[논문]LSTM-VAE를 활용한 기계시설물 장치의 이상 탐지 시스템

서재홍; 박준성; 유준우; 박희준

doi:10.7469/jksqm.2021.49.4.581

LSTM-VAE를 활용한 기계시설물 장치의 이상 탐지 시스템
Anomaly Detection System in Mechanical Facility Equipment: Using Long Short-Term Memory Variational Autoencoder 원문보기

品質經營學會誌 = Journal of Korean society for quality management, v.49 no.4, 2021년, pp.581 - 594

서재홍 (연세대학교 산업공학과) , 박준성 (연세대학교 산업공학과) , 유준우 (연세대학교 산업공학과) , 박희준 (연세대학교 산업공학과)

Abstract ▼ AI-Helper

Purpose: The purpose of this study is to compare machine learning models for anomaly detection of mechanical facility equipment and suggest an anomaly detection system for mechanical facility equipment in subway stations. It helps to predict failures and plan the maintenance of facility. Ultimately it aims to improve the quality of facility equipment. Methods: The data collected from Daejeon Metropolitan Rapid Transit Corporation was used in this experiment. The experiment was performed using Python, Scikit-learn, tensorflow 2.0 for preprocessing and machine learning. Also it was conducted in two failure states of the equipment. We compared and analyzed five unsupervised machine learning models focused on model Long Short-Term Memory Variational Autoencoder(LSTM-VAE). Results: In both experiments, change in vibration and current data was observed when there is a defect. When the rotating body failure was happened, the magnitude of vibration has increased but current has decreased. In situation of axis alignment failure, both of vibration and current have increased. In addition, model LSTM-VAE showed superior accuracy than the other four base-line models. Conclusion: According to the results, model LSTM-VAE showed outstanding performance with more than 97% of accuracy in the experiments. Thus, the quality of mechanical facility equipment will be improved if the proposed anomaly detection system is established with this model used.

주제어

표/그림 (11)

그림 Figure 1. Architecture of Autoencoder
그림 Figure 2. Anomaly detection system of mechanical facility equipment
표 Table 1. Total volume of data collected
그림 Figure 3. Vibration and current value of normal data
그림 Figure 4. Data of rotating body failure
그림 Figure 5. Data of axis alignment failure
표 Table 2. Model Parameter
그림 Figure 7. Distribution of loss function value
그림 Figure 8. Loss value of rotating body failure
그림 Figure 9. Loss value of axis alignment failure
표 Table 3. Model performance evaluation of Random, IF, OSVM, AE and LSTM-VAE

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