随着国内煤炭需求量的增加以及相关设备科技水平的提高，煤炭企业对机电设备可靠性要求也越来越高。采煤机作为煤矿开采过程中的核心设备之一，在开采任务当中承担着重要角色，其正常运转是煤炭生产计划有序进行的重要保障，直接影响着煤炭的开采效率、人员安全以及企业的经济效益等，也对于煤矿企业制定采煤机检修计划以及进行采煤机寿命预测起着至关重要的作用，因此对于采煤机健康状态评价研究具有重要意义。基于此背景，本文以采煤机为研究对象，分别从采煤机健康状态评价体系建立、权重计算、状态评价模型构建以及采煤机健康状态评价系统开发四个方面对采煤机健康状态评价开展了研究。论文主要研究内容如下：

首先，构建科学、合理的评价体系是采煤机健康状态准确评价的基础。本文以MG200-AWD型采煤机作为研究对象，针对采煤机结构复杂、状态指标繁杂等问题，通过对采煤机常见的故障类型和原因进行分析，并根据相关评价原则，选取截割部、牵引部、变频部、调高调压四个方面共10个影响因素作为采煤机健康状态评价指标，建立其评价体系，划分健康状态等级。

其次，在权重计算方法上，为了避免单一权重计算方法存在的弊端，例如受决策者的主观影响或者过于依赖客观数据，本文从主观和客观两个角度对采煤机健康状态进行评估，分别采用层次分析法（AHP）和主成分分析法（PCA）获取各指标权重，再利用最小鉴别信息原理融合主观和客观权重，求得综合权重。

然后，针对采煤机运行状态评价模型的建立，本文提出了基于灰云模型的采煤机健康状态评价方法来解决评价过程中评估信息的模糊性、随机性以及不确定性。采用灰云模型代替传统的白化权函数，构建指标云模型代替指标量化值计算云关联度，能够更好的体现采煤机状态等级的不确定性；利用关联规则和灰云聚类得到各故障层状态，再利用变权融合得到采煤机整体状态，从而使评价结果更加客观、准确。

最后，结合采煤机健康状态评价现状，利用Java Swing和Vue设计并开发了采煤机健康状态评价系统。通过分析健康状态评价系统的非功能性需求和功能性需求，构建了系统总体结构，明确各模块应当实现的功能，并进行了数据库设计，最后形成一个完整的采煤机健康状态评价系统。该系统可以实现用户权限管理、运行状态监测、阈值报警、健康状态评估、历史记录管理，运行参数可视化展示等功能。

With the increase in domestic coal demand and the improvement of related equipment technology, coal enterprises have increasingly high requirements for the reliability of mechanical and electrical equipment. As one of the core equipment in the coal mining process, the coal mining machine plays an important role in the mining task. Its normal operation is an important guarantee for the orderly implementation of coal production plans, directly affecting the efficiency of coal mining, personnel safety, and economic benefits of the enterprise. It also plays a crucial role in formulating coal mining machine maintenance plans and predicting the life of coal mining machines for coal mining enterprises, Therefore, it is of great significance to study the health status evaluation of coal mining machines. Based on this background, this article takes coal mining machines as the research object, and conducts research on the health status evaluation of coal mining machines from four aspects: establishment of a health status evaluation system, weight calculation, construction of a state evaluation model, and development of a health status evaluation system for coal mining machines. The main research content of the paper is as follows:

Firstly, building a scientific and reasonable evaluation system is the foundation for accurate evaluation of the health status of coal mining machines. This article takes the MG200-AWD coal mining machine as the research object. In response to the complex structure and complex status indicators of the coal mining machine, common fault types and reasons of the coal mining machine are analyzed. According to relevant evaluation principles, a total of 10 influencing factors from four aspects, namely the cutting part, traction part, frequency conversion part, and height and pressure regulation, are selected as the health status evaluation indicators of the coal mining machine. The evaluation system is established and the health status levels are divided.

