Abstract
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.
Highlights
Economic and social development in most countries has increased considerably the requirement for transportation capability
A novel technique that combines a Doppler transient model and parameter identification based on the Laplace wavelet and a spectrum correlation assessment is proposed for real locomotive bearing fault detection
The conventional bearing fault detection methods have been developed for situations with no relative movement between the signal acquisition system and the defective bearing, and the acquired signal is not affected by the Doppler effect, locomotive bearing signals suffer from high frequency shifts, frequency band expansions, and amplitude modulations due to the Doppler effect
Summary
Economic and social development in most countries has increased considerably the requirement for transportation capability. Once one of these components suffers from a local defect, approximately periodic impacts will be generated when the defective surface comes into contact with the rollers [2] These transient interaction components contain important information about the health status of the bearing. The Laplace wavelet parameters, through which the local maxima are derived, are regarded as the closest to the observed the model parameters of the system Based on these fundamentals, Wang et al [19,20] proposed a method that incorporates a transient model and parameter identification based on wavelets and correlation filtering to achieve bearing fault feature detection. A novel technique that combines a Doppler transient model and parameter identification based on the Laplace wavelet and a spectrum correlation assessment is proposed for real locomotive bearing fault detection.
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