Study On Grey System Theory And Its Application In Ferrographic Image Processing

Ferrography is one of important technologies in machine condition monitoring and fault diagnosis.It takes wear particle as its research objects.Machine states can be monitored and fault can be diagnosed by analyzing ferrography because wear particle image reflects the information of mechanical equipment's wearing and tearing. The recognition of wear particle is the core of ferrography for its direct relation to the correctness of monitoring.With the rapid development of the computer technology, some methods have been applied in ferrography technique such as computer vision, expert system,artificial neural network.Since wear particle images have some grey characteristics such as the grey level of image pixel,image edge,image noise and the threshold of image segmentation and so on,the grey system theory has aroused researchers' attention and its feasibility and validity in the field of image process becomes a brand-new area.In this thesis,grey system theory and its application in ferrographic image processing has been studied by the numbers,which conducts the research of ferrography wear particle image's processing,feature extraction,image quality assessment as fellows:1.The research of enhancing grey prediction model precisionIt is shown by study that the factors affecting grey prediction model precision primarily are the smooth degree of primary data sequence,the background value and the initial value of grey model,in order to increase grey model precision,the research is conducted separately:(1) Based on function x^-a(a＞0) transformation and based on multiplex composite function transformation,the methods of enhancing smooth degree of data sequence are proposed respectively.It has been proved that the discrete data after transformation can greatly advance its smooth degree.Moreover,the property of transformation function is summarized. (2) The structuring method of background value based on the integral definition and the exponential function are put up,and the fitting and prediction precision is analyzed.The results show that these two kinds of improvement grey model may be used not only in short-term forecasting,medium-term but also in long-term forecasting.(3) The revision initial value and the time-variable initial value are advanced,and the method of its parameter identification based on the adaptive genetic algoritlm is given.In order to obtain the higher modeling and prediction precision,the grey model may be ameliorated from the sequence smooth degree,the background value and the initial value at the same time.2.The research of ameliorating grey correlation degreeThe research has discovered that the existing grey correlation degree has some flaws such as contradiction between the grey correlation four axioms and the computational methods of grey correlation degree,the influence of sequence's non-dimensionalization against the computed result of correlation.Based on this,the grey average correlation degree and grey T's correlation degree have been studied, and ameliorating grey Euclid correlation degree and grey T's correlation degree are proposed separately:(1)The ameliorating grey Euclid correlation degree is given.It considers not only the correlation coefficient fluctuation of each spot to its mean value but also the ideal related and negative ideal related.It is proved that it has parallelism,standardization, integration,even symmetry and appropinquity.(2)The grey T's correlation degree is supposed.It can reflect the positive and negative relation of sequence and has symmetry,uniqueness,comparability, appropinquity,standardization,and the rank preservation to the non-quantification processing.3.The research of image processing algorithm based on grey system theory.(1) The new three methods of edge detection are proposed according to grey correlation degree and derivative operator,stepwise ratio,higher-dilnension space and correlativity respectively.These algorithlns can detect the edges of different direction and adjust edge detail detected by means of threshold.These algorithms have suppression ability against some kinds of noise such as gaussian,speckle and poisson noise specially.Moreover,these algorithms can locate accurately and the computation load is small.(2) A new method of detection for noise spots is proposed based on grey correlation coefficients.This algorithm that distinguishes noise spots between noise image and mean image according to grey coefficients has used view image information including noise statistical information.It can distinguish the noise spots of image effectively.(3) Kinds of adaptive weighted filter are proposed based on grey correlation degree,grey model,mean image and median image.These algorithms take mean image or median image of noised image as its foundations,and use grey correlation degree and grey forecast model to process noise spots separately.These methods can overcome noise spot's influence effectively and reduce the image fuzziness,it can preserve integrity of edge.(4) Based on lifting wavelet transform and grey prediction model,an image compression algorithm is proposed.Firstly,this algorithm transforms image to frequency range by using promotion wavelet,and differentiates significant coefficients and insignificant coefficients by using zero-tree in various wave bands. Moreover,it scans various wave bands to one dimension by using Hilbert curve. Finally,it carries on predictive coding by using grey forecast model.The simulation shows that this algorithm can enhance image compression ratio and compression quality effectively.4.The research of the image quality assessment based on the correlativity.Based on the characteristics of wavelet coefficients of image and the correlativity index,a novel image quality assessment is proposed.The algorithm makes full use of perfect integral comparison mechanism of correlativity index,it can not only evaluate the quality of image accurately but also bears more consistency with human visual system. 5.The research of wear particle image pre-processing,feature extraction and recognition algorithm.This thesis makes a review of achievement in the field of ferrography especially in image processing,feature distilling and particle recognition.On the image processing,the wear particle image pre-processing can be done by means of image edge detection and image smoothing which are proposed by this thesis.On feature extraction,about 54 parameters of characteristics are extracted.On wear particle recognition,the characteristic parameters are simplified and optimized by using grey correlation degree to carry on the wear particle recognition.The wear particle recognition algorithm is proposed based on grey correlation neural network.The neural network takes the 9 above-mentioned characteristic parameters and wear particle type as its input and output respectively,and the neuron number of hidden layer is optimized by use of grey correlation degree.The algorithm can optimize the structure of neural network greatly,enhance network's study efficiency and the accuracy of wear particle recognition.Experiment result shows that the classification accuracy is more than 97.5%.