of the partition software. By proving that each transition in the SPN model of the partition software approximately obeying exponential distribution, and according to the reachable state tree of the SPN isomorphic to a homogeneous Markov chain (MC), the steady-state probability of the partition software in the fault state is calculated to be $5.2778^\ast 10^{-9}$ by using MC stochastic process theory. The factors affecting the reliability of the partition software are obtained, and the sensitivity of each factor to the model is studied. Finally, the relevant conclusions are drawn to provide guidance for improving the reliability of partition software.

In the replacement of items that fail suddenly, it is always better to compare the replacement policy adopted whether it is with Pure individual replacement or Group replacement after optimum period. In pure individual policy, failed item is replaced immediately by procuring in the local available market spending the amount at market price. As the usage time increases, the rate of failure increases. In that case, the individual replacement cost always increase proportional to usage period. Replacing the total lot of items as a whole or lot is a better option to consider. In group replacement, lot is replacement at a specified period irrespective of the usage period and condition of the item. As the failure rate in the initial days of the usage period, item replacement cost in the individual replacement case as well as group replacement case is more or less in a narrow price region. We need to take a better decision by comparing Average cost in both the cases. In the present paper, a novel approach is proposed to minimize the number of steps to find the average cost using Table method. The proposed algorithm is programmed in EXCEL. The proposed algorithm can be extended for any number of periods of operation.

Integrating renewable energy sources (RESs) into electrical power systems has gotten highly noticeable among researchers and those interested in electrical energy production due to the increase in energy demands, fossil fuel exhaustion, and ecological effects. PV-based renewable energy generation is one of the essential RESs that has appeared and had played a vital role in electrical power systems recently due to their advantages. In this regard, this paper presents a multi-objective computation problem for optimal siting and the design of grid-tied PV systems to achieve optimum generating reliability, considering some states of different generation probabilities. The proposed paper studies the evaluation of the grid-tied PV systems reliability, the states of generation probabilities, the generation buses availabilities, the capacities of the generation’s system in or out of service for each failure state, and the frequency and mean duration of generation failure states. The presented multi-objective computation problem is optimized using a modified adaptive accelerated particle swarm optimization (MAACPSO) algorithm. The effectiveness of the proposed method is demonstrated through IEEE_EPS_24_bus integrated with PV systems. Results revealed the ability of MAACPSO to solve the multi-objective optimization problem presented, consequently supporting the system reliability.

For complex and durable products such as automobiles and construction machinery, the quality and reliability characteristics of these products become more and more complex. However, in many cases, the technical ability of users is not enough to perfect the daily maintenance of the product. Warranty policy plays an increasingly important role between manufacturers and users. Aiming at the problem that the availability of the two-dimensional (2D) warranty product is difficult to meet the actual needs of users during the warranty period, this paper makes use of the accelerated failure time (AFT) model to construct the failure rate function of the product based on imperfect preventive maintenance (PM), establishes the 2D warranty availability model and gives an effective solution. On this basis, this paper obtains the optimal PM interval with the numerical algorithm and particle swarm optimization (PSO) taking the maximal availability of the 2D warranty product as the optimization objective and compares the results and speed of the two algorithms. Finally, the validity and accuracy of the model are verified by a numerical example which provides a scientific basis for the manufacturer to formulate the 2D warranty strategy.