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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

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

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Abstract

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。

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ENResearch on Key Technology of Industrial Artificial Intelligence and Its Application in Predictive Maintenance
Introduction
  • With the rapid development of artificial intelligence technology and its effective application in industrial system, industrial intelligentization has become an important trend of current industrial production transformation.
  • School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074 2.
  • 根据设备运行的监测数据和退化机理 模型的先验知识, 利用人工智能技术, 及时检测到异 常并预测设备剩余使用寿命 (Remaining useful life, RUL), 接着设计合理的最优维修方案, 将有效地保 障设备运行的安全性和可靠性.
  • 基于寿命预测和维 修决策的预测性维护技术 (Predictive maintenance, PdM) 技术[16] 是实现以上功能的一项关键技术, 它 不仅能够保障设备的可靠性和安全性, 而且能够有 效降低维修成本、减少停机时间以及提高任务的完 成率.
  • 因此, PdM 技术广泛应用到航空航天、武器装 备、石油化工装备、船舶、高铁、电力设备、数控机床 以及道路桥梁隧道等领域[17].
  • PdM 技术主要由数据采集与处理、状态监测、 健康评估与 RUL 预测及维修决策等模块组成[18−20], 它是故障诊断思想和内涵的进一步发展, 其核心功 能是根据监测数据预测设备的 RUL, 然后利用获得 的预测信息和可用的维修资源, 设计合理的维修方 案, 实现降低保障费用、增加使用时间、提高设备安
  • 文献 [23] 把设备 RUL 预测 方法分为三类: a) 退化模型方法: b) 数据驱动方法; c) 模型和数据融合的方法.
  • 23] 根据所采用的 RUL 预测方法, 把预测性维 修决策分为退化模型驱动的预测性维修决策和数据 驱动的预测性维修决策.
Highlights
  • With the rapid development of artificial intelligence technology and its effective application in industrial system, industrial intelligentization has become an important trend of current industrial production transformation
  • 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 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
Results
  • 为了实 现 PdM 的功能需要数据采集装置和数据处理芯片 等硬件.
  • 动力学来预测其剩余寿命, 当设备比较简单且退化 是由单一退化因素造成时, 物理退化模型的 RUL 预 测精度较高[34−37].
  • 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
  • 确定性经验退化模型 (非随机经验退化模型) 采用典型的分布曲线 (指数模型[47−48]、威布尔模型[49]、 比例风险模型[50] 等) 来拟合设备的退化过程, 并通 过外推获得 RUL 预测结果.
  • 在设备单一、工况 简单的情况下, 此类方法能够比较准确预测 RUL.
  • 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
  • 1) 指数模型(锂电池[47]、轴承[48]等).
  • 1) 构建高性能机器学习模型需要特定领域和 1) 支持/相关向量机(锂电池[85]、轴承[87]等).
  • 基于深度学习的寿命预测已经引起了广泛的 关注[62], 其代表性方法包括: 卷积神经网络 (Convolutional neural networks, CNN)[73−74]、深度置信网 络[75−76]、循环神经网络 (Recurrent neural network, RNN)[77−78] 以及迁移学习[79−80] 等预测方法.
  • 此类方法常利用信号处理方法提取设备退化特征, 再利用深度学习方法学习健康指标和退化关键特征 之间的映射关系, 进而实现设备的 RUL 预测.
  • 相对于 CNN 是对人类视觉的仿真, RNN 可以 看作是对人类记忆能力的模拟, 它具有短期记忆能 力.
  • GRU 网络是另一种典型的基于门控制的循环神 经网络, 它由更新门和重置门组成, 更新门是由 LSTM 中的输入门和遗忘门合并而成.
  • 基 于 GPR 方法的预测, 一方面是直接预测型, 如文献 [88] 利用瑞利熵提取轴承的退化特征, 输入 GPR 模型, 预测了轴承的剩余寿命: 文献 [90] 首先对电池容量 曲线进行 EMD 分解, 然后对分解后的固有模态函 数和残差部分分别采用不同高斯回归和逻辑回归, 最后将两部分的结果相加, 较准确地预测了电池剩 余寿命.
