Intelligent fault diagnosis of rolling bearing using the ensemble self-taught learning convolutional auto-encoders

The lack of labelled data presents a common challenge in many fault diagnosis and machine learning tasks. It requires the model to be able to efficiently capture useful fault features from a smaller amount of labelled data. In this paper, a method to train multiple convolutional auto-encoders by self-learning method and integrate them using ensemble learning, called ensemble self-taught learning convolutional auto-encoders (STL-CAEs), is proposed, which can effectively extract features of bearing vibration signals. First, an ensemble learning strategy is proposed to obtain two auto-encoders that satisfy the strategy by optimizing the model parameters and structure. Then, a self-taught learning training method is proposed to solve the problem of little label data. Finally, ensemble learning and fault diagnosis is achieved by the SoftMax classifier. Applying the proposed method to the bearing data from Case Western Reserve University, the STL-CAEs have higher accuracy and generalization than common fault diagnosis methods such as CAE, CNN, SAE and EMD, and also have significant advantages in terms of diagnostic time and training time.

Automated summary

The paper introduces a method to extract features from bearing vibration signals using ensemble self-taught learning convolutional auto-encoders (STL-CAEs), addressing the challenge of lack of labelled data. The proposed method achieves higher accuracy and generalization than common fault diagnosis methods and also has advantages in diagnostic time and training time when applied to bearing data from Case Western Reserve University.