Incipient fault detection for dynamic chemical processes based on enhanced CVDA integrated with probability information and fault-sensitive features

In recent years, deep learning has shown its unique potential and advantages in feature extraction and pattern recognition [...] 

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Deep Learning-Based Machinery Fault Diagnostics

Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics T2 and Q. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the k-nearest neighbor (kNN) technique is applied to design the statistics nearest neighbor distance DT2 and DQ as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVAkNN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.

Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method

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Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method