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http://dx.doi.org/10.9717/kmms.2022.25.11.1547

Anomaly Diagnosis of Rotational Machinery Using Time-Series Vibration Data Based on Time-Distributed CNN-LSTM  

Kim, Min-Ki (Dept. of Computer Science, Gyeongsang National University, Engineering Research Institute)
Publication Information
Journal of Korea Multimedia Society / v.25, no.11, 2022 , pp. 1547-1556 More about this Journal
Abstract
As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.
Keywords
Anomaly Detection; Fault Diagnosis; CNN-LSTM;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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