With the relationships between industrial process variables becoming more complex, linear and nonlinear relationships coexist in most processes, both of which should be considered simultaneously to improve monitoring effect. Focusing on this issue, the paper proposes a novel principal component analysis-stacked autoencoder (PCA-SAE) model for fault detection. In this model, PCA and SAE respectively deals with linear and nonlinear components. Besides, PCA plays a role in separating the two components. As a linear mapping method, PCA is supposed to extract only linear features and leave the nonlinear part. And this is accomplished by adjusting its cumulative percent variance (CPV) of features. After that, the remaining nonlinear part is modeled by SAE. Comprehensive statistics are established to monitor the two parts of processes. The proposed method achieves 86.5% average fault detection rate in Tennessee Eastman (TE) process, higher than pure PCA, pure SAE, and many other conventional methods; and it successfully detects the fault that neither PCA nor SAE is able to achieve in a wind power generation process.

随着工业过程变量之间的关系变得越来越复杂，在大多数过程中线性和非线性关系共存，应同时考虑两者，以提高监控效果。针对这一问题，本文提出了一种用于故障检测的新颖的主成分分析堆叠自动编码器（PCA-SAE）模型。在此模型中，PCA和SAE分别处理线性和非线性分量。此外，PCA在分离两个组件方面也起着作用。作为线性映射方法，PCA只能提取线性特征，而保留非线性部分。这是通过调整其特征的累积百分比差异（CPV）来实现的。之后，剩余的非线性部分由SAE建模。建立了全面的统计信息以监视流程的两个部分。该方法在田纳西州伊士曼（TE）流程中的平均故障检测率达到86.5％，高于纯PCA，纯SAE和许多其他常规方法；它成功检测出PCA和SAE在风力发电过程中均无法实现的故障。