Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (2): 267-279.doi: 10.13229/j.cnki.jdxbgxb20211080

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Research progress and development trend of health assessment of electromechanical equipment

Guo-fa LI1,2(),Yan-bo WANG1,2,Jia-long HE1,2(),Ji-li WANG1,2   

  1. 1.Key Laboratory of CNC Equipment Reliability，Ministry of Education，Jilin University，Changchun 130022，China
    2.College of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
  • Received:2021-10-22 Online:2022-02-01 Published:2022-02-17
  • Contact: Jia-long HE E-mail:ligf@jlu.edu.cn;hejl@jlu.edu.cn

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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

CLC Number: 

  • TH17

Fig.1

Health assessment flow chart of electromechanical equipment"

Table 1

Monitoring signals of common electromechanical equipment"

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］

Table 2

Comparison of time frequency analysis methods"

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］

Table 3

Advantages and disadvantages of data driven methods"

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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