Fault diagnosis is an important task for the chemical process, and the efficiency of the diagnostic approach depends critically on the location of sensors monitoring the process variables.
故障诊断是化工企业安全生产中一项重要的工作，其诊断方法的效率主要取决于监视过程变量传感器的配置。
Probabilistic principal component analysis (PPCA) can realize the process monitoring (according) to the whiten values of process variables' prediction error and their scores.
概率主元分析(PPCA)能够根据过程变量的预测误差及其主元的白化值实现对过程的监控。
This paper carries out a study on soft sensors model of wastewater treatment process control parameters and water variables, which enables further control and parameters 'on-line monitoring.
论文针对建立污水处理过程控制参数及水质参数的软测量模型进行了研究，为进一步实现污水处理过程的自动控制和参数在线检测创造条件。
This paper carries out a study on soft sensors model of wastewater treatment process control parameters and water variables, which enables further control and parameters 'on-line monitoring.
论文针对建立污水处理过程控制参数及水质参数的软测量模型进行了研究，为进一步实现污水处理过程的自动控制和参数在线检测创造条件。
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