A physics-informed deep learning approach for bearing fault detection
| Autor: | Venkat P. Nemani, Shawn Kenny, Mohammadkazem Sadoughi, Hao Lu, Jeff Sidon, Chao Hu, Adam Thelen, Sheng Shen, Keith E. Webster, Matthew J. Darr |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Bearing (mechanical) business.industry Computer science Deep learning Big data Condition monitoring 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Field (computer science) Fault detection and isolation law.invention 020901 industrial engineering & automation Artificial Intelligence Control and Systems Engineering law 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business computer Computer technology |
| Zdroj: | Engineering Applications of Artificial Intelligence. 103:104295 |
| ISSN: | 0952-1976 |
| Popis: | In recent years, advances in computer technology and the emergence of big data have enabled deep learning to achieve impressive successes in bearing condition monitoring and fault detection. While existing deep learning approaches are able to efficiently detect and classify bearing faults, most of these approaches depend exclusively on data and do not incorporate physical knowledge into the learning and prediction processes—or more importantly, embed the physical knowledge of bearing faults into the model training process, which makes the model physically meaningful. To address this challenge, we propose a physics-informed deep learning approach that consists of a simple threshold model and a deep convolutional neural network (CNN) model for bearing fault detection. In the proposed physics-informed deep learning approach, the threshold model first assesses the health classes of bearings based on known physics of bearing faults. Then, the CNN model automatically extracts high-level characteristic features from the input data and makes full use of these features to predict the health class of a bearing. We designed a loss function for training and validating the CNN model that selectively amplifies the effect of the physical knowledge assimilated by the threshold model when embedding this knowledge into the CNN model. The proposed physics-informed deep learning approach was validated using (1) data from 18 bearings on an agricultural machine operating in the field, and (2) data from bearings on a laboratory test stand in the Case Western Reserve University (CWRU) Bearing Data Center. |
| Databáze: | OpenAIRE |
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