中文摘要：

工业过程对象特性变化会导致软测量的测量精度下降甚至失真,需要对软测量模型进行校正。首先构建软测量模型性能评价指标用于对模型性能进行监测。当性能评价指标超过统计限时,对过程特性变化类型进行诊断：若过程特性渐变,则对模型进行递推校正;若过程特性发生突变,则对模型进行重构校正。通过对连续搅拌釜式反应器和DAISY（database for identification of systems）数据库提供的蒸发器实际生产数据进行仿真实验,验证了本文方法的有效性。实验结果表明,该方法避免了传统校正方法存在的盲目校正、受离线分析噪声影响严重等问题,有效地提高了软测量模型对对象特性变化的适应能力。

英文摘要：

The estimation performance of a soft-sensor would deteriorate when the process characteristics change.To cope with such changes,recursive PLS and just-in-time modeling have been developed.However,these methods update a soft-sensor model whenever the new input-output data are sampled no matter whether the estimation performance of the soft-sensor deteriorates or not.Therefore,an approach for adaptive correction of a soft-sensor was proposed based on model performance monitoring and assessment.Firstly,an estimation performance assessment index of soft sensors was designed to objectively detect the degradation of a soft-sensor relative to their design performance.Then,the statistical limits of the index could be determined based on the time-series index data collected under normal conditions.The soft sensor would be updated only when the assessment index exceeded the corresponding statistical limits.A state classifier could diagnose the type of change of process characteristics according to the discrete Fourier transforms of the indexes series.The adaptive correction method would update model recursively when process characteristics were gradually changing and reconstructed the local model in a neighborhood around the query point when process characteristics had an abrupt change.The usefulness of the proposed method was demonstrated through a case study of a continuous stirred tank reactor（CSTR）process and the real industrial data of an industrial evaporator from DAISY（database for identification of systems）.The result showed that the method could avoid large fluctuation of measurement caused by blind correction and serious off-line effects of analysis noise.The method could cope with changes in process characteristics and improve the estimation performance,and could have the potential for realizing efficient maintenance of soft-sensors in the actual industrial process.