Design of matched wavelets based on generalized Mexican-hat function
Signal Processing
A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Fault diagnosis of ball bearings using machine learning methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid time-frequency method based on improved Morlet wavelet and auto terms window
Expert Systems with Applications: An International Journal
EEMD method and WNN for fault diagnosis of locomotive roller bearings
Expert Systems with Applications: An International Journal
A New Hybrid Method for Image Approximation Using the Easy Path Wavelet Transform
IEEE Transactions on Image Processing
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A new de-noising method based on parameter optimized Mexican hat wavelet was put forward in this paper. For the similar shape to the mechanical shock vibration signal, the Mexican hat wavelet is chosen as the mother wavelet and improved by the shape parameters optimization. The noise jamming in the raw vibration signals can be filtered by the continue wavelet transform (CWT) using the improved Mexican hat wavelet as the mother wavelet. The shape parameters of the Mexican hat wavelet are optimized by the cross-validation method (CVM). In the CWT process, the optimal scale factor is also obtained by the circle CVM. The useful components can be extracted by the CWT with the optimal shape parameters and scale factor. The experimental result shows that the proposed method can not only de-noise the useless noise effectively but also extract the fault feature availably.