Power Transformer Anomaly Detection Based on Adaptive Kernel Fuzzy C-Means Clustering and Kernel Principal Component Analysis

2018 ◽  
Author(s):  
Kan Tang ◽  
Tingzhang Liu ◽  
Xiaoye Xi ◽  
Yue Lin ◽  
Jianfei Zhao
Keyword(s):  
Principal Component Analysis ◽  
Anomaly Detection ◽  
Power Transformer ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Adaptive Kernel

scholarly journals Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yunpeng Fan ◽  
Wei Zhang ◽  
Yingwei Zhang
Keyword(s):  
Principal Component Analysis ◽  
Time Delay ◽  
Principal Component ◽  
Component Analysis ◽  
Adaptive Method ◽  
Kernel Principal Component Analysis ◽  
Moving Window ◽  
Data Space ◽  
Weighted Principal Component ◽  
Adaptive Kernel

A new adaptive kernel principal component analysis (KPCA) algorithm for monitoring nonlinear time-delay process is proposed. The main contribution of the proposed algorithm is to combine adaptive KPCA with moving window principal component analysis (MWPCA) algorithm, and exponentially weighted principal component analysis (EWPCA) algorithm respectively. The new algorithm prejudges the new available sample with MKPCA method to decide whether the model is updated. Then update the KPCA model using EWKPCA method. And also extend MPCA and EWPCA from linear data space to nonlinear data space effectively. Monitoring experiment is performed using the proposed algorithm. The simulation results show that the proposed method is effective.

Failure Prediction and Wear State Evaluation of Power Shift Steering Transmission

2015 ◽  
Vol 741 ◽  
pp. 183-187 ◽  
Author(s):  
Yong Liu ◽  
Biao Ma ◽  
Yu Yan ◽  
Chang Song Zheng
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Atomic Emission ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
State Evaluation ◽  
Power Shift ◽  
Fuzzy C Means Clustering ◽  
Friction Surfaces

Within the vehicle transmission, the friction surfaces of mechanical parts were consecutively worn-out and ultimately up to the degradation failures. For assessing the wear progress effectively, wear particles should be generally monitored by measuring the element concentration through Atomic emission (AE) spectroscopy. Herein, the spectral data sampled from life-cycle test has been processed by both the Principal Component Analysis (PCA) and further Kernel Principal Component Analysis (KPCA). Results show that KPCA acts more effectively in variable-dimensions reduction due to fewer principle components and higher cumulative contributing rate. To detect the threshold point at where the wear-stage upgraded, the Fuzzy C-means clustering algorithm was applied to process the eigenvalues of principle components. Furthermore, it is demonstrated that the principle components relate to the worn-out state of friction pairs or transmission parts. In general, the introduction of KPCA has contributed to assess the wear-stage at where the machine situates and the accurate worn-out state of various transmission parts.

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