FFT-Guided Multi-Window USAD with DTW–Isolation Forest for Reliable Anomaly Detection in Industrial Power Time-Series

Background: Industrial power time-series exhibit strong daily/weekly periodicities and nonstationary behaviors that challenge generic deep autoencoders. Methods: We take first differences of the signal, compute the FFT spectrum, and map top spectral peaks to a small set of modeling window sizes. For each window, a GELU-activated CNN–GRU autoencoder is trained under the Unsupervised Anomaly Detection (USAD) paradigm (one encoder, two decoders with an adversarial phase). Reconstruction errors are measured with Dynamic Time Warping (DTW) to mitigate phase jitter, and final anomaly decisions are obtained by fitting an Isolation Forest to the error distribution. On a three-year, single-site dataset (15 min sampling), the approach detects abrupt spikes/drops and slow drifts across sub-daily to daily rhythms; FFT-selected windows of 11, 16, 24, 32, and 96 time steps (15 min units) cover the dominant cycles. Conclusions: FFT-guided multi-window training and inference, combined with a USAD-based model, DTW-aware scoring, and Isolation Forest, yields a practical unsupervised detector for smart-factory monitoring and near-real-time deployment.