Autoencoder Based N-Segmentation Frequency Domain Anomaly Detection for Optimization of Facility Defect Identification 


Vol. 13,  No. 3, pp. 130-139, Mar.  2024
https://doi.org/10.3745/TKIPS.2024.13.3.130


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  Abstract

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

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[IEEE Style]

K. Park and Y. Lee, "Autoencoder Based N-Segmentation Frequency Domain Anomaly Detection for Optimization of Facility Defect Identification," The Transactions of the Korea Information Processing Society, vol. 13, no. 3, pp. 130-139, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.3.130.

[ACM Style]

Kichang Park and Yongkwan Lee. 2024. Autoencoder Based N-Segmentation Frequency Domain Anomaly Detection for Optimization of Facility Defect Identification. The Transactions of the Korea Information Processing Society, 13, 3, (2024), 130-139. DOI: https://doi.org/10.3745/TKIPS.2024.13.3.130.

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