Degradation feature extraction using multi-source monitoring data via logarithmic normal distribution based variational auto-encoder

Degeneration features extraction from multi-source monitoring data is significant for data-driven based state assessment and RUL prediction of complex equipment. However, the massive data gathered continuously from condition monitoring systems has created challenges to extract degradation features effectively, and meanwhile the current existing feature extraction methods such as PCA pays more attention to the integrity of the extracted information, but ignores the priori information. In this paper, we developed logarithmic normal distribution based variational auto-encoder algorithms which can ensure that the final feature extraction results follow the lognormal prior hypothesis to address above problems. The main procedure of the developed method is as follows. First, create the multi-source information dataset consist of different monitoring indicators, and normalize the dataset. Then, the KL divergence regularization is deduced based on the principle of variational Bayes inference. Next, the three-layer variational auto-encoder network model is constructed, and normalized datasets is used to train and test the model. The experiment results demonstrate that the degradation trajectory obtained using the proposed method offers stronger predictive capabilities than do those obtained using original methods, thereby improving the accuracy of predictions of the equipment remaining useful life.