Envelope Harmonic Noise Ratio Based Adaptive Kurtogram and Its Application in Bearing Compound Fault Identification
Wenyi Wu, Cai Yi, Jie Bai, Yan Huang, Jianhui Lin
Abstract
Fast kurtogram (FK) is an effective tool in fault diagnosis, but it still has two defects. Firstly, its indicator is easy to be affected by the random impact. Secondly, its fixed frequency band segmentation rules might lead to over-decomposition or under-decomposition problems. Therefore, by combining a more robust indicator, envelope harmonic-to-noise ratio (EHNR), with an adaptive frequency band segmentation method based on scale-space representation (SSR), a completely parameterless adaptive spectrum analysis technology, EHNR-SSR, is constructed. The EHNR is more robust to random impact and independent of prior parameters, and the SSR-based frequency band segmentation has adaptive adjustment ability. Additionally, the EHNR can characterize the signal with specific periodicity, which makes the proposed method has the capability of compound fault detection. The superiority of EHNR-SSR is verified through simulated signals and experimental tests. The results reveal that EHNR-SSR can identify bearing fault features from vibration mixture and realize compound fault detection.