A review on data-driven fault severity assessment in rolling bearings

Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive

A review of stochastic resonance in rotating machine fault detection

Bearing remaining useful life prediction based on deep autoencoder and deep neural networks

A novel method is proposed for quantitative and localization diagnosis of a bearing outer ring defect in this paper. This method aims to distinguish these defects with different angular positions and different sizes based on the natural multiples of the ball angular spacing. Defects that differ in size by natural multiples of the ball angular spacing will have the same time interval, which may lead to misdiagnosis. Distinguishing such defects accurately is the goal for some industries. Therefore, the synchronized signal in the vertical and horizontal directions of the bearing systems with different angular positions and different size defects are quantitatively analyzed based on the presented five indices to explore the mechanism and method of distinguishing these defects. The simulation and experimental results show that the vertical–horizontal synchronized root mean square (VHSRMS) index can distinguish such defects effectively, and the defect angle position can be diagnosed using the VHSRMS–1 . More importantly, the VHS analysis method expands the location of the outer ring defect to 265–275° in quantitative diagnosis and realizes the localization diagnosis of the defect angle position. The research results provide a new approach and method for quantitative and localization diagnosis of a defective bearing.

Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical–Horizontal Synchronization Signal Analysis

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.

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A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects.

Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

The phenomenon of Doppler shift in the acoustic signal acquired by a microphone amounted beside the railway leads to the difficulty for fault diagnosis of train bearings with a high moving speed. To enhance the condition monitoring performance of the bearings on a passing train using stationary microphones, the elimination of the Doppler shift should be implemented firstly to correct the severe frequency-domain distortion of the acoustic signal recorded in these conditions. In this paper, a Doppler shift removal method is proposed based on instantaneous frequency (IF) estimation (IFE) for analyzing acoustic signals from train bearings with a high speed. Specifically, the IFE based on short-time Fourier transform is firstly applied to attain the IF vector. According to the acoustic theory of Morse, the data fitting is then carried out to achieve the fitting IFs with which the resampling sequence can be established as the resampling vector in time domain. The resampled signal can be finally reconstructed to realize fault diagnosis of train bearings. To demonstrate the effectiveness of this method, two simulations and an experiment with practical acoustic signals of train bearings with a crack on the outer raceway and the inner raceway have been carried out, and the comparison results have been presented in this paper. [DOI: 10.1115/1.4026431]

Doppler Shift Removal Based on Instantaneous Frequency Estimation for Wayside Fault Diagnosis of Train Bearings

In a wayside acoustic defective bearing detector system, bearing faults are detected through the analysis of the acoustic signal generated by the bearings of a passing vehicle. As vehicles pass by with high speeds, the acoustic signal recorded by the stationary microphone is disturbed by the Doppler effect. The reduction of the frequential structure disturbance of signals facilitates the efficient diagnosis of bearing faults. This study proposes a Doppler effect reduction scheme for the removal of the frequential structure disturbances of Doppler-shifted signals in the acoustic defective bearing detector system. First, the parameters, including the initial speed and the initial acceleration of the vehicle, are estimated by the acceleration-based Dopplerlet transform via the matching pursuit algorithm. Second, the time vector for resampling is calculated according to the estimated initial speed, the initial acceleration in the first step, the sound speed, and the measured geometric parameters of the ADBD s...

Doppler effect reduction scheme via acceleration-based Dopplerlet transform and resampling method for the wayside acoustic defective bearing detector system

Ao Zhang

Papers

A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

Doppler Shift Removal Based on Instantaneous Frequency Estimation for Wayside Fault Diagnosis of Train Bearings

Doppler effect reduction scheme via acceleration-based Dopplerlet transform and resampling method for the wayside acoustic defective bearing detector system

Wayside acoustic defective bearing detection based on improved Dopplerlet transform and Doppler transient matching