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Reliability Prediction Using Degradation Data – A Preliminary Study Using Neural Network-based Approach

One of the most critical aspects of degradation-based estimation of reliability lies in the modeling of degradation data. A variety of techniques ranging from mixed-effects non linear regression to techniques like Neural Networks have been applied for capturing the behaviour of the degradation paths. In this work, we propose to study the behaviour of the distributional parameters of degradation signals across time using a Neural Networks approach. We used data generated from an underlying process of a Gaussian distribution. A neural network is trained to recognize the variation pattern of the distributional characteristics of the process. The neural network is then used to predict these characteristics’ values at future time intervals. These predictions were found to be comparable to the theoretical values and were subsequently used to arrive at reliability estimates.