摘要
煤矿机电设备智能化维护是智慧矿山建设的重要组成部分。从煤矿机电设备故障机理、设备状态监测、信号分析与处理、故障诊断与预测算法4个方面总结了煤矿机电设备的智能故障诊断与预测性维护研究现状:①设备故障机理研究主要是针对不同设备,采用不同方法建立设备故障的分析模型,并对模型施加激励以获得设备故障动态响应,从而为后续的故障诊断提供评判依据;②设备状态监测研究针对煤矿机电设备建立了较为完备的状态监测系统,能够准确及时地获取设备参数,为设备的故障诊断提供数据支持;③信号分析与处理研究除了采用传统的时域、频域和时频域分析方法外,还将多种方法结合的手段用于信号处理与特征提取,提高了信号处理效率和处理结果的可靠性;④故障诊断与预测算法主要采用人工神经网络及机器学习、深度学习等智能算法建立设备故障诊断与预测模型,从而实现故障的智能诊断和预测。指出煤矿机电设备智能化维护研究存在的问题:①设备故障机理的研究缺少多故障复合状态下的故障机理研究,需要更多地对设备某部分的故障带来的连锁反应进行研究;②模拟环境下所获得的故障数据不能完全真实反映设备实际的运行状况,需长时间不断采集现场监测数据,最好是设备全生命周期数据;③目前采用组合式的算法研究较少,并且研究对象更多局限在设备某个部分或零部件。最后给出煤矿机电设备智能化维护的发展趋势:①研究应用灵敏度更高的智能传感器来监测设备,结合随机共振、盲源分离等方法从强噪声中提取微弱的特征信号,及时地识别出设备早期故障,从而实现预测性维护;②采用独立的诊断方法已经不能适应实际设备的诊断需求,基于多传感器信息融合技术的诊断和预测可准确有效地识别出设备存在的所有故障;③将迁移学习算法作为“桥梁”,建立仿真、试验数据与现场数据的相关性,为解决仿真与试验条件和现场条件差异的问题提供数据支持和保障。
The intelligent maintenance of coal mine electromechanical equipment is an important part of the construction of intelligent mines.The research status of intelligent fault diagnosis and predictive maintenance of coal mine electromechanical equipment is summarized from four aspects,coal mine electromechanical equipment fault mechanism,equipment condition monitoring,signal analysis and processing,and fault diagnosis and prediction algorithm.①Equipment fault mechanism research is mainly for different equipment,which uses different methods to establish equipment fault analysis models and applies incentives to the models to obtain dynamic response to equipment fault so as to provide a basis for subsequent fault diagnosis.②Equipment condition monitoring research has established a relatively complete condition monitoring system for coal mine electromechanical equipment,which can obtain equipment parameters accurately and timely and provide data support for equipment fault diagnosis.③Signal analysis and processing research not only uses traditional time domain,frequency domain and time-frequency domain analysis methods,but also combines multiple methods for signal processing and characteristic extraction,which improves the efficiency of signal processing and the reliability of processing results.④Fault diagnosis and prediction algorithms mainly use artificial neural networks,including machine learning,deep learning and other intelligent algorithms to establish equipment fault diagnosis and prediction models so as to achieve intelligent diagnosis and prediction of faults.It is pointed out the problems of intelligent maintenance research of coal mine electromechanical equipment are as follows.①The study of equipment fault mechanism lacks the study of fault mechanism under multiple fault compound state.And more research is needed on the chain reaction caused by the fault of a certain part of the equipment.②The fault data obtained in the simulated environment cannot fully reflect the actual operating conditions of the equipment,and it is necessary to continuously collect on-site monitoring data for a long time,preferably the whole life cycle data of the equipment.③At present,there is few research using the combined algorithm,and the research object is more limited to a certain part or component of the equipment.Finally,the development trend of intelligent maintenance of coal mine electromechanical equipment is proposed as follows.①It is suggested to study the application of intelligent sensors with higher sensitivity to monitor equipment,combine the methods such as random resonance and blind source separation to extract weak characteristic signals from strong noise to identify early equipment faults in a timely manner,thus realizing predictive maintenance.②The use of independent diagnosis method can no longer meet the diagnosis needs of actual equipment.The diagnosis and prediction based on multiple sensor information fusion technology can identify all faults in the equipment accurately and effectively.③It is proposed to use the migration learning algorithm as a'bridge'to establish the correlation between simulation,test data and field data,and provide data support and guarantee for solving the problem of the difference between simulation and test conditions and field conditions.
作者
刘媛媛
LIU Yuanyuan(CCTEG China Coal Research Institute,Beijing 100013,China)
出处
《工矿自动化》
北大核心
2021年第7期79-84,共6页
Journal Of Mine Automation
基金
天地科技股份有限公司科技创业资金专项项目(2020-TD-MS001)。
关键词
煤矿智能化
煤矿机电设备故障诊断
智能化维护
预测性维护
故障机理
设备状态监测
信号分析与处理
coal mine intelligence
coal mine electromechanical equipment fault diagnosis
intelligent maintenance
predictive maintenance
fault mechanism
equipment condition monitoring
signal analysis and processing
