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大型重载支承轴隐蔽部位因发生不可观测的突发性疲劳断裂事故,严重影响生产的正常进行,给企业带来重大的经济损失-在分析支承轴振动特性的基础上,通过在机械结构外部便于检测部位提取反映结构疲劳裂纹状态的特征信号,编写相应的MAT-LAB程序构建时序模型,然后模拟支承轴实际工况,将裂纹产生、发展、断裂过程划分为5种状态,建立标准故障模式特征向量空间．运用模糊聚类分析方法,结合Euclide距离判别函数,找出与标准故障模式特征向量中隶属度最大的特征向量,从而有效诊断支承轴隐蔽部位疲劳裂纹状态的程度,最后通过实例验证了该方法的正确性和可行性,图2,参5。
Due to the unobservable sudden fatigue fracture accident, the hidden parts of the large heavy load bearing shaft seriously affect the normal production and bring great economic losses to the enterprise. Based on the analysis of the vibration characteristics of the bearing shaft, It is convenient for the detection site to extract the characteristic signals that reflect the fatigue crack state of the structure. The corresponding MAT-LAB program is used to construct the time series model. Then the actual working conditions of the support shaft are simulated. The generation, development and fracture processes of cracks are divided into five states, Eigenvector space. Fuzzy clustering analysis method and Euclidean distance discriminant function are used to find the eigenvector with the highest degree of membership in the eigenvector of the standard fault mode so as to effectively diagnose the degree of fatigue crack in the concealed part of support shaft. Finally, Correctness and feasibility, Figure 2, reference 5.