LSTM-GAN-AE: A Promising Approach for Fault Diagnosis in Machine Health Monitoring. Liu, H., Zhao, H., Wang, J., Yuan, S., & Feng, W. IEEE Transactions on Instrumentation and Measurement, 2021. Conference Name: IEEE Transactions on Instrumentation and Measurementdoi abstract bibtex Recent years have witnessed that, real-time health monitoring for machine gains more and more importance with the goal of achieving fault diagnosis and predictive maintenance. Conventional diagnosis methods face formidable challenges imposed by the high requirement for expert knowledge and extensive labor. The diagnosis scheme based on deep learning (DL) models has served as a promising solution and achieved great success. However, many of these DL-based models are fail to extract critical temporal information thoroughly. In addition, it is difficult to apply them to machine health monitoring (MHM) in real-time as those methods take long time for diagnosis in practice. To address the aforementioned issues, this paper introduces a novel intelligent fault diagnosis algorithm with three stages for MHM. It is a hybrid framework that combines generative adversarial networks (GAN) and auto-encoder (AE) based on the bi-directional long short-term memory (bi-LSTM). Firstly, GAN is employed to obtain the reconstruction residual and learn the discriminative representation. Then, AE is used to perform the critical temporal features extraction and dimension reduction. Finally, the supervised learning model is constructed to integrate feature information and predict diagnosis results. To verify the effectiveness of the proposed algorithm, typical rolling bearing datasets are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the competing methods.
@article{liu_lstm-gan-ae_2021,
title = {{LSTM}-{GAN}-{AE}: {A} {Promising} {Approach} for {Fault} {Diagnosis} in {Machine} {Health} {Monitoring}},
issn = {1557-9662},
shorttitle = {{LSTM}-{GAN}-{AE}},
doi = {10.1109/TIM.2021.3135328},
abstract = {Recent years have witnessed that, real-time health monitoring for machine gains more and more importance with the goal of achieving fault diagnosis and predictive maintenance. Conventional diagnosis methods face formidable challenges imposed by the high requirement for expert knowledge and extensive labor. The diagnosis scheme based on deep learning (DL) models has served as a promising solution and achieved great success. However, many of these DL-based models are fail to extract critical temporal information thoroughly. In addition, it is difficult to apply them to machine health monitoring (MHM) in real-time as those methods take long time for diagnosis in practice. To address the aforementioned issues, this paper introduces a novel intelligent fault diagnosis algorithm with three stages for MHM. It is a hybrid framework that combines generative adversarial networks (GAN) and auto-encoder (AE) based on the bi-directional long short-term memory (bi-LSTM). Firstly, GAN is employed to obtain the reconstruction residual and learn the discriminative representation. Then, AE is used to perform the critical temporal features extraction and dimension reduction. Finally, the supervised learning model is constructed to integrate feature information and predict diagnosis results. To verify the effectiveness of the proposed algorithm, typical rolling bearing datasets are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the competing methods.},
journal = {IEEE Transactions on Instrumentation and Measurement},
author = {Liu, Haoqiang and Zhao, Hongbo and Wang, Jiayue and Yuan, Shuai and Feng, Wenquan},
year = {2021},
note = {Conference Name: IEEE Transactions on Instrumentation and Measurement},
keywords = {Data mining, Deep learning, Fault diagnosis, Feature extraction, Generative adversarial networks, Logic gates, Monitoring, Support vector machines, ecml, fault diagnosis, long short-term memory (LSTM), machine health monitoring},
pages = {1--1},
}
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In addition, it is difficult to apply them to machine health monitoring (MHM) in real-time as those methods take long time for diagnosis in practice. To address the aforementioned issues, this paper introduces a novel intelligent fault diagnosis algorithm with three stages for MHM. It is a hybrid framework that combines generative adversarial networks (GAN) and auto-encoder (AE) based on the bi-directional long short-term memory (bi-LSTM). Firstly, GAN is employed to obtain the reconstruction residual and learn the discriminative representation. Then, AE is used to perform the critical temporal features extraction and dimension reduction. Finally, the supervised learning model is constructed to integrate feature information and predict diagnosis results. To verify the effectiveness of the proposed algorithm, typical rolling bearing datasets are taken as trial data. 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Conventional diagnosis methods face formidable challenges imposed by the high requirement for expert knowledge and extensive labor. The diagnosis scheme based on deep learning (DL) models has served as a promising solution and achieved great success. However, many of these DL-based models are fail to extract critical temporal information thoroughly. In addition, it is difficult to apply them to machine health monitoring (MHM) in real-time as those methods take long time for diagnosis in practice. To address the aforementioned issues, this paper introduces a novel intelligent fault diagnosis algorithm with three stages for MHM. It is a hybrid framework that combines generative adversarial networks (GAN) and auto-encoder (AE) based on the bi-directional long short-term memory (bi-LSTM). Firstly, GAN is employed to obtain the reconstruction residual and learn the discriminative representation. Then, AE is used to perform the critical temporal features extraction and dimension reduction. 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