NDLI: Fault Diagnosis of the Blast Furnace Based on the Bayesian Network Model

Content Provider	IEEE Xplore Digital Library
Author	Pan Lian Ning Ning Chen Aiping Tong Yaobin
Copyright Year	2010
Abstract	Blast furnace condition and stable operation for blast furnace is crucial, The production practice shows that only by maintaining the stability of blast furnace condition, could it achieve 'high yield, high quality, low consumption, longevity', obtain good technical and economic indexes. However, the process of smelting furnace is influenced by many factors, which will definitely cause the fluctuation in furnace condition. Therefore, the operator must be timely to judge change-trend and amplitude at the blast furnace condition, figure out the reasons for changing conditions in smelted induction, and adjust it promptly, only through which could maintain the stable smelted in induction. Due to the different levels of the operators, so the adjustment method is also different. Moreover, it will inevitably result in the fluctuation in furnace condition, for the operators have to face with a lot of operating parameters. Therefore, it is absolutely useful and necessary to summarize the experiences of excellent operators, to make the furnace operation unification and standardization through establishing the model of intelligent diagnosis, to forecast online as well as gives practical guidance in furnace condition. To solve the problem of blast furnace failure mode and effect analysis are unable to quantitative description of the occurrence probability of fault model. This paper puts forward a Bayesian network topology structure based on the FMEA--CFE (Cause Failure Effect)-type Bayesian diagnostic network. On the basis of the relationship between 'failure reason', 'failure mode' and 'failure fault' in FMEA, the diagnostic network topology structure can be confirmed, and the causality and gradation in system fault can be described. So, we can solve the probability of failure mode by adopting Bayesian network fault diagnosis of decision-making techniques. This method will provide theoretical basis for blast furnace fault diagnosis, and through the actual research, the validity of this method finally has been verified. It provides effective guidance to the actual production of blast furnace
Starting Page	990
Ending Page	993
File Size	371428
Page Count	4
File Format	PDF
ISBN	9781424468805
e-ISBN	9781424468812
DOI	10.1109/iCECE.2010.251
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Bayesian network model Network topology Bayesian methods FMEA method Materials Blast furnaces Blast furnace condition
Content Type	Text
Resource Type	Article

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