CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring

Kwangsuk Lee; Jae Kyeong Kim; Jaehyong Kim; Kyeon Hur; Hagbae Kim

doi:10.1109/ICKII.2018.8569155

CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring

Kwangsuk Lee, Jae Kyeong Kim, Jaehyong Kim, Kyeon Hur, Hagbae Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

26 Citations (Scopus)

Abstract

This paper proposes a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) combination scheme for anomaly detection to train feature extraction and to test anomaly prediction by using Stacked Convolutional Neural Networks (S-CNNs), Stacked Gated Recurrent Units (S-GRUs) as the typical model of RNNs, and a linear regression layer. In this proposed model, the S-CNNs layers firstly capture spatial feature extraction of the input sequence data of vibration sensor, and the result is used to temporal feature learning secondly with the S-GRUs. After this procedure, finally a regression layer predicts an anomaly detection. The experimental results of bearing data in NASA prognostics data repository not only show the accuracy of the proposed model in anomaly prediction for rotating machinery diagnostics, but also suggest the better performance than other state-of-the-art algorithms such as a plain RNN and GRU of the individual model.

Original language	English
Title of host publication	1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018
Editors	Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	102-105
Number of pages	4
ISBN (Electronic)	9781538652671
DOIs	https://doi.org/10.1109/ICKII.2018.8569155
Publication status	Published - 2018 Dec 7
Event	1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018 - Jeju Island, Korea, Republic of Duration: 2018 Jul 23 → 2018 Jul 27

Publication series

Name	1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018

Other

Other	1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018
Country/Territory	Korea, Republic of
City	Jeju Island
Period	18/7/23 → 18/7/27

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Graphics and Computer-Aided Design
Computer Networks and Communications
Computer Science Applications
Information Systems and Management
Media Technology

Access to Document

10.1109/ICKII.2018.8569155

Cite this

Lee, K., Kim, J. K., Kim, J., Hur, K., & Kim, H. (2018). CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring. In T-H. Meen (Ed.), 1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018 (pp. 102-105). [8569155] (1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICKII.2018.8569155

Lee, Kwangsuk ; Kim, Jae Kyeong ; Kim, Jaehyong et al. / CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring. 1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018. editor / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 102-105 (1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018).

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title = "CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring",

abstract = "This paper proposes a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) combination scheme for anomaly detection to train feature extraction and to test anomaly prediction by using Stacked Convolutional Neural Networks (S-CNNs), Stacked Gated Recurrent Units (S-GRUs) as the typical model of RNNs, and a linear regression layer. In this proposed model, the S-CNNs layers firstly capture spatial feature extraction of the input sequence data of vibration sensor, and the result is used to temporal feature learning secondly with the S-GRUs. After this procedure, finally a regression layer predicts an anomaly detection. The experimental results of bearing data in NASA prognostics data repository not only show the accuracy of the proposed model in anomaly prediction for rotating machinery diagnostics, but also suggest the better performance than other state-of-the-art algorithms such as a plain RNN and GRU of the individual model.",

author = "Kwangsuk Lee and Kim, {Jae Kyeong} and Jaehyong Kim and Kyeon Hur and Hagbae Kim",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018 ; Conference date: 23-07-2018 Through 27-07-2018",

year = "2018",

month = dec,

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doi = "10.1109/ICKII.2018.8569155",

language = "English",

series = "1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018",

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Lee, K, Kim, JK, Kim, J, Hur, K & Kim, H 2018, CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring. in T-H Meen (ed.), 1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018., 8569155, 1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018, Institute of Electrical and Electronics Engineers Inc., pp. 102-105, 1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018, Jeju Island, Korea, Republic of, 18/7/23. https://doi.org/10.1109/ICKII.2018.8569155

CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring. / Lee, Kwangsuk; Kim, Jae Kyeong; Kim, Jaehyong et al.

1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018. ed. / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 102-105 8569155 (1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Kim, Hagbae

PY - 2018/12/7

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N2 - This paper proposes a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) combination scheme for anomaly detection to train feature extraction and to test anomaly prediction by using Stacked Convolutional Neural Networks (S-CNNs), Stacked Gated Recurrent Units (S-GRUs) as the typical model of RNNs, and a linear regression layer. In this proposed model, the S-CNNs layers firstly capture spatial feature extraction of the input sequence data of vibration sensor, and the result is used to temporal feature learning secondly with the S-GRUs. After this procedure, finally a regression layer predicts an anomaly detection. The experimental results of bearing data in NASA prognostics data repository not only show the accuracy of the proposed model in anomaly prediction for rotating machinery diagnostics, but also suggest the better performance than other state-of-the-art algorithms such as a plain RNN and GRU of the individual model.

AB - This paper proposes a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) combination scheme for anomaly detection to train feature extraction and to test anomaly prediction by using Stacked Convolutional Neural Networks (S-CNNs), Stacked Gated Recurrent Units (S-GRUs) as the typical model of RNNs, and a linear regression layer. In this proposed model, the S-CNNs layers firstly capture spatial feature extraction of the input sequence data of vibration sensor, and the result is used to temporal feature learning secondly with the S-GRUs. After this procedure, finally a regression layer predicts an anomaly detection. The experimental results of bearing data in NASA prognostics data repository not only show the accuracy of the proposed model in anomaly prediction for rotating machinery diagnostics, but also suggest the better performance than other state-of-the-art algorithms such as a plain RNN and GRU of the individual model.

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Lee K, Kim JK, Kim J, Hur K, Kim H. CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring. In Meen T-H, editor, 1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 102-105. 8569155. (1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018). doi: 10.1109/ICKII.2018.8569155

CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring

Abstract

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