Calibration of a Hub Dynamic Balancing Machine Based on the Least Squares Method and Systematic Error Analysis
To resolve issues such as excessive residual vibrations and unsatisfactory balance effects in the balancing process, the particle swarm optimization (PSO)algorithm is combined with the least squares influence coefficient method of rotor dynamic balance to perform dynamic balance calibration based on...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/2ea3dddd339d448489f1d1f7944acd3d |
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| Sumario: | To resolve issues such as excessive residual vibrations and unsatisfactory balance effects in the balancing process, the particle swarm optimization (PSO)algorithm is combined with the least squares influence coefficient method of rotor dynamic balance to perform dynamic balance calibration based on the research of the least squares influence coefficient method of wheel dynamic balance. The influence coefficient generally has a large error due to the influence of the vibration measured value, thereby lowering the accuracy of the calibrated influence coefficient. Therefore, the maximum likelihood estimate (MLE) method is employed to address the influence coefficient error, and the result is compared with the calibration value of the influence coefficient (IC) method. The analysis results indicate that the residual value generated by the calibration of the influence coefficient through the maximum likelihood estimate (MLE) is 1.036 while the residual value obtained through the influence coefficient (IC) method is 1.513, suggesting that the former exhibits a smaller systematic error and is closer to the true value. |
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