Sparse Time-Frequency Analysis Based on Instantaneous Frequency Estimation and Fault Diagnosis Application

Huang, Ying; Bao, Wenjie; Li, Fucai; Li, Xiaolu

doi:10.1109/tim.2022.3210987

Cited by 3 publications

(1 citation statement)

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“…However, it difficult to improve the energy concentration of TFR for nonstationary signals with fast time-varying frequency components [ 20 ]. To overcome this drawback, Huang et al [ 21 ] proposed a sparse TFA method combining sparse time–frequency analysis with IF estimation based on the basic weighted sparse model for analyzing nonstationary signals with large IF variations, which improves both energy concentration and accuracy. Hu et al [ 22 ] proposed an adaptive time domain signal segmentation method that adopts cubic spline interpolation to envelope the original signal.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Parameterized Domain Mapping Method and Its Application in Wheel–Rail Coupled Fault Diagnosis for Rail Vehicles

Yang

Yao

et al. 2023

Sensors

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The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel–rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel–rail coupled faults will improve the safety of rail vehicles’ operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel–rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time–frequency analysis method is proposed in this paper based on the PDMF adopting Rényi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Rényi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles’ variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method.

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Section: Introductionmentioning

confidence: 99%