Clustering Empirical Failure Rate Curves for Reliability Prediction Purposes in the Case of Consumer Electronic Products

In this paper, a methodology based on the combination of time series modeling and soft computational methods is presented to model and forecast bathtub‐shaped failure rate data of newly marketed consumer electronics. The time‐dependent functions of historical failure rates are typified by parameters of an analytic model that grabs the most important characteristics of these curves. The proposed approach is also verified by the presentation of an industrial application brought along at an electrical repair service provider company. The prediction capability of the introduced methodology is compared with moving average‐based and exponential smoothing‐based forecasting methods. According to the results of comparison, the presented method can be considered as a viable alternative reliability prediction technique.