Research On The Method Of Wear Pertern Recognition Based On Wear Particle Analysis

As a technical method, ferrography analysis is used widely in wear fault diagnosis and condition monitoring. The recognition of wear particle is regarded as a key point in ferrography analysis technology. With the rapid development of the computer technology and artificial intelligence technology, some methods were applied in the ferrography technology, such as computer vision technology, expert system, artificial neural network and fuzzy theory. Intelligentization of the recognition of wear particle is a important and difficult issue.The thesis introduces a new method for ferrography analysis based on Support Vector Machine (SVM) for the first time. SVM is a kind of novel machine learning methods, which based on Statistics Learning. It becomes a study hotspot in the international machine learning comparing with other methods because of its excellent performance, e.g., limited samples, overfitting and local convergence problems. Moreover, it has better generalization ability.This paper is summarized as follows:1. The development and up-to-date status of wear particle analysis technology at home and abroad are evaluated synthetically. The study plan and main content are presented.2. Wear mechanism and classification including wear particle classification and characters are analyzed and discussed. The inner relations between each basic kind of wear particles and wear type, wear particle characters, wear mechanism, machine running status are analyzed and expatiated.3. The method about pretreatments of debris image and extracting morphologic characters of wear particles is studied. The popular intelligent recognition methods based on artificial neural network and fuzzy theory are studied, and their difficulties and shortcomings are pointed out. The SVM technique based on the Statistics Learning Theory and limited sample is studied. The classifying mechanism of SVM is discussed, and the framework of wear particle recognition system based on SVM is built.4. The SVM is applied to wear pattern recognition, and the wear particle classifier is designed. The detailed design of SVM wear particle classifier is performed including the build-up of wear sample data, the training algorithm, the multi-class pattern, the kernel function. The main data structures, classes and functions of the SVM wear particle classifier are analyzed, and the interface of procedures when running is presented.5. 100 wear particle samples are cliosen, and their morphologic characters are taken as the input of SVM wear particle classifier including roundness, slightness, scatter andconcavity, the sliding, cutting, normal, fatigue erosion wear are taken as the output of SVM wear particle classifier. The effect when applying different kernel parameters for SVM wear particle classifier is studied. Choosing the appropriate parameters to prove the validity by experiment, the correct of the classifier Is up to 96%.6. The performances of the classifiers based on SVM and on the BP neural network are tested by using the same wear particle samples. The result indicates that the correct ratio based on SVM is beyond BP neural network 6%. The superiorities of SVM classifier are presented by the comparing experiment and analyzed from theory.The wear pattern recognition method based on SVM and proposed by this thesis, gives a new way to wear fault diagnosis, wear condition monitoring and intelligentization of the ferrography technology.This project is supported by a grant from the National Natural Science Foundation of China which the authorizing number is 50375141.