Secondly, in terms of weight calculation methods, in order to avoid the drawbacks of a single weight calculation method, such as subjective influence by decision-makers or excessive reliance on objective data, this article evaluates the health status of coal mining machines from both subjective and objective perspectives. Analytic Hierarchy Process (AHP) and Principal Component Analysis (PCA) are used to obtain the weights of each indicator, and then the principle of minimum discriminant information is used to fuse subjective and objective weights, Obtain comprehensive weights.

Then, in response to the establishment of an evaluation model for the operational status of coal mining machines, this article proposes a method for evaluating the health status of coal mining machines based on the grey cloud model to address the fuzziness, randomness, and uncertainty of evaluation information during the evaluation process. Using the grey cloud model instead of the traditional whitening weight function and constructing an indicator cloud model to replace the quantitative value of the indicator to calculate the cloud correlation degree can better reflect the uncertainty of the state level of the coal mining machine; Using association rules and grey cloud clustering to obtain the states of each fault layer, and then using variable weight fusion to obtain the overall state of the coal mining machine, making the evaluation results more objective and accurate.

Finally, based on the current status of coal mining machine health status evaluation, a coal mining machine health status evaluation system was designed and developed using Java Swing and Vue. By analyzing the non-functional requirement and functional requirements of the health status evaluation system, the overall structure of the system is constructed, the functions that each module should achieve are defined, and the database design is designed, finally forming a complete health status evaluation system for coal cutters. This system can achieve functions such as user permission management, operation status monitoring, threshold alarm, health status evaluation, historical record management, and visual display of operation parameters.

[1]中华人民共和国统计局.中国统计年鉴[M].北京：中国统计出版社,2021.

[2]杨富强,吴迪.“十四五”时期我国能源转型实现碳达峰的路径建议[J].可持续发展经济导刊,2021(Z2):21-22.

[3]刘一博,白云虎,侯建国.浅谈综采放顶煤开采的发展及存在的问题与对策[J].煤矿安全,2011,42(06):160-162.

[4]张培森,牛辉,朱慧聪,等.2019-2020年我国煤矿安全生产形势分析[J].煤矿安全,2021,52(11):245-249.

[5]任立民,薛晓,陈华敏.基于物联网的煤矿井下机电设备安全监测系统设计[J].煤炭技术,2021,40(10):166-168.

[6]沈江飞,潘天成,毛晓明,等.核电厂重大设备健康状态的模糊综合评价方法[J].核动力工程,2018,39(06):104-110.

[7]李俊卿,陈雅婷,孙福春,等.大型同步调相机油系统的健康状态评价[J].电力科学与工程,2020,36(02):1-6.

[8]郑文光,谢红玲,李燕青,等.基于WPHM模型和模糊综合评估的变压器健康状态评价[J].电力科学与工程,2021,37(02):32-41.

[9]孙世卓,李峰,司佳佳,等.航天器蓄电池组在轨健康状态评价方法研究[J].电源技术,2020,44(11):1658-1661.

[10]房友龙,贺星,刘东风,等.燃气轮机健康状态组合法综合评价[J].推进技术,2020,41(08):1903-1913.

[11]沈功田,刘渊,张君娇,等.在役大型游乐设施健康评价方法研究[J].机械工程学报,2020,56(10):1-11.

[12]龙建平,江平,丁伟.基于数据挖掘的燃煤机组健康状态评价方法研究[J].广西电力,2019,42(03):36-39.

[13]张晗.基于大数据的充电桩健康状态分析系统的研究[D].西安石油大学,2019.

[14]马威.基于健康指数的配电设备状态评估研究[D].西安理工大学,2021.

[15]Zhao W,Cui M.Real-time health status evaluation for electric power equipment based on cloud model[J]. International Journal of Simulation and Process Modelling, 2020, 15(1-2): 134-144.

