In the industrial community it is well known that the failure rate of the manufactured units vary with time due to a variety of causes, namely, engineering design, manufacturing process, maintenance and quality inspection procedures and various assignable and non-assignable factors. Such failure rates invariably exhibit changes in both level and slope and at times exhibit periodic patterns as well. Therefore it would be quite inappropriate and erroneous to analyze such stochastic series of observations using the usual failure distribution approach. Since such data can be construed as time series, we suggest in this paper the time series techniques including the Kalman filter for their analysis. Other advantages of using the latter techniques are that the periodicities, if any, can be taken into account and short-term forecasts can be made which otherwise would not have been possible.
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