Hyper-parameter optimization based nonlinear multistate deterioration modeling for deterioration level assessment and remaining useful life prognostics

Complex equipment deterioration refers to a nonlinear multistate deterioration process, where the deterioration curve may not follow a typical shape such as exponential or linear function. A general solution is presented to nonlinear multistate deterioration modeling for deterioration level assessment and remaining useful life prognostics under no-label lifetime data including multi signals. In the solution, a three layer nonlinear multistate deterioration model of complex equipment is established based on hyper-parameter optimization. Hyper-parameter I and M, which determine the first two layers, are optimized by the proposed unsupervised extraction method based on greedy kernel principal components analysis and the improved Mann–Kendall criterion, respectively. As a determinant of the third layer, hyper-parameter N is optimized by the improved Bayesian information criterion to obtain optimized model, when parameters have been estimated under each alternative model structure at different N. The no-label lifetime dataset of turbofan engines are adopted for case study, and more accurate deterioration level and remaining useful life are obtained. The results verify the effectiveness of the presented solution. The study indicates that: nonlinearity makes an important effect on multistate deterioration modeling through hyper-parameters; the solution deals with nonlinearity in a systematic manner by hyper-parameter optimization of three layers.