基于数据挖掘的电子皮带秤皮带跑偏检测
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摘要
为提高电子皮带秤连续累计称量精度,针对严重影响精度的电子皮带秤跑偏,采用对皮带秤现有原始传感器的数据挖掘实现跑偏量实时在线检测,以取代传统硬件检测设备。引入流形学习和深度学习,分别提出了基于局部切空间排列(local tangent space alignment,LTSA)+广义回归神经网络(generalized regression neural networks,GRNN)和基于连续深度置信网络(continuous deep belief networks,CDBN)的在线跑偏特征提取模型,再结合极限学习机(extreme learning machine,ELM)以跑偏特征为模型输入进行跑偏量预测。最后通过试验对该文提出的在线跑偏量预测模型的性能进行了验证:LTSA+GRNN+ELM平均跑偏预测精度为93.33%,平均每组预测时间38.29 ms;CDBN+ELM预测精度则高达98.61%,平均每组预测时间1.47 ms。二者预测精度和实时性皆表明能取代传统硬件检测装置,为皮带跑偏检测提供了一种方法,为进一步的皮带秤在线精度补偿和故障预测提供了必要依据。
At present, belt weigher has been widely used in various transportation and trade occasions of industry and agriculture. Belt deviation is one of the most important indicators of accuracy of belt weigher, and it is also one of the most common faults. In this paper, aiming at the problem of belt deviation, we obtained the real-time online detection of deviation by the data mining based on the existing original sensor data of belt weigher, instead of traditional hardware testing equipment in which CCD, PSD and array phototransistor are usually used as the specialized sensor for detecting deviation. At first, in order to reduce the dimension of existing original data and the complexity of the subsequent detection mode of belt deviation, the online features extraction models of belt deviation based on LTSA(Local Tangent Space Alignment) + GRNN(Generalized Regression Neural Networks), and CDBN(Continuous Deep Belief Networks) were proposed respectively, through introducing manifold learning and deep learning. GRNN was applied to construct the explicit nonlinear mapping from the original data of high dimension to the features of belt deviation extracted by LTSA. CDBN was proposed by introducing CRBM(Continuous Restricted Boltzmann Machine) and combining with the "dropout". Unlike LTSA, CDBN can be used to construct the explicit nonlinear mapping while extracting the deviation features from the original data, which needed more training time. Subsequently, the feature extraction experiments of belt deviation at different flow rates showed that the models based on LTSA+GRNN, and CDBN both had very good feature detection effect which meant that the two features extraction models could effectively reduce the redundancy of the original data while retaining enough features of belt deviation. And the experiments also revealed that, in case of belt deviation, the bigger the flow rate was, the greater the amount of belt deviation was, and vice versa. Further, SVM(Support Vector Machine), ELM(Extreme Learning Machine) and other regression analysis methods were used to build the online prediction models of belt deviation where the deviation features extracted by LTSA+GRNN and CDBN were taken as the input. Finally, the performances of two proposed online detection models of belt deviation based on LTSA+GRNN+ELM and CDBN+ELM respectively were verified through the experiments: the average prediction accuracy of deviation prediction model based on LTSA+GRNN+ELM was 93.33%, while its average prediction time of each group was 38.29 ms and its average training time was 18.91 s; the average prediction accuracy of deviation prediction model based on CDBN was as high as 98.61%, while its average prediction time of each group was as short as 1.47 ms and its average training time was 139.96 s. Besides, the experiments also showed that ELM was more suitable than SVM for the belt deviation, because ELM had almost the same prediction accuracy as SVM while the training and prediction time of ELM was far less than that of SVM. Both the prediction and real-time of the two models mentioned above showed that the two models could be a new approach for online detection of belt deviation and replaced traditional hardware detection device. Moreover, this study provided the necessary basis for the further online precision compensation and fault prediction of belt weigher.
