An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error

Due to the powerful ability of artificial neural network in data fitting, it has been applied to describe the mean function in Wiener process for degradation modeling and estimating reliability. However, the previously published method neglects the measurement error and individual difference, which need to be considered in reliability estimation. In order to handle the above issues, the artificial neural network supported Wiener process is improved. Both the individual difference and measurement error are considered. The measurement error is described by a normal distribution with zero mean. The individual difference is described by introducing hyper parameters. In order to demonstrate the proposed method, a simulation study and a case study have been performed. It can be seen that, considering the measurement error and individual difference can improve the reliability and lifetime estimation accuracies, resulting in the proposed method is more powerful and more practical in engineering practice.