Research On The Key Technical Problems On Dynamic Balancing Measurement

Dynamic balancing performance is the determinant factor for the service life, working noise and energy consumption of motors. With the increasing of motor rotating speed, the requirements on motor dynamic balancing performance are more and more stringent, which then demands more accurate dynamic balancing machines. Currently, the poor accuracy of the measurement system on domestic dynamic balancing products is the bottleneck to improve their quality of. Due to the fact that system measurement error can be eliminated and the influence of random measurement error on measurement results can be estimated, the most economic and effective way to improve the accuracy of measurement systems is to adopt real time system error elimination technology and to real time evaluate the influence of random error on measurement error. The system error of dynamic balancing measurement process was featured by the estimating accuracy of influence coefficients. Aiming at this situation, the main contributions are listed below:(1) Under the circumstance that recalibration was adopted to periodically calibrate measurement systems, aiming at the conflict between the number of samples of vibration responses of trial weights and the stoppage time of product assembly line, a hierarchical Bayesian method for dealing with calibration data was proposed. Through this method, the prior information of the vibration responses of trial weights and their estimation variance was sufficiently employed. The joint distribution model of vibration responses and their measurement variance was established to embody the influence of measurement error on the estimator of the true value of vibration responses; furthermore, the Gibbs sampling method of Markov Chain Monte Carlo (MCMC) method was used to obtain their maximum posterior estimation. This method reduced the dependency of estimation accuracy of calibration data on the number of samples and implemented accurate estimation of calibration data under the circumstance of small sample. This could reduce the time of the calibration process and then decrease the loss resulting from the stoppage of the whole assembly line.(2) When standard measurement data with higher accuracy was available, an improved Kalman filtering method for the regulation of dynamic balancing measurement process was presented. Aiming at the drawback that conventional state estimation method could not give attention to both the ability of track state change and the ability of suppress the disturbance of random measurement errors, Kalman filtering was combined with multivariate statistical process control. The measurement residual was monitored by theχ2 value. When the measurement process was under control, normal Kalman filtering was adopted to make the best use of its ability in suppressing random measurement errors; when the measurement process was out of control, theχ2 value was used to actively regulate the state prediction covariance to enhance the ability of track state change of Kalman filtering, which made the best tradeoff the conflict between tracking state change and suppressing random errors and improved the robustness of process regulation method.(3) Aiming at the problem that the method provided by ISO-GUM was incapable in solving the nonlinearity of dynamic balancing measurement process and embodying the dynamics of measurement systems in their lifecycle, a dynamic measurement uncertainty evaluation method for dynamic balancing measurement systems was proposed. By establishing the probability propagation relationship between unbalance and its vibration response, the distribution of unbalance was determined, which took the Monte Carlo method as calculating tools. Meantime, through the dynamic estimation of influence coefficients and the statistical characteristics of random measurement error of vibration responses, the dynamic information of the measurement system was fused into the process of evaluating measurement uncertainty. Ultimately, the accurate evaluation of measurement uncertainty during the lifecycle of measurement systems was achieved.(4) Regarding to the two wrong decisions of accepting a bad rotor and rejecting a good rotor due to measurement uncertainty, an evaluating method of rotor dynamic balancing performance based on Bayesian minimum decision cost method was proposed. This method adopted the information of the variation of manufacturing processes and decision cost into the decision model, which provided more reference for decision-makers. Meantime, to guarantee the correctness of the above information, the statistical characteristics of the variation of manufacturing process were dynamically estimated by Bayesian statistical estimation method. And finally the reliability of quality decision was improved.