基于弹性核凸包张量机的机械设备热成像故障诊断方法#br#

doi:10.3969/j.issn.1004-132X.2021.12.009

中国机械工程 ›› 2021, Vol. 32 ›› Issue (12): 1456-1461，1478.DOI: 10.3969/j.issn.1004-132X.2021.12.009

基于弹性核凸包张量机的机械设备热成像故障诊断方法#br#

何知义1,2;邵海东1,2;程军圣1,2;杨宇1,2

1.湖南大学汽车车身先进设计制造国家重点实验室，长沙，410082
2. 湖南大学机械与运载工程学院，长沙，410082

出版日期:2021-06-25 发布日期:2021-07-08
通讯作者: 邵海东（通信作者），男，1990年生，博士后研究人员、讲师。研究方向为机械设备智能故障诊断和寿命预测。发表论文30余篇。E-mail:hdshao@hnu.edu.cn。
作者简介:何知义，男，1989年生，博士。研究方向为机械设备故障诊断与健康管理。发表论文10余篇。E-mail:hezhiyihnu@126.com。
基金资助:
国家自然科学基金（51905160）

A Fault Diagnosis Method of Mechanical Equipment Based on FKCH-TM and Thermal Images#br#

HE Zhiyi1,2;SHAO Haidong1,2;CHENG Junsheng1,2;YANG Yu1,2

1.State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha，410082
2.College of Mechanical and Vehicle Engineering,Hunan University,Changsha,410082

Online:2021-06-25 Published:2021-07-08

摘要/Abstract

摘要： 针对机械设备热成像故障诊断中图像样本向量化维数灾难和结构信息破坏的问题，提出了基于弹性核凸包张量机的机械设备热成像故障诊断方法。首先定义样本特征张量空间的弹性核凸包，然后求解弹性核凸包间的最优超平面来构建弹性核凸包张量机模型，最后以机械设备不同故障状态下的热成像图像为输入，实现机械设备的故障诊断。实验结果表明：该方法可以准确、稳定地对机械设备进行故障诊断。

关键词: 机械设备, 故障诊断, 热成像, 弹性核凸包张量机

Abstract: Aiming at problems of dimensionality disaster and structure information destruction in thermal imaging fault diagnosis of mechanical equipment, a fault diagnosis method of mechanical equipment thermal images was proposed based on FKCH-TM. Firstly, flexible kernel convex hull(FKCH) of each class in feature tensor space was defined. Then, the optimal hyperplane between the FKCH was found, and FKCH-TM model was built. Finally, thermal images of mechanical equipment under different fault conditions were directly used as the input of FKCH-TM for fault diagnosis. Experimental results show that the proposed method may identify and diagnose the faults of mechanical equipment accurately and stably.

Key words: mechanical equipment, fault diagnosis, thermal image, flexible kernel convex hull based on tensor machine(FKCH-TM)

中图分类号:

TH878

何知义, 邵海东, 程军圣, 杨宇, . 基于弹性核凸包张量机的机械设备热成像故障诊断方法#br#[J]. 中国机械工程, 2021, 32(12): 1456-1461，1478.

HE Zhiyi, SHAO Haidong, CHENG Junsheng, YANG Yu, . A Fault Diagnosis Method of Mechanical Equipment Based on FKCH-TM and Thermal Images#br#[J]. China Mechanical Engineering, 2021, 32(12): 1456-1461，1478.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2021.12.009

