Accelerated Life Testing (ALT) is very important in evaluating the reliability of highly reliable products. According to ALT procedure, products undergo higher stress levels than normal conditions to reduce the failure times. ALTs have been studied for various condition More
Accelerated Life Testing (ALT) is very important in evaluating the reliability of highly reliable products. According to ALT procedure, products undergo higher stress levels than normal conditions to reduce the failure times. ALTs have been studied for various conditions and stresses. In addition to common stress such as temperature and humidity, random usage can also be considered as another stress that can cause failure. Design of ALT plan for products which are exposed to random usage process have not been studied in the literature. Therefore, a procedure for designing ALT plan for these products is studied in this paper. To do so, hazard rate of products is formulated based on the random usage process and other stresses. Then, the variance of the hazard rate is estimated over a predetermined time period. Optimum stress levels and the number of units at every stress level are obtained by numerically minimizing the variance of the hazard rate estimate. Numerical example and sensitivity analysis are performed to show the application and robustness of the model to parameter deviations. The results show that the proposed procedure is robust to parameter changes and can be used for ALT planning of products under random usage.
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When a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. Upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. However, searching for assignable cause around th More
When a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. Upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. However, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always lead to successful identification of assignable cause(s). If process engineers could identify the change point, i.e. the time when the disturbance first manifested itself into the process, then corrective actions could be directed towards effective elimination of the source of disturbance. In this paper we develop a maximum likelihood estimator (MLE) for process change point designed to detect changes in process variance of a normal quality characteristic when the change follows a linear trend. We describe how this estimator can be used to identify the change point when a Shewhart S-control chart signals a change in the process variance. Numerical results reveal that the proposed estimator outperforms the MLE designed for step change when a linear trend disturbance is present.
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