Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 9, Pages 5139-5148Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2899118
Keywords
Classification; domain adaptation (DA); deep learning; fault diagnosis; power plant; thermal system
Categories
Funding
- Natural Science Foundation of Hebei Province of China [F2016502093]
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Fault diagnosis of a thermal system under varying operating conditions is of great importance for the safe and reliable operation of a power plant involved in peak shaving. However, it is a difficult task due to the lack of sufficient labeled data under some operating conditions. In practical applications, the model built on the labeled data under one operating condition will be extended to such operating conditions. Data distribution discrepancy can be triggered by variation of operating conditions and may degenerate the performance of the model. Considering the fact that data distributions are different but related under different operating conditions, this paper proposes a hierarchical deep domain adaptation (HDDA) approach to transfer a classifier trained on labeled data under one loading condition to identify faults with unlabeled data under another loading condition. In HDDA, a hierarchical structure is developed to reveal the effective information for final diagnosis by layer wisely capturing representative features. HDDA learns domain-invariant and discriminative features with the hierarchical structure by reducing distribution discrepancy and preserving discriminative information hidden in raw process data. For practical applications, the Taguchi method is used to obtain the optimized model parameters. Experimental results and comprehensive comparison analysis demonstrate its superiority.
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