Journal
CONTROL ENGINEERING PRACTICE
Volume 98, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2020.104358
Keywords
Autoencoder; Ensemble learning; Fault diagnosis; Imbalanced data; Incomplete data
Funding
- National Key RAMP
- D Program of China [2018YFB1201500, 2018YFB1703000]
- National Natural Science Foundation of China [61873201, 61773313, 61773016]
- Key research and development plan of Shaanxi Province [2018GY-139]
- Natural Science Foundation of Shaanxi Provincial Department of Education [19JS051]
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Skewed distribution and incompleteness of monitored data might cause feature submergence and information loss, rendering the fault diagnosis from imbalanced and incomplete data commonly existing in industrial systems is still an intractable problem. Therefore, in order to improve the accuracy of fault diagnosis from imbalanced and incomplete data, this paper proposes a fusion autoencoder (FAE) network and an ensemble diagnosis scheme. A designed multi-level denoising strategy and a variable-scale resampling strategy are adopted as compensation for information loss and skewed distribution. The multiple FAE networks are constructed by combining the advantages of improved sparse autoencoder (SAE) with denoising autoencoder (DAE) to enhance the adaptability. Different hyper parameters are configured for each FAE to ameliorate the diagnostic flexibility, and Bagging strategy is employed to integrate each network into a complete FAE fault diagnosis model. Furthermore, evaluation criteria are suggested and the application range of the model is tested. Finally, different experiments are conducted to verify the effectiveness and practicability of the proposed method.
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