Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 9, Pages 6379-6389Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2999323
Keywords
Optimization; Feature extraction; Indexes; Monitoring; Sorting; Sociology; Statistics; Data driven; health monitoring; multiobjective optimization; stacked autoencoder
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Funding
- National Natural Science Foundation of China [61803390, 61773407, 61790571]
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61621062]
- Hunan Provincial Key Laboratory [2017TP1002, 2019T120713]
- Project of State Key Laboratory of High Performance Complex Manufacturing, Central South University [ZZYJKT2020-14, ZZYJKT2019-14]
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This article introduces a new data-driven health monitoring method using multiobjective optimization and stacked autoencoder for health indicator construction. Through simulation experiments, the proposed method accurately identifies equipment status and effectively reduces the complexity of the diagnostic model.
This article proposes a new data-driven health monitoring method, which uses multiobjective optimization and stacked autoencoder based health indicator. Specifically, the proposed method proposes an improved nondominated sorting genetic algorithm-II (NSGA-II) to perform multiobjective optimization on a large number of candidate features extracted from the sensor measurements. Then, a stacked autoencoder model is used to construct health indicators from the selected features. In the improved NSGA-II algorithm, the optimization goals of feature selection are defined as the minimum gap of health indicators between different states and the number of features. Comparisons between the proposed method and the state-of-the-art methods on simulation experiments show that the proposed method can accurately identify the status of the equipment and effectively limit the complexity of the diagnostic model.
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