A health assessment method for aero-engines under variable operating conditions based on operating condition identification and Tanimoto distance
A technology of aero-engine and Tanimoto distance, applied in the direction of instruments, simulators, control/regulation systems, etc., can solve the problem that performance parameters cannot reflect the health status of the system, and achieve a comprehensive effect of measurement
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[0023] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0024] The present invention is based on working condition identification and Tanimoto distance health assessment method for aero-engine complete machine under variable working conditions. The specific steps are as follows:
[0025] 1. Working condition identification based on K-means clustering
[0026] The K-means clustering algorithm was proposed by MacQue, which is widely used in data mining and is one of the classic clustering algorithms. Let X={X 1 ,X 2 ,...,X n} is a known data set, X in X 1 ,X 2 ,...,X n is n data objects and each data object is N-dimensional, that is, X i =(x i1 ,x i2 ,...,x iN ). The K-means clustering algorithm is to find a set C={C that contains K cluster centers 1 ,C 2 ,...,C K}={(c 11 ,c 12 ,...,c 1N ),(c 21 ,c 22 ,...,c 2N ),...,(c K1 ,c K2 ,...,c KN )} such that the objective function
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