A boundary division guiding synchrosqueezed wave packet transform method for rolling bearing fault diagnosis

Self Cite

In practical industrial applications, rolling bearing generally operates under variable conditions and its vibration signal significantly fluctuates in amplitude and frequency. This increases the feature distribution differences of the bearing fault samples and makes the health status identification of the bearing more difficult. To this end, a new intelligent fault diagnosis method for bearing under time-varying speed conditions is proposed based on time-characteristic order (TCO) spectrum and multi-scale domain adaptation network (MSDAN). Firstly, by using the good noise robustness and high time-frequency aggregation properties of the synchrosqueezed wave packet transform (SSWPT), the TCO spectrum method based on SSWPT is proposed to eliminate the impact of speed fluctuation, reducing the distribution shift of bearing data under time-varying speeds. Secondly, an MSDAN model based on global-local feature fusion is established to extract the domain-invariant features closely related to the bearing fault state from the TCO spectrum. Finally, the local maximum distribution discrepancy (LMMD) is introduced to capture the discriminative fine-grained features. The feasibility of the proposed method is verified in various transfer tasks on two different bearing datasets with time-varying speeds. Compared with some state-of-art methods, the proposed method can eliminate the sample distribution differences under time-varying speeds, significantly improving its accuracy and generalization performance in cross-domain fault diagnosis of rolling bearing.

“…More details of SSWPT can refer to [30]. Figure 1 shows the SSWPT spectrums of two inner race fault (IF) samples under different speed stages.…”

Section: Time-characteristic Order (Tco) Spectrummentioning

confidence: 99%

Rolling bearing fault diagnosis under time-varying speeds based on time-characteristic order spectrum and multi-scale domain adaptation network

Xu,

Tang,

Pang

et al. 2023

Self Cite

“…Rolling bearings are indispensable key components in rotating machinery, which are widely used in industrial fields [1][2][3]. Rolling bearings are highly susceptible to failure because of the complex working conditions and severe working environment [4,5]. Bearing failures significantly reduce the operational reliability of machinery, resulting in economic losses and casualties [6].…”

Section: Introductionmentioning

confidence: 99%

An adaptive spectrum segmentation-based optimized VMD method and its application in rolling bearing fault diagnosis

Meng¹,

Wang²,

Liu³

et al. 2022

Variational Mode Decomposition (VMD) is a signal decomposition algorithm with excellent denoising ability. However, the drawback that VMD is unable to determine the input parameters adaptively seriously affects the decomposition results. For this issue, an optimized VMD method based on modified scale-space representation (MSSR-VMD) is proposed. Firstly, MSSR is proposed to segment the fault signal spectrum, acquiring modes' number and the initial center frequency for each mode adaptively. Moreover, a pre-decomposition step is added to the original VMD, which selects a target mode from divided frequency bands. Finally, the penalty factor of the target mode is adjusted during the iterative update of the VMD to achieve accurate extraction for the fault features. MSSR-VMD and other adaptive decomposition algorithms are employed to handle the simulated and experimental signals separately. By comparing the analysis results, the method has certain superiority in rolling bearing fault feature extraction.

“…Health monitoring and fault diagnosis play a significant role in finding potential faults and ensuring the normal operation of equipment [1][2][3]. Due to vibration signals containing rich information related to machinery health conditions, difficulties in machinery health monitoring and fault diagnosis [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Maximum reweighted-kurtosis deconvolution: a fully blind and adaptive method for restoration of gear fault impulse trains

Wang

Zhang

et al. 2022

Deconvolution based on vibration signals has been proven to be an effective tool in gear fault diagnosis. However, for many common methods, precisely restoring the fault impulse train is still a challenging task due to the great dependence on prior knowledge and the empirical determination of filter parameters. In this paper, a fully blind and adaptive method termed maximum reweighted-kurtosis deconvolution (MRKD) is proposed. A new deconvolution criterion, i.e., reweighted-kurtosis, is defined. This criterion possesses great robustness to impulse interferencesand thus has great potential to solve the problem of previous kurtosis-based methods in which a single dominant impulse is deconvolved instead of the impulse train induced by a localized fault. Furthermore, a parameter-adaptive strategy is developed to adaptively determine the appropriate filter parameters. As such, the proposed method does not require any prior knowledge of the target fault impulse train and addresses the critical issue of many common methods specifying filter parameters empirically. The proposed method is validated through simulated and real vibration signals. Comparison with the most popular deconvolution methods indicates that MRKD outperforms other methods for the restoration of a gear fault impulse train.