Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery
| Autor: | Niao He, Yuquan Meng, Siyuan Chen, Haichuan Tang, Chenhui Shao, Yin Tian |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network business.industry Computer science Deep learning Kernel density estimation Condition monitoring Pattern recognition 02 engineering and technology Convolutional neural network Computer Science Applications Vibration 020901 industrial engineering & automation Control and Systems Engineering Robustness (computer science) Artificial intelligence Electrical and Electronic Engineering business Classifier (UML) |
| Zdroj: | IEEE/ASME Transactions on Mechatronics. 25:2167-2176 |
| ISSN: | 1941-014X 1083-4435 |
| DOI: | 10.1109/tmech.2020.3007441 |
| Popis: | Fault diagnosis for rolling elements in rotating machinery persistently receives high research interest due to the said machinery's prevalence in a broad range of applications. State-of-the-art methods in such setups focus on effective identification of faults that usually involve a single component while rejecting noise from limited sources. This article studies the data-based diagnosis of mixed faults coming from multiple components with an emphasis on model robustness against a wide spectrum of external perturbation. A dataset is collected on a rotor and bearing system by varying the levels and types of faults in both the rotor and bearing, which results in 48 machine health conditions. A duplet classifier is developed by combining two 1-D convolutional neural networks (CNNs) that are responsible for the diagnosis of the rotor and bearing faults, respectively. Experimental results show that the proposed classifier can reliably identify the onset and nature of mixed faults. In addition, one-vs-all classifiers are built using the features generated by the developed 1-D CNNs as predictors to recognize previously unlearned fault types. The effectiveness of such classifiers is demonstrated using data collected from four new fault types. Finally, the robustness and ability to reject external perturbation of the duplet classification model are analyzed using kernel density estimation. The code for the proposed classifiers is available at https://github.com/siyuanc2/machine-fault-diag . |
| Databáze: | OpenAIRE |
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