Toward accurate extraction of bearing fault modulation characteristics with novel time–frequency modulation bispectrum and modulation Gini index analysis

Zou, Xue; Zhang, Huaming; Jiang, Zuhua; Zhang, Kun; Xu, Yonggang

doi:10.1016/j.ymssp.2024.111629

Cited by 10 publications

(1 citation statement)

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“…Even a minor damage can potentially lead to severe accidents. Hence, monitoring the condition and diagnosing faults in rolling bearings is imperative [3][4][5]. Local defects in rolling bearings typically generate repetitive transient pulses in their vibration signals.…”

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%