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.

Fatigue Life Prediction of Buckling String with Cracks in Horizontal Wells of Mining Engineering

2012 ◽  
Vol 577 ◽  
pp. 127-131 ◽  
Author(s):  
Peng Wang ◽  
Tie Yan ◽  
Xue Liang Bi ◽  
Shi Hui Sun
Keyword(s):  
Fatigue Life ◽  
Prediction Model ◽  
Life Prediction ◽  
Horizontal Well ◽  
Drill Pipe ◽  
Prediction Method ◽  
Drill String ◽  
Fatigue Life Prediction ◽  
Mining Engineering ◽  
Life Prediction Model

Fatigue damage in the rotating drill pipe in the horizontal well of mining engineering is usually resulted from cyclic bending stresses caused by the rotation of the pipe especially when it is passing through curved sections or horizontal sections. This paper studies fatigue life prediction method of rotating drill pipe which is considering initial crack in horizontal well of mining engineering. Forman fatigue life prediction model which considering stress ratio is used to predict drill string fatigue life and the corresponding software has been written. The program can be used to calculate the stress of down hole assembly, can predict stress and alternating load in the process of rotating-on bottom. Therefore, establishing buckling string fatigue life prediction model with cracks can be a good reference to both operation and monitor of the drill pipe for mining engineering.

scholarly journals Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Xiaochen Zhang ◽  
Dongxiang Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Life Prediction ◽  
Ball Screw ◽  
Support Vector ◽  
Remaining Life ◽  
Quantum Genetic Algorithm ◽  
Training Samples ◽  
Life Prediction Model

To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA) and support vector machine (SVM) is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap), the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

scholarly journals A Zoning Earthquake Casualty Prediction Model Based on Machine Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 30
Author(s):  
Boyi Li ◽  
Adu Gong ◽  
Tingting Zeng ◽  
Wenxuan Bao ◽  
Can Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Prediction Method ◽  
Historical Earthquakes ◽  
Support Vector ◽  
Learning Methods ◽  
Support Vector Regression Model ◽  
Machine Learning Methods ◽  
Earthquake Casualty

The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction.

scholarly journals Life Prediction Model of Mineral Admixture Cement Based-Materials under Early Age CO2-Erosion

Coatings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 413
Author(s):  
Saisai Wang ◽  
Jian Chen ◽  
Xiaodong Wen
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Experimental Tests ◽  
Mineral Admixture ◽  
Model Parameters ◽  
Influence Coefficient ◽  
Test Pieces ◽  
Cement Based Materials ◽  
Particle Group ◽  
Life Prediction Model

Most of the existing models of structural life prediction in early carbonized environment are based on accelerated erosion after standard 28 days of cement-based materials, while cement-based materials in actual engineering are often exposed to air too early. These result in large predictions of the life expectancy of mineral-admixture cement-based materials under early CO2-erosion and affecting the safe use of structures. To this end, different types of mineral doped cement-based material test pieces are formed, and early CO2-erosion experimental tests are carried out. On the basis of the analysis of the existing model, the influence coefficient of CO2-erosion of the mineral admixture Km is introduced, the relevant function is given, and the life prediction model of the mineral admixture cement-based material under the early CO2-erosion is established and the model parameters are determined by using the particle group algorithm (PSO). It has good engineering applicability and guiding significance.

The construction of the life prediction model of 1N and 4N monopropellant thrusters

2013 ◽  
Author(s):  
Go Fujii ◽  
Daisuke Goto ◽  
Hideshi Kagawa ◽  
Shingo Murayama ◽  
Kenichi Kajiwara ◽  
...  
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Life Prediction Model

SUPPORT VECTOR REGRESSION MODEL FOR DIRECT METHANOL FUEL CELL

2012 ◽  
Vol 23 (07) ◽  
pp. 1250055 ◽  
Author(s):  
J. L. TANG ◽  
C. Z. CAI ◽  
T. T. XIAO ◽  
S. J. HUANG
Keyword(s):  
Fuel Cell ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Direct Methanol ◽  
Methanol Fuel Cell

The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R2) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.

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