A Novel Fault-Type Intrinsic Feature Decoupling Method for Rotating Machinery Fault Diagnosis Using Decoupling Generative Adversarial Network

Deep learning has been widely applied in the field of rotating machinery fault diagnosis. Existing deep learning-based fault diagnosis methods commonly utilize deep neural networks to extract features from raw data and employ fully connected layers for classification of the extracted features. However, in existing methods, fault type-related information is coupled with irrelevant information (e.g., load variations, fault sizes, and system intrinsic properties) at the fully connected layer. The lack of decoupling may result in fault type-related information being overwhelmed by irrelevant factors, thereby hindering accurate fault type determination. We propose that there exists an intrinsic feature in the data, where information is interrelated and closely tied to the fault type. This feature remains highly stable under changes in irrelevant factors, such as load and fault size, and provides an accurate representation of the fault type. By extracting and analyzing fault-type intrinsic features, the interference caused by irrelevant factors on fault type determination can be effectively eliminated. To address this, this article proposes a novel decoupling generative adversarial network for decoupling the intrinsic feature of fault type. The proposed method effectively extracts the intrinsic features of fault types that are unaffected by irrelevant factors, significantly improving fault diagnosis accuracy and robustness, and, to some extent, endowing the fault-type intrinsic features with interpretability and physical rationality.