Hierarchical Approach to Rolling Bearing Failure Detection Using Spectrogram Images Classification

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Abstract Early detection of rolling bearing faults is essential for preventing failures in rotating machinery. Although vibration-based analysis is widely used for fault diagnosis, it requires additional sensors, such as accelerometers, which increases system cost and complexity. Motor current signals, inherently available in industrial drive systems, offer a cost-effective alternative; however, their indirect sensitivity to mechanical defects makes reliable fault diagnosis challenging. This paper proposes a hierarchical motor current-based diagnostic framework that integrates domain knowledge with data-driven image analysis. Raw current signals are transformed into time-frequency spectrograms, which provide enhanced representations of faults compared to pure time or frequency-domain features. The proposed approach consists of three stages. First, bearing health is characterized using a rule-based method to compute the characteristic frequencies associated with different fault types, incorporating domain knowledge into the diagnosis process. Second, this information is used to guide a hierarchical classification strategy that separates health assessment from fault localization. Finally, global image descriptors extracted from the spectrograms are employed to classify the bearing condition as healthy or faulty and to identify the fault location (internal or external). By explicitly combining physics-informed fault characterization with image-based machine learning, the proposed approach improves interpretability while maintaining competitive diagnostic performance. The effectiveness of the method is validated on a benchmark bearing dataset, demonstrating its potential for cost-effective industrial condition monitoring.

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