http://www.cmemo.org.cn/CN/Y2021/V32/I12/1456

参考文献

［1］郭亮, 董勋, 高宏力, 等. 无标签数据下基于特征知识迁移的机械设备智能故障诊断［J］. 仪器仪表学报, 2019, 40(8):58-64.
GUO Liang, DONG Xun, GAO Hongli, et al. Feature Knowledge Transfer Based Intelligent Fault Diagnosis Method of Machines with Unlabeled Data［J］. Chinese Journal of Scientific Instrument, 2019, 40 (8):58-64.
［2］雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战［J］．机械工程学报, 2018, 54(5):94-104.
LEI Yaguo, JIA Feng, KONG Detong, et al. Opportunities and Challenges of Machinery Intelligent Fault Diagnosis in Big Data Era［J］. Journal of Mechanical Engineering, 2018，54(5):94-104.
［3］陈雪峰, 郭艳婕, 许才彬, 等. 风电装备故障诊断与健康监测研究综述［J］. 中国机械工程, 2019, 31 (2):175-189.
CHEN Xuefeng, GUO Yanjie, XU Caibin, et al. Review of Fault Diagnosis and Health Monitoring for Wind Power Equipment［J］. China Mechanical Engineering, 2019, 31(2):175-189.
［4］XIANG Z, ZHANG X N, ZHANG W W, et al. Fault Diagnosis of Rolling Bearing under Fluctuating Speed and Variable Load Based on TCO Spectrum and Stacking Auto-encoder［J］. Measurement, 2019, 138:162-174.
［5］XU F, HUANG Z L, YANG F F, et al. Constructing a Health Indicator for Roller Bearings by Using a Stacked Auto-encoder with an Exponential Function to Eliminate Concussion［J］. Appl. Soft Comput., 2020, 89:106119.
［6］NASIRIA A, TAHERI-GARAVANDB A, OMIDA M, et al. Intelligent Fault Diagnosis of Cooling Radiator Based on Deep Learning Analysis of Infrared Thermal Images［J］. Appl. Therm. Eng., 2019, 163:114410.
［7］JANSSENS O, WALLE R, LOCCUFIER M, et al. Deep Learning for Infrared Thermal Image Based Machine Health Monitoring［J］ IEEE-ASME Trans. Mech., 2018, 23(1):151-159.
［8］JIA Z, LIU Z B, VONG C, et al. A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images［J］. IEEE Access, 2019, 7:12348-12359.
［9］HAN L, WU Z, ZENG K, et al. Online Multilinear Principal Component Analysis［J］. Neurocomputing, 2018, 275:888-896.
［10］KOTSIA I, GUO W, PATRAS I. Higher Rank Support Tensor Machines for Visual Recognition［J］. Pattern Recognition, 2012, 45:4192-4203.
［11］HAO Z, HE L, CHEN B, et al. A Linear Support Higher-order Tensor Machine for Classification［J］. IEEE Transactions on Image Processing, 2013, 22:2911-2920.
［12］HE H, TAN Y, ZHANG W. A Wavelet Tensor Fuzzy Clustering Scheme for Multi-sensor Human Activity Recognition［J］. Engineering Applications of Artificial Intelligence, 2018, 70:109-122.
［13］DENG X, JIANG P, PENG X, et al. An Intelligent Outlier Detection Method with One Class Support Tucker Machine and Genetic Algorithm toward Big Sensor Data in Internet of Things［J］. IEEE Transactions on Industrial Electronics, 2019, 66:4672-4683.
［14］ZENG M, YANG Y, ZHENG J D, et al. Maximum Margin Classification Based on Flexible Convex Hulls for Fault Diagnosis of Roller Bearings［J］. Mechanical Systems and Signal Processing, 2016, 66/67:533-545.
［15］HE Z Y, SHAO H D, CHENG J S, et al. Support Tensor Machine with Dynamic Penalty Factors and Its Application to the Fault Diagnosis of Rotating Machinery with Unbalanced Data［J］. Mechanical Systems and Signal Processing, 2019:106441.

[1]	罗洁思, 于德介, 史美丽. 基于SVD和线调频小波路径追踪的转速波动齿轮箱故障诊断 [J]. J4, 201016, 21(16): 1947-1951.
[2]	肖刚, 顾海瑞, 董锦锦, 王琪冰, 陆佳炜. 仿真数据驱动的长期服役电梯导轨故障迁移诊断方法[J]. 中国机械工程, 2024, 35(01): 125-135.
[3]	于树博, 刘占生, 赵辰. 动力学仿真数据驱动的域自适应智能诊断方法[J]. 中国机械工程, 2023, 34(23): 2832-2841.
[4]	陈剑, 许畅, 徐庭亮, . 基于位错叠加法和改进概率神经网络的离心泵故障诊断方法[J]. 中国机械工程, 2023, 34(23): 2854-2861.
[5]	秦国浩, 张楷, 丁昆, 黄锋飞, 郑庆, 丁国富, . 动态宽卷积残差网络的轴承故障诊断方法[J]. 中国机械工程, 2023, 34(18): 2212-2221.
[6]	董绍江, 周存芳, 陈里里, 徐向阳. 基于判别性特征提取和双重域对齐的轴承跨域故障诊断[J]. 中国机械工程, 2023, 34(15): 1856-1863.
[7]	蒙康, 滕伟, 彭迪康, 向玲, 柳亦兵. 运行机理与数据双驱动的风电齿轮箱系统故障预警[J]. 中国机械工程, 2023, 34(12): 1476-1485.
[8]	郑近德, 陈焱, 童靳于, 潘海洋. 精细广义复合多元多尺度反向散布熵及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2023, 34(11): 1315-1325.
[9]	张龙, 胡燕青, 赵丽娟, 张号. 多通道信息融合与深度迁移学习的旋转机械故障诊断[J]. 中国机械工程, 2023, 34(08): 966-975.
[10]	张龙, 张号, 王朝兵, 王晓博, 文培田, 赵丽娟. 显式动力学驱动的轴承变工况故障诊断方法[J]. 中国机械工程, 2023, 34(08): 982-992.
[11]	董绍江, 朱朋, 朱孙科, 刘兰徽, 邢镔, 胡小林. 基于仿真数据驱动和领域自适应的滚动轴承故障诊断方法[J]. 中国机械工程, 2023, 34(06): 694-702.
[12]	赵靖, 杨绍普, 李强, 刘永强, . 一种残差注意力迁移学习方法及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2023, 34(03): 332-343.
[13]	赵艺珂, 王家序, 张新, 吴磊, 刘治汶. 基于增强最小熵解卷积的航空发动机故障诊断[J]. 中国机械工程, 2023, 34(02): 193-200.
[14]	李可, 燕晗, 顾杰斐, 宿磊, 苏文胜, 薛志钢. 基于shapelets时间序列的多源迁移学习滚动轴承故障诊断方法[J]. 中国机械工程, 2022, 33(24): 2990-2996,3006.
[15]	郭伟, 邢晓松. 基于改进卷积生成对抗网络的少样本轴承智能诊断方法[J]. 中国机械工程, 2022, 33(19): 2347-2355.

基于弹性核凸包张量机的机械设备热成像故障诊断方法#br#

A Fault Diagnosis Method of Mechanical Equipment Based on FKCH-TM and Thermal Images#br#

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics