Convolution neural network based particle filtering for remaining useful life prediction of rolling bearing

Aiming at the problem of remaining useful life prediction of rolling bearing in aero engine, a data-driven prediction method based on deep learning and particle filter is proposed. Initially, only the vibration data of rolling bearing in normal stage are trained by the deep convolution neural network. According to the feature distance between normal and degraded samples, the evolution features during the whole lifetime are extracted adaptively, and the health index of rolling bearing is constructed. Then, the alarm and failure threshold are determined by unsupervised clustering algorithm. Combined with the extracted feature, remaining useful life of rolling bearing is tracked and predicted by particle filter algorithm based on four parameter exponential model. Finally, the effectiveness of the proposed method is verified by three groups of whole lifetime test data of rolling bearings. Results show that the degradation feature extracted by deep learning method has higher prediction accuracy of 2.19%, 0.93%, and 1.43% respectively than RMS values, and has more stable performance and less influenced by the number of particles or resampling methods, which can better reflect the evolution trend of rolling bearing than the traditional feature.

Automated summary

This study proposes a data-driven prediction method based on deep learning and particle filter for the remaining useful life prediction of rolling bearings in aero engines. The degradation feature of the rolling bearings is extracted using a deep learning method, and a health index is constructed. The remaining useful life is then tracked and predicted using a particle filter algorithm. Experimental results show that the degradation feature extracted by the deep learning method has higher prediction accuracy and more stable performance compared to the traditional RMS values, better reflecting the evolution trend of the rolling bearings.