At present, belt weigher has been widely used in various transportation and trade occasions of industry and agriculture. Belt deviation is one of the most important indicators of accuracy of belt weigher, and it is also one of the most common faults. In this paper, aiming at the problem of belt deviation, we obtained the real-time online detection of deviation by the data mining based on the existing original sensor data of belt weigher, instead of traditional hardware testing equipment in which CCD, PSD and array phototransistor are usually used as the specialized sensor for detecting deviation. At first, in order to reduce the dimension of existing original data and the complexity of the subsequent detection mode of belt deviation, the online features extraction models of belt deviation based on LTSA(Local Tangent Space Alignment) + GRNN(Generalized Regression Neural Networks), and CDBN(Continuous Deep Belief Networks) were proposed respectively, through introducing manifold learning and deep learning. GRNN was applied to construct the explicit nonlinear mapping from the original data of high dimension to the features of belt deviation extracted by LTSA. CDBN was proposed by introducing CRBM(Continuous Restricted Boltzmann Machine) and combining with the "dropout". Unlike LTSA, CDBN can be used to construct the explicit nonlinear mapping while extracting the deviation features from the original data, which needed more training time. Subsequently, the feature extraction experiments of belt deviation at different flow rates showed that the models based on LTSA+GRNN, and CDBN both had very good feature detection effect which meant that the two features extraction models could effectively reduce the redundancy of the original data while retaining enough features of belt deviation. And the experiments also revealed that, in case of belt deviation, the bigger the flow rate was, the greater the amount of belt deviation was, and vice versa. Further, SVM(Support Vector Machine), ELM(Extreme Learning Machine) and other regression analysis methods were used to build the online prediction models of belt deviation where the deviation features extracted by LTSA+GRNN and CDBN were taken as the input. Finally, the performances of two proposed online detection models of belt deviation based on LTSA+GRNN+ELM and CDBN+ELM respectively were verified through the experiments: the average prediction accuracy of deviation prediction model based on LTSA+GRNN+ELM was 93.33%, while its average prediction time of each group was 38.29 ms and its average training time was 18.91 s; the average prediction accuracy of deviation prediction model based on CDBN was as high as 98.61%, while its average prediction time of each group was as short as 1.47 ms and its average training time was 139.96 s. Besides, the experiments also showed that ELM was more suitable than SVM for the belt deviation, because ELM had almost the same prediction accuracy as SVM while the training and prediction time of ELM was far less than that of SVM. Both the prediction and real-time of the two models mentioned above showed that the two models could be a new approach for online detection of belt deviation and replaced traditional hardware detection device. Moreover, this study provided the necessary basis for the further online precision compensation and fault prediction of belt weigher.
引文
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[23]Kim Kyoungok,Lee Daewon.Inductive manifold learning using structured support vector machine[J].Pattern Recognition,2014,47(1):470-479.
[24]Liu Bing,Xia Shixiong,Meng Fanrong,et al.Extreme spectral regression for efficient regularized subspace learning[J].Neurocomputing,2015,149:171-179.
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[28]Mihoub Alaeddine,Bailly Gérard,Wolf Christian,et al.Graphical models for social behavior modeling in face-to face interaction[J].Pattern Recognition Letters,2016,74:82-89.
[29]Hinton G E.A practical guide to training restricted boltzmann machines[J].Momentum,2010,9(1):599-619.
[30]Tran Van Tung,Al Thobiani Faisal,Ball Andrew.An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks[J].Expert Systems with Applications,2014,41(9):4113-4122.
[31]胡昭华,樊鑫,梁德群等.基于双向非线性学习的轨迹跟踪和识别[J].计算机学报,2007(8):1389-1397.Hu Zhaohua,Fan Xin,Liang Dequn,et al.Trajectory tracking and recognition using bi-directional nonlinear Learning[J].Chinese Journal of Computer,2007(8):1389-1397.(in Chinese with English abstract)
[32]胡昭华,宋耀良.基于Autoencoder网络的数据降维和重构[J].电子与信息学报,2009(5):1189-1192.Hu Zhaohua,Song Yaoliang.Dimensionality reduction and reconstruction of data based on autoencoder network[J].Journal of Electronics and Information Technology,2009(5):1189-1192.(in Chinese with English abstract)
[33]Hinton G E,Srivastava N,Krizhevsky A,et al.Improving neural networks by preventing co-adaptation of feature detectors[J].Computer Science,2012,3(4):212-223.
[34]Hopfield J.J.Neurons with graded response have collective computational properties like those of two-state neurons.[J].Proceedings of the National Academy of Sciences,1984,81(10):3088-3092.
[35]Bishop Christopher M.Pattern Recognition and Machine Learning[M].Springer,2006.
[36]Huang Guang Bin.An insight into extreme learning machines:random neurons,Random Features and Kernels[J].Cognitive Computation,2014,6(3):376-390.
