Many fault detection and identification methods have been developed in recent years; each method works under its own assumption, which means a method that works well under one condition may not provide a satisfactory performance under another condition. In this paper, several data-based process monitoring methods are used in order to provide an effective monitoring scheme for processes under various conditions, and then the analytic hierarchy process approach is introduced for model prioritization. Compared with conventional ensemble systems, the proposed method is able to provide different priorities for different models in monitoring different process faults. Furthermore, a new fuzzy decision fusion system is designed for the purpose of online process monitoring. The effectiveness of the developed method is verified through using the Tennessee Eastman benchmark process.

中文翻译：

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基于层次分析的模糊决策融合系统在模型优先和过程监控中的应用

近年来，已经开发了许多故障检测和识别方法。每种方法都在其自己的假设下工作，这意味着在一个条件下工作良好的方法可能无法在另一条件下提供令人满意的性能。本文使用了几种基于数据的过程监控方法，以便为各种条件下的过程提供有效的监控方案，然后引入了层次分析法来进行模型优先排序。与传统的集成系统相比，该方法能够为不同的模型提供不同的优先级，以监视不同的过程故障。此外，设计了一种新的模糊决策融合系统，用于在线过程监控。