Hongtian Chen

Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway

Incipient faults in high-speed railway have been rarely considered before developing into faults or failures. In this paper, a new data-driven incipient fault estimate (FE) methodology is proposed under multivariate statistics frame, which incorporates with Kullback-Leibler divergence (KLD) in information domain and neural network approximation in machine learning. By defining one sensitive fault indicator (SFI), the incipient fault amplitude can be precisely estimated. According to the experimental platform of China Railway High-speed 2 (CRH2), the proposed incipient FE algorithm is examined, and the more sensitivity and accuracy to tiny abnormality are demonstrated. Followed by the incipient FE results, several factors on FE performance are further analyzed.

Multi-mode kernel principal component analysis–based incipient fault detection for pulse width modulated inverter of China Railway High-speed 5:

This article deals with incipient fault of insulated-gate bipolar transistors to improve the safety of traction systems of China Railway High-speed 5. Combining with the pulse width modulated strategy which makes signals variate periodically, the multi-mode kernel principal component analysis algorithm is proposed. It can effectively not only capture the tiny changes caused by incipient faults but also detect short-faults of insulated-gate bipolar transistors in electrical systems. In feature space of every mode, different thresholds will be formed corresponding to defined modes. The proposed scheme is tested in experimental setup of traction system of China Railway High-speed 5 with incipient fault and short-circuit fault, and experimental results show that the multi-mode kernel principal component analysis has superior monitoring performance compared to other five methods.

Real-time incipient fault detection for electrical traction systems of CRH2

Abstract Electrical traction systems in a high-speed train are the core parts to provide traction force for the whole train. Due to performance degradation of electronic components and the prolonged operation under variously complicated operating environments, incipient faults will inevitably happen and will evolve into faults or failures if they are not successfully detected. Currently, the univariate control charts are used to monitor electrical traction systems of high-speed trains. However, this primitive solution is unable to deal with incipient faults with satisfactory performance. In this paper, a Kullback–Leibler divergence (KLD) and independent component analysis (ICA)-based method is proposed to perform incipient fault detection (FD) in electrical traction systems. Compared with the existing ICA-based methods, the proposed strategy is more sensitive to incipient faults; meanwhile it has low computational load because estimating the probability density functions (PDFs) of the derived independent components and the residuals is avoided. On the experimental platform of the traction system for China Railway High-speed 2-type (CRH2) trains, three typical incipient faults are successfully injected, and the proposed method is successful in detecting these incipient faults.

An improved incipient fault detection method based on Kullback-Leibler divergence

This paper presents an improved incipient fault detection method based on Kullback-Leibler (KL) divergence under multivariate statistical analysis frame. Different from the traditional multivariate fault detection methods, this methodology can detect slight anomalous behaviors by comparing the online probability density function (PDF) online with the reference PDF obtained from large scale off-line data set. In the principal and residual subspaces obtained via PCA, a symmetric evaluation function is defined for both single variate and multivariate cases. The uniform form of probability distribution and fault detection thresholds associated with all eigenvalues are given. In addition, the robust performance is analyzed with respect to a wide range of Signal to Noise Ratio (SNR). Case studies are conducted with three types of incipient faults on a numerical example; combining with two nonlinear projections, the proposed scheme is successfully used for incipient fault detection in non-Gaussian electrical drive system. The results can demonstrate the superiority of the proposed method than several other methods.