Defect Detection for Wheel-Bearings with Time-Spectral Kurtosis and Entropy

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.

System Entropy Measurement of Stochastic Partial Differential Systems

“…Information entropy characterizes uncertainty caused by random parameters of a random system and measurement noise in the environment [6]. Entropy has been used for information retrieval such as systemic parametric and nonparametric estimation based on real data, which is an important topic in advanced scientific disciplines such as econometrics [1,2], financial mathematics [4], mathematical statistics [3,4,6], control theory [5,7,8], signal processing [9], and mechanical engineering [10,11]. Methods developed within this framework consider model parameters as random quantities and employ the informational entropy maximization principle to estimate these model parameters [6,9].…”

Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals

“…HABDs are prone to false alarms while in some cases they may be unable to detect faulty overheating bearings due to environmental condition effects from the surroundings of the bearing concerned. 5 Due to their high cost, the number of HABDs deployed along a line depends on the budget available to procure and maintain such systems. Normally, HABDs are installed at locations where trains travel at their maximum speed.…”

“…The TSK technique can indicate not only the transients in frequency domain but also their locations in the time domain. 5 Defects in rolling element bearings, which are usually similar to short impulses caused by the impact from the defect, can be determined using the TSK technique. The kurtosis value of a normal distribution signal is around 3.…”

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

“…Chen et al proposed a fault classification method combining time-spectral kurtosis, entropy and support vector machines (SVM). Combining the advantages of time-spectral kurtosis (T-SK) and entropy, this method makes fault extraction more obvious [16].…”