scholarly journals A Novel Decentralized Weighted ReliefF-PCA Method for Fault Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 140478-140487 ◽  
Author(s):  
Yinghua Yang ◽  
Xiangming Chen ◽  
Yue Zhang ◽  
Xiaozhi Liu
Keyword(s):  
Fault Detection ◽  
Pca Method

scholarly journals Condition Monitoring of Sensors in a NPP Using Optimized PCA

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Wei Li ◽  
Minjun Peng ◽  
Yongkuo Liu ◽  
Shouyu Cheng ◽  
Nan Jiang ◽  
...  
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Nuclear Power ◽  
Model Simulation ◽  
Principal Component ◽  
Fault Detection And Isolation ◽  
Pca Method ◽  
The Common ◽  
Simulation Results ◽  
Selection Stage

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.

An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis

2017 ◽  
Vol 142 ◽  
pp. 167-178 ◽  
Author(s):  
Yabin Guo ◽  
Guannan Li ◽  
Huanxin Chen ◽  
Yunpeng Hu ◽  
Haorong Li ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Pca Method ◽  
Detection And Diagnosis

scholarly journals Hotelling T2Index Based PCA Method for Fault Detection in Transient State Processes

2016 ◽  
Vol 22 (4) ◽  
pp. 276-280
Author(s):  
Furqan Asghar ◽  
Muhammad Talha ◽  
Se-Yoon Kim ◽  
SungHo Kim
Keyword(s):  
Fault Detection ◽  
Transient State ◽  
Pca Method

scholarly journals An Improved Principal Component Analysis in the Fault Detection of Multi-sensor System of Mobile Robot

2018 ◽  
Vol 14 (09) ◽  
pp. 82
Author(s):  
Zhaihe Zhou ◽  
Qianyun Zhang ◽  
Qingtao Zhao ◽  
Ruyi Chen ◽  
Qingxi Zeng
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Mobile Robots ◽  
Measurement Data ◽  
Principal Component ◽  
Component Analysis ◽  
Allocation Model ◽  
Dynamic Conditions ◽  
Pca Method ◽  
Detection Capabilities

<p class="0abstract"><span lang="EN-US">To cope with the fault detection in dynamic conditions of inertial components in the mobile robots, an improved principal component analysis (PCA) method was proposed. This work took a five gyroscopes redundancy allocation model to realize the measurement of the attitude. It is hard to distinguish the fault message from dynamic message in dynamic system that results in false alarm and missing inspection, so we firstly used the parity vector to preprocess the measurement data from the sensors. A fault was detected when the preprocessed data was dealt with PCA method. The effectiveness of the improved PCA method introduced in this paper was verified by comparing fault detection capabilities of conventional PCA method under the dynamic conditions of the step fault. The results of the simulation and experimental verification of the method was expected to contribute to the fault detection and improve the accuracy and reliability of the multi-sensors system in dynamic conditions.</span></p>

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