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Repairable system reliability analysis is very important to industry and, for complex systems, replacing a failed component is the most commonly used corrective maintenance action as it is an inexpensive way to restore the system to its functional state. However, failure data analysis for repairable system is not an easy task and usually a number of assumptions which are difficult to validate have to be made. Despite the fact that time series models have the advantage of few such assumptions and they have been successfully applied in areas such as chemical processes, manufacturing and economics forecasting, its use in the field of reliability prediction has not been that widespread. In this paper, we examine the usefulness of this powerful technique in predicting system failures. Time series models are statistically and theoretically sound in their foundation and no postulation of models is required when analysing failure data. Illustrative examples using actual data are presented. Comparison with the traditional Duane model, which is commonly used for repairable system, is also discussed. The time series method gives satisfactory results in terms of its predictive performance and hence can be a viable alternative to the Duane model.

The purpose of this paper is to propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

This study proposes a new method for predicting the reliability of repairable systems. The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Findings – The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Genetic algorithms are used to globally optimize the number of neurons in the hidden layer, the learning rate and momentum of neural network architecture. Research limitations/implications – This study only adopts real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems. Practical implications – Based on the more accurate analytical results achieved by the proposed method, engineers or management authorities can take follow‐up actions to ensure that products meet quality requirements, provide logistical support and correct product design. Originality/value – The proposed method is superior to other prediction techniques in predicting the reliability of repairable systems.

The purpose of this study is to find the reliability of the three-component three-phased mission system, which can be repaired by considering the exponential distribution for repair and failure rates in the transitions between the phases based on states with Markov approach. Also, multilevel-phased mission systems are calculated based on states for partially working states.

The reliabilities of the repairable two-level and three-level three-component three-phased mission systems based on states are calculated with the Markov approach. The structure functions are obtained for each phase of the systems, and differential equations are created by the failure and repair of each working state component. These equations are solved using Laplace method.

Reliability values of two-level and three-level three-component three-phased systems with different failure, repair, and time intervals are calculated and compared. The intermediate states that multilevel systems handle differently from two-level systems provide a better investigation of the systems. So, these repairable systems offer transparent information in complex systems like transportation and energy, ensuring appropriate timing and cost for repair operations.

This study is original in terms of calculating the reliability of the repairable phased mission system based on the states using Markov method. It is also important in calculating the reliability of the repairable multilevel phased mission system based on states and making reliability comparisons according to different repair and failure rates, equal and different time intervals.

The purpose of this paper is to derive a closed-form expression for the steady-state availability of a cold standby repairable k-out-of-n system. This makes the availability calculation much easier and accurate.

Assuming exponential distributions for system failure and repair, the Markov method is employed to derive the formula.

The proposed formula establishes an easier and faster venue and provides accurate steady-state availability.

The formula is valid for the case when the probability density function of the component failure and the repair is exponential.

The Markov method has never been used in the literature to derive the steady-state availability of a cold standby repairable k-out-of-n: G system.

The purpose of this paper is to examine the cost/benefit (C/B) analysis of four configurations for a repairable system with two primary components/units and one standby.

The four configurations are set to the status of the detection and switching failure of standby, as well as the possible reboot of failed units. The time to failure for each of the primary and standby is assumed to follow an exponential distribution. The time to repair and the time to reboot is assumed to have a k‐stage Erlang distribution. The paper develops the explicit expressions of the mean time to failure (or MTTF) and the steady‐state availability (or A) for four various configurations and performed some comparative analysis. Based on the C/B criterion, comparisons are made for specific values of distribution parameters and of the costs of the units. The four various configurations for a repairable system are ranked by using MTTF, A and C/B, where B is either MTTF or A.

Although it is uncertain which configuration is the optimal one among the four ones, the paper provides much comparative information to manager and manufacturers. Managers can use these results to choose the best configuration according to the used data of parameters and selections of the weight of MTTF or Cost/MTTF.

This paper shows a comparative analysis for a two‐unit online repairable system with one standby under four different configurations. It is the first discussion of comparable work on reliability and availability models for redundant repairable systems in which the units are characterized by detection, switching failure and reboot.

The purpose of this paper is to propose a procedure to construct the membership functions for a one-unit repairable system, which has both active and standby redundancy. The coverage factor is the same for the operating and standby unit failure.

The α-cut approach is used to extract a family of conventional crisp intervals from the fuzzy repairable system for the desired system characteristics. This can be determined with a set of non-linear parametric programing using the membership functions.

When system characteristics are governed by the membership functions, more information is provided to use by management. On the other hand, fuzzy theory is applied for the redundant system; therefore, the results are more useful for designers and practitioners.

Different from other studies, the authors’ model provides more accurate estimation compared to uncertain environments based on fuzzy theory. The research would help managers and manufactures to make a better decision in order to have the optimal maintenance strategy based on the desired mean time to failure and availability of the systems.

Considers trend testing in the context of reliability/survival applications. Suggests that the very common tendency in reliability testing to fit lifetime distributions to reliability/maintenance data might occasionally be invalid. Details the appropriate methods to assess the validity, or otherwise, of such a procedure. More specifically, discusses ROCOF curves and the Laplace test for trend, and demonstrates their use by means of a practical, reliability example.

To propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

This study proposes a method for analysing and forecasting field failure data for repairable systems. The novel method constructs a predictive model by combining the seasonal autoregressive integrated‐moving average (SARIMA) method and neural network model.

Current methods for analysing and forecasting field failure data for repairable systems do not consider the seasonal effect in the data. The proposed method can not only analyse the trends and seasonal vibration of the data, but can also forecast the short‐ and long‐term reliability of the system based on only a small amount of historical data.

This study adopts only real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method.

Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.

The proposed method is superior to other prediction techniques in predicting future real failure data.

Points out certain ambiguities in the usage of some reliability parameters in their application to repairable systems and presents a digital simulation model for analysing their reliability. The proposed model is useful for assessing intended changes in systems design or improvements in operational and maintenance procedures on system reliability. Outlines different steps of a computational algorithm for solving the proposed model. Describes an illustrative application of the proposed model to a fleet of trainer aircraft.

The purpose of this paper is to study repairable consecutive‐k‐out‐of‐n: systems with r repairmen and a sensing device.

The system can either be a circular C(k, n: G) system or a linear C(k, n: G) system. The working time and the repair time of each component in the system and the sensor detection time are exponentially distributed. Every component after repair is perfect. Each component is classified as either a key component, or an ordinary one according to its priority role to system's repair. A sensing device is introduced to detect the failure of each component in the system in advance and completion of repair of components. If the repair is completed, the sensor will send the component to standby according to its priority. The state transition probabilities of the system are derived using the definition of generalized transition probability. To obtain the reliability and availability Laplace transform techniques have been used.

The Kolmogorov‐Feller forward equations are derived for both linear and circular systems. Reliability and MTTF of both the systems are derived using Laplace transforms. Numerical examples are given in detail to demonstrate the theoretical results and these verify the validity of the studied system.

A consecutive‐k‐out‐of‐n system consists of a sequence of n‐ordered components along a line or a circle such that the system is good if and only if at least k consecutive components in the system are good. Each component in the system is classified as key component or ordinary component according to its priority in system functioning. By using a sensing device the failure can be detected in advance.

This study indicates that by using a sensing device we can detect the failure in advance. Thus, the reliability and MTTF of the system can be improved.