The amplitude modulation bispectrum: A weak modulation features extracting method for bearing fault diagnosis

Yang, Miaorui; Zhang, Kun; Sheng, Zhipeng; Zhang, Xiangfeng; Xu, Yonggang

doi:10.1016/j.ress.2024.110241

Cited by 13 publications

(1 citation statement)

References 32 publications

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“…Envelope analysis is one of the most commonly used methods in rolling bearing fault diagnosis, renowned for its exceptional performance in extracting fault information from raw signals [12,13]. In the field of rolling bearing fault diagnosis, commonly employed techniques include envelope spectrum (ES) analysis and squared envelope spectrum analysis.…”

Section: Introductionmentioning

confidence: 99%

HTG transformation: an amplitude modulation method and its application in bearing fault diagnosis

Qiao,

Zhang,

Zhang

et al. 2024

Meas. Sci. Technol.

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Rolling bearings are critical components in modern mechanical equipment, and the health monitoring and predictive maintenance of bearings are crucial for the normal operation of machinery. Hence, there is a compelling need to delve into advanced methodologies for enhancing the detection of fault characteristics in bearings. Faulty bearings produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, in some complex conditions, the relevant fault frequencies may be hidden within interfering components. This paper presents an amplitude modulation technique called the hyperbolic tangent Gaussian (HTG) transformation, designed to extract weak fault components from signals. Firstly, a family of amplitude modulation functions, known as the HTG functions, is constructed. These functions modulate signals with normalized amplitudes to obtain a series of modulated signals. Simultaneously, a frequency domain amplitude ratio (FDAR) metric is used for the automatic selection of the optimal components. Finally, the HTGgram is introduced, a spectral decomposition method based on trend components, aiming to identify the best combination of filtering and modulation components. Simulations with multi-component bearing fault signals and experimental signals with composite bearing faults demonstrate that this method not only highlights fault features and suppresses noise interference but also adaptively selects frequency bands related to faults, enhancing fault information. This approach exhibits excellent adaptability and effectiveness in complex operating conditions with multiple interference components.

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Section: Introductionmentioning

confidence: 99%