基于边缘计算的轴向柱塞泵磨损状态辨识方法研究

doi:10.3901/JME.2024.04.189

机械工程学报 ›› 2024, Vol. 60 ›› Issue (4): 189-199.doi: 10.3901/JME.2024.04.189

• 特邀专栏：智能液压元件及系统基础技术 • 上一篇下一篇

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基于边缘计算的轴向柱塞泵磨损状态辨识方法研究

王丹丹¹, 黄伟迪¹, 张军辉¹, 赵守军^2,3, 于斌^2,3, 刘施镐¹, 吕飞¹, 苏琦¹, 徐兵¹

1. 浙江大学流体动力基础件与机电系统全国重点实验室杭州 310027;
2. 北京精密机电控制设备研究所北京 100076;
3. 航天伺服驱动与传动技术实验室北京 100076

收稿日期:2023-03-21 修回日期:2023-11-07 出版日期:2024-02-20 发布日期:2024-05-25
通讯作者: 苏琦,男,1987年出生,博士,助理研究员。主要研究方向为智能液压阀、电液比例伺服控制等。E-mail:471068186@qq.com
作者简介:王丹丹,女,2000年出生。主要研究方向为轴向柱塞泵故障诊断。E-mail:3190103682@zju.edu.cn
基金资助:
国家重点研发计划(2019YFB2004504); 国家自然科学基金(51835009,52105075); 浙江省自然科学基金(LQ21E050022); 航天伺服驱动与传动技术实验室开放基金(LASAT-20210104)资助项目

Wear State Identification Method for Axial Piston Pumps Based on Edge Computing

WANG Dandan¹, HUANG Weidi¹, ZHANG Junhui¹, ZHAO Shoujun^2,3, YU Bin^2,3, LIU Shihao¹, LÜ Fei¹, SU Qi¹, XU Bing¹

1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027;
2. Beijing Institute of Precision Mechatronics and Controls, Beijing 100076;
3. Laboratory of Aerospace Servo Actuation and Transmission, Beijing 100076

Received:2023-03-21 Revised:2023-11-07 Online:2024-02-20 Published:2024-05-25

摘要/Abstract

摘要： 判断轴向柱塞泵的磨损状态对维护轴向柱塞泵正常运行具有重要意义。然而,现有的轴向柱塞泵故障诊断方法大多为离线式和基于云计算的在线式,存在延时长和数据量大的问题,无法满足轴向柱塞泵磨损状态辨识的实时性需求。为了减少延迟时间和传输数据量,提出一种基于边缘计算的轴向柱塞泵磨损状态辨识方法。构造出一个集成信号采集、信号预处理、特征提取和磨损状态分类的边缘节点,用于正确和实时地辨识磨损状态。设置4种轴向柱塞泵滑靴副磨损状态作为故障源,并构建相应的磨损故障数据集。为了减少边缘节点计算量,在上位机中利用随机森林包外误差选择敏感特征值。为了实现磨损状态辨识,在上位机中训练磨损状态分类的特征选择人工神经网络模型,并将信号预处理和特征提取功能算法以及模型参数嵌入边缘节点。通过与其他方法的比较和在线磨损状态辨识试验证明所提方法的正确性和实时性。

关键词: 轴向柱塞泵, 边缘计算, 磨损状态辨识, 特征敏感程度, 人工神经网络

Abstract: Wear state identification is of great significance to maintain the normal operation of axial piston pump. However, most existing fault diagnosis methods for axial piston pump are offline while those online are based on cloud computing, producing a long delay or a large amount of data, which cannot meet the real-time requirements of wear state identification. To cut down latency and data amount, a wear state identification method for axial piston pump based on edge computing is proposed. An edge node integrating the functions of data acquisition, data pre-processing, feature extraction and wear state classification is constructed to identify the wear state accurately and timely. Four kinds of wear states of axial piston pump are set as fault sources, and the corresponding wear fault dataset is established. To reduce the computational load of the edge node, the out-of-bag error of random forest is employed to select the sensitive features at the host computer. To classify the wear state, a feature-selected artificial neural network for wear state classification is trained at the host computer, and the data pre-processing and feature extraction algorithm together with model parameters are embedded into the edge node. The accuracy and real-time performance of the proposed method are demonstrated through comparisons with other methods and the online wear state identification experiment.

Key words: axial piston pump, edge computing, wear state identification, feature sensitivity, artificial neural network

中图分类号:

TH322

王丹丹, 黄伟迪, 张军辉, 赵守军, 于斌, 刘施镐, 吕飞, 苏琦, 徐兵. 基于边缘计算的轴向柱塞泵磨损状态辨识方法研究[J]. 机械工程学报, 2024, 60(4): 189-199.

WANG Dandan, HUANG Weidi, ZHANG Junhui, ZHAO Shoujun, YU Bin, LIU Shihao, LÜ Fei, SU Qi, XU Bing. Wear State Identification Method for Axial Piston Pumps Based on Edge Computing[J]. Journal of Mechanical Engineering, 2024, 60(4): 189-199.

