Research On Technologies Of Oil Wear Particle Monitoring Based On Electrostatic Induction And Microscopical Image

Oil wear particle monitoring is one of the important means for condition monitoring of mechanical equipments, which can provide analysis basis for fault diagnosis and condition based maintenance of mechanical equipments. Based on the study of the current technologies for oil wear particle monitoring, for the shortage of the current oil wear particle monitoring technologies and the demand of oil wear particle monitoring, a new oil wear particle monitoring method based on electrostatic sensor and microscopical image analysis is proposed. The main contents are listed as follows.1. An electrostatic sensor with ring-shaped electrode is designed for wear particle on-line monitoring. The structural model of the electrostatic sensor is given. The analysis method for electrostatic field based on finite element method is studied. Numerical solution of the sensor electrostatic field is carried out by using the finite element analysis software ANSYS. The spatial sensitivity of the sensor is derived. The effect factors of the sensor sensitivity are researched in detail. The frequency response characteristic of the electrostatic sensor and its effect factors are investigated theoretically and experimentally. The optimal method on structural dimension design of the electrostatic sensor is proposed.2. An on-line oil wear particle monitoring system based on microscopical image analysis is designed. The main procedure of wear particle object extraction is given according to the image characteristic. The degrading mechanism of the blurred wear particle image is analyzed. The degradation model of the blurred wear particle image is derived. The method on the blur parameters calculation of the blurred wear particle image based on difference and autocorrelation is proposed. On this basis, the restoration method of the blurred wear particle image is studied. The wear particle image segmentation method based on oil background image and Otsu is proposed. The characteristic parameter system of wear particle is optimized based on rough sets. The classifier for two kinds of wear particle is designed based on the least squares support vector machines, and the parameters of this model are optimized by particle swarm optimization algorithm. Based on this classifier, the integrative wear particle classifier is designed according to the wear particle recognition system.3. The detection method for large wear particle based on the digital video recorder and common camera is proposed. A large wear particle detection system based on microscopical image is designed .The image fusion method based on color space conversion and wavelet analysis is studied. Image mosaic method based on phase correlation and wavelet fusion is researched. The classifier for ferromagnetic wear particle is designed using the least squares support vector machine.4. The experiment platform for wear electrostatic monitoring is established, and the preliminary experiment research is carried out. An on-line oil wear particle monitoring system based on microfluidic chip and microscopical image is developed, and the system's performance is tested by the particle counter and the ferrography technology. A large wear particle detection system based on microscopical image fusion and mosaic is developed. The integrated monitoring method based on electrostatic sensor and microscopical image analysis is studied.