Reliability analysis of a mine production system-a case study

In this paper, an efficient method for the reliability analysis of systems with nonlinear limit states is described. It combines optimization-based and simulation-based approaches and is particularly applicable for problems with highly nonlinear and implicit limit state functions, which are difficult to solve by conventional reliability methods. The proposed method consists of two major parts. In the first part, an optimization-based method is used to search for the most probable point (MPP) on the limit state. This is achieved by using adaptive response surface approximations. In the second part, a multi-modal adaptive importance sampling method is proposed using the MPP information from the first part as the starting point. The proposed method is applied to the reliability estimation of a vehicle body-door subsystem with respect to one of the important quality issues -- the door closing effort. The superiority of the proposed method, in terms of efficiency and accuracy, is demonstrated with a numerical example of highly nonlinear limit state problem, as well as an automotive door closing effort application. A generalized framework for reliability estimation is also proposed for problems with large numbers of random variables and complicated limit states.

Modified subset simulation method for reliability analysis of structural systems

The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic framework to facilitate application of the reliability analysis technique for knowledge-based CAD systems that are not feature-based. The study was based on an information-theoretic CAD system developed for detection of masses in screening mammograms from the Digital Database for Screening Mammography (DDSM). The experimental results reveal that the query-specific reliability estimate provided by the proposed probabilistic framework is an accurate predictor of CAD performance for the query case. It can also be successfully applied as a base for stratification of CAD predictions into clinically meaningful reliability groups (i.e., HIGH, MEDIUM, and LOW). Based on a leave-one-out sampling scheme and ROC analysis, the study demonstrated that the diagnostic performance of the IT-CAD is significantly higher for cases with HIGH reliability (A(z) = 0.92 +/- 0.03) than for those stratified as MEDIUM (A(z) = 0.84 +/- 0.02) or LOW reliability predictions (A(z) = 0.78 +/- 0.02).

Machining center is a complex mechatronical product. Because of numerous influencing factors on its reliability, parts of their inaccurate description and the uncertainty of its failure behaviors, it is very difficult to analyze its reliability by traditional reliability model which is based on probability theory. To solve this problem, grey system theory combined with GO method reliability analysis was presented, and sequentially a practical reliability analysis model considering various operating conditions of the components of machining center was established. The method was used in reliability analysis of hydraulic system of Tray Automatic Exchange Device (TAED) of machining center. Firstly, through a systematical analysis of hydraulic system of tray automatic exchange device of machining center, its basic structure model was summarized, and the model of GO chart for the hydraulic system was established. Then, the gray dynamic analysis for the hydraulic system was derived, and a feasible method of dynamic analysis for the performance reliability of hydraulic system was provided. Finally, the availability and correctness of this method in the analysis and reliability evaluation of machining center was verified by comparing the calculated result of this method with that of the traditional static GO methodology and the FTA. This approach provided a theoretical basis for the prevention of failures of the hydraulic system of tray automatic exchange device, the improvement of the system reliability and security, and the development of security controls.

PurposeDegraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow exponential distribution. Therefore, it becomes important to carry out reliability and availability analysis of such systems. From the reported literature, it is learnt that models are available for the situations where the degraded failures as well as critical failures follow exponential distribution. The purpose of this paper is to present models suitable for reliability and availability analysis of systems where the degradation process follows Weibull distribution and critical failures follow exponential distribution.Design/methodology/approachThe research uses Semi-Markov modeling using the approach of method of stages which is suitable when the failure processes follow Weibull distribution. The paper considers various states of the system and uses state transition diagram to present the transition of the system among good state, degraded state and failed state. Method of stages is used to convert the semi-Markov model to Markov model. The number of stages calculated in Method of stages is usually not an integer value which needs to be round off. Method of stages thus suffers from the rounding off error. A unique approach is proposed to arrive at failure rates to reduce the error in method of stages. Periodic inspection and repairs of systems are commonly followed in industries to take care of system degradation. This paper presents models to carry out reliability and availability analysis of the systems including the case where degraded failures can be arrested by appropriate inspection and repair.FindingsThe proposed method for estimating the degraded failure rate can be used to reduce the error in method of stages. The models and the methodology are suitable for reliability and availability analysis of systems involving degradation which is very common in systems involving moving parts. These models are very suitable in accurately estimating the system reliability and availability which is very important in industry. The models conveniently cover the cases of degraded systems for which the model proposed by Hokstad and Frovig is not suitable.Research limitations/implicationsThe models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.Practical implicationsThese models can be suitably used to deal with reliability and availability analysis of systems where the degradation process is non-exponential. Thus, the models can be practically used to meet the industrial requirement of accurately estimating the reliability and availability of degradable systems.Originality/valueA unique approach is presented in this paper for estimating degraded failure rate in the method of stages which reduces the rounding error. The models presented for reliability and availability analyses can deal with degradable systems where the degradation process follows Weibull distribution, which is not possible with the model presented by Hokstad and Frovig.

