A reference learning network for fault diagnosis of rotating machinery under strong noise

The strong noise often masks the fault characteristics of equipment, which reduces the accuracy of fault diagnosis and even leads to the inability of intelligent fault diagnosis algorithms to be applied in industrial environments. This has always been a challenge in the field of mechanical fault diagnosis. As known that equipment failure results from the continuous degradation of the equipment's state, with the failure state evolving from the healthy state. Considering that both healthy signals and fault signals contain similar noise, this paper proposes a Reference Learning Network (RLNet) model. The model aims to enhance the distinguishing features between healthy and faulty samples through reference units, thereby eliminating the influence of noise on feature distribution. Firstly, the impact of variable speed on the model's robustness is mitigated using the computed order tracking method. Then, the difference features between healthy samples and a class of fault samples are extracted through the binary classification reference learning unit (RLU). Next, the extracted differential features are used to train the state classifier. Finally, membership weights are employed to effectively combine the feature recognition results, reducing the influence of fault features from mismatched RLUs. The robustness and superiority of the proposed method were verified by comparing it with five other intelligent fault diagnosis methods on the gear and bearing datasets. RLNet is of great significance for the engineering application of intelligent fault diagnosis methods in industrial noise environments.