Railroad track monitoring systems are used for finding rail defects that may lead to a derailment of the train. The classical limit-value based defect detection systems are simple but are limited in their capability to detect small defects. As a cutting-edge supervision method, signal derivative filters can help to reveal information in the acceleration signal collected while the train is moving on the rail. The derivative filters are designed based on the required performance of the application. However, their design should be done with caution because they can greatly amplify the noise in the data, especially in high frequencies.
Derivative filters can be implemented in the sample domain of space or time. The derivative filters in time domain are not always sufficient to study all the features of a signal. To explore the signal content, wavelet transformation was chosen, because it gives accurate description of the frequency contents according to their position in time. It should be noted that the wavelet transform that gives the derivative of a signal, has the properties of smoothing and differentiation.
The proposed algorithm processes the data using continuous and discrete derivative wavelets filters, and is able to locate defects and provide information that may help to distinguish between various types of rail and wheel defects, including rail cracks, squats, corrugation, and wheel out-of-rounds. The wavelet-based algorithm developed was applied to a sample accelerometer signal and the results show the potential of this algorithm to locate and diagnose defects from limited bogie vertical acceleration data.