A combined physics of failure and Bayesian network reliability analysis method for complex electronic systems

Reliability and safety analysis for systems of fusion device

Reliability is the probability that the system will perform its intended function under specified working condition for a specified period of time. It is the analysis of failures, their causes and consequences. Reliability analysis is the most important characteristic of product quality as things have to be working satisfactorily before considering other quality attributes. Usually, specific performance measures can be embedded into reliability analysis by the fact that if the performance is below a certain level, a failure can be said to have occurred. Markov model is widely used technique in reliability analysis. The hypoexponential distribution is used in modeling multiple exponential stages in series. This distribution can be used in many domains of application. In this chapter we find a modified and simple form of the probability density function for the general case of the hypoexponential distribution. The new form obtained is used in an application of Markovian systems to find its reliability.

PurposeThis paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems.Design/methodology/approachFirstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis.FindingsIn this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis.Originality/valueThe proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

Reliability analysis of hierarchical computer-based systems subject to common-cause failures

This paper proposes a new goal-oriented (GO) method for reliability analysis of repairable systems with multiple-input and multi-function component (MIMFC). First, a new GO operator combination, which is composed of a new function GO operator and a new auxiliary GO operator, is created to represent MIMFC. The new function GO operator named as Type 22 operator is created to represent MIMFC itself, and the auxiliary GO operator named as Type 15B operator is created to represent multi-condition control signals of MIMFC. Then, GO operation formulas of the new GO operator combination are deduced based on logical relationships among inputs, outputs, and the component itself. The reliability analysis process of the new GO method is formulated. Furthermore, this new GO method is applied for the first time in steady availability analysis and qualitative analysis of the fan drive system of a power-shift steering transmission. Finally, the results obtained by the new GO method are compared with the results of fault tree analysis (FTA) and Monte Carlo simulation (MCS), and the comparison results show that this new GO method is reasonable and advantageous in reliability analysis of repairable systems with MIMFC. Moreover, the analysis process shows that it is more advantageous in the aspect of building system models and conducting reliability analysis. Overall, this paper not only improves the basic theory of the GO method and expands the application of the GO method, but it also provides a new approach for reliability analysis of repairable systems with MIMFC.

Ever shrinking device structures are one of the main reasons for a growing inherent unreliability of embedded system components. As a remedy, various means to increase the reliability of complex embedded systems at several levels of abstraction are available. In fact, their efficient application is a key factor for the successful design of reliable embedded systems. While analysis approaches that evaluate these techniques and their advantages and disadvantages at particular levels exist, an overall system analysis that has to work cross-level is still lacking. This paper introduces a framework for cross-level reliability analysis that enables a seamless and flexible combination of various reliability analysis techniques across different levels of abstraction. For this purpose, a proposed framework provides mechanisms for (a) the composition and decomposition of the system during analysis and (b) the connection of different levels of abstraction by adapters that convert and abstract analysis results. As a case-study, the framework extends and combines three analysis approaches from the MPSoC domain: (I) a BDD-based reliability analysis considers redundancies in the system structure, (II) an analytical behavioral model to consider computational activity, and (III) a temperature simulator for processor cores. This enables to capture thermal reliability threats at transistor level in an overall system analysis. The approach is seamlessly integrated in an automatic Electronic System Level (ESL) tool flow.KeywordsReliability AnalysisEmbed SystemField Programmable Gate ArrayReliability FunctionDesign Space ExplorationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Traditional reliability analysis generally uses probability approach to quantify the uncertainty, while it needs a great amount of information to construct precise distributions of the uncertain parameters. In this paper, a new reliability analysis technique is developed based on a hybrid uncertain model, which can deal with problems with limited information. All uncertain parameters are treated as random variables, while some of their distribution parameters are not given precise values but variation intervals. Due to the existence of the interval parameters, a limit-state strip enclosed by two bounding hyper-surfaces will be resulted in the transformed normal space, instead of a single hyper-surface as we usually obtain in conventional reliability analysis. All the limit-state strips are then summarized into two different classes and corresponding reliability analysis models are proposed for them. A monotonicity analysis is carried out for probability transformations of the random variables, through which effects of the interval distribution parameters on the limit state can be well revealed. Based on the monotonicity analysis, two algorithms are then formulated to solve the proposed hybrid reliability models. Three numerical examples are investigated to demonstrate the effectiveness of the present method.