[16]Sarkar D,Gunturi S K.Wind turbine blade structural state evaluation by hybrid object detector relying on deep learning models[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12: 8535-8548.

[17]潘子涵.基于信息熵的集装箱码头设备群状态评价及仿真研究[D].武汉理工大学,2020.

[18]冯乐乐.多设备生产线健康状态评估与预防性维护研究[D].电子科技大学,2021.

[19]Liu C.Fuzzy comprehensive evaluation method of variable weight for power distribution equipment group[C]//Journal of Physics: Conference Series. IOP Publishing,2020, 1633(1): 012112.

[20]Xue W ,Li X ,Huang B .Health diagnosis of nuclear power plant[J]. International Journal of Advanced Robotic Systems, 2019, 16(5):172988.

[21]何宗政,雷一楠,曹现刚,等.基于劣化度的采煤机健康状态评价方法研究[J].煤矿机械,2019,40(12):54-57.

[22]曹现刚,雷一楠,宫钰蓉,等.基于组合赋权法的采煤机健康状态评估方法研究[J].煤炭科学技术,2020,48(06):135-141.

[23]曹现刚,李彦川,雷卓,等.采煤机健康状态智能评估方法研究[J].工矿自动化,2020,46(06):41-47.

[24]郑云龙.基于BP神经网络的刮板输送机健康状态实时评估[J].煤矿机械,2017,38(06):148-150.

[25]刘训非.基于改进遗传算法的刨煤机故障诊断研究[J].煤矿机械,2013,34(12):259-261.

[26]马旭东,王跃龙,田慕琴,等.液压支架健康评估与寿命预测模型研究[J].煤炭科学技术,2021,49(03):141-148.

[27]雷一楠.采煤机健康状态监测与识别方法研究[D].西安科技大学,2020.

[28]吴少杰.基于数据挖掘的煤矿机电设备运行状态预测方法研究[D].西安科技大学,2019.

[29]高畅,刘涛.科普发展综合评价方法研究[J].数学的实践与认识,2019,49(18):89-97.

[30]陈大川,彭文开.基于改进模糊层次分析法的化工园区建筑物结构抗爆安全性能风险评估[J].安全与环境工程,2021,28(06):52-60+66.

[31]陈灏,张义敏,张晓琳,等.基于层次分析法的长时间尺度水资源综合评估[J].人民珠江,2021,42(11):53-59.

[32]李昱瑾,赵慧.公路网现状评价体系及应用[J].综合运输,2022,44(02):138-144.

[33]王艳丽,金宇宁.基于层次分析与云模型的TOD站点衔接设计评价[J].深圳大学学报(理工版),2022,39(02):193-200.

[34]何稳,李成标.国有企业治理体系与治理能力现代化评价研究[J].财会通讯,2021(20):103-107.

[35]徐银健,贾汉森,刘诗琦,等.基于模糊层次分析法的木质成型颗粒环境影响评价[J].太阳能学报,2021,42(09):377-386.

[36]彭庭睿,刘海鹏,刘彦,等.基于模糊层次分析法和图像对比毁伤评估法的目标权重计算方法[J].兵工学报,2021,42(S1):173-180.

[37]杨向升,郭广礼,郭松,等.基于改进模糊层次分析法的高压输电线路场地稳定性评价[J].金属矿山,2022(01):231-236.

[38]Li R,Wang X.Self-adaptive weighted majority vote algorithm based on entropy[C]// Intelligent Robot Systems. IEEE, 2017:73-77.

[39]Tian Y,Sun X.Risk evaluation index system of navigation environment of Qiongzhou Strait based on FAHP[C]// International Conference on Transportation Information & Safety. IEEE, 2015.

[40]Jiang L,Wang F,Yu D.Determining the weight of evaluation index based on FAHP and evidence theory[C]// IEEE International Conference on Electronics Information & Emergency Communication. IEEE, 2017:560-563.

