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Development of Anomaly Detector for Motor Bearing Condition Monitoring using Fast Fourier Transform (FFT) and Long Short Term Memory (LSTM)-Autoencoder

Bashir Sulaimon Adebayo*, Oladebo Suliat Jimoh**, Idris Mohammed Kolo***, Enesi Femi Aminu****

*-**** Department of Computer Science, School of Information and Communication Technology, Federal University of Technology, Minna, Nigeria.

Periodicity:January - June'2023
DOI : https://doi.org/10.26634/jpr.10.1.19762

Abstract

Anomaly detection in motor bearings is a critical task for preventing downtime and ensuring efficient operation. This paper proposes a novel approach for anomaly detection using Fast Fourier Transform (FFT) and Long Short-Term Memory (LSTM)-Autoencoder (AE). A data processing approach based on FFT was developed to pre-process the raw sensor data. This helped to reduce noise and improve the Signal-to-Noise Ratio (SNR). Additionally, an anomaly detection model based on LSTM-Autoencoder was developed and trained on the pre-processed data. The proposed approach was able to detect anomalies at a low threshold and achieved a high accuracy score.

Keywords

Motor Bearing, Anomaly Detection, Deep Learning, Fast Fourier Transform, Long Short Term Memory, Autoencoder.

How to Cite this Article?

Bashir, S. A., Jimoh, O. S., Kolo, I. M., and Aminu, E. F. (2023). Development of Anomaly Detector for Motor Bearing Condition Monitoring using Fast Fourier Transform (FFT) and Long Short Term Memory (LSTM)-Autoencoder. i-manager’s Journal on Pattern Recognition, 10(1), 1-15. https://doi.org/10.26634/jpr.10.1.19762

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