Failure Data Acquisition System Based on WSN for Vehicle PHM Technology
International Journal of Sensors and Sensor Networks
Volume 6, Issue 1, March 2018, Pages: 16-25
Received: Feb. 10, 2018; Accepted: Apr. 9, 2018; Published: May 5, 2018
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Zhiqiang Pan, China National Instruments Import & Export (Group) Corporation, Beijing, China
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Prognostic and health management (PHM) technology needs plenty of historical failure data to build accurate model for failure analysis, diagnostic and prognostic. However the existing data collection equipment is not flexible enough for special vehicle because of the wired connection or lack of real-time information feedback in real vehicle experiment. A new failure data acquisition system based on wireless sensor network is proposed to acquire the sufficient historical failure data for the development of vehicle PHM technology. The proposed system architecture consists of several wireless failure data acquisition (WFDA) nodes, a gateway node, monitoring software and probability density ratio (PDR) algorithm working on a base station. Compared with other related acquisition systems, the WFDA node is small enough and suitable for working in a narrow space inside the vehicle. A double-buffer resampling strategy is specifically developed in this node to solve the contradiction of high sampling rate and low wireless bandwidth. The PDR algorithm embedded in monitoring software is used to detect abnormal data and show researchers the analysis results which can be relied to change the test item in time. Experiments results in the laboratory preliminary verified the effectiveness of system.
Vehicle Failure Data, Wireless Sensor Network, PHM, Vehicle Test, Abnormal Detection
To cite this article
Zhiqiang Pan, Failure Data Acquisition System Based on WSN for Vehicle PHM Technology, International Journal of Sensors and Sensor Networks. Vol. 6, No. 1, 2018, pp. 16-25. doi: 10.11648/j.ijssn.20180601.13
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Z. S Chen, Y. M Yang, Zheng Hu. A Technical Framework and Roadmap of Embedded Diagnostics and Prognostics for Complex Mechanical Systems in Prognostics and Health Management Systems. IEEE TRANSACTIONS ON RELIABILITY, 2012; 61(2): 314-322.
Nikhil M. Vichare, Michael G. Pecht. Prognostics and health management of electronics. IEEE Transactions on Components and Packing Technology 2006, 29(1): 222-229.
Jiajie Fan, Yung KC, Pecht M. Physics-of-Failure-Based Prognostics and Health Management for High-Power White Light-Emitting Diode Lighting. IEEE Transactions on Device and Materials Reliability 2011, 11 (3): 407-416.
Qiang Miao, Azarian M, Pecht M. Cooling Fan Bearing Fault Identification Using Vibration Measurement, Proc. IEEE Int. Conf. Prognostics and Health Management, Chengdu, China, Sep. 2011, pp. 1-5.
Ping Zhou, Dongfeng Liu. Research on marine diesel's fault prognostic and health management based on oil monitoring. Conf. Prognostics and System Health Management, Shenzhen, China, May 2011, pp. 1-4.
Bulter A, Hopf J, Jacob J, et al. Laboratory validation of sensors for a corrosion prognostic health management system for use with military aircraft. Annual Conf. of the Australasian Corrosion Association, Melbourne, Australia, Sep. 2014.
Chen, G S, Ma S. Research on prognostic and health management technology of unmanned aerial vehicle. Proc. Int. Conf. Control Engineering and Information System, Shijiazhuang, China, June, 2014, pp. 791-794.
Zhang Jinyu, Huang Xianxiang, Cai Wei. Research on prognostic and health monitoring system for large complex equipment. Proc. Int. Conf. Control, Automation and Systems Engineering, Xi'an, China, July, 2009, pp. 3-8.
Mueller I, Larrosa C, Roy S, et al. An integrated diagnostic and prognostic SHM technology for structural health management. Proc. 7th Int. workshop on Structural Health Monitoring, CA, United States, Sep. 2009, pp. 399-409.
Mark P Zachos, Karl E Schohl. Bridging design and implementation for a more practical CBM+ solution, IEEE Instrumentation & Measurement Magazine 2011; 14(4): 34-38.
[11] Banks Jeff, Brought Mark, Estep Jason, et al. Health and Usage Monitoring for Military Ground Vehicle Power Generating Devices. IEEE Aerospace Conference Proceedings, PA, United States, March, 2011, pp. 1-17.
Sreerupa Das, Richard Hall, Amar Patel, et al. An Open Architecture for Enabling CBMPHM Capabilities in Ground VEHICLE. 2012 IEEE Conference on Prognostics and Health Management, Denver, CO, United States, June, 2012, pp. 1-8.
Omer Faruk Eker, Fatih Camci, Adem Guclu, et al. A Simple State-Based Prognostic Model for Railway Turnout Systems. IEEE Transactions on Industrial Electronics 2011; 58(5): 1718-1726.
Kwok L Tsui, Nan Chen, Qiang Zhou, et al. Prognostics and Health Management: A Review on Data Driven Approaches. Mathematical Problems in Engineering 2015: 1-17.
Wang Jing, Liu Tingting. Application of wireless sensor network in Yangtze River basin water environment monitoring. 2015 27th Chinese Control and Decision Conference, Qingdao, China, pp. 5981-5985.
Sabrine Khriji, Dhouha El Houssaini, Mohamed Wassim Jmal, et al. Precision irrigation based on wireless sensor network. IET Science, Measurement and Technology 2014; 8(3): 98-106.
Yong Cui, Jianxun Lv, Haiwen Yuan, et al. Development of a Wireless Sensor Network for Distributed Measurement of Total Electric Field under HVDC Transmission Lines. International Journal of Distributed Sensor Networks 2014; 1: 790-797.
Shunfeng Cheng, Kwok Tom, Larry Thomas, et al. A Wireless Sensor System for Prognostics and Health Management. IEEE SENSORS JOURNAL 2010; 10(4): 856-862.
Jan Neuzil, Ondrej Kreibich, Radislav Smid. A Distributed Fault Detection System Based on IWSN for Machine Condition Monitoring. IEEE Transactions on Industrial Informatics 2014; 10 (2): 1118-1123.
Abel C Lima-Filho, Ruan D Gomes, Marc´eu O Adissi. Embedded System Integrated Into a Wireless Sensor Network for Online Dynamic Torque and Efficiency Monitoring in Induction Motors, IEEE/ASME Transactions on Mechatronics, 2012, 17, (3), pp. 404-414.
Joana R C Faria, Sónia M V Semedo1, Francisco J A Cardoso. Condition Monitoring and Diagnosis of Steam Traps with Wireless Smart Sensors. Proc. of the 8th Int. Conf. Sensing Technology, Sep., 2014, Liverpool, UK, pp. 57-62.
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