An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

• Published in: IEEE transactions on industrial electronics (1982) Vol. 63; no. 5; pp. 3137 - 3147
• Main Authors: Lei, Yaguo, Jia, Feng, Lin, Jing, Xing, Saibo, Ding, Steven X.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bearing; Big Data; Data management; Datasets; Diagnosis; Fault diagnosis; Feature extraction; Filtering; Human; intelligent fault diagnosis; Learning; Learning (artificial intelligence); Mechanical big data; Motors; Neural networks; softmax regression; sparse filtering; unsupervised feature learning; Vibrations; Intelligent fault diagnosis; unsupervised feature learning; softmax regression; mechanical big data; sparse filtering
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2016.2519325

Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time-consuming and labor-intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two-layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.