[41]Zhen H,Yu L,Ping G,et al.Evaluation on equipment maintenance support ability based on FAHP[C]//2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). IEEE, 2013: 1492-1495.

[42]Peng L,Li N.FAHP-based Evaluation of the Group-buying Website[C]//ICSSSM11. IEEE, 2011: 1-6.

[43]陈丽,周宏.基于模糊综合评价和主成分分析法的岩溶流域水资源承载力评价[J].安全与环境工程,2021,28(06):159-173.

[44]卜兴兵,俸强,廖翀,等.基于主成分分析法的高速公路土壤重金属污染研究[J].安全与环境学报,2022,22(04):2241-2247.

[45]王鹤,余中枢,李筱婧,等.基于主成分分析方法的多类型电动汽车接入配电网的综合风险评估[J].电力自动化设备,2021,41(11):57-65.

[46]杨瑾,王忠伟,庞燕.基于熵权TOPSIS法的油茶产业发展绩效评价[J].中南林业科技大学学报,2021,41(12):168-177.

[47]阮超,张延军,李胡爽,等.基于改进熵权-未确知测度模型的城市污水深隧下穿既有铁路施工风险评价[J].安全与环境工程,2021,28(06):84-90.

[48]Hadri A,Chougdali K,Touahni R.Intrusion detection system using PCA and Fuzzy PCA techniques[C]//2016 International Conference on Advanced Communication Systems and Information Security (ACOSIS). IEEE, 2016: 1-7.

[49]Li W,Yang X,Huang J,et al.Research on integrated evaluation of military software supportability based on FAHP[C]//2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE, 2016: 1-7.

[50]Zhi H,Zhang G,Liu Y,et al.A novel risk assessment model on software system combining modified fuzzy entropy-weight and AHP[C]//2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2017: 451-454.

[51]周金柱.矿山设备维修手册[M].北京：中国电力出版社，2019.

[52]王萌.基于数据驱动的采煤机关键零部件故障诊断系统[D].太原理工大学,2021.

[53]李赟恒.基于BP神经网络的采煤机截割部故障诊断研究[D].西安科技大学,2017.

[54]吴少杰.基于数据挖掘的煤矿机电设备运行状态预测方法研究[D].西安科技大学,2019.

[55]许满贵,梁念兴,宝银昙.煤矿安全评价关键问题及对策[J].西安科技大学学报,2013,33(02):136-142.

[56]李哲.基于云边协同的大型电力变压器状态评估与故障预警方法研究[D].华北电力大学,2021.

[57]韩利,梅强,陆玉梅,等.AHP-模糊综合评价方法的分析与研究[J]. 中国安全科学学报,2004, 14(7):86-89.

[58]汤飞,赵方,宁雪,等.铁路领域科技创新平台评价研究——基于PCA-灰色聚类综合评价模型[J].北京交通大学学报(社会科学版),2022,21(03):124-136.

[59]江婷婷,姚传勤,童张俊.基于博弈论组合赋权的地铁盾构施工安全管理模糊综合评价[J].华北科技学院学报,2021,18(04):86-92.

[60]王智越.基于PCA与层次分析法的电力变压器健康状态评估方法研究[D].华北水利水电大学,2020.

[61]刘传修,张菁,刘小康,等.基于IVIF-AHP与改进CRITIC法的配电网规划方案综合评估[J].控制工程,2022,29(02):322-329.

[62]邓聚龙.灰色系统理论简介[J].内蒙古电力,1993(03):51-52.

[63]张琳琳.基于组合赋权云模型的装配式建筑投资风险评价[D].青岛理工大学,2022.

[64]赵元路.基于灰云模型和BP神经网络的风电齿轮箱评估诊断系统[D].天津理工大学,2019.

[65]郑雯.光电关键设备的健康状态评估分析方法与系统实现[D].重庆大学,2020.

[66]张晓平.设备分析评价与决策系统的设计与实现[D].中国石油大学,2010.