Gearbox Fault Diagnosis Using Multiscale Sparse Frequency-Frequency Distributions

Zhang, Luke; Li, Yang; Dong, Lincheng; Yang, Xiaoqing; Ding, Xiaoxi; Zeng, Qiang; Wang, Li Ming; Shao, Yimin

doi:10.1109/access.2021.3104281

Cited by 17 publications

(2 citation statements)

References 31 publications

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“…When gear faults occur, the gear fault-related information will be distributed in the frequency band due to the influence of complex modulation mechanism [25]. As a consequence, detecting the spectrum characterisation information of gear faults, we can determine whether the gear is faulty and initially identify the type of fault.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Metamaterial-based acoustic enhanced sensing for gearbox weak fault feature diagnosis

Pan,

Ding,

Qiao

et al. 2023

Smart Mater. Struct.

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It is widely known that the gear fault signal is affected by the interference of strong background noise and the loss of internal fault excitation transmission, which weakens the features of incipient fault signals and makes gear fault diagnosis difficult. Motivated by the acoustic manipulation capabilities of acoustic metamaterials and the gear modulation principle, this study proposes a front-end perception method for weak gear fault feature diagnosis via gradient acoustic metamaterials (GAM), which utilizes the acoustic rainbow capture and compression ability to reduce the difficulty of gear incipient fault diagnosis. Considering the FM/AM modulation principle, acoustically enhanced sensing for fault features can be achieved by collecting components from the selected air gaps in the GAM structure. The feasibility of GAM-based gear weak fault feature diagnosis is proved by experiments, which illustrate the multiscale feature denoising and frequency-selective enhancement characteristics of GAM. The results show that the amplitude of the target signal is amplified by more than eight times, the sideband component containing the fault signal is clearly enhanced, and the effect of denoising outside the target sideband is evident. This allows the weak fault features to be easily identified. Compared to digital filtering in traditional post-processing methods, the proposed approach is more straightforward for extracting weak fault features. Additionally, this method adopts a non-contact measurement method with the MEMS microphone for front-end enhancement and sensing. Finally, it can be seen that the proposed method has great potential for structurally enhancing the perception of gearbox weak fault signal denoising and feature diagnosis.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Metamaterial-based acoustic enhanced sensing for gearbox weak fault feature diagnosis

Pan,

Ding,

Qiao

et al. 2023

Smart Mater. Struct.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Wavelet transform was also used for detecting angular misalignment in (Saari et al, 2015). There are also several studies where some kind of sparse representations in some basis are sought and these sparse decompositions also turn out to be useful for gear fault (Zhang et al, 2021;Li et al, 2018) and bearing fault diagnostics (Li et al, 2019;Chen et al, 2017;He et al, 2016).…”

Section: Introductionmentioning

confidence: 99%