A Fusion CWSMM-Based Framework for Rotating Machinery Fault Diagnosis Under Strong Interference and Imbalanced Case
Xin Li, Jian Cheng, Haidong Shao, Kan Liu, Baoping Cai
Abstract
Vibration signals and infrared images have different advantages and characteristics. Although a few recent researches have explored their information fusion in rotating machinery fault diagnosis, they show limited performance when facing strong interference and imbalanced cases. Therefore, a fusion framework based on confidence weight support matrix machine (CWSMM) is proposed. In this framework, CWSMM can not only fully leverage the structure information of infrared thermography images and vibration time–frequency images, but also has the following novelties. First, CWSMM uses dynamic penalty factors for different class samples to address the class imbalance problem. Second, by using the prior knowledge of matrix samples, a confidence weight assignment strategy is designed for CWSMM to improve the robustness. Last, the Dempster–Shafer (D-S) evidence theory is applied to fuse the posterior probability outputs of CWSMMs using different measurements. Experiment results demonstrate that the proposed method has promising fault diagnosis performance, specifically under strong interference and imbalanced datasets.