Lennox J, Rosen C
Advanced Wastewater Management Centre, Department of Chemical Engineering, The University of Queensland, St Lucia, Australia.
Water Sci Technol. 2002;45(4-5):227-35.
Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.
故障检测与隔离（FDI）是工业过程监测与监督中的重要步骤。生物废水处理（WWT）厂由于生物反应的复杂性，以及工厂进水和干扰具有高度的变异性和/或不可测量性，因此难以建模，进而难以进行监测。在过去几十年中，针对各种情况开发了多元统计模型，并在许多应用中取得了成功。在本文中，我们基于主成分分析（PCA）开发了一种新的监测算法。它可以等效地看作是使多尺度PCA（MSPCA）具有适应性，或者是自适应PCA的多尺度分解。自适应多尺度PCA（AdMSPCA）利用了不同时间尺度上变量之间不断变化的多元关系。随着时间的推移对尺度PCA模型进行自适应调整，使它们能够跟踪过程、输入或干扰的演变。对AdMSPCA和自适应PCA在实际WWT数据集上的性能进行了比较和对比。观察到的最显著差异是AdMSPCA能够适应更广泛的变化范围。这主要是由于允许每个尺度模型在未发出该尺度异常事件信号时进行自适应调整所带来的灵活性。仅简要考察了相对检测速度，但似乎取决于故障/干扰的特征。对于突然变化，算法的结果相似，但AdMSPCA似乎对较慢的变化更敏感。
