Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning

• Published in: IEEE transactions on instrumentation and measurement Vol. 67; no. 1; pp. 185 - 195
• Main Authors: Sun, Jiedi, Yan, Changhong, Wen, Jiangtao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Bearing fault; Compressed sensing; compressed sensing (CS); Data acquisition; Deep learning; deep neural network (DNN); Dependence; Diagnostic systems; Fault diagnosis; Feature extraction; intelligent diagnosis; Machine learning; Machinery condition monitoring; Neural networks; Roller bearings; Rotating machinery; Rotating machines; Signal processing; stacked sparse autoencoder (SSAE); Time-domain analysis; Vibration monitoring
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2017.2759418

Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault diagnosis has some deficiencies, such as complex computation of feature vectors, excessive dependence on prior knowledge and diagnostic expertise, and limited capacity for learning complex relationships in fault signals. Furthermore, following the increase in condition data, how to promptly process the massive fault data and automatically provide accurate diagnosis has become an urgent need to solve. Inspired by the idea of compressed sensing and deep learning, a novel intelligent diagnosis method is proposed for fault identification of rotating machines. In this paper, a nonlinear projection is applied to achieve the compressed acquisition, which not only reduces the amount of measured data that contained all the information of faults but also realizes the automatic feature extraction in transform domain. For exploring the discrimination hidden in the acquired data, a stacked sparse autoencoders-based deep neural network is established and performed with an unsupervised learning procedure followed by a supervised fine-tuning process. We studied the significance of compressed acquisition and provided the effects of key factors and comparison with traditional methods. The effectiveness of the proposed method is validated using data sets from rolling element bearings and the analysis shows that it is able to obtain high diagnotic accuracies and is superior to the existing methods. The proposed method reduces the need of human labor and expertise and provides new strategy to handle the massive data more easily.