  • [95−98] 目前, 为了提高设备的 RUL 预测精度, 许多 学者对 HSMM 算法进行了改进, 如文献 [96] 利用 改进的 HSMM 模型来描述相邻刀具磨损状态依赖
  • 数据与模型的融合主要以随机滤波 (KF/EKF/ UKF/PF) 为桥梁, 基于特征工程建立健康指标, 并选 择合适的退化模型, 融合随机滤波方法或者参数辨 识方法确定模型的参数, 通过模型外推获得预测结 果, 实现数模方法的优势互补, 提高预测精度.
  • 扩展卡 尔曼滤波 (Extended Kalman filter, EKF) 和无迹卡尔 曼滤波 (Unscented Kalman filter, UKF)都可以用来 处理具有高斯噪声的非线性退化过程.
  • 其中 EKF 主 要基于非线性退化过程的偏导建立雅可比行列式, 进而实现线性化处理; UKF 利用无迹变换实现非线 性退化过程的近似计算.
  • 这类方法的基本思路是假 设设备退化符合某种退化机理模型, 接着通过建立 扩展向量把数学模型和关键参数融合成离散时间的 状态空间模型, 然后利用 KF/EKF/UKF 实现状态的 更新与预测.
  • 研究锂离子电池和滚动轴承的 RUL 预 测时, 退化机理模型常选择指数函数及其扩展模型[99−103]、 二次函数模型[48] 以及融合曲线模型[104] 等.
  • 为了获 得较高的预测精度, 常把 EKF/UKF 和 SVM/RVM 等机器学习方法融合使用[100−102, .
  • 退化模型是影响 PF 的融合方法[111−114] 预测效 果的关键因素之一, 其中指数模型是滚动轴承和锂 离子电池 RUL 预测广泛使用的模型, 针对指数模型 存在主观选择第一预测时间和随机误差等缺点, 文 献 [115] 提出了一种改进的指数模型, 基于 3 区间 建立了自适应第一预测时间选择方法, 并利用粒子 滤波来减少随机误差.
  • 3) 状态依赖的维修决策 此类维修决策分为: 预防性维修 (Preventive maintenance, PM)、基于状态的维修 (Conditionbased maintenance, CBM) 以及预测性维修 (Predictive maintenance, PdM) 等.
  • 其中, PM 是一种基于时 间的计划维修方案[133−134], CBM 是基于设备当前健 康状态的维修方案 而 PdM 是基于设备未来的退化 趋势制定的维修方案[135].
  • 目前应用广泛的是 PdM 策略 与 CBM 策略, 它们各有所长, 可以优势互补.
  • 另一方面, 由于设备或生产系统的实际运行中偶尔发生一些不 可预测的突发故障, 或者受不确定工况等因素的影 响, 寿命预测误差较大, CBM 策略可以为 PdM 策略 提供支持和补充.
Conclusion
  • 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
  • Exponential model+ GA-SVR+AUKF (锂电池[105]) 刀具磨损模型+ BLSTM+PF+SVR (刀 具[124]) FNN+CNN+LSTM (锂电池[120])
  • RNN+CNN (轴承和铣刀[122])
  • 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.
  • 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.
  • 数模融合(非随机滤波) 维修决策
  • Dual-Task Deep LSTM+ Weibull (涡轮 基于RMSE性能标准, 获得比SVR, RVR, CNN, Deep LSTM 等方法更好的
  • LSTM+DPM (涡轮发动机[151])
Summary
  • With the rapid development of artificial intelligence technology and its effective application in industrial system, industrial intelligentization has become an important trend of current industrial production transformation.
  • School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074 2.
  • 根据设备运行的监测数据和退化机理 模型的先验知识, 利用人工智能技术, 及时检测到异 常并预测设备剩余使用寿命 (Remaining useful life, RUL), 接着设计合理的最优维修方案, 将有效地保 障设备运行的安全性和可靠性.
