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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publication1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018
EditorsTeen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages102-105
Number of pages4
ISBN (Electronic)9781538652671
DOIs
Publication statusPublished - 2018 Dec 7
Event1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018 - Jeju Island, Korea, Republic of
Duration: 2018 Jul 232018 Jul 27

Publication series

Name1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018

Other

Other1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018
CountryKorea, Republic of
CityJeju Island
Period18/7/2318/7/27

Fingerprint

Bearings (structural)
Rotating machinery
Health
Neural networks
Monitoring
Feature extraction
Network layers
Linear regression
NASA
Machinery
Anomaly detection
Sensors
Anomaly
Prediction

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

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 ; Hur, Kyeon ; Kim, Hagbae. / 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).
@inproceedings{b26577c37a374d1d83fe96a02cd12d4a,
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",
year = "2018",
month = "12",
day = "7",
doi = "10.1109/ICKII.2018.8569155",
language = "English",
series = "1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "102--105",
editor = "Teen-Hang Meen",
booktitle = "1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018",
address = "United States",

}

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; Hur, Kyeon; Kim, Hagbae.

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 proceedingConference contribution

TY - GEN

T1 - CNN and GRU combination scheme for Bearing Anomaly Detection in Rotating Machinery Health Monitoring

AU - Lee, Kwangsuk

AU - Kim, Jae Kyeong

AU - Kim, Jaehyong

AU - Hur, Kyeon

AU - Kim, Hagbae

PY - 2018/12/7

Y1 - 2018/12/7

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.

UR - http://www.scopus.com/inward/record.url?scp=85060738510&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060738510&partnerID=8YFLogxK

U2 - 10.1109/ICKII.2018.8569155

DO - 10.1109/ICKII.2018.8569155

M3 - Conference contribution

T3 - 1st IEEE International Conference on Knowledge Innovation and Invention, ICKII 2018

SP - 102

EP - 105

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

A2 - Meen, Teen-Hang

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

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). https://doi.org/10.1109/ICKII.2018.8569155