To deal with the inaccurate monitoring caused by the non-stationary characteristics of the xylenol tail gas treatment process, a layered monitoring scenario utilizing stationary subspace analysis has been presented in the present study. First, principal component analysis (PCA) is applied to establish the upper-level stationary monitoring model. Then the sliding time window is used to capture the dynamic information of the remaining non-stationary features, update the model, and establish the lower non-stationary monitoring model. Next, the Bayesian information criterion is employed to build the global monitoring model according to the detection results of the upper and lower layers. Finally, the industrial data collected from the industrial boiler is used to evaluate the effectiveness and applicability of the proposed method. It is demonstrated that layered monitoring has higher accuracy than conventional non-stationary process monitoring techniques. Moreover, it can capture the faults in the stationary features masked by the non-stationary features.

中文翻译：

---

基于平稳子空间分析的二甲酚尾气处理过程分层监测

针对二甲酚尾气处理过程的非平稳特性导致的监测不准确，本研究提出了一种利用平稳子空间分析的分层监测方案。首先，应用主成分分析（PCA）建立上层静态监测模型。然后利用滑动时间窗捕捉剩余非平稳特征的动态信息，更新模型，建立下层非平稳监测模型。接下来，根据上下层的检测结果，采用贝叶斯信息准则建立全局监控模型。最后，使用从工业锅炉收集的工业数据来评估所提出方法的有效性和适用性。结果表明，分层监控比传统的非固定过程监控技术具有更高的准确性。此外，它可以捕获被非平稳特征掩盖的静止特征中的故障。