An industrial machine coupling fault diagnosis method based on adaptive feature clustering under low-labeled samples

Abstract

This invention proposes an adaptive feature clustering-based method for diagnosing coupled faults in industrial machinery under low-labeled sample conditions. It relates to the field of intelligent fault diagnosis for industrial equipment and addresses the technical problems of existing fault diagnosis methods, such as high dependence on labeled data, poor multi-source signal feature fusion, insufficient deep feature mining of coupled faults, and weak adaptability to complex operating conditions. First, heterogeneous kernel density adaptive normalization is performed on multi-sensor signals. Then, three-dimensional weighted primary and secondary features are adaptively mined to generate pre-trained weights. A diagnostic clustering model is constructed through iterative fusion of channel attention to complete fault identification. Finally, the model is optimized, and the source and evolution of the fault are quantified. This invention is suitable for low-labeled industrial scenarios, provides high accuracy in coupled fault diagnosis, exhibits strong robustness under conditions such as sample imbalance and limited channels, can quantify the source of faults, reveal evolution patterns, and provide accurate operation and maintenance suggestions. It also has good scalability and is easy to implement in engineering.

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