A hybrid model-based prognostics approach for estimating remaining useful life of rolling bearings

Data-driven machine learning (ML) for rolling bearing remaining useful life (RUL) prediction is a promising method in condition-based maintenance. However, due to the uncertainty of optimal hyperparameter tuning of the ML model, it is very difficult for a data-driven method to accurately predict the RUL of rolling bearings. Aiming to address this problem, this paper proposes a hybrid model-based on continuous wavelet transform (CWT), convolutional neural network (CNN), Bayesian network and long short-term memory network for estimating the remaining usage of rolling bearings lifetime. Firstly, the one-dimensional vibration signal of a bearing is divided into six segments and then it is converted into the corresponding two-dimensional time-frequency feature images via CWT. Secondly, the two-dimensional images are input into the two-dimensional CNN for deep feature extraction in order to obtain a series of one-dimensional feature vectors. Finally, it is input into a Bayesian-optimized long short-term memory model to obtain a prediction of the RUL of the bearing. The effectiveness of the proposed method is verified using bearing data. The verification results show that the proposed method has better prediction accuracy than the other two compared prediction methods, which indicates that the proposed method can effectively extract the bearing fault features and accurately predict the RUL of rolling bearings.