Soochow University

Nan Chen

Jun Wang

Jun Zhu

Weiguo Huang

Zhongkui Zhu

Juanjuan Shi

Changqing Shen

Dongmiao Song

Jianqin Zhou

The present invention discloses a method for predicting bearing life based on a hidden Markov model (HMM) and transfer learning, including the following steps: (1) acquiring an original signal of full life of a rolling bearing; and extracting a feature set including a time domain feature, a time-frequency domain feature, and a trigonometric function feature; (2) inputting the feature set into an HMM to predict a hidden state, to obtain a failure occurrence time (FOT); (3) constructing a multilayer perceptron (MLP) model, obtaining a domain invariant feature and an optimal model parameter, and obtaining a neural network life prediction model; and (4) inputting the remaining target domain feature sets into the neural network life prediction model, and predicting the remaining life of the bearing. In the present invention, MLP-based transfer learning is used to resolve distribution differences in a source domain and a target domain caused by different operating conditions.

Method for predicting bearing life based on hidden Markov model and transfer learning

Growing community of inventors

Suzhou, China

Nan Chen