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.
EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning
University of Applied Science and Technology（应用科技大学）；MZG Consulting（MZG咨询公司）；Institute of Electrical and Electronics Engineers（电气与电子工程师协会）；Baku State University（巴库国立大学）
Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets for Vision
Dept. of Electrical and Electronics Engineering, Koç University（电子工程系，科奇大学）；Codeway AI Research（Codeway人工智能研究）；Dept. of Computer Engineering, Middle East Technical University（计算机工程系，中东技术大学）
该研究提出小波微调（WaveFT），利用权重矩阵小波域的稀疏更新实现细粒度参数控制，在视觉任务的参数高效微调方法中达最优，已纳入Hugging Face PEFT库。
Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)
Dongguk University（东国大学）；University of Nottingham（诺丁汉大学）
