Rotating machinery structural faults feature enhancement and diagnosis base on low-pass Teager energy operator intrinsic time-scale decomposition

Abstract Aiming to address the issue of the complex and harsh working environment of rotating machinery, the features of vibration signals associated with structural faults are often obscured by noise, resulting in low accuracy in fault diagnosis. This paper proposes a method for feature enhancement and diagnosis of rotating machinery structural faults, which combines the low-pass Teager energy operator intrinsic time-scale decomposition (LTEO-ITD) recurrence plot (RP) with the ResNet18 network. Firstly, the low-frequency components of the vibration signal are extracted and enhanced using the LTEO. The method effectively suppresses noise interference and enhances fault features. Then, the fault features are extracted using ITD. The component that contains the highest number of fault features is selected based on kurtosis analysis, followed by the generation of the corresponding RP. Finally, the data is input into the ResNet18 network for diagnostic verification. The effectiveness and feasibility of the proposed method are verified through vibration signals from the rotating machinery experimental platform and the comprehensive rotating machinery experimental platform. The proposed method achieves a diagnostic accuracy of 100% on both datasets. The comparative validation was conducted using five distinct image encoding methods. The experimental results show that the proposed method effectively extracts fault features of structural faults, thereby enhancing the accuracy of fault diagnosis.