Ferrograph abrasive particle composite characteristic construction method based on heredity programming

Abstract

The invention discloses a ferrograph abrasive particle composite characteristic construction method based on heredity programming, which comprehensively utilizes the characteristics that the ferrograph abrasive particle is direct in form and easy to obtain and the advantage of the heredity programming on the composite characteristic structure. The invention is applicable to the extraction of the oil-water ferrograph abrasive particle graph and the machine wearing state mode identification in the mechanical operation state monitoring. The ferrograph abrasive particle composite characteristic construction method takes the form characteristics of various ferrograph abrasive particles as a sample population, performs combination and optimization on the multiple form characteristics through the heredity programming to obtain composite characteristics, and is used for the classification of various fault abrasive particle. The invention overcomes the blindness in the traditional characteristic construction which is time-consuming and labor-wasting, breaks through the tradition that the prior art only can perform analysis on the off-line image, integrates the multiple form characteristic information of the ferrograph abrasive particles, can effectively improve the capability of utilizing the ferrograph image identification machinery device common fault and provides the strong support to the healthy and reliable operation of the machinery device.

Description

Technical field: [0001] The invention belongs to the field of fault diagnosis of mechanical equipment, and in particular relates to a method for constructing composite features of ferrographic wear particles based on genetic programming. Background technique: [0002] For the status monitoring of mechanical equipment, the characteristics of ferrographic wear particles are very valuable information resources. The characteristics of wear particles can be used to judge the operating status of mechanical equipment and the wear type of the machine. Wear particle characteristics can be acquired using ferrography and computer vision techniques. Therefore, researchers of ferrographic abrasive grains divide the characteristics of abrasive grains into morphological features, color features, and texture features according to different types. Morphological features are easy to obtain and can reflect the severity of machine wear, so they are most widely used in industry. The morphologi...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings of time-consuming, labor-intensive and blindness in the traditional feature construction process, and to improve the ability of ferrographic abrasive grain morphology to identify faulty abrasive grains with different morphology characteristics, and to propose a ferrographic abrasive grain based on genetic programming. Composite feature construction method

Images