|Table of Contents|

[1] Chen Hao, Zhu Yikai, Lei Bo, Weng Zhihai, et al. Sensor fault self-detection based on the mean shift method [J]. Journal of Southeast University (English Edition), 2024, 40 (2): 140-147. [doi:10.3969/j.issn.1003-7985.2024.02.004]
Copy

Sensor fault self-detection based on the mean shift method()
基于均值漂移方法的传感器故障自检测
Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
40
Issue:
2024 2
Page:
140-147
Research Field:
Civil Engineering
Publishing date:
2024-06-13

Info

Title:
Sensor fault self-detection based on the mean shift method
基于均值漂移方法的传感器故障自检测
Author(s):
Chen Hao1;  Zhu Yikai1;  Lei Bo2;  Weng Zhihai3;  Xu Hongchang2;  Wan Huaping1
1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2China Construction Third Engineering Bureau Co., Ltd., Wuhan 430064, China
3Huzhou City Investment and Development Group Co., Ltd., Huzhou 313000, China
陈昊1;  朱一凯1;  雷波2;  翁志海3;  徐鸿昌2;  万华平1
1浙江大学建筑工程学院, 杭州 310058; 2中建三局集团有限公司, 武汉 430064; 3湖州市城市投资发展集团有限公司, 湖州 313000
Keywords:
sensor fault detection;  mean shift;  sliding window;  machine learning
传感器故障检测;  均值漂移;  滑动窗口;  机器学习
PACS:
TU317
DOI:
10.3969/j.issn.1003-7985.2024.02.004
Abstract:
To accurately identify sensor faults caused by complex environmental conditions and ensure that structural health monitoring systems correctly perceive the structural state, a self-detection method for sensor nodes based on mean shift and sliding window techniques was proposed. The self-detection method comprises two stages, i.e., fault prescreening and fault self-detection. During the fault prescreening stage, the method rapidly identifies potentially abnormal data using the quartile method combined with the sliding window technique, significantly improving the efficiency of the method. During the fault self-detection stage, the method employs the mean shift algorithm to perform adaptive clustering of the abnormal data, effectively detecting various faults. Data from the Canton Tower were used to test the effectiveness of the method by setting four types of sensor faults, i.e., offset, drift, gain, and stuck. Then, the proposed method was compared with extremely randomized trees, random forests, support vector data description, and one-class support vector machines. Results show that the proposed method can detect the four aforementioned faults with high accuracy and computational efficiency.
为准确识别复杂环境导致的传感器故障, 确保结构健康监测系统能正确感知结构状态, 提出了一种基于均值漂移和滑动窗技术的传感器节点自检测方法.该方法包含故障预筛选和故障自检测2个阶段.在故障预筛选阶段, 结合四分位数法和滑动窗口技术快速识别出潜在的异常数据, 有效地提升了方法运行效率;在故障自检测阶段, 结合均值偏移算法对异常数据实施自适应聚类, 有效地检测了多种故障.利用广州塔实测数据验证所提方法的有效性, 设置偏移、漂移、增益和卡滞4种故障来模拟传感器故障, 并与极端随机树、随机森林、支持向量数据描述和单类支持向量机方法进行对比.结果表明, 该方法能够检测出4种故障, 并具有较高的正确率和计算效率.

References:

