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A general review has been conducted to emphasize the increasing concern with reliability in the engineering industry. The latest books and publications available to the authors have been reviewed during the survey to identify the latest thinking on the topic. Emphasizes the prediction of reliability and its use for further reliability analysis methods. Describes and briefly explains modern methods and tools for reliability prediction, to give an overview to engineers and managers interested in the subject. Includes a small case study of a subsystem of an aircraft system as example of an application of the subject. Includes in the reference section the books and papers used during the review and references for further reading into the subject.

The purpose of this paper is to present the durability analysis in predicting the reliability life cycle for an automobile crankshaft under random stress load using the stochastic process. Due to the limitations associated with the actual loading history obtained from the experimental analysis or due to the sensitivity of the strain gauge, the fatigue reliability life cycle assessment has lower accuracy and efficiency for fatigue life prediction.

The proposed Markov process embeds the actual maximum and minimum stresses by a continuous updating process for stress load history data. This is to reduce the large credible intervals and missing loading points used for fatigue life prediction. With the reduction and missing loading intervals, the accuracy of fatigue life prediction for the crankshaft was validated using the statistical correlation properties.

It was observed that fatigue reliability corresponded well by reporting the accuracy of 95–98 per cent with a mean squared error of 1.5–3 per cent for durability and mean cycle to failure. Hence, the proposed fatigue reliability assessment provides an accurate, efficient, fast and cost-effective durability analysis in contrast to costly and lengthy experimental techniques.

An important implication of this study is durability-based life cycle assessment by developing the reliability and hazard rate index under random stress loading using the stochastic technique in modeling for improving the sensitivity of the strain gauge.

The durability analysis is one of the fundamental attributes for the safe operation of any component, especially in the automotive industry. Focusing on safety, structural health monitoring aims at the quantification of the probability of failure under mixed mode loading. In practice, diverse types of protective barriers are placed as safeguards from the hazard posed by the system operation.

Durability analysis has the ability to deal with the longevity and dependability of parts, products and systems in any industry. More poignantly, it is about controlling risk whereby engineering incorporates a wide variety of analytical techniques designed to help engineers understand the failure modes and patterns of these parts, products and systems. This would enable the automotive industry to improve design and increase the life cycle with the durability assessment field focussing on product reliability and sustainability assurance.

The accuracy of the simulated fatigue life was statistically correlated with a 95 per cent boundary condition towards the actual fatigue through the validation process using finite element analysis. Furthermore, the embedded Markov process has high accuracy in generating synthetic load history for the fatigue life cycle assessment. More importantly, the fatigue reliability life cycle assessment can be performed with high accuracy and efficiency in assessing the integrity of the component regarding structural integrity.

This research aims to develop an approach to assess the reliability of integrated construction supply chains via an integrated model of building information modelling (BIM) and the lean supply chain (LSC). It reflects the synergistic workflow between BIM and LSC as a novel approach to improve the reliability of construction projects.

This research evaluates the reliability of the BIM-LSC approach through a combination of entropy theory, set pair analysis (SPA), and Markov chains (EESM). An exploratory survey was conducted to collect data from 316 industry professionals experienced in BIM and LSC. Subsequently, multiple cycles of calculations were performed with indirect data inputs. Finally, a reliability evaluation index is established for the BIM-LSC approach and potential applications are identified.

The results show that the EESM model of BIM-LSC developed in this study can handle not only supply chain reliability evaluation at a given state but also the prediction of reliability in supply chain state transitions due to changing project conditions. This is particularly relevant to the current environment of the construction project, which is characterised by an increasing level of complexity in terms of labour, technology, and resource interactions.

Future research could consider the accuracy and validity of the proposed model in real-life scenarios with by considering both quantitative and qualitative data across the entire lifecycle of projects.

The research offers a model to evaluate the reliability of the BIM-LSC approach. The accuracy of BIM supply chain reliability analysis and prediction in an uncertain environment is improved.

The BIM-LSC reliability evaluation and prediction presented in this study provides a theoretical foundation to enhance understanding of the BIM-LSC in the construction project context.

While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and imperfect planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require significant amount of extra capital investment. Traditional preventive/planned maintenance is usually scheduled at a fixed interval based on maintenance personnel's experience and it can result in decreasing reliability. This paper deals with reliability analysis and prediction for mining machinery. A software tool called GenRel is discussed with its theoretical background, applied algorithms and its current improvements. In GenRel, it is assumed that failures of mining equipment caused by an array of factors (e.g. age of equipment, operating environment) follow the biological evolution theory. GenRel then simulates the failure occurrences during a time period of interest based on Genetic Algorithms (GAs) combined with a number of statistical procedures. The paper also discusses a case study of two mine hoists. The purpose of this paper is to investigate whether or not GenRel can be applied for reliability analysis of mine hoists in real life.

Statistical testing methods are applied to examine the similarity between the predicted data set with the real-life data set in the same time period. The data employed in this case study is compiled from two mine hoists from the Sudbury area in Ontario, Canada. Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.

The case studies shown in this paper demonstrate successful applications of a GAs-based software, GenRel, to analyze and predict dynamic reliability characteristics of two hoist systems. Two separate case studies in Mine A and Mine B at a time interval of three months both present acceptable prediction results at a given level of confidence, 5 percent.

Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.

Compared to conventional mathematical models, GAs offer several key advantages. To the best of the authors’ knowledge, there has not been a wide application of GAs in hoist reliability assessment and prediction. In addition, the authors bring discrete distribution functions to the software tool (GenRel) for the first time and significantly improve computing efficiency. The results of the case studies demonstrate successful application of GenRel in assessing and predicting hoist reliability, and this may lead to better preventative maintenance management in the industry.

