Exploring Wavelet Transformations for Deep Learning-based Machine Condition Diagnosis

TL;DR

This study demonstrates that wavelet transform-based 2D representations combined with deep learning significantly improve motor fault diagnosis accuracy over previous methods, offering a promising non-intrusive condition monitoring approach.

Contribution

The paper introduces a novel combination of wavelet transform techniques with CNN models for motor fault diagnosis, achieving higher accuracy than existing 2D image-based methods.

Findings

WT-Morse achieved 93.73% accuracy, surpassing previous methods.

WT-based deep learning methods outperform traditional 2D-image-based approaches.

WSST methods faced challenges in fault classification accuracy.

Abstract

Deep learning (DL) strategies have recently been utilized to diagnose motor faults by simply analyzing motor phase current signals, offering a less costly and non-intrusive alternative to vibration sensors. This research transforms these time-series current signals into time-frequency 2D representations via Wavelet Transform (WT). The dataset for motor current signals includes 3,750 data points across five categories: one representing normal conditions and four representing artificially induced faults, each under five different load conditions: 0, 25, 50, 75, and 100%. The study employs five WT-based techniques: WT-Amor, WT-Bump, WT-Morse, WSST-Amor, and WSST-Bump. Subsequently, five DL models adopting prior Convolutional Neural Network (CNN) architecture were developed and tested using the transformed 2D plots from each method. The DL models for WT-Amor, WT-Bump, and WT-Morse showed remarkable effectiveness with peak model accuracy of 90.93, 89.20, and 93.73%, respectively, surpassing previous 2D-image-based methods that recorded accuracy of 80.25, 74.80, and 82.80% respectively using the identical dataset and validation protocol. Notably, the WT-Morse approach slightly exceeded the formerly highest ML technique, achieving a 93.20% accuracy. However, the two WSST methods that utilized synchrosqueezing techniques faced difficulty accurately classifying motor faults. The performance of Wavelet-based deep learning methods offers a compelling alternative for machine condition monitoring.