Application of optimization feature extraction from bearing based on genetic programming for life prediction

In the field of the rolling bearing fault diagnosis, the remaining useful life prediction is very important. This paper proposes an approach based on genetic programming for features extraction, and multiple features are combined into a feature tree, so multi-dimensional input transfers to single-dimensional input. Furthermore, using the improved fitness to estimate the quality of the feature tree. After repeated iterations, the final output is the feature tree, whose fitness is the maximal. Besides, the curve of this feature tree is the closest to the linear trend in the time domain, hence, it is regarded as an independent feature named the optimization feature. This paper uses the vibration signal of the entire bearing life to predict the remaining useful life of the bearing with the optimization feature as the prediction model, and verifies the accuracy of prediction. © 2021, Editorial Board of Journal of Vibration Engineering. All right reserved.