Analyzing and forecasting reliability is increasingly important for enterprises. An accurate product reliability forecasting model cannot only learn and track a product's reliability and operational performance, but can also offer useful information that allows managers to take follow‐up actions to improve the product's quality and cost. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) model is already extensively used to analyze and forecast time series data. However, the GARCH model has not been used to analyze and forecast the failure data of repairable systems. Based on these concerns, this study proposes the GARCH model to analyze and forecast the field failure data of repairable systems.

This paper proposes the GARCH model to analyze and forecast the field failure data of repairable systems. Empirical results from electronic systems designed and manufactured by suppliers of the Chrysler Corporation are presented and discussed.

The proposed method can analyze and forecast failure data for repairable systems. Not only can this method analyze failure data volatility, it can also forecast the future failure data of repairable systems.

Advanced progress in the field of reliability prediction estimation can benefit engineers or management authorities by providing important decision support tools in which the prediction accuracy suggests financial and business outcomes as well as other outcome application results.

Discusses the implementation of failure mode and effects analysis (FMEA) for both product design and process control. FMEA is implemented in two ways to ensure that the reliability requirements are met for the production of an airbag inflator. Design FMEA is performed to generate a process control plan, visual aids, and a process verification list. Design FMEA and process FMEA are integrated through reliability prediction and supplier PPM reports. The supplier PPM reports contain the information that can be employed to update the probabilities used in design FMEA. The results of reliability predictions are fed back to eliminate the design weakness. Demonstrates the integrated procedure of the FMEA approach and discusses the relationships among useful tools.

The purpose of this paper is to analyze the dependent competing failure reliability of harmonic drive (HD) with strength failure and degradation failure.

Based on life tests and stiffness degradation experiments, Wiener process is used to establish the accelerated performance degradation model of HD. Model parameter distribution is estimated by Bayesian inference and Markov Chain Monte Carlo (MCMC) and stiffness degradation failure samples are obtained by a three-step sampling method. Combined with strength failure samples of HD, copula function is used to describe the dependence between strength failure and stiffness degradation failure.

Strength failure occurred earlier than degradation failure under high level accelerated condition; degradation failure occurred earlier than strength failure under medium- or low-level accelerated condition. Gumbel copula is the optimum copula function for dependence modeling of strength failure and stiffness degradation failure. Dependent competing failure reliability of HD is larger than independent competing failure reliability.

The reliability evaluation method of dependent competing failure of HD with strength failure and degradation failure is first proposed. Performance degradation experiments during accelerated life test (ALT), step-down ALT and life test under rated condition are conducted for Wiener process based step-down accelerated performance degradation modeling.

The purpose of this paper is to investigate the application of reliability engineering to oil and gas (O&G) pipeline systems with the aim of identifying means through which reliability engineering can be used to improve pipeline integrity, specifically with regard to man-made incidents (e.g. material/weld/equipment failure, corrosion, incorrect operation and excavation damages).

A literature review was carried out on the application of reliability tools to O&G pipeline systems and four case studies are presented as examples of how reliability engineering can help to improve pipeline integrity. The scope of the paper is narrowed to four stages of the pipeline life cycle; the decommissioning stage is not part of this research. A survey was also carried out using a questionnaire to check the level of application of reliability tools in the O&G industry.

Data from survey and literature show that a reliability-centred approach can be applied and will improve pipeline reliability where applied; however, there are several hindrances to the effective application of reliability tools, the current methods are time based and focus mainly on design against failure rather than design for reliability.

The tools identified do not cover the decommissioning of the pipeline system. Research validation sample size can be broadened to include more pipeline stakeholders/professionals. Pipeline integrity management systems are proprietary information and permission is required from stakeholders to do a detailed practical study.

This paper proposes the minimum applied reliability tools for application during the design, operation and maintenance phases targeted at the O&G industry. Critically, this paper provides a case for an integrated approach to applying reliability and maintenance tools that are required to reduce pipeline failure incidents in the O&G industry.

The purpose of this paper is to analyze the fuzzy reliability of the compressor house unit (CHU) system in a coal fired thermal power plant under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process. This paper uses different fuzzy membership functions to quantify uncertainty and access the system reliability in terms of different fuzzy reliability indices having symmetric shapes.

This study analyses the fuzzy reliability of the CHU system in a coal fired thermal power plant using T_ω-based generalized fuzzy Lambda-Tau (TBGFLT) technique. This approach applies fault tree, Lambda-Tau method, different fuzzy membership functions and α-cut coupled T_ω-based approximate arithmetic operations to compute various reliability parameters (such as failure rate, repair time, mean time between failures, expected number of failures, availability and reliability) of the system. The effectiveness of TBGFLT technique has been demonstrated by comparing the results with results obtained from four different existing techniques. Moreover, this paper applies the extended Tanaka et al. (1983) approach to rank the critical components of the system when different membership functions are used.

The adopted TBGFLT technique in the present study improves the shortcomings of the existing approaches by reducing the accumulating phenomenon of fuzziness, accelerating the computation process and getting symmetric shapes for computed reliability parameters when different membership functions are used to quantify data uncertainty.

In existing fuzzy reliability techniques which are developed for repairable systems either triangular fuzzy numbers, triangle vague sets or triangle intuitionistic fuzzy sets have been used for quantifying uncertainty. These approaches do not examine the systems for components with different membership functions. The present study is an effort in this direction and evaluates the fuzzy reliability of the CHU system in a coal fired thermal power plant for components with different membership functions. This is the main contribution of the paper.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.