参考文献

[1] GUO S,CHEN J,LU Y,et al. Hydraulic piston pump in civil aircraft:Current status,future directions and critical technologies[J]. Chinese Journal of Aeronautics,2020,33(1):16-30.
[2] PANG H,WU D,DENG Y,et al. Effect of working medium on the noise and vibration characteristics of water hydraulic axial piston pump[J]. Applied Acoustics,2021,183:108277.
[3] 刘思远,王闯,杨梦雪,等.斜盘泵滑靴副剧烈磨损过程的动态特性分析[J].液压与气动,2017(1):1-5.LIU Siyuan,WANG Chuang,YANG Mengxue,et al.Analysis of dynamic characteristics for severe wear process of swash plate axial piston pump slipper pair[J].Chinese Hydraulic & Pneumatics,2017(1):1-5.
[4] LÜ F,ZHANG J,ZHAO S,et al. Coupled evolution of piston asperity and cylinder bore contour of piston/cylinder pair in axial piston pump[J]. Chinese Journal of Aeronautics,2022,36(8):395-407.
[5] 谷立臣,马子文,田晴晴,等.瞬时转速波动在轴向柱塞泵故障诊断中的应用[J].排灌机械工程学报,2021,39(7):740-746.GU Lichen, MA Ziwen, TIAN Qingqing, et al.Application of instantaneous speed fluctuation signal in fault diagnosis of axial piston pump[J]. Journal of Drainage and Irrigation Machinery Engineering,2021,39(7):740-746.
[6] WANG S, XIANG J, ZHONG Y, et al. A data indicator-based deep belief networks to detect multiple faults in axial piston pumps[J]. Mechanical Systems and Signal Processing,2018,112:154-170.
[7] CHAO Q,GAO H,TAO J,et al. Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network[J]. Frontiers of Mechanical Engineering,2022,17(3):36.
[8] TANG S,ZHU Y,YUAN S. Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform[J]. Reliability Engineering & System Safety,2022,224:108560.
[9] HE Y,TANG H,REN Y,et al. A semi-supervised fault diagnosis method for axial piston pump bearings based on DCGAN[J]. Measurement Science and Technology,2021,32(12):125104.
[10] JIANG W,LI Z,ZHANG S,et al. Hydraulic pump fault diagnosis method based on EWT decomposition denoising and deep learning on cloud platform[J]. Shock and Vibration,2021,2021:1-18.
[11] OU Y,HUANG D,HU C,et al. A gear fault diagnosis method based on EEMD cloud model and PSO_SVM[C]//2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). New York:IEEE,2021:860-865.
[12] HUANG X,QI G,MAZUR N,et al. Deep residual networks-based intelligent fault diagnosis method of planetary gearboxes in cloud environments[J]. Simulation Modelling Practice and Theory,2022,116:102469.
[13] SATYANARAYANAN M. The emergence of edge computing[J]. Computer,2017,50(1):30-39.
[14] SHI W,CAO J,ZHANG Q,et al. Edge computing:Vision and challenges[J]. IEEE Internet of Things Journal,2016,3(5):637-646.
[15] QIAN G,LU S,PAN D,et al. Edge computing:A promising framework for real-time fault diagnosis and dynamic control of rotating machines using multi-sensor data[J]. IEEE Sensors Journal,2019,19(11):4211-4220.
[16] WANG X,LU S,HUANG W,et al. Efficient data reduction at the edge of industrial internet of things for PMSM bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement,2021,70:1-12.
[17] LAI Y,ZHANG Y,FANG L,et al. Fault diagnosis of motor based on low cost edge computing technology[C]//2020 7th International Conference on Information Science and Control Engineering (ICISCE). New York:IEEE,2020:635-638.
[18] LEI Y,YANG B,JIANG X,et al. Applications of machine learning to machine fault diagnosis:A review and roadmap[J]. Mechanical Systems and Signal Processing,2020,138:106587.
[19] MERAINANI B,RAHMOUNE C,BENAZZOUZ D,et al. A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform,singular value decomposition,and SOM neural network[J].Journal of Vibration and Control, 2018, 24(12):2512-2531.
[20] SARAVANAN N, SIDDABATTUNI V N S K,RAMACHANDRAN K I. Fault diagnosis of spur bevel gear box using artificial neural network (ANN),and proximal support vector machine (PSVM)[J]. Applied Soft Computing,2010,10(1):344-360.
[21] CHO S,CHOI M,GAO Z,et al. Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks[J]. Renewable Energy,2021,169:1-13.
[22] LIU P,ZHANG Y,WU H,et al. Optimization of edge-PLC-based fault diagnosis with random forest in industrial internet of things[J]. IEEE Internet of Things Journal,2020,7(10):9664-9674.
[23] LI H,HU G,LI J,et al. Intelligent fault diagnosis for large-scale rotating machines using binarized deep neural networks and random forests[J]. IEEE Transactions on Automation Science and Engineering,2022,19(2):1109-1119.
[24] BUCHAIAH S,SHAKYA P. Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection[J]. Measurement,2022,188:110506.
[25] ZHANG C,HE Y,DU B,et al. Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning[J]. Future Generation Computer Systems,2020,108:533-545.
[26] 徐兵,童章谦,周高明.轴向柱塞泵的振动噪声测试分析及基于壳体的结构优化[J].机床与液压,2010,38(13):116-121.XU Bing,TONG Zhangqian,ZHOU Gaoming. Analysis of the noise emission of the axial piston pump and the structure optimization of its housing[J]. Machine Tool & Hydraulic,2010,38(13):116-121.
[27] XIA S,ZHANG J,YE S,et al. A spare support vector machine based fault detection strategy on key lubricating interfaces of axial piston pumps[J]. IEEE Access,2019(7):178177-178186.

基于边缘计算的轴向柱塞泵磨损状态辨识方法研究

Wear State Identification Method for Axial Piston Pumps Based on Edge Computing

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