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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基于层次分析过程的模糊决策融合系统，用于模型优先级排序和过程监控应用


近年来发展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，这意味着在一种条件下运行良好的方法在另一种条件下可能无法提供令人满意的性能。本文采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，然后引入层次分析过程方法来进行模型优先级排序。与传统的集成系统相比，该方法能够为不同模型在监测不同过程故障时提供不同的优先级。此外，还设计了一种新的模糊决策融合系统，用于在线过程监控。通过田纳西州伊士曼基准流程验证了所开发方法的有效性。