  • 基于寿命预测和维 修决策的预测性维护技术 (Predictive maintenance, PdM) 技术[16] 是实现以上功能的一项关键技术, 它 不仅能够保障设备的可靠性和安全性, 而且能够有 效降低维修成本、减少停机时间以及提高任务的完 成率.
  • 因此, PdM 技术广泛应用到航空航天、武器装 备、石油化工装备、船舶、高铁、电力设备、数控机床 以及道路桥梁隧道等领域[17].
  • PdM 技术主要由数据采集与处理、状态监测、 健康评估与 RUL 预测及维修决策等模块组成[18−20], 它是故障诊断思想和内涵的进一步发展, 其核心功 能是根据监测数据预测设备的 RUL, 然后利用获得 的预测信息和可用的维修资源, 设计合理的维修方 案, 实现降低保障费用、增加使用时间、提高设备安
  • 文献 [23] 把设备 RUL 预测 方法分为三类: a) 退化模型方法: b) 数据驱动方法; c) 模型和数据融合的方法.
  • 23] 根据所采用的 RUL 预测方法, 把预测性维 修决策分为退化模型驱动的预测性维修决策和数据 驱动的预测性维修决策.
  • 为了实 现 PdM 的功能需要数据采集装置和数据处理芯片 等硬件.
  • 动力学来预测其剩余寿命, 当设备比较简单且退化 是由单一退化因素造成时, 物理退化模型的 RUL 预 测精度较高[34−37].
  • 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
  • 确定性经验退化模型 (非随机经验退化模型) 采用典型的分布曲线 (指数模型[47−48]、威布尔模型[49]、 比例风险模型[50] 等) 来拟合设备的退化过程, 并通 过外推获得 RUL 预测结果.
  • 在设备单一、工况 简单的情况下, 此类方法能够比较准确预测 RUL.
  • 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
  • 1) 指数模型(锂电池[47]、轴承[48]等).
  • 1) 构建高性能机器学习模型需要特定领域和 1) 支持/相关向量机(锂电池[85]、轴承[87]等).
  • 基于深度学习的寿命预测已经引起了广泛的 关注[62], 其代表性方法包括: 卷积神经网络 (Convolutional neural networks, CNN)[73−74]、深度置信网 络[75−76]、循环神经网络 (Recurrent neural network, RNN)[77−78] 以及迁移学习[79−80] 等预测方法.
  • 此类方法常利用信号处理方法提取设备退化特征, 再利用深度学习方法学习健康指标和退化关键特征 之间的映射关系, 进而实现设备的 RUL 预测.
  • 相对于 CNN 是对人类视觉的仿真, RNN 可以 看作是对人类记忆能力的模拟, 它具有短期记忆能 力.
  • GRU 网络是另一种典型的基于门控制的循环神 经网络, 它由更新门和重置门组成, 更新门是由 LSTM 中的输入门和遗忘门合并而成.
  • 基 于 GPR 方法的预测, 一方面是直接预测型, 如文献 [88] 利用瑞利熵提取轴承的退化特征, 输入 GPR 模型, 预测了轴承的剩余寿命: 文献 [90] 首先对电池容量 曲线进行 EMD 分解, 然后对分解后的固有模态函 数和残差部分分别采用不同高斯回归和逻辑回归, 最后将两部分的结果相加, 较准确地预测了电池剩 余寿命.
  • [95−98] 目前, 为了提高设备的 RUL 预测精度, 许多 学者对 HSMM 算法进行了改进, 如文献 [96] 利用 改进的 HSMM 模型来描述相邻刀具磨损状态依赖
  • 数据与模型的融合主要以随机滤波 (KF/EKF/ UKF/PF) 为桥梁, 基于特征工程建立健康指标, 并选 择合适的退化模型, 融合随机滤波方法或者参数辨 识方法确定模型的参数, 通过模型外推获得预测结 果, 实现数模方法的优势互补, 提高预测精度.