[1] Ghoshal A, Sundaresan M J, Schulz M J, et al. Structural health monitoring techniques for wind turbine blades[J].Journal of Wind Engineering and Industrial Aerodynamics, 2000, 85(3): 309-324. DOI: 10.1016/S0167-6105(99)00132-4.
[2] Ko J M, Ni Y Q. Technology developments in structural health monitoring of large-scale bridges[J].Engineering Structures, 2005, 27(12): 1715-1725. DOI: 10.1016/j.engstruct.2005.02.021.
[3] Spencer B F Jr, Ruiz-Sandoval M E, Kurata N. Smart sensing technology: Opportunities and challenges[J]. Structural Control and Health Monitoring, 2004, 11(4): 349-368. DOI: 10.1002/stc.48.
[4] Wan H P, Ni Y Q. Bayesian multi-task learning methodology for reconstruction of structural health monitoring data [J].Structural Health Monitoring, 2019, 18(4): 1282-1309. DOI: 10.1177/1475921718794953.
[5] Hu J, Guo J, Ou J P, et al. Measurement of wind field characteristics at a long-span suspension bridge [J]. Journal of Southeast University(English Edition), 2011, 27(3): 328-334. DOI: 10.3969/j.issn.1003-7985.2011.03.020.
[6] Liu X L, Huang Q, Ren Y, Fan Y H, et al. Extraction of cable forces due to dead load in cable-stayed bridges under random vehicle loads [J]. Journal of Southeast University(English Edition), 2015, 31(3): 407-411. DOI: 10.3969/j.issn.1003-7985.2015.03.019.
[7] Wang H, Li A Q, Guo T, et al. Comparable study on typhoon and strong northern wind characteristics of the Runyang Suspension Bridge based on field tests [J]. Journal of Southeast University(English Edition), 2009, 25(1): 99-103. DOI: 10.3969/j.issn.1003-7985.2009.01.021.
[8] Mao J X, Wang H, Spencer B F Jr. Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders[J].Structural Health Monitoring, 2021, 20(4): 1609-1626. DOI: 10.1177/1475921720924601.
[9] Mahapatro A, Khilar P M. Fault diagnosis in wireless sensor networks: A survey[J]. IEEE Communications Surveys & Tutorials, 2013, 15(4): 2000-2026. DOI: 10.1109/SURV.2013.030713.00062.
[10] Zidi S, Moulahi T, Alaya B. Fault detection in wireless sensor networks through SVM classifier[J]. IEEE Sensors Journal, 2018, 18(1): 340-347. DOI: 10.1109/JSEN.2017.2771226.
[11] Magalhães F, Cunha Á, Caetano E. Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection[J]. Mechanical Systems and Signal Processing, 2012, 28: 212-228. DOI: 10.1016/j.ymssp.2011.06.011.
[12] Mao J X, Wang H, Feng D M, et al. Investigation of dynamic properties of long-span cable-stayed bridges based on one-year monitoring data under normal operating condition[J].Structural Control and Health Monitoring, 2018, 25(5): e2146. DOI: 10.1002/stc.2146.
[13] Muhammed T, Shaikh R A. An analysis of fault detection strategies in wireless sensor networks[J].Journal of Network and Computer Applications, 2017, 78: 267-287. DOI: 10.1016/j.jnca.2016.10.019.
[14] Lau B C P, Ma E W M, Chow T W S. Probabilistic fault detector for Wireless Sensor Network[J].Expert Systems with Applications, 2014, 41(8): 3703-3711. DOI: 10.1016/j.eswa.2013.11.034.
[15] Abid A, Kachouri A, Guiloufi A B F, et al. Centralized KNN anomaly detector for WSN[C]//2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices(SSD15). Mahdia, Tunisia, 2015: 1-4. DOI: 10.1109/SSD.2015.7348091.
[16] Warriach E U, Tei K. Fault detection in wireless sensor networks: A machine learning approach[C]//2013 IEEE 16th International Conference on Computational Science and Engineering. Sydney, Australia, 2014: 758-765. DOI: 10.1109/CSE.2013.116.
[17] Panda M, Khilar P M. Energy efficient distributed fault identification algorithm in wireless sensor networks[J]. Journal of Computer Networks and Communications, 2014, 2014: 1-16. DOI: 10.1155/2014/323754.
[18] Chen J R, Kher S, Somani A. Distributed fault detection of wireless sensor networks[C]//Proceedings of the 2006 Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks. Los Angeles, CA, USA, 2006: 65-72. DOI: 10.1145/1160972.1160985.
[19] Obst O. Distributed fault detection in sensor networks using a recurrent neural network[J].Neural Processing Letters, 2014, 40(3): 261-273. DOI: 10.1007/s11063-013-9327-4.
[20] Younis O, Fahmy S, Santi P. An architecture for robust sensor network communications[J].International Journal of Distributed Sensor Networks, 2005, 1(3/4): 305-327. DOI: 10.1080/15501320500330786.
[21] Barborak M, Dahbura A, Malek M. The consensus problem in fault-tolerant computing[J]. ACM Computing Surveys, 1993, 25(2): 171-220. DOI: 10.1145/152610.152612.
[22] Ni K, Ramanathan N, Chehade M N H, et al. Sensor network data fault types[J].ACM Transactions on Sensor Networks, 2009, 5(3): 1-29. DOI: 10.1145/1525856.1525863.
[23] Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition[J].IEEE Transactions on Information Theory, 1975, 21(1): 32-40. DOI: 10.1109/TIT.1975.1055330.
[24] Xiao C X, Liu M. Efficient mean-shift clustering using Gaussian KD-tree[J].Computer Graphics Forum, 2010, 29(7): 2065-2073. DOI: 10.1111/j.1467-8659.2010.01793.x.
[25] Ni Y Q, Xia Y, Liao W Y, et al. Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower[J].Structural Control and Health Monitoring, 2009, 16(1): 73-98. DOI: 10.1002/stc.303.
[26] Ni Y Q, Xia Y, Lin W, et al. SHM benchmark for high-rise structures: A reduced-order finite element model and field measurement data[J].Smart Structures and Systems, 2012, 10(4/5): 411-426. DOI: 10.12989/sss.2012.10.4_5.411.
[27] Saeed U, Jan S U, Lee Y D, et al. Fault diagnosis based on extremely randomized trees in wireless sensor networks[J].Reliability Engineering & System Safety, 2021, 205: 107284. DOI: 10.1016/j.ress.2020.107284.
[28] Noshad Z, Javaid N, Saba T, et al. Fault detection in wireless sensor networks through the random forest classifier[J]. Sensors, 2019, 19(7): 1568. DOI: 10.3390/s19071568.

Memo

Memo:
Biographies: Chen Hao(2000—), male, Ph.D. candidate; Wan Huaping(corresponding author), male, doctor, professor, hpwan@zju.edu.cn.
Foundation items: The National Key Research and Development Program of China(No. 2021YFF0501001), Zhejiang Provincial Natural Science Foundation(No. LR23E080003).
Citation: Chen Hao, Zhu Yikai, Lei Bo, et al. Sensor fault self-detection based on the mean shift method[J].Journal of Southeast University(English Edition), 2024, 40(2):140-147.DOI:10.3969/j.issn.1003-7985.2024.02.004.
Last Update: 2024-06-20