Incipient fault diagnosis and amplitude estimation based on K-L divergence with a Gaussian mixture model
Dongnian Jiang1, Wei Li1, Fuyuan Shen1
1College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Abstract:
In this paper, a method of incipient fault diagnosis and amplitude estimation based on Kullback-Leibler (K-L) divergence is proposed. An incipient fault is usually regarded as the precursor of a significant system fault, but due to a low amplitude and non-obvious characteristics, it is easy for such a fault to be hidden by disturbance and noise. Based on this and considering the sensitivity of the K-L divergence method in data feature extraction, a method of diagnosing incipient faults is designed. In order to consider the safety performance and lay a foundation for the fault tolerance of the system, an amplitude estimation method for incipient faults is also proposed. By mapping the characteristic change in the residual data to the numerical change in the K-L divergence, the amplitude of the incipient fault can be measured with high sensitivity. Considering the generality of the method, a Gaussian mixture model is used to model the residual data in order to increase the accuracy of fault amplitude estimation. Finally, the effectiveness of the proposed method for incipient fault diagnosis and amplitude estimation is verified by experiment.
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