GAN-SAE based fault diagnosis method for electrically driven feed pumps

• Published in: PloS one Vol. 15; no. 10; p. e0239070
• Main Authors: Han, Hui, Hao, Lina, Cheng, Dequan, Xu, He
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 22.10.2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Automation; Back propagation networks; Biology and Life Sciences; Classification; Coders; Computer and Information Sciences; Condition monitoring; Datasets; Deep learning; Engineering and Technology; Fault diagnosis; Fault location (Engineering); Feature extraction; Feeds; Feedwater pumps; Game theory; Generative adversarial networks; Impact analysis; Learning theory; Mechanical engineering; Mechanical properties; Medical diagnosis; Medicine and Health Sciences; Methods; Neural networks; Physical Sciences; Power plants; Propagation; Research and Analysis Methods; Sample size; Teaching methods; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0239070

The running of high-speed electrically driven feed pump has a direct impact on the safety of personnel equipment and economic benefits of power plant, as the result, intelligent condition monitoring and fault diagnosis of electrically driven feed pump becomes an urgent need. In the practical process of electrically driven feed pump fault diagnosis, the running of the equipment is in normal state for a long time, occasionally, with faults, which makes the fault data very rare in a large number of monitoring data, and makes it difficult to extract the internal fault features behind the original time series data, When the deep learning theory is used in practice, the imbalance between the fault data and the normal data occurs in the operation data set. In order to solve the problem of data imbalance, this paper proposes a fault diagnosis method of GAN-SAE. This method first makes compensation for the imbalance of sample data based on the Generative Adversarial Network (GAN), and then uses the Stacked Auto Encoder (SAE) method to extract the signal features. By designing the fault diagnosis program, compared with only using SAE, back propagation neural networks (BP) and multi-hidden layer neural networks(MNN) method, the GAN-SAE method can offer better capability of extracting features, and the accuracy of fault diagnosis of electrically driven feed pump could be improved to 98.89%.