A Dynamic Health Assessment Method for Industrial Equipment Based on SG-FCM Clustering Algorithm

The health status assessment of industrial equipment will be affected by the actual operation environment and condition. In order to improve the accuracy of equipment health status assessment, this paper proposed a dynamic health assessment method based on SG-FCM clustering with fuzzy clustering theory, which can generate multiple health status recognizers by clustering degradation states of several typical cases from actual operation data, and assess the degradation states by matching the recognizer. Compared with other methods, the experimental results show that, the proposed method can assess the health status of equipment more accurately under different environments and operation conditions, and provide more accurate bases for the fault prediction and residual life calculation of equipment.

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