矿井提升机健康状态评估与预测系统研究

王琛; 杨岸

doi:10.13272/j.issn.1671-251x.2023030092

矿井提升机健康状态评估与预测系统研究

doi: 10.13272/j.issn.1671-251x.2023030092

王琛,
杨岸^,

安徽理工大学电气与信息工程学院，安徽淮南　232000

详细信息

作者简介:
王琛（1999—），男，安徽宿州人，硕士研究生，主要研究方向为矿山电力系统自动化，E-mail：35045472@qq.com

通讯作者:
杨岸（1965—），男，安徽寿县人，副教授，硕士，主要研究方向为矿山电力系统自动化，E-mail：1084538989@qq.com。

中图分类号: TD633
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出版历程
- 收稿日期: 2023-03-28
- 修回日期: 2023-10-15
- 网络出版日期: 2023-10-24

Research on the health evaluation and prediction system for mine hoists

WANG Chen,
YANG An^,

School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China

摘要

摘要: 针对目前对矿井提升机整个系统进行健康状态评估与预测的相关研究相对较少的问题，建立了矿井提升机健康状态评估指标体系和评语集，设计了矿井提升机健康状态评估与预测系统。针对矿井提升机各组成系统的监测数据无法充分利用、健康状态评估结果不能满足实际生产需求的问题，提出了一种提升机健康状态模糊综合评估方法：引入相对劣化度表征提升机不同类型指标的健康度，并利用健康度对矿井提升机的健康状态进行量化；采用模糊综合评估法计算矿井提升机的健康状态，使用指数标度代替1—9标度对层次分析法（AHP）进行改进，以降低计算复杂度；采用CRITIC客观赋权法，结合主客观权重计算各子系统和指标的综合权重；根据模糊综合评估计算过程和最大隶属原则，得到矿井提升机的健康状态评估结果和故障原因。在提升机健康状态评估结果基础上，利用哈里斯鹰优化（HHO）算法优化支持向量回归（SVR）模型的重要参数，构建HHO−SVR模型对矿井提升机的健康状态进行预测，提高健康预测结果的准确性。实验结果表明：模糊综合评估方法能够准确实现提升机健康状态评估；与粒子群优化支持向量回归（PSO−SVR）、遗传算法优化支持向量回归（GA−SVR）、灰狼算法优化支持向量回归（GWO−SVR）模型相比，HHO−SVR模型的预测结果更接近实际值，具有更好的预测效果。
- 矿井提升机 /
- 健康状态评估与预测 /
- 模糊综合评估 /
- 层次分析法 /
- 哈里斯鹰优化算法 /
- 支持向量回归 /
- HHO−SVR模型
Abstract: In response to the relatively limited research on health evaluation and prediction of the entire system of mine hoists, a health evaluation index system and comment set for mine hoists have been established. The health evaluation and prediction system for mine hoists has been designed. A fuzzy comprehensive evaluation method for the health of mine hoists is proposed to address the issues of insufficient utilization of monitoring data from various components of mine hoists, and the inability of health evaluation results to meet actual production needs. The method introduces relative degradation degree to characterize the health of different types of indicators of the hoist. The method uses health degree to quantify the health of mine hoists. The fuzzy comprehensive evaluation method is used to calculate the health of mine hoists. The analytic hierarchy process (AHP) is improved by replacing the 1-9 scale with an exponential scale to reduce computational complexity. The method uses CRITIC objective weighting method and combines subjective and objective weights to calculate the comprehensive weights of each subsystem and indicator. Based on the fuzzy comprehensive evaluation calculation process and the maximum membership principle, the health evaluation results and fault causes of the mine hoist are obtained. On the basis of the health evaluation results of the mine hoist, the Harris hawks (HHO) algorithm is used to optimize the important parameters of the support vector regression (SVR) model. The HHO-SVR model is constructed to predict the health of the mine hoist, improving the accuracy of the health prediction results. The experimental results show that the fuzzy comprehensive evaluation method can accurately evaluate the health of the hoist. Compared with particle swarm optimization support vector regression (PSO-SVR), genetic algorithm optimization support vector regression (GA-SVR), and grey wolf algorithm optimization support vector regression (GWO-SVR) models, the prediction results of the HHO-SVR model are closer to the actual values and have better prediction performance.
- mine hoist /
- health evaluation and prediction /
- fuzzy comprehensive evaluation /
- analytic hierarchy process /
- Harris hawks optimization algorithm /
- support vector regression /
- HHO-SVR model

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图 1 矿井提升机健康状态评估与预测系统结构

Figure 1. Structure of the health evaluation and prediction system for mine hoists