  • 扩展卡 尔曼滤波 (Extended Kalman filter, EKF) 和无迹卡尔 曼滤波 (Unscented Kalman filter, UKF)都可以用来 处理具有高斯噪声的非线性退化过程.
  • 其中 EKF 主 要基于非线性退化过程的偏导建立雅可比行列式, 进而实现线性化处理; UKF 利用无迹变换实现非线 性退化过程的近似计算.
  • 这类方法的基本思路是假 设设备退化符合某种退化机理模型, 接着通过建立 扩展向量把数学模型和关键参数融合成离散时间的 状态空间模型, 然后利用 KF/EKF/UKF 实现状态的 更新与预测.
  • 研究锂离子电池和滚动轴承的 RUL 预 测时, 退化机理模型常选择指数函数及其扩展模型[99−103]、 二次函数模型[48] 以及融合曲线模型[104] 等.
  • 为了获 得较高的预测精度, 常把 EKF/UKF 和 SVM/RVM 等机器学习方法融合使用[100−102, .
  • 退化模型是影响 PF 的融合方法[111−114] 预测效 果的关键因素之一, 其中指数模型是滚动轴承和锂 离子电池 RUL 预测广泛使用的模型, 针对指数模型 存在主观选择第一预测时间和随机误差等缺点, 文 献 [115] 提出了一种改进的指数模型, 基于 3 区间 建立了自适应第一预测时间选择方法, 并利用粒子 滤波来减少随机误差.
  • 3) 状态依赖的维修决策 此类维修决策分为: 预防性维修 (Preventive maintenance, PM)、基于状态的维修 (Conditionbased maintenance, CBM) 以及预测性维修 (Predictive maintenance, PdM) 等.
  • 其中, PM 是一种基于时 间的计划维修方案[133−134], CBM 是基于设备当前健 康状态的维修方案 而 PdM 是基于设备未来的退化 趋势制定的维修方案[135].
  • 目前应用广泛的是 PdM 策略 与 CBM 策略, 它们各有所长, 可以优势互补.
  • 另一方面, 由于设备或生产系统的实际运行中偶尔发生一些不 可预测的突发故障, 或者受不确定工况等因素的影 响, 寿命预测误差较大, CBM 策略可以为 PdM 策略 提供支持和补充.
  • 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
  • Exponential model+ GA-SVR+AUKF (锂电池[105]) 刀具磨损模型+ BLSTM+PF+SVR (刀 具[124]) FNN+CNN+LSTM (锂电池[120])
  • RNN+CNN (轴承和铣刀[122])
  • 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.
  • 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.
  • 数模融合(非随机滤波) 维修决策
  • Dual-Task Deep LSTM+ Weibull (涡轮 基于RMSE性能标准, 获得比SVR, RVR, CNN, Deep LSTM 等方法更好的
  • LSTM+DPM (涡轮发动机[151])
Tables
  • Table1: Research summary of RUL prediction with model and data method
  • Table2: Research summary of remaining useful life and maintenance decision based on fusion method
Download tables as Excel
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  • 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 (ZHANG Yong Professor at the School of Information Science and Engineering, Wuhan University of Science and Technology. He received his Ph. D. degree from Huazhong University of Science and Technology in 2010. His research interest covers artificial intelligence, safety of equipment and system. Corresponding author of this paper.)
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  • 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 (YUAN Ye Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. He received his Ph. D. degree from University of Cambridge in 2012. His research interest covers artificial intelligence, cyber-physical systems (CPS), intelligent manufacturing, and medical treatment.)
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  • 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 (DING Han Professor at the School of Mechanical Science and Engineering, Huazhong University of Science and Technology, academician of the Chinese Academy of Sciences. He received his Ph. D. degree from Huazhong University of Technology in 1989. His research interest covers theory and technology of robotics and digital manufacturing.)
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Author
袁烨
袁烨
张永
张永
丁汉
丁汉
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