期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 33, 期 11, 页码 6789-6801出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3083401
关键词
Hydraulic systems; Fault diagnosis; Sensors; Valves; Temperature sensors; Feature extraction; Monitoring; Convolutional neural network (CNN); deep learning (DL); fault diagnosis; hydraulic system; multirate data samples
类别
资金
- National Natural Science Foundation of China [62073340, 61973319, 61860206014]
- National Key Research and Development Program of China [2019YFB1705300]
- Innovation-Driven Plan in Central South University, China [2019CX020]
- Excellent Youth Natural Science Foundation of Hunan Province [2019JJ30032]
- State Key Laboratory of Robotics and Systems (HIT) [SKLRS-2020-KF-14]
- 111 Project, China [B17048]
Hydraulic systems, as a typical complex nonlinear system, pose challenges in fault diagnosis; a deep learning model with multirate data samples is proposed, capable of automatically extracting features and suitable for industrial environments; experimental results demonstrate high diagnostic accuracy even in the case of imbalanced sample data.
Hydraulic systems are a class of typical complex nonlinear systems, which have been widely used in manufacturing, metallurgy, energy, and other industries. Nowadays, the intelligent fault diagnosis problem of hydraulic systems has received increasing attention for it can increase operational safety and reliability, reduce maintenance cost, and improve productivity. However, because of the high nonlinear and strong fault concealment, the fault diagnosis of hydraulic systems is still a challenging task. Besides, the data samples collected from the hydraulic system are always in different sampling rates, and the coupling relationship between the components brings difficulties to accurate data acquisition. To solve the above issues, a deep learning model with multirate data samples is proposed in this article, which can extract features from the multirate sampling data automatically without expertise, thus it is more suitable in the industrial situation. Experiment results demonstrate that the proposed method achieves high diagnostic and fault pattern recognition accuracy even when the imbalance degree of sample data is as large as 1:100. Moreover, the proposed method can increase about 10% diagnosis accuracy when compared with some state-of-the-art methods.
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