基于粗糙集和证据理论的设备状态评估方法

doi:10.3969/j.issn.1001-506X.2020.01.19

Abstract

Abstract:

To assess the health condition of complex equipment accurately, a health condition assessment method based on rough sets and D-S evidence theory is proposed. Firstly, given that only discrete attributes could be processed by using rough sets, a discretization method for continuous attributes based on the dynamic fuzzy C-means clustering algorithm is put forward. Secondly, the reduction attributes are obtained by using the reduction algorithm based on mutual information. Thirdly, the assessment decision table is processed and the basic probability assignment function is set up. Finally, assessment indexes are fused by the evidence theory to get the health condition grade, and the relationship between assessment indexes and health conditions is mined further. The case study and comparative analysis show that this method can improve the decision credibility effectively and reduce the uncertainty of assessment.

Key words: rough set, D-S evidence theory, health condition assessment, fuzzy C-means clustering

CLC Number:

TJ07

Liang WANG, Zhanyi LU, Zhuoyu LI. Condition assessment method of equipment based on rough sets and evidence theory[J]. Systems Engineering and Electronics, 2020, 42(1): 141-147.

Figures/Tables 6

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Table 1

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References 21

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序号	c₁	c₂	c₃	c₄	c₅	c₆	状态
1	2.5	6.8	24.4	0.4	7.3	14.9	良好
2	3.1	7.6	25.6	0.7	6.5	13.7	良好
3	2.8	6.5	25.2	0.8	8.2	14.3	良好
4	6.8	8.1	21.8	0.9	13.6	12.1	正常
5	7.5	13.2	22.1	1.5	12.3	11.4	正常
6	7.8	12.8	22.8	1.6	11.9	9.9	正常
7	8.1	16.3	20.1	1.9	18.5	9.3	一般
8	4.2	15.9	17.8	2.4	15.8	8.1	一般
9	5.6	17.4	18.4	2.5	17.3	7.7	一般
10	8.6	19.2	13.7	3.1	22.6	6.9	恶化
11	9.4	20.1	12.2	3.3	23.4	7.3	恶化
12	8.8	18.9	14.1	2.9	25.1	5.8	恶化
13	14.4	23.1	11.5	4.2	26.3	5.4	病态
14	12.6	21.9	9.9	4.9	25.6	6.1	病态
15	13.7	22.6	10.6	5.2	27.7	5.5	病态

样本	指标	m(1)	m(2)	m(3)	m(4)	m(5)	m(Θ)
E₁	c₃	0	0	0	0.720 7	0.179 3	0.1
	c₆	0	0	0	0.657 0	0.243 0	0.1
	c₂	0	0	0	0.595 5	0.204 5	0.2
	c₄	0	0	0	0.560 5	0.239 5	0.2
E₂	c₃	0.782 7	0.117 3	0	0	0	0.1
	c₆	0.559 3	0.340 7	0	0	0	0.1
	c₂	0.371 3	0.428 7	0	0	0	0.2
	c₄	0.491 4	0.308 6	0	0	0	0.2
E₃	c₃	0	0	0.656 8	0.243 2	0	0.1
	c₆	0	0.270 7	0.629 3	0	0	0.1
	c₂	0	0	0.529 6	0.270 4	0	0.2
	c₄	0	0.144 7	0.655 3	0	0	0.2
E₄	c₃	0	0	0.656 8	0.243 2	0	0.1
	c₆	0	0.270 7	0.629 3	0	0	0.1
	c₂	0.8	0	0	0	0	0.2
	c₄	0	0.144 7	0.655 3	0	0	0.2

样本	合成指标	m(1)	m(2)	m(3)	m(4)	m(5)	m(Θ)	结果
E₁	c₃⊕c₆	0	0	0	0.864 5	0.121 3	0.014 2	4
	c₂⊕c₃⊕c₆	0	0	0	0.927	0.069 2	0.003 8	4
	c₄⊕c₃⊕c₆	0	0	0	0.917 9	0.078 2	0.003 9	4
	c₂⊕c₄⊕c₃⊕c₆	0	0	0	0.956 6	0.042 4	0.001	4
E₂	c₃⊕c₆	0.856 6	0.128 4	0	0	0	0.015	1
	c₂⊕c₃⊕c₆	0.845 9	0.149	0	0	0	0.005 1	1
	c₄⊕c₃⊕c₆	0.891 6	0.104	0	0	0	0.004 5	1
	c₂⊕c₄⊕c₃⊕c₆	0.882 4	0.116 2	0	0	0	0.001 6	1
E₃	c₃⊕c₆	0	0.044 9	0.898 2	0.040 3	0	0.016 6	3
	c₂⊕c₃⊕c₆	0	0.012 8	0.948 9	0.033 5	0	0.004 8	3
	c₄⊕c₃⊕c₆	0	0.022 1	0.963 8	0.01	0	0.004 1	3
	c₂⊕c₄⊕c₃⊕c₆	0	0.006 2	0.984 6	0.008 1	0	0.001 2	3
E₄	c₃⊕c₆	0	0.044 9	0.898 2	0.040 3	0	0.016 6	3
	c₂⊕c₃⊕c₆	0.062 3	0.042 1	0.842 3	0.037 8	0	0.015 5	3
	c₄⊕c₃⊕c₆	0	0.022 1	0.963 8	0.01	0	0.004 1	3
	c₂⊕c₄⊕c₃⊕c₆	0.016 2	0.021 8	0.948 2	0.009 8	0	0.004	3

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Condition assessment method of equipment based on rough sets and evidence theory

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