This article proposes an intelligent software system for detecting and diagnosing motor faults in near–real-time conditions. The approach extracts time–frequency features (mean, RMS, skewness, kurtosis, crest factor, peak-to-peak, spectral centroid, band powers, and 60–150 Hz / 0–60 Hz ratios) from current and vibration signals using 200 ms windows with a 50 ms stride.
For anomaly detection, an unsupervised Isolation Forest model is used, and real-time decision scores are evaluated using a threshold selected based on the 95th percentile of normal windows.

In the simulation, a 30 Hz mechanical component, white noise, fault-interval spectral signatures around 120 Hz, and impulsive rubbing events were introduced. The results show high sensitivity and reliability: ROC AUC ≈ 0.991, precision ≈ 0.894, recall ≈ 1.000, F1 ≈ 0.944—meaning no fault window was missed, and false alarms can be controlled by tuning the threshold.
The proposed architecture is modular, easily deployable on edge devices, and can be extended with adaptive thresholds, online retraining, and multisensor integration (e.g., acoustic emission). It has practical value for early warning and predictive maintenance in industrial drives.

real-time diagnostics, anomaly detection, isolation forest, vibration signal, spectral features, kurtosis, ROC, threshold, unsupervised approach

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