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图 2 矿井提升机健康状态评估指标体系

Figure 2. Index system for health evaluation of mine hoists

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图 3 矿井提升机健康状态评估流程

Figure 3. Process of health evaluation of mine hoists

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图 4 健康度与健康等级的隶属函数

Figure 4. Membership function of health degree and health level

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图 5 HHO−SVR模型建立流程

Figure 5. The process of Harris hawks optimization- support vector regression model

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图 6 实验现场

Figure 6. Experimental site

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图 7 各优化算法的适应度曲线

Figure 7. Fitness curves of each optimization algorithm

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图 8 不同模型的预测结果对比

Figure 8. Comparison of prediction results of different models

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图 9 不同模型的R²对比

Figure 9. Comparison of R² of different models

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图 10 不同模型的误差对比

Figure 10. Comparison of errors of different models

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表 1 提升机主要故障位置及其原因

Table 1. Main fault positions and causes of hoists

故障位置	主要原因
拖动部分	电动机超速；电动机电流过大；电动机温度过高；电动机轴承过热；电动机停转；变速器轴承变形；齿轮磨损严重；减速器轴承磨损
制动部分	制动转矩过大；制动转矩过小；制动失效；制动油温过高；制动油压过小；制动闸片温度过高；制动闸片间隙过大等
滑动控制部分	油温过高；油压过高；油压过低；油量过少；油中杂质太多
钢丝绳部分	钢丝绳磨损；钢丝绳腐蚀；钢丝绳变形

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表 2 矿井提升机健康状态描述和检修决策

Table 2. Health status description and maintenance decision of mine hoists

健康等级	描述	维修决策
健康	所有子系统和指标值都很健康，指标值接近预期	矿井提升机处于健康状态，不需要维修
亚健康	子系统处于健康和警告的状态之间，指标值接近阈值	不影响正常运行，但要注意定期检测
警告	一些子系统处于亚健康状态，一些指标值在故障阈值附近波动	短时间内仍能正常运行，但要提高监测频率
故障	至少有1个子系统处于故障状态，传感器值大幅超过故障阈值	建议立即停车进行维修

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表 3 矿井提升机健康等级与健康度之间的关系

Table 3. The relationship between the health level and health degree of mine hoists

健康状态	健康等级	健康度
健康（HS）	V₁	0.75≤d≤1.0
亚健康（SH）	V₂	0.4≤d＜0.75
警告（CS）	V₃	0.15≤d＜0.4
故障（FS）	V₄	0≤d＜0.15

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表 4 1—9标度与指数标度的关系

Table 4. The relationship between 1-9 scale and exponential scale

1—9 标度	相对重要性	指数标度
1	同等重要	q⁰
3	前者比后者稍微重要一些	q²
5	前者明显比后者更重要	q⁴
7	前者比后者非常重要	q⁶
9	前者绝对比后者更重要	q⁸
2，4，6，8	2个指标的中间值之间的逆向比较	q¹，q³，q⁵，q⁷

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表 5 实验平台主要参数

Table 5. Main parameters of the experimental platform

参数	值
提升高度/m	750
电动机功率/kW	5300
转速/（r·min⁻¹）	49.7
钢丝绳直径/cm	10
提升速度/（m·s⁻¹）	11
载质量/t	15
刹车数	5

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表 6 不同指标在不同运行阶段的数值

Table 6. Values of different indicators at different operating stages

指标	类型	加速	匀速	减速
X₁₁	成本型	2.7	2.38	2.57
X₁₂	成本型	187.16	114.16	113.28
X₁₃	成本型	38	36	37
X₁₄	区间型	4.28	3.23	3.75
X₂₁	区间型	0.73	0.63	0.66
X₂₂	区间型	4.07	3.18	3.66
X₂₃	成本型	36	35	35
X₃₁	区间型	28	27	27
X₃₂	区间型	30	28	28
X₃₃	成本型	1.1	1.1	1.1
X₃₄	成本型	2.0	1.8	1.9
X₄₁	区间型	0.4	0.4	0.4
X₄₂	成本型	4	2	3
X₅₁	区间型	0.56	0.56	0.56
X₅₂	区间型	20	20	23
X₅₃	成本型	0.2	0.2	0.2

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表 7 指标层和子系统层的综合权重

Table 7. Comprehensive weights of indicator layer and subsystem layer

子系统层	子系统层综合权重	指标层	指标层综合权重
X₁	0.2572	X₁₁	0.2982
		X₁₂	0.3055
		X₁₃	0.1702
		X₁₄	0.2261
X₂	0.2334	X₂₁	0.3573
		X₂₂	0.3605
		X₂₃	0.2822
X₃	0.1853	X₃₁	0.3276
		X₃₂	0.3021
		X₃₃	0.1780
		X₃₄	0.1923
X₄	0.1570	X₄₁	0.6873
X₄	0.1570	X₄₂	0.3127
X₅	0.1671	X₅₁	0.3534
		X₅₂	0.3150
		X₅₃	0.3316

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