Anomaly Detection Framework for Rotary Equipment Using ContinuousWavelet Transform and U-Net Autoencoders

Recent advances in data-driven methods, particularly deep learning, have transformed predictive maintenance for rotary machinery. These methods enable intelligent, sensor-based condition monitoring from unlabeled operational data, even under rare-fault conditions. This study proposes an unsupervised anomaly detection framework for rotary equipment that utilizes continuous wavelet transform (CWT) to transform unlabeled, multichannel vibration signals into stacked time-frequency scalograms using complex Morlet wavelet. These scalograms are then processed by an enhanced U-Net deep convolutional autoencoder (CWT-U-Net CAE), which learns features of healthy operational conditions and detects anomalies by identifying significant deviations in reconstruction error. Coupled with its edge-compatibility, the framework enables scalable real-time condition monitoring in industrial environments. A custom test bench with an induction motor was used to obtain realistic vibrational signatures under normal operating conditions, assessing the effectiveness of the proposed approach.