System and measurement uncertainties play a key role in finding the reliability of complex and safety-critical structural systems such as buildings and bridges. Current mainstream techniques in structural reliability analysis use an iterative first-order reliability method that performs an indirect uncertainty evaluation by finding the most probable failure point. However, such an approach could lead to high computational time due to slower convergence, and in some cases, poor convergence that leads to unreliable results. In order to address the shortcomings, this paper applies a new high-order moment propagation technique (that was initially developed for the measurement uncertainty analysis) to structural reliability analysis problems aiming to obtain results that are simultaneously accurate and computational efficient. The proposed approach was implemented on a three-story elastic frame structure, and the presented results prove that the application of a technique from the field of measurement uncertainly can indeed be applied to complex problems in the structural reliability analysis in order to reduce the trade-off between accuracy and computational efficiency.

1 INTRODUCTION Since the invention of the Wheel, man has worked to develop systems that were reliable as well as functional. Until the end of World War II however, the bulk of his efforts in reliability enhancement were devoted to making his equipment bigger and stronger to withstand the effects of heavier loads and more hostile environments. Unfortunately, there was no satisfactory way of measuring or quantifying this seemingly elusive characteristic. Designs were based upon past experience and very often on inadequate data. Many of these reliability improvement efforts achieved some measure of success in prolonging the operating life of the equipment, but the associated costs were distressingly high; and reliability overkill or unfavorable economic balance resulted. In the last twenty-five years, a new discipline has found increasing application within governmental and industrial communities. Reliability engineering, proven in an outstandingly successful space program, is an integral requirement in virtually every significant military or space procurement. The techniques of reliability engineering and measurement are used extensively by the safety-conscious, commerical aircraft manufacturers. The intensely competitive automotive industry has enjoyed the benefits of reliability analyses for many years. The public utilities have long recognized the need for reliability measurement and prediction. Recently, the petroleum industry has identified the need for measuring production equipment reliability from the standpoint of potential pollution as well as profit enhancement. II RELIABILITY DEFINED Reliability is defined as:The probability that a system will operate satisfactorily, for a specific period of time under stated conditions.Consequently, satisfactory system operation, time and conditions of use are the essential elements of reliability quantification. II I ANALYSIS OUTPUTS Traditionally, in reliability analysis, the principal output is a number between 0 and 1 which satisfies the definition of reliability as stated above. This number, however suitable for measuring the reliability of many types of systems, does not provide a unit operator with an indication of the penalties he must pay for each failure. For example: a platform production system may have a reliability of .2. At first glance, this appears to be an extremely unreliable system. It may well be, however, that the failures producing this apparently low reliability are of relatively little significance, i.e., they cause no pollution, negligible shut in time, and small repair costs. Therefore, the operator may derive little or no benefit from an expenditure to improve the reliability of his facility. Conversely, a reliability of .95 indicating relatively few failures, could conceal a small number of disasterous failure events creating catastrophic consequences. Given this situation, the operator would benefit materially from a reliability improvement program. The "Event-Consequence" approach to reliability analysis was developed especially for the unique problems of the petroleum industry.