[37]邹伟东,张百海,姚分喜等.基于改进型极限学习机的日光温室温湿度预测与验证[J].农业工程学报,2015,31(24):194-200.Zou Weidong,Zhang Baihai,Yao Fenxi,et al.Verification and forecasting of temperature and humidity in solar greenhouse based on improved extreme learning machine algorithm[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(24):194-200.(in Chinese with English abstract)
[38]朱亮,李东波,李妍,等.基于RS485/Modbus皮带跑偏检测传感网络的研究与实现[J].中国农机化学报,2014(4):217-220.Zhu Liang,Li Dongbo,Li Yan,et al.Design and implementation of belt deviation detection sensor network based on RS485/Modbus[J].Journal of Chinese Agricultural Mechanization,2014(4):217-220.(in Chinese with English abstract)
[39]宦娟,刘星桥.基于K-means聚类和ELM神经网络的养殖水质溶解氧预测[J].农业工程学报,2016,32(17):174-181.Huan Juan,Liu Xingqiao.Dissolved oxygen prediction in water based on K-means clustering and ELM neural network for aquaculture[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(17):174-181.(in Chinese with English abstract)
[2]董甲东,谷立臣.带式输送机纠偏装置液压伺服系统仿真设计[J].起重运输机械,2010(11):17-20.Dong Jiadong,Gu Lichen.Simulation design of the hydraulic servo system of the belt conveyor correcting device[J].Hoisting and Conveying Machinery,2010(11):17-20.(in Chinese with English abstract)
[3]刘文亮,张晓伟,董秋艳.连续皮带机胶带跑偏原因与力学分析[J].水利水电技术,2006,37(3):26-27.Liu Wenliang,Zhang Xiaowei,Dong Qiuyan.Cause and dynamic analysis of deviation of belt for belt conveyer[J].Water Resources and Hydropower Engineering,2006,37(3):26-27.(in Chinese with English abstract)
[4]张佳伟,楼佩煌.输送皮带跑偏自动化检测及液压矫正技术[J].机械工程师,2008(10):26-28.Zhang Jiawei,Lou Peihuang.Automatic detection and hydraulic correction technology of belt deviation[J].Mechanical Engineer,2008(10):26-28.(in Chinese with English abstract)
[5]段洁,孙向阳,蔡敬海,等.PSD在激光位移检测系统中的应用研究[J].红外与激光工程,2007(S1):281-284.Duan Jie,Sun Xiangyang,Cai Jinghai,et al.Applications research to PSD in the laser displacement detecting system[J].Infrared and Laser Engineering,2007(S1):281-284.(in Chinese with English abstract)
[6]李妍,朱亮,袁延强,等.基于阵列式光电三极管的皮带跑偏检测技术研究与系统设计[J].机床与液压,2014(3):127-131.Li Yan,Zhu Liang,Yuan Yanqiang,et al.Research and design of detection system for belt deviation based on array phototransistors[J].Machine Tool and Hydraulics,2014(3):127-131.(in Chinese with English abstract)
[7]朱亮,李东波,李妍,等.基于FPGA+MCU的皮带跑偏检测系统研究与设计[J].机床与液压,2014(7):86-89,95.Zhu Liang,Li Dongbo,Li Yan,et al.Research and design of detection system for belt deviation based on FPGA+MCU[J].Machine Tool and Hydraulics,2014(7):86-89,95.(in Chinese with English abstract)
[8]庞苏娟.基于DSP的电子皮带秤设计与实现[D].西安:陕西科技大学,控制科学与工程,2012.Pang Sujuan.Design and Implementation of Electronic Belt Scale Based On DSP[D].Xian:Shaanxi University of Science and Technology,Control Science and Engineering,2012.(in Chinese with English abstract)
[9]黄添强.数据挖掘算法与应用[M].厦门:厦门大学出版社,2011.
[10]王珏,周志华,周傲英.机器学习及其应用[M].北京:清华大学出版社,2006.
[11]高亚文,汤海青,欧昌荣,等.基于前表面荧光光谱鉴别新鲜与冻融大黄鱼[J].农业工程学报,2016,32(16):279-285.Gao Yawen,Tang Haiqing,Ou Changrong,et al.Differentiation between fresh and frozen-thawed large yellow croaker based on front-face fluorescence spectroscopy technique[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(16):279-285.(in Chinese with English abstract)
[12]岳学军,全东平,洪添胜,等.不同生长期柑橘叶片磷含量的高光谱预测模型[J].农业工程学报,2015,31(8):207-213.Yue Xuejun,Quan Dongping,Hong Tiansheng,et al.Prediction model of phosphorus content for citrus leaves during different growth periods based on hyperspectrum[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(8):207-213.(in Chinese with English abstract)
[13]Timonen J,Stirland M,Pilling D J,et al.An information geometry of statistical manifold learning[J].Physical Review Letters,2015,56(21):2233-2236.
[14]Wang Qian,Wang Weiguo,Nian Rui,et al.Manifold learning in local tangent space via extreme learning machine[J].Neurocomputing,2016,174:18-30.
