耐火磚全尺寸模糊分類方法及其執(zhí)行機(jī)構(gòu)動態(tài)控制的研究
[Abstract]:Whether the shape and size of refractory brick meets the allowable tolerance range is one of the key indexes for the quality of refractory brick. The theoretical research shows that strictly controlling the shape and size of refractory brick is beneficial to the masonry stability of kiln to a certain extent, and ultimately affects and determines the service life of kiln. With the development of modern science and technology, it is possible to detect the size of refractory brick by online automation. On the basis of the existing detection level, the size of refractory brick is further refined and segmented and clustering effectively, so as to establish a new fuzzy classification model of refractory brick. More detailed and reasonable classification can not only meet the needs of product export, but also reduce the cumulative error of size in kiln masonry, prolong the service life of kiln, reduce the risk of collapse and increase the economic benefit. In this paper, the full size fuzzy classification method of refractory brick and the dynamic control of its actuator are studied and discussed. In the process of research, the influence mechanism of refractory brick service life and various mathematical clustering algorithms are studied and studied systematically. based on this, the widely used and theoretically mature FCM algorithm is selected for clustering analysis. In the second step, by comparing the advantages and disadvantages of various existing clustering algorithms, the limitations of FCM algorithm are analyzed. In order to solve the problem that FCM needs the subjectivity of artificial clustering number, according to the principle of clustering validity, a method of combining clustering validity function with FCM algorithm is proposed to determine the optimal clustering number adaptively, and the Matlab simulation of known clustering center data is carried out. The stability and reliability of the algorithm are verified. In the third step, in order to effectively overcome the fact that the algorithm can not deal with symbolic data, a processing scheme is proposed to overcome the limitations of the algorithm to a certain extent. The clustering results are compared by Matlab simulation analysis, and the fluctuation of clustering center is investigated by increasing the number of samples, thus a stable fuzzy classification model is constructed. Finally, the fuzzy classifier model is embedded into PLC control program, the man-machine interface is designed, and the human-computer communication is established, and the dynamic control of its actuator is completed by program simulation. The results show that the clustering results of fuzzy classifiers are stable and objective, and the actuator can accurately complete the classification output.
【學(xué)位授予單位】:遼寧工業(yè)大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2015
【分類號】:TQ175.7
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