Data Reduction Techniques for Intelligent Fault Diagnosis in Automotive Systems

2006 ◽  
Author(s):  
Kihoon Choi ◽  
Jianhui Luo ◽  
Krishna R. Pattipati ◽  
Setu Madhavi Namburu ◽  
Liu Qiao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Reduction ◽  
Intelligent Fault Diagnosis ◽  
Automotive Systems ◽  
Reduction Techniques

Flight Test Execution and Data Reduction Techniques of Exhaust Gas Reingestion on the CH-53K King Stallion

2021 ◽  
Author(s):  
Noopur Joshi ◽  
Noah Becker ◽  
Roger Tull ◽  
James Kenna ◽  
Christopher Adams ◽  
...  
Keyword(s):  
Data Reduction ◽  
Flight Test ◽  
Exhaust Gas ◽  
Test Execution ◽  
Reduction Techniques

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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