Journal of Process Control ( IF 4.2 ) Pub Date : 2022-08-06 , DOI: 10.1016/j.jprocont.2022.07.012 Jianfeng Zhang, Chunhui Zhao
For industrial processes, the observed data usually has strong nonlinearity and non-Gaussian properties. The whole industrial process shows typical nonstationary characteristics due to frequent time-varying behaviors. This makes the nonstationary process monitoring a challenging task along time direction. It is recognized that the nonstationary process characteristic can change with a certain rule along condition direction instead of time direction. In the present work, a novel condition-driven probabilistic adversarial autoencoder (CPAAE) algorithm is designed to address this problem. CPAAE divides the whole nonstationary process into multiple condition slices by cutting the indicator variables of the industrial process into multiple equal intervals. Subsequently, probabilistic adversarial autoencoder (PAAE) models for different condition slices can be established to extract nonlinear Gaussian features. The condition slices will be aggregated into different condition modes by evaluating the similarity of Gaussian features, and multiple monitoring models can be established for different condition modes to replace the conventional time-driven method. In this way, the nonstationary changes along the time direction can be restored to different condition modes, revealing similar process characteristics in the same condition mode. Finally, the nonstationary industrial process can be monitored by checking the changes of both Gaussian features and reconstruction errors for different condition modes. A numerical case and a real thermal power plant process are adopted to validate the feasibility of the proposed method.
中文翻译:
具有非线性高斯特征学习的条件驱动概率对抗自动编码器用于非平稳过程监控
对于工业过程,观测数据通常具有很强的非线性和非高斯特性。由于频繁的时变行为,整个工业过程表现出典型的非平稳特征。这使得非平稳过程监控沿时间方向成为一项具有挑战性的任务。认识到非平稳过程特性可以沿条件方向而不是时间方向按一定规律变化。在目前的工作中,设计了一种新的条件驱动概率对抗自动编码器(CPAAE)算法来解决这个问题。CPAAE通过将工业过程的指标变量切割成多个等间隔,将整个非平稳过程分成多个条件切片。随后,可以建立不同条件切片的概率对抗自动编码器(PAAE)模型来提取非线性高斯特征。通过评估高斯特征的相似性,将条件切片聚合成不同的条件模式,可以针对不同的条件模式建立多个监测模型,以取代传统的时间驱动方法。这样,沿时间方向的非平稳变化可以恢复到不同的条件模式,在相同的条件模式下表现出相似的过程特征。最后,可以通过检查不同条件模式下高斯特征和重建误差的变化来监控非平稳工业过程。