Development of anomaly detector for motor bearing condition monitoring using Fast Fourier Transform (FFT) and Long Short Term Memory (LSTM)-Autoencoder

Author:

Sulaimon A. Bashir1,Oladebo Suliat Jimoh1,Idris Mohammed Kolo1,Enesi Femi Aminu1

Affiliation:

1. Federal University of Technology

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.

Publisher

i-manager Publications

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference37 articles.

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