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Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery
- Source :
- IEEE/ASME Transactions on Mechatronics. 25:2167-2176
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- 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 .
- Subjects :
- 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)
Subjects
Details
- ISSN :
- 1941014X and 10834435
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- IEEE/ASME Transactions on Mechatronics
- Accession number :
- edsair.doi...........508102b94eac999801713e30ebfeaa0b