Wavelet Analysis Application On Eddy Current Testing Signal Processing Of Carburized Layer Depth

Nondestructive testing and quality control of metal hardened-depth is an important problem to be solved in machinery industry. It belongs to material property testing. The metal carburized layer depth is one of the major technical parameter to evaluate the quality of carburized components. Eddy current testing is a method suitable for testing changes occurred in the surface or subsurface of specimen. When using this method to test carburized components, the variable of eddy current signal is depend on the change of constituents' physical property in carburized layer. So it can be used to test carburized components nondestructively. However, many factors may affect eddy current testing and it is hard to classify them, and how to extract information which indicating carburized layer depth in signals is very important. So this paper is used Wavelet transform method to analyze signals and extract features in them, then classify them by neutral network. Wavelet transform is a method to analyze signal in the time-scale (time-frequency) plane. It has a character of multi-resolution, and can be used to characterize detailed features in both time and frequency field. The dimension of the window is constant, but its shape is variable. That is to say, in low frequency band, it has high frequency resolution and low time resolution; in high frequency band, it has low frequency resolution and high time resolution ,which is fit for analyzing requirement of engineering signal . This paper is described Wavelet transform theory , mother wavelet choice , the method to filter signal by Wavelet transform and the result , prospered a way to extract feature originated from Wavelet theory, which using Wavelet packet analyzing method to subdivide signal both in low frequency and high frequency field, and consider energy of every layer as feature in frequency field, and in conjunction with the detailed analyzing character of Wavelet packet in time-frequency plane, consider several minimum or maximum points in the lowest frequency band as features in the time field. This is a very good method to charactering feature both in the time field and in the frequency field, and this method is superior to Fourier transform method.BP neutral network is one of the representative neutral network models, it is suitable for classifying. This paper is introduced BP neutral network character, algorithm , designing principal on its construction and the designed product . Input features extracted by the way described above into a BP neutral network, and using it to classify seven type of different carburized layer depthspecimen .The result is indicated, using Wavelet packet method to extract features and BP neutral network to classifying, is effective and precise to classify different metal carburized layer depth. It is useful and economical.