Industrial processes often show typical nonstationarity, resulting from frequent variation of working condition. It is rather challenging to assess the operating performance of nonstationary processes, since the process property is time-variant and the standard of performance levels may differ from working conditions. Besides the nonstationarity, nonlinearity widely exists in industrial processes, making it hard to analyze process characteristics. To solve the above problems, a three-dimensional (3-D) online modeling and assessment method of operating performance is proposed in the present work. Considering that the inner drive of nonstationarity is often the fluctuation of working condition, 3-D space is established, including sample, variable, and working condition axes. Thus, the process can be modeled and assessed online along direction of the working condition indicator to handle both nonstationarity and nonlinearity. Within the 3-D space, the selection of online training data and establishment of assessing model are solved simultaneously through multi-objective optimization, to make the training data suitable for performance assessment and preserve high assessment accuracy. Bayesian inference based criterion is then defined to determine the performance level. Finally, the validation on a three-phase flow facility and a coal mill of a power plant show feasibility and superiority of the proposed method on performance assessment for nonstationary industrial processes with nonlinearity.

由于工作条件的频繁变化，工业过程通常表现出典型的不稳定状态。评估非平稳过程的操作性能非常具有挑战性，因为过程属性是随时间变化的，并且性能水平的标准可能与工作条件有所不同。除了非平稳性之外，工业过程中还广泛存在非线性，这使得分析过程特征变得困难。为了解决上述问题，本工作提出了一种三维（3D）在线建模与运行绩效评估方法。考虑到非平稳性的内部驱动因素通常是工作条件的波动，因此建立了3D空间，包括样本，变量和工作条件轴。因此，可以沿着工作状态指示器的方向在线建模和评估该过程，以处理非平稳性和非线性。在3-D空间内，通过多目标优化同时解决了在线训练数据的选择和评估模型的建立，使训练数据适合于绩效评估，并保持较高的评估精度。然后定义基于贝叶斯推理的标准，以确定性能水平。最后，通过对三相流设施和电厂磨煤机的验证，证明了所提出方法对非线性非平稳工业过程性能评估的可行性和优越性。通过多目标优化同时解决在线培训数据的选择和评估模型的建立，使培训数据适合绩效评估，并保持较高的评估准确性。然后定义基于贝叶斯推理的标准，以确定性能水平。最后，通过对三相流设施和电厂磨煤机的验证，证明了所提出方法对非线性非平稳工业过程性能评估的可行性和优越性。通过多目标优化同时解决在线培训数据的选择和评估模型的建立，使培训数据适合绩效评估，并保持较高的评估准确性。然后定义基于贝叶斯推理的标准，以确定性能水平。最后，通过对三相流设施和电厂磨煤机的验证，证明了所提出方法对非线性非平稳工业过程性能评估的可行性和优越性。