Failure prediction with statistical analysis of bearing using deep forest model and change point detection.

Current failure prediction methods of bearings have less uncertainty analysis with interpretability, less correlation analysis between degradation characteristics and prediction error. Moreover, there are multiple degradation stages in entire life cycle and prediction performance cannot meet practical demands. Therefore, this paper proposes a new approach for failure prediction of bearings. The change point detection method achieves multi-stage division of degradation data. The improved hybrid deep forest with best dissimilarity sequence (BDS) is studied and a new pretrained algorithm with pruning operation is developed. The convergence theorem is proved. A novel multi-stage failure prediction algorithm based on improved hybrid deep forest, hypothesis testing and interpretability analysis, is developed to get better prediction result. The time complexity of proposed algorithm is analyzed. The datasets of NASA and FEMTO-ST institute are utilized and experimental results show that: 1) Our approach with model interpretability has better prediction performances than support vector machine (SVR), recurrent neural network (RNN), long short-term memory (LSTM), and deep forest (DF); 2) The non-normal distribution characteristics, monotonic degradation trend and effect size of multiple stages are analyzed based on hypothesis testing methods; 3) The positive and inverse relation analysis achieves the correlation interpretability between multi-stage degradation characteristics and failure prediction results. • EWMA, CUSUM, and K-means clustering are used to obtain real change points for multi-stage division. • An improved hybrid deep forest model with BDS is presented to improve prediction performance. • This paper proposes a new pretrained algorithm to achieve a better tradeoff between accuracy and runtime cost. • A multi-stage failure prediction algorithm with model interpretability is developed. • Hypothesis testing and correlation analysis are utilized to enhance the interpretability of degradation characteristics. [ABSTRACT FROM AUTHOR]