Multi-steps degradation process prediction for bearing based on improved back propagation neural network

Abstract

Bearing degradation process prediction is extremely important in industry. This article proposed a new method to achieve multi-steps bearing degradation prediction based on an improved back propagation neural network model. Firstly, time domain and time–frequency domain features extraction methods are employed to extract the original features from the mass vibration signals. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis is used to merge the original features and reduce the dimension, the typical sensitive features can be extracted. Then, based on the extracted features, the improved three-layer back propagation neural network model is constructed and trained for multi-steps bearing degradation process prediction. The phase space construction method is used to determine the embedding dimension of the back propagation neural network model. An accelerated bearing run-to-failure experiment was carried out, the results proved the effectiveness of the methodology.