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Several stochastic models for repairable system are developed by assuming different time trends, and repair effects. The new model is convenient and provides accurate estimation results when compared to the existing models.

This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed from SPC techniques, are used to monitor process changes and estimate the disturbance magnitudes during production. Based on the SPC monitoring results, different supervisory strategies are used to revise the disturbance models and the control law in the GPC to achieve a satisfactory control performance. A case study is provided to demonstrate the developed methodology. [DOI: 10.1115/1.2114912] Keywords: ARMAX model predictive control, statistical process control supervisory strategies, thin film deposition

Budgets and maintenance programs in fossil power plants are frequently set based on engineering judgment rather than a Probabilistic Risk Assessment (PRA). Fossil power plants seldom use PRA due to a lack of practical processes, especially when field reliability data are not readily available. In the absence of field data, using general industry data may not be right for conducting PRA for a given organization as the operation and maintenance conditions significantly vary between organizations. To have a successful PRA, an organization needs to use its own field data, and have an efficient process for its organization. Studies have suggested using field data provides a robust model and accurate results. Use of Reliability Block Diagrams for Reliability, Availability, and Maintainability analysis is not new. However, applications of these techniques are new to fossil power plants. In this paper, we propose a practical process by integrating several existing reliability techniques for fossil plants to apply PRA. The process was tested at many power plants, and the results aligned well with the actual values. The methodology outlined in this paper is a forward‐looking tool for managers to predict system reliability, and proactively develop maintenance plans and budgets. [ABSTRACT FROM AUTHOR]

As reliability requirements become increasingly demanding for many engineering systems, conventional system reliability demonstration testing (SRDT) based on the number of failures depends on a large sample of system units. However, for many safety critical systems, such as missiles, it is prohibitive to perform such testing with large samples. To reduce the sample size, existing SRDT methods utilize test data from either system level or component level. In this paper, an aggregation-based SRDT methodology is proposed for hierarchical systems by utilizing multilevel reliability information of components, subsystems, and the overall system. Analytical conditions are identified for the proposed method to achieve lower consumer risk. The performances of different SRDT design strategies are evaluated and compared according to their consumer risks. A numerical case study is presented to illustrate the proposed methodology and demonstrate its validity and effectiveness. [ABSTRACT FROM AUTHOR]

The generalized renewal process (GRP) has been widely used for modeling repairable systems under general repairs. Unfortunately, most of the related work does not provide closed-form solutions for predicting the reliability metrics of such systems, such as the expected number of failures, and the expected failure intensity, at a future point in time. A technical approach reported in literature is to conduct simulations to predict the reliability metrics of interest; however, simulations can be time-consuming. To reduce computational efforts for failure prediction, we propose an analytical approach that does not rely on simulations. Our idea is to predict the system's mean residual life based on its virtual age after each repair. The predicted mean residual life is then used to determine the expected time to the next failure. To illustrate this approach, we use a log-linear failure intensity function, and provide a detailed procedure for obtaining the maximum likelihood estimates (MLE) of the model parameters. A numerical study shows that this simple yet effective approach can provide failure predictions as accurate as the simulation alternative. We then demonstrate how the proposed approach can evaluate different maintenance strategies more efficiently compared to using simulations. [ABSTRACT FROM PUBLISHER]

This article proposes a new method to develop multiscale monitoring control charts for an autocorrelated process that has an underlying unknown ARMA(2, 1) model structure. The Haar wavelet transform is used to obtain effective monitoring statistics by considering the process dynamic characteristics in both the time and frequency domains. Three control charts are developed on three selected levels of Haar wavelet coefficients in order to simultaneously detect the changes in the process mean, process variance, and measurement error variance, respectively. A systematic method for automatically determining the optimal monitoring level of Haar wavelet decomposition is proposed that does not require the estimation of an ARMA model. It is shown that the proposed wavelet-based Cumulative SUM (CUSUM) chart on Haar wavelet detail coefficients is only sensitive to the variance changes and robust to process mean shifts. This property provides the separate monitoring capability between a variance change and a mean shift, which shows its advantage by comparison with the traditional CUSUM monitoring chart. For the purpose of mean shift detection, it is also shown that using the proposed wavelet-based Exponentially Weighted Moving Average (EWMA) chart to monitor Haar wavelet scale coefficients will more successfully detect small mean shifts than direct-EWMA charts. [ABSTRACT FROM PUBLISHER]

Reliability Demonstration Testing (RDT) has been widely used in industry to verify whether a product has met a certain reliability requirement with a stated confidence level. To design RDTs, methods have been developed based on either the number of failures or the failure times. However, practitioners often have difficulty in determining which method to use for a specific design problem. In particular, the method based on the number of failures cannot be used when all the units are tested to failure, while the alternative based on failure times falls short in dealing with cases where no failures are expected. This paper elaborates on the two methods, and compares them from both practical and theoretical standpoints. The detailed discussions regarding the relationship between the two methods will help practitioners design RDTs, and understand when the two methods will lead to similar designs. A Weibull distribution is used in the relevant mathematical derivations, but the results can be extended to other widely used failure time distributions. Case studies are provided to demonstrate the use of the two methods in practice, and in developing equivalent RDT designs. [ABSTRACT FROM PUBLISHER]

It is difficult to estimate the confidence interval of a system's reliability with zero failures experienced. We approach this problem by proposing a hybrid model that integrates the Bayesian model with the variance propagation technique. The Bayesian model will compute the moments of component reliability estimates, and the variance propagation technique is used to estimate the system reliability variance. The confidence interval for the system reliability is then derived by matching the moments with a beta distribution. As a major contribution, the distribution for reliability estimates with zero failures is explicitly derived. The performance of the new model is compared with existing methods, and further validated by simulation data. The results show that the hybrid model generally outperforms existing methods in terms of estimation accuracy. Because the new model does not require multiple integral calculations, it can be applied to design complex systems configured in mixed series-parallel or networked components. [ABSTRACT FROM AUTHOR]