research-article

Fault Diagnosis Based on Modified BiLSTM Neural Network

Authors:

Dailiang MaAuthors Info & Claims

ICIIT '20: Proceedings of the 2020 5th International Conference on Intelligent Information Technology

Pages 21 - 26

https://doi.org/10.1145/3385209.3385212

Published: 06 June 2020 Publication History

Abstract

Fault diagnosis of industrial processes has recently received widespread attention. Some studies applied neural networks to fault classification algorithms, which directly take the raw data as input of the neural network for training, and then use classifier to classify faults. However, the raw data of industrial usually has high dimension and the variables has linear correlations between each other, which may not only lead to higher computational complexity and cause the neural network difficult to converge. In this paper, a fault diagnosis algorithm based on a modified bidirectional long-short term memory network (BiLSTM) is investigated. The proposed method introduces the linear discriminant analysis (LDA) before the architecture of networks to reduce the dimension of input data. Then the LDA-BiLSTM is tested in Tennessee Eastman Process (TEP) platform. Experiment results show that the dimension reduction procedure can speed up the convergence of neural network, and may improve the classification accuracy. Extensive experimental results indicate that developed algorithm provides better diagnosis performance.

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Cited By

Bai GSun WCao CWang DSun QSun L(2024)GAN-Based Bearing Fault Diagnosis Method for Short and Imbalanced Vibration SignalIEEE Sensors Journal10.1109/JSEN.2023.333727824:2(1894-1904)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JSEN.2023.3337278
Bais PLondhe NChintapalli KNallana VMittal S(2024)Fault Classification Using Hybrid FCN-BiLSTM Model in Grid Integrated with Distributed Generators2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)10.1109/ICDCOT61034.2024.10515975(1-6)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICDCOT61034.2024.10515975
Kim SAzad MSong JKim H(2023)Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data AugmentationApplied Sciences10.3390/app13211183713:21(11837)Online publication date: 29-Oct-2023
https://doi.org/10.3390/app132111837
Show More Cited By

Index Terms

Fault Diagnosis Based on Modified BiLSTM Neural Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICIIT '20: Proceedings of the 2020 5th International Conference on Intelligent Information Technology

February 2020

163 pages

ISBN:9781450376594

DOI:10.1145/3385209

Copyright © 2020 ACM.

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Publication History

Published: 06 June 2020

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ICIIT 2020

ICIIT 2020: 2020 5th International Conference on Intelligent Information Technology

February 19 - 22, 2020

Hanoi, Viet Nam

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Cited By

Bai GSun WCao CWang DSun QSun L(2024)GAN-Based Bearing Fault Diagnosis Method for Short and Imbalanced Vibration SignalIEEE Sensors Journal10.1109/JSEN.2023.333727824:2(1894-1904)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JSEN.2023.3337278
Bais PLondhe NChintapalli KNallana VMittal S(2024)Fault Classification Using Hybrid FCN-BiLSTM Model in Grid Integrated with Distributed Generators2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)10.1109/ICDCOT61034.2024.10515975(1-6)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICDCOT61034.2024.10515975
Kim SAzad MSong JKim H(2023)Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data AugmentationApplied Sciences10.3390/app13211183713:21(11837)Online publication date: 29-Oct-2023
https://doi.org/10.3390/app132111837
Li XJiang XWang QYang LWang ZShen CZhu Z(2022)Multi-perspective deep transfer learning modelKnowledge-Based Systems10.1016/j.knosys.2022.108443243:COnline publication date: 11-May-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108443
Sun HZhao S(2021)Fault Diagnosis for Bearing Based on 1DCNN and LSTMShock and Vibration10.1155/2021/12214622021:1Online publication date: 19-Oct-2021
https://doi.org/10.1155/2021/1221462

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