scholarly journals A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qinming Liu ◽  
Wenyi Liu ◽  
Jiajian Mei ◽  
Guojin Si ◽  
Tangbin Xia ◽  
...  
Keyword(s):  
Missing Data ◽  
Support Vector Regression ◽  
Prediction Method ◽  
Small Sample ◽  
Support Vector ◽  
Equipment Failure ◽  
Single Variable ◽  
Sample Data ◽  
Data Missing ◽  
Health Diagnosis

Actually, it is difficult to obtain a large number of sample data due to equipment failure, and small sample data may also be missing. This paper proposes a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve equipment health diagnosis effect. First, the genetic algorithm is used to optimize support vector regression, and a new method GA-SVR can be proposed. The GA-SVR model is trained by using other data of the variable to which the missing data belongs, and the single-variable prediction method can be obtained. The correlation analysis is used to reconstruct the training set, and the GA-SVR is trained by using the data of the variables related to the missing data to obtain the multivariate prediction method. Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. The filled data are used as input of GA-SVM to diagnose equipment failure. Finally, a case study is given to verify the applicability and effectiveness of the proposed method.

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

scholarly journals Copper Price Prediction Using Support Vector Regression Technique

2020 ◽  
Vol 10 (19) ◽  
pp. 6648
Author(s):  
Gabriel Astudillo ◽  
Raúl Carrasco ◽  
Christian Fernández-Campusano ◽  
Máx Chacón
Keyword(s):  
Support Vector Regression ◽  
Mining Industry ◽  
Prediction Method ◽  
Real Data ◽  
Copper Price ◽  
Support Vector ◽  
Metal Exchange ◽  
Potential Growth ◽  
Support Vector Regression Model ◽  
Price Prediction

Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (C, ε, γ) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the 2.2% for prediction periods of 5 and 10 days.

Damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression

Optik ◽  
2019 ◽  
Vol 180 ◽  
pp. 244-253 ◽  
Author(s):  
Shizeng Lu ◽  
Mingshun Jiang ◽  
Xiaohong Wang ◽  
Hongliang Yu ◽  
Chenhui Su
Keyword(s):  
Support Vector Regression ◽  
Fiber Bragg Grating ◽  
Prediction Method ◽  
Support Vector ◽  
Bragg Grating ◽  
Damage Degree

scholarly journals Research on water pollution prediction of township enterprises based on support vector regression machine

2021 ◽  
Vol 228 ◽  
pp. 02014
Author(s):  
Yue Wang ◽  
Song Xue ◽  
Junming Ding
Keyword(s):  
Water Pollution ◽  
Support Vector Regression ◽  
Rural Areas ◽  
Rural Economy ◽  
Negative Impact ◽  
Water Environment ◽  
Small Sample ◽  
Support Vector ◽  
Rural Water

The construction and development of township enterprises plays a key role in promoting the development of rural economy. With the implementation of the rural revitalization strategy, township enterprises develop rapidly, but there are problems in the development process that have a negative impact on the quality of local rural water environment. Rural water environment is related to the health of farmers, the healthy development of agriculture and the sustainable development of rural areas, so it is necessary to predict the water pollution of township enterprises. The application of support vector regression forecasting model to the prediction of water pollution of township enterprises can better predict the water pollution of township enterprises with the characteristics of complexity, nonlinear and small sample. This intelligent forecasting method will help to scientifically prevent the development of township enterprises from having negative impact on the quality of local water environment.

scholarly journals Fuzzy reliability analysis based on support vector machine

2021 ◽  
Author(s):  
xiao bo Nie ◽  
Haibin Li ◽  
Hongxia Chen ◽  
Ruying Pang ◽  
Honghua Sun
Keyword(s):  
Support Vector Machine ◽  
Probability Distribution ◽  
Reliability Analysis ◽  
Small Sample ◽  
Learning Ability ◽  
Support Vector ◽  
Fuzzy Reliability ◽  
Performance Function ◽  
Sample Data ◽  
Distribution Parameters

Abstract For a structure with implicit performance function structure and less sample data, it is difficult to obtain accurate probability distribution parameters by traditional statistical analysis methods. To address the issue, the probability distribution parameters of samples are often regarded as fuzzy numbers. In this paper, a novel fuzzy reliability analysis method based on support vector machine is proposed. Firstly, the fuzzy variable is converted into an equivalent random variable, and the equivalent mean and equivalent standard deviation are calculated. Secondly, the support vector regression machine with excellent small sample learning ability is used to train the sample data. Subsequently, and the performance function is approximated. Finally, the Monte Carlo method is used to obtain fuzzy reliability. Numerical examples are investigated to demonstrate the effectiveness of the proposed method, which provides a feasible way for fuzzy reliability analysis problems of small sample data.

scholarly journals Life prediction of slewing bearing based on isometric mapping and fuzzy support vector regression

2019 ◽  
Vol 42 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Weigang Bao ◽  
Hua Wang ◽  
Jie Chen ◽  
Bo Zhang ◽  
Peng Ding ◽  
...  
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Life Prediction ◽  
Feature Fusion ◽  
Prediction Method ◽  
Support Vector ◽  
Isometric Mapping ◽  
Slewing Bearing ◽  
Time Frequency ◽  
Life Prediction Model

The monitoring data of slewing bearing is massive. In order to establish accurate life prediction model from complex vibration signal of slewing bearing, a life prediction method based on manifold learning and fuzzy support vector regression (SVR) is proposed. Firstly, the multiple features are extracted from time domain and time-frequency domain. Then isometric mapping (ISOMAP) is used to reduce high-dimensional features to low-dimensional features that can reflect degeneration of slewing bearing well. Finally, the fuzzy SVR is used to predict the life degradation trend of slewing bearing. The results show that: (1) Multi-feature fusion after ISOMAP can obtain more comprehensive degradation indicator. (2) The complexity of the life prediction model is simplified and the real-time life degradation trend of slewing bearing can be well predicted by fuzzy SVR, so it is very suitable to predict life degradation trend of slewing bearing based on massive data well. The time of prediction on average is reduced by 72.7%. The mean absolute error (MAE) and root mean square error (RMSE) of prediction are reduced by 73% and 59% respectively compared with traditional methods. The accuracy of prediction is greatly improved.

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