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A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing
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DOI：10.1109/ACCESS.2020.3009644.png)
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Abstract
En 中文
Bearing fault diagnosis is of great significance to ensure the safe operation of mechanical equipment. This paper proposes an intelligent fault diagnosis method of rolling bearings based on deep belief network (DBN) with hyperparameter optimization by using parallel computing. Different with traditional diagnosis methods that extract the features manually depending on much prior knowledge about signal processing techniques and diagnostic expertise, DBN extracts fault features automatically by machine learning mechanism. Considering the time consuming problem, parallel computing is adopted to the DBN training process by using a Master/Slave mode to improve the training speed so that the global optimization with Genetic Algorithm and higher diagnosis accuracy can be achieved. Finally, the proposed method is verified with the public datasets provided by Case Western Reserve University (CWRU) with various fault depths in different locations and loads of rolling bearings. The results indicate that the proposed method can identify bearing faults under different conditions correctly which significantly enhances the intelligence of fault classification and reduces the time for parameter selection of deep learning models.
Keywords:
Fault diagnosis
Parallel processing
Machine learning
Optimization
Feature extraction
Training
Rolling bearings
Deep belief network
hyperparameter optimization
parallel computing
fault diagnosis
Journal
IF:
3.6
Papers:
9.8W
Citations:
32.6W
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