Exploring Wavelet Transformations for Deep Learning-based Machine Condition Diagnosis
<|reference_start|>Exploring Wavelet Transformations for Deep Learning-based Machine Condition Diagnosis: 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.<|reference_end|>
@article{piedad2024exploring,
    title={Exploring Wavelet Transformations for Deep Learning-based Machine
  Condition Diagnosis},
    author={Eduardo Jr Piedad, Christian Ainsley Del Rosario, Eduardo
  Prieto-Araujo, Oriol Gomis-Bellmunt},
    journal={10.1109/Diagnostika61830.2024.10693895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09644},
    primaryClass={eess.SP cs.AI eess.IV}
}