In economic construction, there are many large and important machinery and equipment. Some equipment will continue to work in a harsh working environment, so many and various failures will occur. Rolling bearings are one of the widely used parts in rotating machinery. They are generally composed of inner ring, outer ring, rolling element and holding. The frame is composed of four parts, the failure of the bearing is particularly important, and its safe operation has a vital impact on the entire equipment, Feature extraction is the key link in the subsequent identification of fault types, Although feature extraction in the time domain and frequency domain is effective, it is also necessary to find new feature extraction methods in new areas. On the basis of the snowflake image obtained by using the principle of SDP(Symmetrized Dot Pattern), a method for extracting fault features of rolling bearings based on image processing is proposed, and the snowflake standard map for different working conditions is constructed. The number of snowflake images under different working conditions is different. The binary matrix of the test image is compared with it, and then classified and identified. Finally, the algorithm is validated, and the ideal result is obtained to verify its rationality and effectiveness.
Agricultural Plant Leaf Disease Detection and Diagnosis Using Image Processing Based on Morphological Feature Extraction
