机电装备健康状态评估研究进展及发展趋势

doi:10.13229/j.cnki.jdxbgxb20211080

摘要/Abstract

摘要：

机电装备的维修、保养直接影响其综合利用效率和使用寿命，健康状态评估是制定装备维护策略、管理维修资源的重要依据，是实现装备预测性维护、故障预测和健康管理的前提。传统的机电装备健康状态评估方法存在过分依赖专家经验、无法处理大规模数据、准确度低等缺点，难以满足现代机电装备健康管理技术的需求。本文在梳理、分析国内外机电装备健康状态评估最新研究成果的基础上，从信号获取、特征提取、健康状态等级划分和健康状态评估四个关键环节，综述了机电装备健康状态评估方法的研究进展和发展动态，指出了机电装备健康状态评估相关技术面临的挑战及解决途径。

关键词: 机械工程, 机电装备, 特征提取, 健康状态等级划分, 健康状态评估

Abstract:

The maintenance and repair of electromechanical equipment such as aerospace equipment， high-grade CNC machine tools and wind power generation equipment directly affect its comprehensive efficiency and service life. Health assessment is the basis of maintenance strategy formulation and maintenance resource management. It is the premise of predictive maintenance， fault prediction and health management. The traditional health assessment method has the disadvantages of relying too much on expert experience， unable to process large-scale data and low accuracy， which is difficult to meet the needs of modern electromechanical equipment health management technology. On the basis of combing and analyzing the latest research results of health status assessment of electromechanical equipment at home and abroad， this paper summarizes the research progress and development trend of health assessment of electromechanical equipment from four key links： signal acquisition， feature extraction， health status division and health assessment； The challenges faced by the related technologies of health assessment of electromechanical equipment are pointed out； Finally， the solutions and development trends to deal with these challenges are discussed.

Key words: mechanical engineering, electromechanical equipment, feature extraction, health status classification, health assessment

中图分类号:

TH17

李国发,王彦博,何佳龙,王继利. 机电装备健康状态评估研究进展及发展趋势[J]. 吉林大学学报(工学版), 2022, 52(2): 267-279.

Guo-fa LI,Yan-bo WANG,Jia-long HE,Ji-li WANG. Research progress and development trend of health assessment of electromechanical equipment[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 267-279.

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研究对象	监测信号	文献
风电机组	齿轮箱温度、转子转速、有功功率、相电压、相电流、电网频率、轴承振动、齿轮箱润滑油压力等	［6-11］
航空发动机	燃油流量等	［12，13］
燃气涡轮发动机	低压转速、高压转速等	［14，15］
涡轮喷气发动机	低压转速、高压转速、高压压气机入口压力和温度等	［16］
涡扇发动机气路风扇	风扇振动、风扇出口温度	［17］
油砂泵	振动	［18，19］
船用柴油机	交流发电机有功功率、冷却系统出口温度、油压、湿度等	［20］
变压器油纸绝缘系统	乙烯、乙烷、一氧化碳、二氧化碳、氢、乙炔、甲烷等的浓度	［21］
在轨卫星	轨道角速度、磁感应强度、俯仰角等	［22］
天波超视距雷达	输出功率、精度、角度等	［23］
飞机液压系统	转速、工作温度、出口压力、流量等	［24］
滚珠丝杠	反向间隙、螺距误差	［25，26］
陀螺仪	振动、温度、漂移（x轴和y轴）、供电电压和功率	［27］
轴承	振动	［28-34］
刀具	振动、噪声、主轴电机电流	［35-37］

方法	窗函数	聚集性	效率	抗噪性	文献
小波分解	多分辨	最低	较高	较高	［38］
小波包分解	多分辨	低	高	高	［41］
变分模态分解	无	高	较低	较低	［42］
局部特征尺度分解	无	较高	低	低	［43］
希尔伯特黄变换	无	最高	最低	最低	［44］

方法	优点	缺点
神经网络	学习能力强、对噪声数据鲁棒性和容错性较强、有联想能力、能逼近任意非线性关系。	需要较多的训练样本、较低的泛化能力、超参数较多、可解释性差、训练过程费时、易陷入局部极值点。
HMM及其变种	对时间序列过程的动态建模能力很强。	初始模型的选取问题、算法下溢问题、过度拟合问题
贝叶斯网络	变量之间的依赖关系采用有向无环图表示、融入了专家领域知识、可以根据训练集的样本实时更新模型的结构和参数。	贝叶斯网络的学习、精确推理和近似推理均是不存在多项式时间算法的问题。
SVM及其变种	良好的泛化能力、适用于高维和小样本问题。	训练样本集的增大会造成过拟合和时空复杂度的增加、核函数必须是半正定的、对缺失数据敏感。
集成学习	对于数据结构复杂、数据量大、数据质量参差不齐等情况，表现优异。	评估结果容易受到弱学习器数目的影响。

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