Exploring Wavelet Transformations for Deep Learning-based Machine Condition Diagnosis

• URL: http://arxiv.org/abs/2408.09644v2
• Date: Mon, 14 Oct 2024 10:08:09 GMT
• Title: Exploring Wavelet Transformations for Deep Learning-based Machine Condition Diagnosis
• Authors: Eduardo Jr Piedad, Christian Ainsley Del Rosario, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt,
• Abstract summary: This research transforms time-series current signals into time-frequency 2D representations via Wavelet Transform. The study employs five WT-based techniques: WT-Amor, WT-Bump, WT-Morse, WSST-Amor, and WSST-Bump. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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