Interpretable fault diagnosis for variable-speed machinery via adaptive ridge extraction and causal learning

Abstract

In most industrial processes, rotating machinery often operates under time-varying speed conditions, making it difficult to extract fault-related features using conventional diagnostic techniques. To address this challenge, a novel diagnostic framework is developed that integrates high-resolution time-frequency analysis with interpretable causal learning. First, a synchrosqueezed continuous wavelet transform (SSQ-CWT) is employed to generate a clean time-frequency representation, upon which a specialized Adaptive Rotational Frequency Ridge Extraction (ARFRE) algorithm operates to accurately estimate the instantaneous speed without a tachometer. Subsequently, features extracted from the resulting order spectrum are screened by a Causal Forest to identify a subset with true causal impact on the fault state. Finally, an interpretable decision tree is constructed using this causally validated feature set. The framework was comprehensively validated on two public benchmarks (SQV, Ottawa) and a proprietary industrial dataset. The results demonstrate superior diagnostic performance, achieving accuracies of 98.49 %, 100.00 %, and 97.44 % respectively, and consistently outperforming baseline methods. This work presents a robust, interpretable, and tachometer-free solution for fault diagnosis in variable-speed machinery, with significant potential for practical industrial application.