An Adaptive Harmonic Product Spectrum for Rotating Machinery Fault Diagnosis

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Yi, Cai, Wang, Hao, Zhou, Qiuyang, Hu, Qiwei, Zhou, Pengcheng, Lin, Jianhui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive segmentation; bearing fault diagnosis; Fault diagnosis; Feature extraction; Frequencies; Frequency conversion; Frequency estimation; Harmonic analysis; harmonic product spectrum (HPS); Kernel functions; optimal resonance frequency band; Optimization; Power harmonic filters; Power spectral density; power spectral density (PSD); Resonance; Resonant frequency; Rotating machinery; Segmentation; Vibrations
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2022.3230462

Although the frequency band segmentation rules used in fast kurtogram (FK) has made great achievements in locating fault resonance band, it still has the problem of under-decomposition or over-decomposition for resonance band. This will affect the performance of the methods based on this band segmentation rule in fault diagnosis. Aiming at this problem, an adaptive harmonic product spectrum (AHPS) is proposed in this paper, which can realize adaptive frequency band segmentation adaptively based on signal power spectral density (PSD) instead of fixed segmentation rules. AHPS can achieve adaptive frequency spectral segmentation by iteratively convolving the PSD with the Gaussian kernel function. The harmonic saliency index (HSI) is used to pave a spectrum plane. The optimal resonance band is determined by maximizing HSI and more accurate fault resonance band location performance is achieved. Both simulation data and measured data are used to verify the performance of the proposed AHPS. The results show that compared with FK and HPS, AHPS has better robustness and fault diagnosis performance.