Trend analysis using real time fault simulation for improved fault diagnosis
Trend analysis using real time fault simulation for improved fault diagnosis
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DOI：
10.1109/icsmc.2007.4414112
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发表时间：
2007-10
期刊：
影响因子：
--
通讯作者：
H. Gabbar;Akinlade Damilola;H. E. Sayed
中科院分区：
文献类型：
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作者：
H. Gabbar;Akinlade Damilola;H. E. Sayed
在任何化工厂中，早期故障检测对于工厂的安全和优化运行和维护至关重要。快速的纠正措施可以帮助减少质量和生产率的偏差，并有助于避免异常情况下的危险后果。本文考虑了基于趋势分析的故障诊断方法，采用趋势匹配的方法对设备的整体行为和运行轨迹进行分析。使用基于神经模糊方法的IF-THEN规则对这些趋势进行定性表示，用于发现任何检测到的异常情况的根本原因、可能和后果。建立了实验装置，为故障检测方法的验证提供实时故障仿真数据。
Early fault detection is critical for safe and optimum plant operation and maintenance in any chemical plant. Quick corrective action can help in minimizing quality and productivity offsets and can assist in averting hazardous consequences in abnormal situations. In this paper, fault diagnosis based on trends analysis is considered where integrated equipment behaviors and operation trajectory are analyzed using a trend-matching approach. A qualitative representation of these trends using IF-THEN rules based on neuro-fuzzy approach is used to find root causes and possible and consequences for any detected abnormal situation. Experimental plant is constructed to provide real time fault simulation data for fault detection method verification.