Abstract
Wheel-bearings easily acquire defects due to their high-speed operating conditions and constant metal-metal contact, so defect detection is of great importance for railroad safety. The conventional spectral kurtosis (SK) technique provides an optimal bandwidth for envelope demodulation. However, this technique may cause false detections when processing real vibration signals for wheel-bearings, because of sparse interference impulses. In this paper, a novel defect detection method with entropy, time-spectral kurtosis (TSK) and support vector machine (SVM) is proposed. In this method, the possible outliers in the short time Fourier transform (STFT) amplitude series are first estimated and preprocessed with information entropy. Then the method extends the SK technique to the time-domain, and extracts defective frequencies from reconstructed vibration signals by TSK filtering. Finally, the multi-class SVM was applied to classify bearing defects. The effectiveness of the proposed method is illustrated using real wheel-bearing vibration signals. Experimental results show that the proposed method provides a better performance in defect frequency detection and classification than the conventional SK-based envelope demodulation.
Highlights
Wheel-bearings are an essential mechanical component of railway vehicles
ENSCO, Inc. has investigated a novel technology, which used accelerometers mounted on the rail, designed to detect bearing defects early. They investigated the transmissibility of the vibration signals from defective bearings to rails, a transient mechanical path formed by the bearing, axle, wheel, rail, and accelerometers
We found that impurities in the lubricant oil for normal wheel-bearings may produce defective frequencies, especially outer race ones, which will lead to the errors in classification
Summary
Wheel-bearings are an essential mechanical component of railway vehicles. The high-speed operating conditions and constant metal-metal contact leads to defects, such as axle burn-off, metal losses and cage fragmentation. Vibration or sound signals generated by bearings usually contain rich information Their corresponding analysis is an effective way to detection defects, and has received wide attention in recent years [1,2,3,4]. ENSCO, Inc. has investigated a novel technology, which used accelerometers mounted on the rail, designed to detect bearing defects early They investigated the transmissibility of the vibration signals from defective bearings to rails, a transient mechanical path formed by the bearing, axle, wheel, rail, and accelerometers. It is shown that SK can indicate transient components in signals, and their locations in the frequency domain, providing the optimal bandwidth for demodulation It may cause false detections when processing real vibration signals collected from wheel-bearings, which usually contain sparse interference impulses. Experimental results show that the proposed method could effectively identify defects from real vibration signals collected from wheel-bearings
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