The Flaw Signal Extraction Of Wire Rope Based On Independent Component Analysis And Wavelet Transform

As wire rope is widely used in many engineering fields,its safety use is critical. With the development of science and technology,material of wire rope is becoming increasingly complex.So the wire rope detection has become increasingly difficult. Present detection techniques are flawed to varying degrees.On the basis of a detailed analysis on state of the wire rope detection technology research at home and aboard,and the principle of wire rope magnetic detection and its detection devices,as well as the features of the defective signal,this paper develops a wire rope flaw detection system. This system uses the embedded systems on wire rope flaw detection the first time.To achieve functions such as signal acquisition and amplification A/D conversion data Storage and so on, the hardware uses embedded systems(s3c44b0) and CPLD(EPM7128BTC100-10) as its core component. Software development is based on uCOSâ…¡platform,it can achieve functions like signal de-noising,judging and show the defection location and the number of defects,communication between the system and the PC,etc.This article focuses on the defective signal extraction method of wire rope.It applies Wavelet theory and Independent Component Analysis theory. After introducing the basic knowledge of Wavelet,this paper analyzes the principle of the wavelet threshold de-noising,and its performance evaluation criteria;then introduces how to ascertain the wavelet function,the best decomposed level and the value of wavelet threshold;and then advances a new wavelet threshold function,this function can be chosen between soft and hard threshold,and has infinite derivative and better continuity;combining with the new threshold function,this paper advances a improved algorithm of Tanslation-invariant wavelet threshold de-noising.Experiment on typical noised signal in Matlab and the real wire rope detecting signal,it can be seen that the improved de-noising algorithm has clear advantage when compared with the traditional soft and hard threshold de-noising algorithm, its SNR has improved above 50%.This proves its validity and applicability.To solve the problem of distilling the defective signal from strong noise background,this paper advances to combine the ICA and Wavelet theory.After introducing the thing of ICA,the paper recommends its math model and its commonly resolution process.It can be seen that using ordinary wavelet de-noising algorithm,the de-noising effect of strong noised signal is not perfect,but it can be good when use the ICA as pretreatment tool.The value of ICA separation performance evaluate criterion and the SNR and RSME of the de-noised signal,can prove the validity of the algorithm.