Open AccessIntelligent fault diagnosis of rolling bearing using the ensemble self‐taught learning convolutional auto‐encoders
Author(s) -
Zhang Yilan,
Wang Jinxi,
Zhang Faye,
Lv Shanshan,
Zhang Lei,
Jiang Mingshun,
Sui Qingmei
Publication year - 2022
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/smt2.12092
Subject(s) - softmax function , computer science , ensemble learning , artificial intelligence , classifier (uml) , autoencoder , fault (geology) , encoder , pattern recognition (psychology) , bearing (navigation) , convolutional neural network , machine learning , generalization , deep learning , mathematics , mathematical analysis , seismology , geology , operating system
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.