Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network
The increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/2684942 |
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| _version_ | 1850164980334198784 |
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| author | Zheng Jiang GuangJian Wang ZuGuang Huang Ye He RuiJuan Xue |
| author_facet | Zheng Jiang GuangJian Wang ZuGuang Huang Ye He RuiJuan Xue |
| author_sort | Zheng Jiang |
| collection | DOAJ |
| description | The increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. It can overcome the disadvantages of conventional methods, which are time consuming and resource intensive. The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. Two common problem of the PSO: local minimization and slow convergence were solved by the IPSO. “Zero failure” data preprocessing, data normalization, and small-sample data enhancement were performed on the original data. A homogenized sampling method is proposed to extract training and testing samples. Experimental results show that the improved PSO-based RBF neural network is superior to back propagation (BP) and RBF networks in terms of accuracy in servo system reliability prediction. |
| format | Article |
| id | doaj-art-f1ef8ce04ab543a787537ed68ecebb9c |
| institution | OA Journals |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-f1ef8ce04ab543a787537ed68ecebb9c2025-08-20T02:21:51ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/2684942Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural NetworkZheng Jiang0GuangJian Wang1ZuGuang Huang2Ye He3RuiJuan Xue4Chongqing UniversityChongqing UniversityGenertec Machine Tool Engineering Research Institute Co.,LtdChongqing UniversityGenertec Machine Tool Engineering Research Institute Co.,LtdThe increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. It can overcome the disadvantages of conventional methods, which are time consuming and resource intensive. The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. Two common problem of the PSO: local minimization and slow convergence were solved by the IPSO. “Zero failure” data preprocessing, data normalization, and small-sample data enhancement were performed on the original data. A homogenized sampling method is proposed to extract training and testing samples. Experimental results show that the improved PSO-based RBF neural network is superior to back propagation (BP) and RBF networks in terms of accuracy in servo system reliability prediction.http://dx.doi.org/10.1155/2022/2684942 |
| spellingShingle | Zheng Jiang GuangJian Wang ZuGuang Huang Ye He RuiJuan Xue Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network Shock and Vibration |
| title | Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network |
| title_full | Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network |
| title_fullStr | Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network |
| title_full_unstemmed | Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network |
| title_short | Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network |
| title_sort | reliability prediction for computer numerical control machine servo systems based on an ipso based rbf neural network |
| url | http://dx.doi.org/10.1155/2022/2684942 |
| work_keys_str_mv | AT zhengjiang reliabilitypredictionforcomputernumericalcontrolmachineservosystemsbasedonanipsobasedrbfneuralnetwork AT guangjianwang reliabilitypredictionforcomputernumericalcontrolmachineservosystemsbasedonanipsobasedrbfneuralnetwork AT zuguanghuang reliabilitypredictionforcomputernumericalcontrolmachineservosystemsbasedonanipsobasedrbfneuralnetwork AT yehe reliabilitypredictionforcomputernumericalcontrolmachineservosystemsbasedonanipsobasedrbfneuralnetwork AT ruijuanxue reliabilitypredictionforcomputernumericalcontrolmachineservosystemsbasedonanipsobasedrbfneuralnetwork |