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The purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.

In this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.

The numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.

The paper provides a useful reference for further theoretical research and engineering application.

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.

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.

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.

The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

The purpose of this paper is to focus on finding the optimal maintenance interval and the minimum maintenance cost for redundant system, considering environment factors.

The authors propose a decision model with environment-based preventive maintenance for the repairable redundant system. Referring to the k-out-of-n model and Proportional Hazard Model, the reliability analysis is completed for the redundant system affected by internal and external issues. Meanwhile, the maintenance cost for the redundant system is divided into two categories: the fixed maintenance cost involving whole system replacement at the time of system failure, and the cost to replace failure components when the system still functions.

Upon the required reliability analysis, an optimal maintenance interval that minimizes the average maintenance cost per unit time is identified. The simulation results indicate that the optimal maintenance interval with consideration of environmental factors is significantly shorter than that without consideration of these factors, with the maintenance cost increase within 10 percent.

The redundant systems have widely been used in industries including the aero craft control system and warship power system. The model could be applied in the more real case considering the types of components and the operation environment, and help production managers better maintain machines by increasing the safety and reliability of the redundant model with the more frequent inspection.

Previous research of redundant system always focuses on internal degradation, while ignoring the reliability analysis for a redundant system with various multiple components under the influence of environment. However, this work could fill the theoretical gap, i.e. simultaneously consider both environmental and internal factors for a redundant system with non-homogeneous components. Meanwhile, the proposed superior model increases the reliability and safety of the k-out-of-n model with reasonable cost. Production managers could benefit a lot from this as well.

This study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.

A total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.

The literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.

This study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.

This is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.

Cumulative capability models are dominating frameworks explaining how manufacturing organizations gain their performance capabilities, such as quality, delivery, flexibility and cost. When innovation capabilities are excluded from the framework, the models are incapable of explaining how companies sustain substantive capabilities in a changing environment. Responding to this gap, the purpose of this paper is to propose and test a “sand cone” cumulative capability model that includes the innovation competitive performance alongside the competitive performance of quality, delivery flexibility and cost.

Two competing cumulative models were proposed. The extended cumulative capability model hypothesizes the development of innovation in sequence with other competitive performance dimensions. The affected with innovation cumulative model hypothesizes innovation performance as a predecessor of other performance dimensions. The models were tested using a multimethod approach on a representative sample of 500 manufacturing companies. An analysis of correlations among competitive performance, frequencies of plants following prescribed sequences, fit statistics of covariance-based structural equation modeling and analysis of strength and statistical significance of path coefficients enabled us to select a model that best represents the collected data.

The findings reveal that innovation competitive performance operates as a predecessor of quality, delivery, flexibility and cost and is developed in relation to these performance dimensions. The modified model also provides a theoretical explanation of how innovation performance helps to sustain reliable production systems that can perform consistently over time within a tolerable range of quality, delivery, flexibility and cost performance.

The results are significant for practitioners, especially for companies that are operating in volatile environments because the results provide insight on how to develop innovation competitive performance in relation to quality, delivery, flexibility and cost performance.

This study extends the cumulative capability models with innovation competitive performance. It advances the contingency approach on cumulative capability models.

The demand of cement in India is expected to increase rapidly as the government has been giving immense boost to various housing facilities, infrastructure projects, road networks and railway corridors. One of the ways to meet this rise in the demand of cement is to increase the capacity utilization of the existing cement plants by improving their availability. The availability of a cement plant can be improved by avoiding failures and reducing maintenance time through reliability, availability and maintainability (RAM) analysis of its subsystems. The paper aims to discuss this issue.

The data related to time between failure (TBF) and time to repair (TTR) of all the critical subsystems of a cement plant were collected over a period of two years for carrying out RAM analysis. Trend test and serial correlation test were performed on TBF and TTR data to verify whether these data are independent and identically distributed or not. Afterwards, the authors use EasyFit 5.6 professional software to find best-fit distribution of TBF and TTR data and their parameters. The effectiveness of a preventive maintenance policy was evaluated by simulating the real and proposed systems.

The results of the analysis show that the raw mill and the coal mill are critical subsystems of a cement plant from a reliability point of view, whereas the kiln is a critical subsystem from an availability point of view. The analysis shows that the repair time of the cement mill should be reduced for improving the availability of the cement plant. The RAM analysis showed that the capacity of the case study company is 17 percent underutilized due to maintenance-related problems and 15 percent underutilized because of management-related problems.

The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Thus, it would serve as a reference for reliability and maintenance managers in deciding maintenance strategies of cement plants as well as in improving their capacity utilization.

The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Even more, using a simulation study, the authors show that preventive maintenance of the cement plant beyond a certain level can be disadvantageous as it leads to an increase in downtime and decrease in availability.

This paper proposes the extension of a prognosis process by means of the integration of maintenance alternative impacts in order to develop a maintenance decision‐making tool.

