RH-KTB真空系统智能故障诊断
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摘要
RH-KTB复杂大型炉外精炼真空系统用于对钢水的提纯处理。在实际精炼中,该系统时常发生各种故障影响了产品的质量甚至生产效率。而现有的针对该系统的监控软件无法实现对精炼过程的状态判断及故障诊断。故障发生时往往要停止生产,人工对故障进行排查和处理。这给生产带来了诸多不便。有时用人工的办法很难准确找出某些故障点和故障原因。因此,建立一套RH-KTB智能故障诊断系统有其实际意义。本文首先研究RH-KTB系统的故障种类及特点,对该系统进行了故障树分析,找出了各种故障间的关系并建立了原始样本采集系统。RH-KTB系统的工艺要求对其进行快速故障诊断。因此本文对该系统进行了无教师快速模糊聚类分析。针对该系统的监测点多且存在大量的重复样本的特点,利用粗糙集理论对原始样本集进行了约简处理。快速诊断要求快速学习和快速分类。本文研究了利用决策树理论对RH-KTB系统的约简集进行学习和分类。给出了基于决策树ID3算法的针对RH-KTB真空系统的故障分类过程。最后,提出了一种基于粗糙集-决策树理论的RH-KTB故障诊断模型。该模型从理论上可以保证及时对干扰数据进行排除并实现快速诊断。给出了基于该模型的故障诊断的完整过程并以实例验证了该模型的实用性。基于该模型开发了一套RH-KTB智能故障诊断系统。应用表明效果良好。
RH-KTB vacuum system is complex-large system used to refine and purify steel.In practice the system often get out of order which effects product quality so far as to production efficiency.However most monitoring software of the system are not able to process state decision and fault diagnosis.When fault occurs,workmen have to manually find position of fault and removal of faults.Obviously it is inconvenience for production.Sometimes special position of fault and failure cause are uneasy to be found accurately.So it is important that an intelligent fault diagnosis system for RH-KTB is developed.In this paper species and features fault of RH-KTB vacuum system is studies and the fault tree is found against the system.The relationship amony faults is obtained,at the same time original information acquisition system is developed.The process conditions of RH-KTB demands for quick-diagnosis.In this paper,we have quick-fuzzy clustering analysis to this system.The original information system is reducted according to rough sets theory.The quick diagnosis means quick learning and classification.In this research decision tree theory is used to learn and classify reducts sets.Finally an intelligent fault diagnosis model based on rough sets-decision tree theory is put forward aiming at the high-duty vacuum metallurgical system of which the possible faults take place frequently and unwarnedly and need quick and exact fault diagnosis. The model is highly self-learning and self-organizing especially applicable for the fault diagnosis to heavy-duty and complicated vacuum systems.Theoretically,disturbed data can be eliminated according to the model. Describing the theory of rough sets-decision tree algorithm,the paper presents the procedure of rough sets-decision tree theory fault diagnosis,with an actual diagnosis process given as an example to show the intelligent fault diagnosis for RH-KTB vacuum metallurgical system.Thus,effectiveness of the algorithm is proved through an analysis of the exemplification.A RH-KTB intelligent fault diagnosis system based on the model is developed.
RH-KTB vacuum system is complex-large system used to refine and purify steel.In practice the system often get out of order which effects product quality so far as to production efficiency.However most monitoring software of the system are not able to process state decision and fault diagnosis.When fault occurs,workmen have to manually find position of fault and removal of faults.Obviously it is inconvenience for production.Sometimes special position of fault and failure cause are uneasy to be found accurately.So it is important that an intelligent fault diagnosis system for RH-KTB is developed.In this paper species and features fault of RH-KTB vacuum system is studies and the fault tree is found against the system.The relationship amony faults is obtained,at the same time original information acquisition system is developed.The process conditions of RH-KTB demands for quick-diagnosis.In this paper,we have quick-fuzzy clustering analysis to this system.The original information system is reducted according to rough sets theory.The quick diagnosis means quick learning and classification.In this research decision tree theory is used to learn and classify reducts sets.Finally an intelligent fault diagnosis model based on rough sets-decision tree theory is put forward aiming at the high-duty vacuum metallurgical system of which the possible faults take place frequently and unwarnedly and need quick and exact fault diagnosis. The model is highly self-learning and self-organizing especially applicable for the fault diagnosis to heavy-duty and complicated vacuum systems.Theoretically,disturbed data can be eliminated according to the model. Describing the theory of rough sets-decision tree algorithm,the paper presents the procedure of rough sets-decision tree theory fault diagnosis,with an actual diagnosis process given as an example to show the intelligent fault diagnosis for RH-KTB vacuum metallurgical system.Thus,effectiveness of the algorithm is proved through an analysis of the exemplification.A RH-KTB intelligent fault diagnosis system based on the model is developed.
引文
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