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摘要: 复杂流程工业过程知识类型多样且含有多种不确定性，针对这些问题提出一种基于D-S融合的混合知识系统故障诊断方法.根据可利用信息的类型建立不同的专家知识系统并进行不确定性推理.通过分析当前信息的数据特点，自适应分配不同专家知识系统可靠性权重，通过权重D-S证据理论融合各专家知识系统的结论.这种方法不仅使用了专家的知识和经验，而且结合了生产过程积累的大量数据信息，提高了信息的利用率.通过融合多个专家知识系统的结论，提高了不确定性系统故障诊断的正确率.将该方法应用于某湿法冶金浓密过程故障诊断，取得了良好的诊断效果.Abstract: There are various types of process knowledge and multiple uncertainties in complex process industry. To address these issues, a fault diagnosis approach which employs D-S knowledge fusion and hybrid knowledge system is proposed. Based on the types of available information, we establish different expert knowledge systems and present uncertainty reasoning respectively. By analyzing the characteristics of the current available data, adaptive weights are calculated for different expert knowledge systems. Then D-S evidence theory is utilized for conclusion fusion. Not only the expert experience knowledge but also a large amount of accumulated data is utilized in this method, which improves the utilization rate of information. The fault diagnosis accuracy for uncertainty systems are increased by the use of D-S conclusion fusion. The proposed method is then applied to fault diagnosis of a thickener in a hydrometallurgy process and satisfactory diagnosis results are achieved.
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Key words:
- Hybrid expert knowledge system /
- adaptive weight /
- D-S evidence theory /
- information fusion /
- thickener
1) 本文责任编委 周傲英 -
图 1 基于D-S融合的混合知识系统故障诊断算法流程
Fig. 1 Fault diagnosis flowchart of method based on D-S fusion theory and hybrid expert knowledge system
图 2 三种诊断方法对浓度偏高的识别效果对比
Fig. 2 Effect comparison of three methods for high concentration fault
表 1 浓密机故障诊断规则
Table 1 Fault diagnosis rules for thickener
序号 规则前件 规则后件 规则强度 1 浓密机运转吃力，噪声大 浓密机压耙 0.8 2 底流流量比较小 底流管道堵塞 0.8 3 矿浆粘稠且起泡 浓度偏高 0.8 4 缓冲槽中液位离槽口过近 缓冲槽冒槽 0.8 
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表 2 压滤机前缓冲槽冒槽原因追溯规则
Table 2 Reasons rules for tank overswelling in front of the fllter press
序号 规则前件 规则后件 规则强度 5 冒槽，浓度不大，流量不大 其他原因致冒槽 0.8 6 冒槽，浓度偏大，流量不大 浓度高致冒槽 0.8 7 冒槽，浓度不大，流量偏大 流量大致冒槽 0.8 8 冒槽，浓度偏大，流量偏大 浓度高流量大致冒槽 0.8
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表 3 专家给出的事件可信度
Table 3 Certainty factors of cases given by expert
事件 专家给出的可信度 浓度偏大 0.78 浓度不大 -0.78 流量偏大 0.81 流量不大 -0.81
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表 4 自适应权重D-S与固定权重D-S融合对比(%)
Table 4 Comparison of adaptive weight D-S and fixed weight D-S (%)
固定权重误报 自适应权重误报 规则信息缺失 6.5 4.0 数据信息缺失 10 6.5 噪声10 % 5.25 4.0 数据较为准确 5 3.5 平均 6.69 4.50
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