The deployment of this extended prognosis process follows a methodology based both on probabilistic and on event approaches.

The importance of the maintenance function has increased due to its role in keeping and improving the system availability and safety but also the product quality. To support this new role, the maintenance concept has undergone several major developments to lead to proactive considerations mainly based on prognosis process allowing one to select the best maintenance plan to be carried out.

Studies over the last 20 years have indicated that around Europe the direct cost of maintenance is equivalent to between 4 and 8 per cent of total sales turnover. The indirect cost of maintenance is likely to be a similar amount. Thus, in the countries where modern maintenance practices have yet to be well adopted by industry, the potential savings from modern maintenance are massive. These modern and efficient maintenances imply identifying the root‐cause of component failures, reducing the failures of production systems, eliminating costly unscheduled shutdown maintenances, and improving productivity as well as quality. It means, for the companies, migrating from their traditional reactive approach, which is “fail and fix”, to “predict and prevent”. The advantage of the latter is that maintenance is performed only when a certain level of equipment deterioration occurs. This “proactive” maintenance is mainly based on prognosis process often considered as the Achilles heel, while its goal is fundamental for implementing anticipation capabilities. This paper looks into this issue by proposing the development of an innovative prognosis process integrating the modelling of maintenance actions and their impacts on system performances. It leads to offering a maintenance aided decision‐making tool cable of assisting the decision‐maker in selecting the best maintenance plan to be carried out.

The feasibility of this new prognosis is experimented on the manufacturing Tele‐Maintenance (TELMA) platform supporting the unwinding of metal bobbins.

The purpose of this paper is to present a new methodology, used for dynamic reliability analysis of a gear transmission system (GTS) of wind turbine (WT), which could be used for assembly decision-making of the parts with errors to improve the GTS’s performance.

This paper involves the dynamic and dynamic reliability analysis of a GTS. The history curves of dynamic responses of the parts are obtained with the developed gear-bearing coupling dynamic model considering the random errors, failure dependency and random load. Then, the surrogate models of the mean and standard deviation of responses are presented by statistics, rain flow counting method and corrected-partial least squares regression response surface method. Further, a novel dynamic reliability model based on the maximum extreme theory, a theory of sequential statistics, equivalent principles and the inverse transform theory of random variable sampling, is developed to overcome the limitations of traditional methods.

The dynamic reliability of GTS considering the different impact factors are evaluated. The proposed reliability methodology not only overcomes the limitations associated with traditional approaches but also provides good guidance to assembly the parts in a GTS to its best performance.

Instead of constant errors, this paper considers the randomness of the impact factors to develop the dynamic reliability model. Further, instead of the limitation of the normal distribution of the random parameters in the traditional method, the proposed methodology can deal with the problems with non-normal distribution parameters, which is more suitable for the real engineering problems.

This paper aims to solve the problem that pipes conveying fluid are faced with severe reliability failures under the complicated working environment.

This paper proposes a dynamic reliability and variance-based global sensitivity analysis (GSA) strategy with non-probabilistic convex model for pipes conveying fluid based on the first passage principle failure mechanism. To illustrate the influence of input uncertainty on output uncertainty of non-probability, the main index and the total index of variance-based GSA analysis are used. Furthermore, considering the efficiency of traditional simulation method, an active learning Kriging surrogate model is introduced to estimate the dynamic reliability and GSA indices of the structure system under random vibration.

The variance-based GSA analysis can measure the effect of input variables of convex model on the dynamic reliability, which provides useful reference and guidance for the design and optimization of pipes conveying fluid. For designers, the rankings and values of main and total indices have essential guiding role in engineering practice.

The effectiveness of the proposed method to calculate the dynamic reliability and sensitivity of pipes conveying fluid while ensuring the calculation accuracy and efficiency in the meantime.

This paper aims to present a dynamic reliability model of scraper chains based on the fretting wear process and propose a reasonable structural optimization method.

First, the dynamic tension of the scraper chain is modeled by considering the polygon effect of the scraper conveyor. Then, the numerical wear model of the scraper chain is established based on the tangential and radial fretting wear modes. The scraper chain wear process is introduced based on the diameter wear rate. Furthermore, the time-dependent reliability of scraper chains based on the fretting wear process is addressed by the third-moment saddlepoint approximation (TMSA) method. Finally, the scraper chain is optimized based on the reliability optimization design theory.

There is a correlation between the wear and the dynamic tension of the scraper conveyor. The unit sliding distance of fretting wear is affected by the dynamic tension of the scraper conveyor. The reliability estimation of the scraper chain with incomplete probability information is achieved by using the TMSA for the method needs only the first three statistical moments of the state variable. From the perspective of the chain drive system, the reliability-based optimal design of the scraper chain can effectively extend its service life and reduce its linear density.

The innovation of the work is that the physical model of the scraper chain wear is established based on the dynamic analysis of the scraper conveyor. And based on the physical model of wear, the time-dependent reliability and optimal design of scraper chains are carried out.