[15]詹宇斌,殷建平,刘新旺.局部切空间对齐算法的核主成分分析解释[J].计算机工程与科学,2010(6):158-161.Zhan Yubin,Yin Jianping,Liu Xinwang.A kernel PCA view of the local tangent space alignment algorithm[J].Computer Engineering and Science,2010(6):158-161.(in Chinese with English abstract)
[16]Zhang Zhenyue,Zha Hongyuan.Principal manifolds and nonlinear dimension reduction via local tangent space alignment[J].Siam Journal of Scientific Computing,2002,26:313-338.
[17]Mendoza Quispe Arturo,Petitjean Caroline,Heutte Laurent.Extreme learning machine for out-of-sample extension in Laplacian eigenmaps[J].Pattern Recognition Letters,2016,74(C):68-73.
[18]Li Bo,Li Jun,Zhang Xiao Ping.Nonparametric discriminant multi-manifold learning for dimensionality reduction[J].Neurocomputing,2015,152:121-126.
[19]Orsenigo Carlotta,Vercellis Carlo.Kernel ridge regression for out-of-sample mapping in supervised manifold learning[J].Expert Systems with Applications,2012,39(9):7757-7762.
[20]Lin Y Y,Liu T L,Fuh C S.Multiple kernel learning for dimensionality reduction[J].IEEE Transactions on Software Engineering,2010,33(6):1147-1160.
[21]Li Xuelong,Lin S,Yan Shuicheng,et al.Discriminant locally linear embedding with high-order tensor data[J].IEEE Transactions on Systems Man&Cybernetics Part B,2008,38(2):342-352.
[22]刘昶,周激流,郎方年,等.基于加权判别局部多线性嵌入的人脸识别[J].仪器仪表学报,2011(10):2248-2255.Liu Chang,Zhou Jiliu,Lang Fangnian,et al.Weighted discriminative locally multi-linear embedding algorithm for face recognition[J].Chinese Journal of Scientific Instrument,2011(10):2248-2255.(in Chinese with English abstract)
[23]Kim Kyoungok,Lee Daewon.Inductive manifold learning using structured support vector machine[J].Pattern Recognition,2014,47(1):470-479.
[24]Liu Bing,Xia Shixiong,Meng Fanrong,et al.Extreme spectral regression for efficient regularized subspace learning[J].Neurocomputing,2015,149:171-179.
[25]Liu Yong,Wang Qicong,Jiang Yi,et al.Supervised locality discriminant manifold learning for head pose estimation[J].Knowledge-Based Systems,2014,66:126-135.
[26]Bengio Yoshua.Learning deep architectures for AI[J].Found.Trends Mach Learn,2009,2(1):1-127.
[27]Hinton G E,Osindero S,Teh Y W.A fast learning algorithm for deep belief nets[J].Neural Comput,2006,18(7):1527-1554.
[28]Mihoub Alaeddine,Bailly Gérard,Wolf Christian,et al.Graphical models for social behavior modeling in face-to face interaction[J].Pattern Recognition Letters,2016,74:82-89.
[29]Hinton G E.A practical guide to training restricted boltzmann machines[J].Momentum,2010,9(1):599-619.
[30]Tran Van Tung,Al Thobiani Faisal,Ball Andrew.An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks[J].Expert Systems with Applications,2014,41(9):4113-4122.
[31]胡昭华,樊鑫,梁德群等.基于双向非线性学习的轨迹跟踪和识别[J].计算机学报,2007(8):1389-1397.Hu Zhaohua,Fan Xin,Liang Dequn,et al.Trajectory tracking and recognition using bi-directional nonlinear Learning[J].Chinese Journal of Computer,2007(8):1389-1397.(in Chinese with English abstract)
[32]胡昭华,宋耀良.基于Autoencoder网络的数据降维和重构[J].电子与信息学报,2009(5):1189-1192.Hu Zhaohua,Song Yaoliang.Dimensionality reduction and reconstruction of data based on autoencoder network[J].Journal of Electronics and Information Technology,2009(5):1189-1192.(in Chinese with English abstract)
[33]Hinton G E,Srivastava N,Krizhevsky A,et al.Improving neural networks by preventing co-adaptation of feature detectors[J].Computer Science,2012,3(4):212-223.
[34]Hopfield J.J.Neurons with graded response have collective computational properties like those of two-state neurons.[J].Proceedings of the National Academy of Sciences,1984,81(10):3088-3092.
[35]Bishop Christopher M.Pattern Recognition and Machine Learning[M].Springer,2006.
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