Ten lectures on wavelets
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Rolling element bearing fault diagnosis using Laplace-wavelet envelope power spectrum
EURASIP Journal on Applied Signal Processing
The optimal morlet wavelet and its application on mechanical fault detection
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
A hybrid time-frequency method based on improved Morlet wavelet and auto terms window
Expert Systems with Applications: An International Journal
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The presence of periodical or nonperiodical impulses in vibration signals often indicates the occurrence of machine faults. This knowledge is applied to the fault diagnosis of such machines as engines, gearboxes, rolling element bearings, and so on. The development of an effective impulse detection technique is necessary and significant for evaluating the working condition of these machines, diagnosing their malfunctions, and keeping them running normally over prolong periods. With the aid of wavelet transforms, a wavelet-based envelope analysis method is proposed. In order to suppress any undesired information and highlight the features of interest, an improved soft threshold method has been designed so that the inspected signal is analyzed in a more exact way. Furthermore, an impulse detection technique is developed based on the aforementioned methods. The effectiveness of the proposed technique on the extraction of impulsive features of mechanical signals has been proved by both simulated and practical experiments.