Study On The Ferrography Wear Particle With Image Processing Technology

The ferrography is a fault diagnosis method based on the analysis of wear particle. In the ferrography, wear particle images reflect the important information of the spare part wearing and tearing within the mechanical equipment. The recognition of the wear particle is the core step of the ferrography. The result of the recognition has direct relation to the correctness of the wear condition monitoring for the equipments. Because of the diversity and complicacy of the wear particle, the recognition procedure is carried out without guidance of mature theory. Currently, the recognition of the wear particle is carried out by experts, the recognition process is very complicated, time-consumed and high cost. Those shortcomings limit the application of the ferrography. With the development of computer image processing and artificial intelligence technologies will be helpful for the improvement of accuracy and automation of ferrography analysis. The recognition of the wear particle is the most important for realizing the automatic ferrography analysis. Fuzzy logic was invented by Zadeh in 1964. The development of fuzzy logic has broken through the obstacles of conventional logic in decision-making. The thesis introduces a new method to ferrography analysis based on the fuzzy cluster theory.The main contents of the thesis are 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 particle and wear type, wear particle characters, wear mechanism, machine running status are analyzed and expatiated.4. According to digital image processing technologies, some methods such as smoothing, filtering, threshold method and HSI color model are adopted to process wear particle images. It successfully reduces the noise and separate wearparticle from background.4. By analyzing and calculating the shape, color and texture characteristic parameters of wear particles, a complete set of wear particle quantification characterization comes into being.5. The principle of fuzzy theory is discussed. A new way adapt to the identification of wear particle is studied based on fuzzy cluster method, then wear particle are recognized on the basis of selecting the near principle in the fuzzy theory. The creditability and validity of the improvement is examined by experiment. The study shows that the effects of automatic recognition are equal to those of manual recognition, and the automation of ferrographical diagnosis has been realized simply and partly.