Condition Monitoring of Sensors in a NPP Using Optimized PCA

An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method

Entropy-based robust PCA for communication network anomaly detection

Principal component analysis (PCA) has received increasing attention as a method to distinguish network traffic anomalies from normal data instances based on its orthogonal linear transformation characteristics and dimensionality reduction technique. To address the issue of parameter sensitivity in the classical PCA, we propose modifications to the classical PCA, called robust PCA in this paper, which exhibits greater flexibility in detecting outliers for different traffic distributions. First, the robust PCA utilizes the Mahalanobis distance function which generates more flexible results than that of the Euclidean distance used in the classical PCA. The second modification to the classical PCA is to take into account the temporal effect of network traffic data by considering the neighbors' corresponding values. Temporal correlation is a practically important feature for network traffic, which the classical PCA does not consider. In addition, the proposed robust PCA also adopts