基于区间智能的故障检测与建模方法

A. Khosravi, Joaquim Armengol Llobet, E. Gelso
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引用次数: 1

摘要

在实际应用中,不确定性因素往往会极大地降低故障检测任务的性能。为了更好地解决这一普遍问题,在本文中,我们开发了一种使用模态区间分析的方法,该方法考虑了植物模型中的这些不确定性。在此基础上提出了一种故障检测方法,该方法对不确定性具有较强的鲁棒性,不会产生误报。一旦检测到故障,就在线训练ANFIS模型来捕获发生故障的主要行为,并将其用于故障调节。仿真结果可以理解地证明了所提出的方法能够适当地完成这两个任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Interval Intelligent-based Approach for Fault Detection and Modelling
Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately.
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