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工业人工智能的关键技术及其在预测性维护中的应用现状

袁烨 张永 丁汉

袁烨, 张永, 丁汉. 工业人工智能的关键技术及其在预测性维护中的应用现状. 自动化学报, 2020, 46(10): 2013−2030 doi: 10.16383/j.aas.c200333
引用本文: 袁烨, 张永, 丁汉. 工业人工智能的关键技术及其在预测性维护中的应用现状. 自动化学报, 2020, 46(10): 2013−2030 doi: 10.16383/j.aas.c200333
Yuan Ye, Zhang Yong, Ding Han. Research on key technology of industrial artificial intelligence and its application in predictive maintenance. Acta Automatica Sinica, 2020, 46(10): 2013−2030 doi: 10.16383/j.aas.c200333
Citation: Yuan Ye, Zhang Yong, Ding Han. Research on key technology of industrial artificial intelligence and its application in predictive maintenance. Acta Automatica Sinica, 2020, 46(10): 2013−2030 doi: 10.16383/j.aas.c200333

工业人工智能的关键技术及其在预测性维护中的应用现状

doi: 10.16383/j.aas.c200333
基金项目: 国家自然科学基金(9174812, 61873197), 江苏省重点研发计划(BE2017002)资助
详细信息
    作者简介:

    袁烨:华中科技大学人工智能与自动化学院教授. 2012年获得剑桥大学博士学位. 主要研究方向为人工智能, 信息物理系统, 智能制造, 医疗.E-mail: yye@hust.edu.cn

    张永:武汉科技大学信息科学与工程学院教授. 2010年获得华中科技大学博士学位. 主要研究方向为人工智能, 设备和系统安全性. 本文通信作者.E-mail: zhangyong77@wust.edu.cn

    丁汉:华中科技大学机械科学与工程学院教授, 中国科学院院士. 1989年获得华中理工大学博士学位. 主要研究方向为机器人与数字制造理论和技术.E-mail: dinghan@hust.edu.cn

Research on Key Technology of Industrial Artificial Intelligence and Its Application in Predictive Maintenance

Funds: Supported by National Natural Science Foundation of China (9174812, 61873197), Primary Research and Development Plan of Jiangsu Province (BE2017002)
  • 摘要: 随着人工智能技术的快速发展及其在工业系统中卓有成效的应用, 工业智能化成为当前工业生产转型的一个重要趋势. 论文提炼了工业人工智能(Industrial artificial intelligence, IAI)的建模、诊断、预测、优化、决策以及智能芯片等共性关键技术, 总结了生产过程监控与产品质量检测等4个主要应用场景. 同时, 论文选择预测性维护作为工业人工智能的典型应用场景, 以工业设备的闭环智能维护形式, 分别从模型方法、数据方法以及融合方法出发, 系统的总结和分析了设备的寿命预测技术和维护决策理论, 展示了人工智能技术在促进工业生产安全、降本、增效、提质等方面的重要作用. 最后, 探讨了工业人工智能研究所面临的问题以及未来的研究方向.
  • 图  1  工业人工智能的整体研究框架.

    Fig.  1  Overall research framework of industrial artificial intelligence in this paper

    图  2  基于工业人工智能的设备预测性维护闭环框架图

    Fig.  2  Closed loop framework of predictive maintenance of equipment based on industrial artificial intelligence

