Abstract
Trackside acoustic signals contain intense noise and nonstationary features even after Doppler distortion correction. Information on bearing defects in these signals is either weak or heavily attenuated. Thus, an improved compound interpolation envelope local mean decomposition (ICIE LMD) method combined with a fast kurtogram (FK) is proposed for wheelset bearings. In this methodology, cubic Hermite interpolation and cubic spline interpolation are employed to find the envelope of the extremal points in the ICIE LMD algorithm to improve accuracy and decrease the computing time of the decomposed signal component. An FK is sensitive to the impact signal and extracts the fault impact features efficiently. In the application, the proposed method uses ICIE LMD to decompose the multicomponent signal into several specific single product function (PF) components. The kurtosis index of the PF is calculated to select the component which contains the most fault information. Then, the selected component of PF is filtered by FK. Finally, the squared envelope spectrum is used to obtain the fault frequency and identify the fault location. The advantages of the ICIE LMD method are verified by simulation analysis. In the application, the results show that the proposed method efficiently extracts the fault features and enhances the target characteristics of the sound signals from a trackside microphone array. Furthermore, the influence of rotating frequency on locating the fault is reduced.
Highlights
The wheelset bearing of a high-speed train is an indispensable component that is prone to damage when the train is running
Bearing damages and defects directly affect the stable operation of equipment [1]. The defects, such as pitting corrosion and cracks, happen on the contact surface of the rolling bearing quickly, when the bearing is under the alternating contact force for a long time, especially when a train is running at high speed and with the heavy loads [2]
Concerning the problems mentioned, the improved compound interpolation envelope (ICIE) Local mean decomposition (LMD) combined with fast kurtogram (FK) is presented to extract the fault feature of the rail-side acoustic bearing signal
Summary
The wheelset bearing of a high-speed train is an indispensable component that is prone to damage when the train is running. Hu et al [16] developed a method to obtain the envelope estimation function by applying cubic spline interpolation to calculate the envelopes of local extreme points This method improves computation time, but undershoot and overshoot problems of the envelope are observed for signals with strong nonstationary features [17]. Concerning the problems mentioned, the improved compound interpolation envelope (ICIE) LMD combined with FK is presented to extract the fault feature of the rail-side acoustic bearing signal. The results show that this method can effectively extract and enhance the fault characteristics of the trackside acoustic rolling bearing and weaken the influence of rotating frequency.
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