Use of a (MSPCA) and (SVM) Method for Diagnosis of Motor Faults

2013 ◽  
Vol 321-324 ◽  
pp. 114-117
Author(s):  
Wen Ying Chen ◽  
Ya Nan Wang ◽  
Xue Fei Wu ◽  
Yu Xiang Qu
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Component Model ◽  
Practical Significance ◽  
Support Vector ◽  
Multi Scale ◽  
Principal Component Model ◽  
Q Statistic

This paper uses the combination between support vector machine and multi-scale principal component analysis. For motor fault detection, the principal component model can be established in various scales. Through T2 and Q statistic judgment whether motor can run normally. The experimental results show that the method of combination vector machine and multi-scale principal component analysis is supported to diagnose motor fault. This offers a new method and idea to diagnose motor. This method improves the accuracy of motor fault detection and practical significance.

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

2021 ◽  
pp. 014233122110447
Author(s):  
Hongjuan Yao ◽  
Xiaoqiang Zhao ◽  
Wei Li ◽  
Yongyong Hui
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Objective Function ◽  
Principal Component ◽  
Batch Process ◽  
Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Order Information ◽  
Multiple Neighborhoods

Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.

Analog Circuits Test by Using Principal Component Analysis

2013 ◽  
Vol 278-280 ◽  
pp. 709-713
Author(s):  
Chao Jie Zhang ◽  
Guang Hui Chang
Keyword(s):  
Principal Component Analysis ◽  
Analog Circuits ◽  
Principal Component ◽  
Component Analysis ◽  
Test Methods ◽  
Component Model ◽  
Suitable Candidate ◽  
Principal Component Model ◽  
Other Test Methods ◽  
Higher Sensitivity

In view of the difficulties caused by determining threshold for analog circuits test, a method based on principal component analysis (PCA) of node voltages was proposed to overcome these difficulties. At first, the principal component model of fault-free circuits was constructed. Then the circuits-under-test was compared with the principal component model to calculate the statistic for fault detection. The proposed method was used to test the output signal amplifying circuit, which is used in the ultrasonic liquid sensor. The testing results show that the PCA based method has a higher sensitivity than other test methods. And the proposed method can overcome the difficulties in determining threshold by the expert’s empirical knowledge. These make it a suitable candidate for analog circuits test.

Application of Principal Component Analysis in Water Quality Evaluation by SPSS Software

2011 ◽  
Vol 403-408 ◽  
pp. 3277-3280 ◽  
Author(s):  
Jun Fang Gu ◽  
Chang Jun Zhu ◽  
Zhen Chun Hao
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Component Model ◽  
Water Quality Evaluation ◽  
Multiple Variables ◽  
Spss Software ◽  
Principal Component Model

In view of the defect of traditional water quality evaluation model, principal component analysis method is developed to evaluate surface water quality in Baoying country. By SPSS software, principal component model is applied to evaluate water quality at representative sections in Baoying surface area. Principal component analysis is a way to reduce orginal dimension, to make multiple variables inti a few comprehensive index. By the combination of variables index, adjusting the combinatorial coefficient to make the new variables representative independent. The process is introduced in the paper in detail.The results indicate that principal component model is suitable for water quality evaluation. By analysis, it is important to pay attention to bring into effective measures for pollution control.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

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