基于动作周期退化相似性度量的机械轴健康指标构建与剩余寿命预测  

Health index construction and remaining useful life prediction of mechanical axis based on action cycle degradation similarity measurement

作　　者：周玉彬 肖红[1] 王涛[2] 姜文超[1,3] 熊梦 贺忠堂 ZHOU Yubin;XIAO Hong;WANG Tao;JIANG Wenchao;XIONG Meng;HE Zhongtang(School of Computer Science,Guangdong University of technology,Guangzhou Guangdong 510006,China;School of Automation,Guangdong University of technology,Guangzhou Guangdong 510006,China;Cloud Computing Center,Chinese Academy of Sciences,Dongguan Guangdong 523808,China)

机构地区：[1]广东工业大学计算机学院,广州510006 [2]广东工业大学自动化学院,广州510006 [3]中国科学院云计算产业技术创新与育成中心,广东东莞523808

出　　处：《计算机应用》2021年第11期3192-3199,共8页journal of Computer Applications

基　　金：国家重点研发计划项目(2018YFB1004202);国家自然科学基金委员会-广东省人民政府联合基金资助项目(U2001201);广东省自然科学基金面上项目(2020A1515010890,2018A030313061);广东省科技计划项目(2019B010139001);广州市科技计划项目(201902020016);2019年佛山市核心技术攻关项目(1920001001367)。

摘　　要：针对工业机器人机械轴健康管理中检测效率和精准度较低的问题,提出了一种机械轴运行监控大数据背景下的基于动作周期退化相似性度量的健康指标(HI)构建方法,并结合长短时记忆(LSTM)网络进行机器人剩余寿命(RUL)的自动预测。首先,利用MPdist关注机械轴不同动作周期之间子周期序列相似性的特点,并计算正常周期数据与退化周期数据之间的偏离程度,进而构建HI;然后,利用HI集训练LSTM网络模型并建立HI与RUL之间的映射关系;最后,通过MPdist-LSTM混合模型自动计算RUL并适时预警。使用某公司六轴工业机器人进行实验,采集了加速老化数据约1500万条,对HI单调性、鲁棒性和趋势性以及RUL预测的平均绝对误差(MAE)、均方根误差(RMSE)、决定系数(R^2)、误差区间(ER)、早预测(EP)和晚预测(LP)等指标进行了实验测试,将该方法分别与动态时间规整(DTW)、欧氏距离(ED)、时域特征值(TDE)结合LSTM的方法,MPdist结合循环神经网络(RNN)和LSTM等方法进行比较。实验结果表明,相较于其他对比方法,所提方法所构建HI的单调性和趋势性分别至少提高了0.07和0.13,RUL预测准确率更高,ER更小,验证了所提方法的有效性。Aiming at the problems of low detection efficiency and accuracy in the health management process of industrial robot axis,a new Health Index(HI)construction method based on action cycle degradation similarity measurement under the background of mechanical axis operation monitoring big data was proposed,and the robot Remaining Useful Life(RUL)prediction was carried out by combining Long Short-Term Memory(LSTM)network.Firstly,MPdist was used to focus on the similarity features of sub-cycle sequences between different action cycles of mechanical axis,and the deviation distance between normal cycle data and degradation cycle data was calculated,so that the HI was constructed.Then,the LSTM network model was trained by HI set,and the mapping relationship between HI and RUL was established.Finally,the MPdist-LSTM hybrid model was used to automatically calculate the RUL and give early warning in time.The six-axis industrial robot of a company was used to carry the experiments,and about 15 million pieces of data were collected.The monotonicity,robustness and trend of HI and Mean Absolute Error(MAE),Root Mean Square Error(RMSE),R-Square(R^2),Error Range(ER),Early Prediction(EP)and Late Prediction(LP)of RUL were tested.The proposed method were compared with the methods such as Dynamic Time Warping(DTW),Euclidean Distance(ED),Time Domain Eigenvalue(TDE)combined with LSTM,MPdist combined with RNN and LSTM.The experimental results show that,compared with other comparison methods,the proposed method has the HI monotonicity and trend higher by at least 0.07 and 0.13 respectively,the higher RUL prediction accuracy,and the smaller ER,which verifies the effectiveness of the proposed method.

关 键 词：MPdist 长短时记忆网络 相似性度量 健康指标构建 剩余寿命预测 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]