Hybrid Approach for Machine Anomaly Detection Across Diverse Operating Conditions: Combining Self-Supervised Learning With Vibration Statistics

Failure detection in rotating machines is critical for reducing maintenance costs, economic losses, and safety-related accidents. Deep learning has become a robust and powerful solution for machine fault diagnosis, but it faces two major challenges: scarcity of labeled faulty data and variability of machine operating conditions. To address these challenges, we propose a novel hybrid machine anomaly detection methodology that integrates an innovative pretext task-based self-supervised learning framework with vibration statistics. First, we designed a pretext task to classify the rotating speeds and trained an auxiliary classifier on it. This auxiliary classifier effectively extracts fault-discriminative features related to rotating speed, an operating condition closely associated with various machine faults. Moreover, the proposed pretext task applies to the fault diagnosis of general rotating machines because the rotating speed can be easily obtained using a Hall sensor. Second, a hybrid feature was generated by combining the auxiliary classifier outputs with six statistical features derived from raw vibration signals. These statistical features augment the fault-discriminative ability of the auxiliary classifier outputs by incorporating the magnitude and asymmetry of the signal, which the auxiliary classifier alone cannot capture. The evaluation results demonstrated that our approach significantly outperformed 11 state-of-the-art anomaly detection methods on three different vibration datasets encompassing multiple operating conditions and a real-world run-to-failure scenario. In addition, our method is capable of performing real-time inference on an edge device platform, demonstrating its applicability to practical industrial setups. Furthermore, feature visualization analysis highlighted how the proposed hybrid feature enables a more distinct separation between faulty and healthy data samples than that achieved with statistical features or auxiliary classifier outputs alone.