外文文獻(xiàn)及翻譯--應(yīng)用高階神經(jīng)網(wǎng)絡(luò)對(duì)旋轉(zhuǎn)機(jī)器的多種故障進(jìn)行診斷-文庫吧
2025-04-17 12:20 本頁面
【正文】 對(duì)于這樣的網(wǎng)絡(luò)，一個(gè)等級(jí)制度的診斷方法能同時(shí)為多個(gè)故障診斷是可實(shí)行的。 參考文獻(xiàn) [1] Duda RO, Hart PE. Pattern Classification and Scene Analysis, Wiley, New York, 1973 [2] Watanabe K, Himmelblau DM. Fault diagnosis in nonlinear chemical process: Theory. AIChE J 1983。 29: 243–250 [3] Venkatasubramanian V, Chen K. A neural work methodology for process fault diagnosis. AIChE J 1989。 35(12): 1993–2020 [4] Yan Tinghu, Zhong Binglin, Huang Ren. Neural work technique and its application in fault diagnosis for rotating machines. J Vibration Eng 1993。 6(3): 205–211 [5] MacIntyre J,Jennings monitoring software that thinks for MAINTEC 97 International Maintenance Conference,Birmingham,UK,1997 [6] MacIntyre works in condition monitoring just another fad?Proc 5th International Conference on Profitable Condition Monitoring,Harrogate,UK,1996 [7] Leonard JA,Kramer of the back propagation approach to fault diagnosis and improvement with basis Annual Meeting,Chicago, IL,1990 [8] Kavuri SN,Venkatasubramanian bounded fault classes using neural works with ellipsoidal activation in Chemical Eng 1993。17(2):139–163. [9] Kavuri SN,Venkatasubramanian fuzzy clustering with ellipsoidal units in neural works for robust fault in Chemical Eng 1993。 17(2):139–163 [10] Pao Pattern Recognition and Neural Networks,AddisonWesley,New York,1989 [11] Hrycej Learning Neural ,New York,1992 [12] Holmstrom L,Koistinen additive noise in back propagation Trans Neural Networks 1992。3(1),24–37 [13] Bishop Networks for Pattern Recognition,Oxford University Press,1995 High Order Neural Networks for Simultaneous Diagnosis of Multiple Faults in Rotating Machines B. Zhong39。， J. MacIntyre2, Y. He` and J. Tait2 Abstract: To overe the limitations of the standard feedforward neural works,highorder neural works( unit works),which are very useful for fault diagnosis applications due to their bounded generalisation and extrapolation,are paper describes the theory and structure of such method for initialising hyperellipsoids and a training algorithm based on the standard backpropagation algorithm are the properties of bounded generalisation and extrapolation inherent in such works,a Hierarchical Diagnostic Artificial Neural Network(HDANN)based on the ellipsoidal unit work is put forward with respect to simultaneous diagnosis of multiple faults on rotating machines,which consists of several subworks and aims at dividing a large pattern space into several smaller subworks can be trained in subspaces respectively and the whole work is capable of simultaneous diagnosis of multiple ,typical fault data from rotating machines are tested in the research results show that HDANN can obtain more accurate and
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