Abnormal Condition Identiflcation and Self-Healing Control Scheme for the Electro-Fused Magnesia Smelting Process
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摘要:
本文提出了基于多源信息融合的电熔镁炉异常工况识别及自愈控制方法.通过分析与三种异常相关的专家知识及操作经验, 本文提取了与异常工况相关的多源信息.通过融合多源信息, 建立了用于异常工况识别的贝叶斯网络模型.根据异常工况的识别结果, 利用剩余生命时间与控制变量调整量间的关系获得自愈控制措施.仿真结果表明提出的方法能够实现异常工况识别, 并且能够区分严重程度, 制定相应的自愈控制方案, 获得比现有方法更好的性能.
Abstract:In this paper, the abnormal condition identification and self-healing scheme is proposed based on the multi-source information fusion. By analyzing the expert knowledge and the experience of operators related with the abnormities, the related multi-source characteristics are extracted. The Bayesian networks are established to identify the abnormities by fusing the multi-source information. Based on the identification results, the self-healing control scheme can be obtained by the relationship between the remaining lifetime and the adjustment of control variables. The simulation results show that the proposed method is effective to identify the abnormal conditions and distinguish the abnormal degree. The corresponding self-healing control scheme can be made to remove the abnormal conditions. The proposed method owns the better performance than the existing research results.
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Key words:
- Electro-fused magnesium furnace /
- multi-source information fusion /
- abnormal condition identification /
- Bayesian network /
- self-healing control
1) 本文责任编委 付俊 -
图 2 用于识别半熔化异常工况的贝叶斯网络模型
Fig. 2 The established Bayesian network for the semimolten condition
图 3 用于识别过加热异常工况的贝叶斯网络模型
Fig. 3 The established Bayesian network for the overheating condition
图 4 用于识别排气异常工况的贝叶斯网络模型
Fig. 4 The established Bayesian network for the abnormal exhausting condition
图 6 电熔镁炉熔炼过程仿真平台
Fig. 6 The simulation platform for the electro-fused magnesia smelting process
图 7 半熔化轻微异常工况的自愈控制效果
Fig. 7 The self-healing control effect for the slight semimolten condition
图 8 过加热轻微异常工况的自愈控制效果
Fig. 8 The self-healing control effect for the slight overheating condition
图 9 排气轻微异常工况的自愈控制效果
Fig. 9 The self-healing control effect for the slight abnormal exhausting condition
表 1 半熔化异常工况的典型事件
Table 1 The typical scenarios for the semimolten condition
事件编号 $A_1$ $B_1$ $C_1$ $D_1$ $E_1$ 1 1 1 1 1 1 2 1 1 1 3 1 3 1 1 1 2 2 4 1 1 1 2 3 5 2 2 2 2 3 6 2 2 2 2 4 7 3 3 3 2 3 8 3 3 3 2 4 
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表 2 过加热异常工况的典型事件
Table 2 The typical scenarios for the overheating condition
事件编号 $A_2$ $B_2$ $C_2$ $D_2$ 1 1 1 2 1 2 1 1 2 2 3 1 1 3 1 4 1 1 3 2 5 1 1 1 3 6 1 1 1 4 7 2 2 1 3 8 2 2 1 4 9 3 3 1 3 10 3 3 1 4
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表 3 排气异常工况的典型事件
Table 3 The typical scenarios for the abnormal exhausting condition
事件编号 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 $A_3$ 1 2 2 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1 $B_3$ 1 2 3 2 3 3 2 3 1 1 1 1 1 1 1 1 1 1 $C_3$ 1 1 1 1 1 1 1 1 3 3 3 3 3 2 2 2 2 2 $D_3$ 1 1 1 1 1 1 1 1 3 3 3 3 3 2 2 2 2 2 $E_3$ 1 1 1 1 1 1 1 1 3 3 3 3 3 2 2 2 2 2 $F_3$ 1 1 1 1 1 2 2 2 3 3 2 3 2 3 2 2 3 3 $G_3$ 1 1 1 1 1 2 2 2 2 4 3 3 4 2 3 4 3 4
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事件编号 1~2 3~5 6 7~8 辨识结果 1 2 3 4
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事件编号 1~4 5~7 8 9~10 辨识结果 1 2 3 4
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事件编号 1 2~5 6~8 9~18 辨识结果 1 2 3 4
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表 7 半熔化异常工况识别结果对比
Table 7 The identification results comparison for the semimolten condition
证据事件编号 3 4 5 6 7 8 正常 0.2654 0.2449 0.0009 0.0009 0.0002 0.0001 轻微异常 0.6032 0.3379 0.4639 0.1827 0.182 0.0438 中度异常 0.1073 0.2949 0.4565 0.6371 0.3401 0.2904 严重异常 0.0241 0.1222 0.0787 0.1793 0.4776 0.6656
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表 8 过加热异常工况识别结果对比
Table 8 The identification results comparison for the overheating condition
证据事件编号 5 6 7 8 9 10 正常 0.13 0.1134 0.001 0.0008 0.0003 0.0002 轻微异常 0.475 0.37 0.5233 0.3935 0.2676 0.1698 中度异常 0.2931 0.3617 0.3347 0.3988 0.3274 0.3291 严重异常 0.1019 0.1548 0.141 0.2069 0.4047 0.5009
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表 9 排气异常工况识别结果对比
Table 9 The identification results comparison for the abnormal exhausting condition
事件编号 1 2~5 6~8 9~18 本文方法辨识结果 1 2 3 4 传统方法辨识结果 1 1 1 4
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