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 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.

[1]  Yansong Fan,et al.  The Optimization of Balancing Least Squares Influence Coefficient Method Based on Particle Swarm Optimization , 2011 .

[2]  Chun-Chieh Wang,et al.  An accuracy improvement for balancing crankshafts , 2003 .

[3]  Chyuan-Yow Tseng,et al.  Dynamic balancing scheme for motor armatures , 2007 .

[4]  H. Taplaka,et al.  Passive balancing of a rotating mechanical system using genetic algorithm , 2012 .

[5]  Thomas P. Goodman,et al.  A Least-Squares Method for Computing Balance Corrections , 1964 .

[6]  Bong-Suk Kim,et al.  Development of the active balancing device for high-speed spindle system using influence coefficients , 2006 .

[7]  Mark S. Darlow,et al.  Balancing of high-speed machinery , 1989 .

[8]  Guoying Meng,et al.  Analytical modelling and numerical experiment for simultaneous identification of unbalance and rolling-bearing coefficients of the continuous single-disc and single-span rotor-bearing system with Rayleigh beam model , 2019, Mechanical Systems and Signal Processing.

[9]  Xian Ying Feng,et al.  Research on Calibration Method of Tire Dynamic Balance Detection System , 2012 .

[10]  Ma,et al.  Hierarchical Bayesian Calibration and On-line Updating Method for Influence Coefficient of Automatic Dynamic Balancing Machine , 2009 .

[11]  Jun Ni,et al.  Adaptive Influence Coefficient Control of Single-Plane Active Balancing Systems for Rotating Machinery , 1999, Manufacturing Science and Engineering.

[12]  Rajiv Tiwari,et al.  Identification of bearing dynamic parameters and unbalance states in a flexible rotor system fully levitated on active magnetic bearings , 2014 .

[13]  R. Tiwari,et al.  Application of active magnetic bearings for in situ flexible rotor residual balancing using a novel generalized influence coefficient method , 2018, Inverse Problems in Science and Engineering.