During the process of quality assessment, aiming at the dynamics of measurement error and the two kinds of decision mistakes, namely accepting a defect and rejecting a qualified component, an online assessment strategy of component quality was proposed. Dynamic measurement process analysis and Bayesian minimal cost decision rule were combined to evaluate the quality of components. A Kalman filtering model for dynamic estimation of systematic measurement error was established and the adaptive windowing approximation method was adopted to online adjust the variance of dynamic random measurement error. In the decision process, Bayesian minimal decision cost method was used to evaluate component quality and the decision confidence was provided as well. Finally, by taking the rotor dynamic balancing process as an example, the proposed method was validated. The results indicated that this method could trace the change of measurement error, decrease the risk of the two wrong decisions, and improve the reliability of quality decisions.%针对测量系统中测量误差的动态性以及由测量误差导致的制造质量评价过程中的误收和误废两类错误决策,提出了动态测量过程分析与贝叶斯最小成本决策准则相结合的零件质量在线评价策略.建立了系统测量误差的动态Kalman滤波模型,并利用自适应估计开窗逼近法对随机测量方差进行在线估计;在决策过程,应用贝叶斯最小决策成本准则对零件质量进行评价,并给出了决策的置信度.最后,以电动机转子动平衡工艺为例对该方法进行了验证,结果表明,该方法能够跟踪测量误差的变化,并降低误收和误废的风险.
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