Development of a new reliability prediction model for predicting the reliability of repairable products

Liang, Yi

doi:10.1504/ijpd.2009.023319

Cited by 4 publications

(6 citation statements)

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“…Traditional time series methods include the exponential smoothing method, the MA method, the time series decomposition method, and the ARIMA and SARIMA models. The exponential smoothing method, the MA method, the time series decomposition method, and the ARIMA and SARIMA models have been used for analyzing and forecasting failure data of repairable systems (Singpurwalla, 1978;Singh et al, 1994;Liang and Tong, 2001;Liang, 2009). Numerous studies have adopted time series analysis to analyze and forecast the failure data of repairable systems.…”

Section: Ijqrm 307mentioning

confidence: 99%

“…Examples of traditional time series methods are the exponential smoothing method, the moving average (MA) method, the time series decomposition method, the autoregressive integrated-moving average (ARIMA) and the seasonal ARIMA (SARIMA) models. Failure data of repairable systems have been analyzed and used for forecasting by exponential smoothing (Healy, 1997), MA (Xie et al, 1997;Wang et al, 2007), and time series decomposition methods (Liang, 2009), as well as by the ARIMA and SARIMA models (Singpurwalla, 1978;Singh et al, 1994;Liang and Tong, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Applying the generalized autoregressive conditional Heteroskedastic model to analyze and forecast the field failure data of repairable systems

Liang

2013

Int J Qual & Reliability Mgmt

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Purpose -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. Design/methodology/approach -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. Findings -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. Originality/value -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.

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Section: Ijqrm 307mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying the generalized autoregressive conditional Heteroskedastic model to analyze and forecast the field failure data of repairable systems

Liang

2013

Int J Qual & Reliability Mgmt

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“…Jiang and Xu (2007) used the time series decomposition method to model and analyze software aging and software failure. Liang (2009) first tried to use the time series decomposition approach to forecast the reliability of repairable systems. However, Liang (2009) did not explain explicitly and discuss sufficiently the proposed method.…”

Section: Introductionmentioning

confidence: 99%

“…Liang (2009) first tried to use the time series decomposition approach to forecast the reliability of repairable systems. However, Liang (2009) did not explain explicitly and discuss sufficiently the proposed method. Liang (2009) also did not compare other reliability prediction methods such as the ARIMA model and neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing and forecasting the reliability for repairable systems using the time series decomposition method

Liang

2011

International Journal of Quality &Amp; Reliability Management

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Purpose -The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems. Design/methodology/approach -This study employs NHPP to model the failure data. Initially, Nelson's graph method is employed to estimate the mean number of repairs and the MCRF value for the repairable system. Second, the time series decomposition approach is employed to predict the mean number of repairs and MCRF values. Findings -The proposed method can analyze and predict the reliability for repairable systems. It can analyze the combined effect of trend-cycle components and the seasonal component of the failure data.Research limitations/implications -This study only adopts simulated data to verify the proposed method. Future research may use other real products' 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 -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. Originality/value -The time series decomposition approach was used to model and analyze software aging and software failure in 2007. However, the time series decomposition approach was rarely used for modeling and analyzing the failure data for repairable systems. This study proposes the time series decomposition approach to analyze and predict the failure data of the repairable systems and the proposed method is better than the ARIMA model and neural networks in predictive accuracy.

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Enhancing reliability of thermal power plant by implementing RCM policy and developing reliability prediction model: a case study

Bhangu

Pahuja

Singh

2016

Int J Syst Assur Eng Manag

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Development of a new reliability prediction model for predicting the reliability of repairable products

Cited by 4 publications

References 0 publications

Applying the generalized autoregressive conditional Heteroskedastic model to analyze and forecast the field failure data of repairable systems

Applying the generalized autoregressive conditional Heteroskedastic model to analyze and forecast the field failure data of repairable systems

Analyzing and forecasting the reliability for repairable systems using the time series decomposition method

Enhancing reliability of thermal power plant by implementing RCM policy and developing reliability prediction model: a case study

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