Jiang Dongnian, Li Wei, Shen Fuyuan
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Rev Sci Instrum. 2020 May 1;91(5):055103. doi: 10.1063/5.0003535.
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.
本文提出了一种基于库尔贝克-莱布勒（K-L）散度的早期故障诊断与幅值估计方法。早期故障通常被视为重大系统故障的前兆，但由于其幅值较低且特征不明显，此类故障很容易被干扰和噪声掩盖。基于此，并考虑到K-L散度方法在数据特征提取方面的敏感性，设计了一种早期故障诊断方法。为了考虑安全性能并为系统的容错奠定基础，还提出了一种早期故障的幅值估计方法。通过将残差数据中的特征变化映射到K-L散度的数值变化上，可以高灵敏度地测量早期故障的幅值。考虑到该方法的通用性，使用高斯混合模型对残差数据进行建模，以提高故障幅值估计的准确性。最后，通过实验验证了所提早期故障诊断与幅值估计方法的有效性。
