A Comparison Study of K-gap Metric Calculation Based on Different Data-driven Stable Kernel Representation Methods

K-gap metric is an innovative tool for fault diagnosis system design and analysis. In this paper, different methods of data-driven stable kernel representation (SKR) are investigated for the purpose of K-gap metric calculation. Three existing methods are compared by definitions of SKR and calculation procedures. The first difference between these methods lies in their different definitions of SKR. The comparison result shows that the one which is unified with model-based SKR is the better definition version. The second difference is the different noise reduction techniques applied to process data, which have influences on numerical result. Based on the above comparison, a new method is proposed with more accuracy in K-gap metric calculation by using the prior knowledge from model-based method. Finally, a numerical example compare and illustrate the accuracy in K-gap computation of four data-driven SKR methods.

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