Abstract
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.
Highlights
The calibration of the dynamic balance of a hub is a reverse mathematical mapping process that estimates the unbalance of the rotor based on the measured vibration response of the support structure
The mass of the counterweight is restricted to the required range [0, qlim], and the quadratic sum of the residual vibrations in the least squares method is set as the fitness function, which is expressed in Eq (11): n min U = |δm|2/m
In particle swarm optimization (PSO), in addition to considering the minimum residual vibration, the weight must be constrained
Summary
The calibration of the dynamic balance of a hub is a reverse mathematical mapping process that estimates the unbalance of the rotor based on the measured vibration response of the support structure. The calibration model and model parameter accuracy are the key factors affecting the accuracy of dynamic balance measurements. Unbalanced vibrations of the rotors are the main excitation sources in rotary machines. The main methods used to balance rotors are the modal balance method and the influence coefficient method. The industrial use of high-speed rotating machines has increased. The characteristics and performance of these rotating systems need to be improved [1].
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