    表  1  基于模型和数据方法的寿命预测研究总结

    Table  1  Research summary of RUL prediction with model and data method

    类型方法优点缺点应用对象
    模型物理
    模型
    1) 刻画了设备退化的实际物理意义.
    2) 设备结构简单时预测结果比较精确.
    1) 建立退化机理模型涉及到多个学科的专业知识, 难以建立准确的物理模型获.
    2) 设备复杂时, 预测的实用性较差.
    1) 累积损伤[29]和裂纹扩展[30]模型等(轴承[34]、IGBT[35]等).
    2) 电化学反应机理模型(锂电池[36] 等).
    3) 电路元件等效电路模型(锂电池[37]等).
    经验
    模型
    (随机)
    1) 描述设备退化过程中的随机时变性.
    2) 获得剩余寿命的解析表达形式.
    1) 需要利用高深的随机过程理论进行数学推导, 不利于工程应用和推广.
    2) 模型参数辨识难, 预测精度有待提高.
    1) 逆高斯模型(激光设备[38]、励磁绕组[39]等).
    2) 维纳过程模型(轴承[42]、高炉炉墙[44]等).
    3) 伽马过程模型(半导体制造设备[45]、二极管[46]等).
    经验
    模型
    (非随机)
    1) 利用回归模型(如多项式、指数模型等)来描述设备的退化趋势.
    2) 通过外推预测设备的剩余寿命.
    1) 忽略了设备退化的内部机理.
    2) 依靠经验构建退化趋势模型具有随意性和不确定性.
    3) 预测结果不准时难以解释退化原理.
    1) 指数模型(锂电池[47]、轴承[48]等).
    2) 威布尔模型(轴承[49] 等).
    3) 比例风险模型(电子元件[53]等).
    数据深度
    学习
    1) 将数据直接输入到深度网络, 训练预测
    模型.
    2) 获得较精确的预测结果.
    1) 需要GPU/CPU、SSD存储、快速和大容量的RAM对数据进行训练.
    2) 缺乏严格的理论基础, 超参数和网络设计困难.
    1) 卷积神经网络(轴承[7374]等).
    2) 深度置信网络(涡轮发动机[7576]等).
    3) 循环神经网络(锂电池[77]、涡轮发动
    [78]等).
    其他
    机器
    学习
    1) 利用特征工程结合机器学习, 获得较好的预测性能.
    2) 算法易解释和理解, 调整超参数和更改模型较方便.
    1) 构建高性能机器学习模型需要特定领域和特定应用的机器学习技术和特征工程.
    2) 预测精度不太高, 且通用性不强.
    1) 支持/相关向量机(锂电池[85]、轴承[87]等).
    2) 高斯过程回归(刀具[91]、锂电池[92]等).
    3) 隐马尔科夫(刀具[9798] 等).
    下载: 导出CSV

    表  2  基于融合方法的寿命预测和维修决策研究总结

    Table  2  Research summary of remaining useful life and maintenance decision based on fusion method

    融合对象融合方式融合方法融合效果
    寿命预测数模融合(随机滤波)Exponential model+ GA-SVR+AUKF (锂电池[105])基于Rt, Rp, ERUL, ERMSE, EMAE, R2, ERA 等性能标准, 获得比Exponential model加上UKF/AUKF, RVR+UKF, SVR+UKF, GA-SVR+ AEKF等更好的结果.
    刀具磨损模型+ BLSTM+PF+SVR (刀具[124])基于RMSE, MAE等性能指标, 获得比KNN, RNN, MLP, AE, LR, LSTM, SAE-DNN等方法更好的结果.
    机器学习融合FNN+CNN+LSTM (锂电池[120])基于EM性能标准, 获得比UKF+CEEMD, UKF+RVM, SVR+PF, BCT+RVM等方法更好的结果.
    RNN+CNN (轴承和铣刀[122])基于CRA和CPE等性能标准, 获得比SVM, FNN, DBN, CNN, CBL STM等方法更好的结果.
    模型融合PHM+Wiener (汽车发动机[118])基于MAE和Bias性能标准, 获得比BPNN, NN更好的预测结果.
    Inverse Gaussian + Wiener (风力发电机轴承[119])基于相对误差和误差时间性能标准, 分别从轴承退化初始阶段、中间阶段、最后阶段展示较好的预测效果.
    数模融合(非随机滤波)Dual-Task Deep LSTM+ Weibull (涡轮发动机[125])基于RMSE性能标准, 获得比SVR, RVR, CNN, Deep LSTM 等方法更好的结果.
    SVR+WPHM (轴承[126])基于MSE, MAE, MAPE等性能标准, 获得比NAR-NN, BPNN, LSTM, GM, ARMA等方法更好的结果.
    维修决策维修决策PM+SM (并行系统[132])针对具有不确定非周期变化的未来使用应力系统, 基于两阶段随机规划模型, 利用SPM和最小维修策略解决不完全维修和紧急故障时的维修问题.
    预测性维修 (退化模型)Wiener+PHM (铅酸电池[146])利用PHM 对退化数据和故障时间数据进行联合建模, 采用维纳过程描述退化过程的随机性, 然后通过最小化系统维护成本得到最优的维护计划.
    预测性维修 (机器学习)LSTM+DPM (涡轮发动机[151])利用LSTM 实现寿命预测, 以平均成本率为目标函数得到动态维护方案, 性能比周期性和理想预测维护策略更好.
    下载: 导出CSV
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  • 收稿日期:  2020-05-20
  • 录用日期:  2020-06-28
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