This paper proposes a non-dismantling functional inspection method for components of the bogie for improved safety and streamlined maintenance. This method uses camera images taken from approximately 2 m away to measure the bogie vibration generated when the vehicles pass over iron plates about 10 mm in thickness and conducts a functional inspection of the bogie components based on changes of their natural frequency. This paper demonstrates the effectiveness of the method by reporting an experiment where some of the bogie components of actual vehicles were replaced and set to different characteristic conditions. It also establishes a simplified and clear dynamic numerical model and theoretically confirms that the measurement results were due to the effect of the replaced component. The potential for efficient maintenance by using the proposed measurement method, analysis method, and numerical model is also presented.
ShirotoriTYasunagaT.A method of sound monitoring for railway vehicles with acceleration on a bogie (in Japanese). Railway Tech Res Inst Rep2011;
8: 17–22.
2.
MakiYAsahinaMKojimaT, et al. Distributed autonomous system for truck condition monitoring. In: 11th World Congress on railway research, Milan, Italy, 29 May–2 June 2016.
3.
MoriHTsunashimaH.Condition monitoring of railway vehicles using multiple model approach (in Japanese). Trans Japan Soc Mech Engrs2010;
76: 12–19.
4.
WardCPWestonPFStewartEJC, et al.
Condition monitoring opportunities using vehicle-based sensors. Proc IMechE, Part F: J Rail and Rapid Transit2011;
225: 202–218. DOI10.117709544097JRRT406.
5.
TsunashimaH. Condition monitoring of railway system – present and challenge (in Japanese). In: The 15th jointed railway technology symposium 2008 J-RAIL, Tokyo, Japan, 16 December–18 December, 2008, pp.49–50.
6.
TsunashimaH.Tetsudo ni okeru jyotaikanshi no genjyo to kadai (in Japanese). Jrea2012;
55: 36526–36528.
7.
NgigiRWPislaruCBallA, et al. Modern techniques for condition monitoring of railway vehicle dynamics. In: 25th international congress on condition monitoring and diagnostic engineering (COMADEM 2012), Huddersfield, UK, 18–20 June 2012, pp. 1–12. Ser. 364. UK: IOP. DOI:10.1088/1742-6596/364/1/012016.
8.
ChengZLiRC.Real-time vehicle performance monitoring system of wheelrail contract stress (in Chinese). Chinese Railways2014;
4: 99–102.
9.
ZhaoCHuHZengY, et al.
Analysis and control of wheel tread damage of freight wagon based on TPDS (in Chinese). Chinese Railways2011;
593: 52–56. 1:
10.
HuW.Analysis on relationship between TADS alarmed axle failure and TPDS inspection results (in Chinese). Chinese Railways2011;
4: 70–72. 586:
11.
RuiyangLAndersonGIraniF. Roller bearing detection and maintenance using acoustic condition monitoring on the Chinese Railways. In: 8th international heavy haul conference, Brazil, 14 June–16 June 2005, pp.801-805.
12.
HeQWangJHuF, et al.
Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement. J Sound Vib2013;
332: 5635–5649.
13.
LiHZhangYPengC.The EMU Trackside Acoustic Detection System (TADS) Technology and its application (in Chinese). Chinese Railways2016;
3: 61–64.
14.
LiuYQianQFuY, et al. Wayside acoustic fault diagnosis of railway wheel-bearing paved with Doppler effect reduction and EEMD-based diagnosis information enhancement. In: 10th international conference on sensing technology, Nanjing, China, 11 November–13 November 2016, New York: IEEE.
15.
ChenGPengCZhangY.An upgrading scheme of on-line detection system for wheel fault of existing EMUs (in Chinese). Chinese Railways2016;
3: 31–35.
16.
GuoX.Temperature control system of infrared hotbox detection car (in Chinese). Chinese Railways2010;
578, 8: 54–57.
17.
YaoHShenG. Study on safety early warning strategies of running gear mechanical fault of the railway vehicle. In: Proceedings of 23rd IAVSD Symposium. 2013, Qingdao, China, 19 August–23 August 2013. paper no. 43.14-ID391. Vienna: IAVSD.
18.
BrickleBMorganRSmithE, et al. Identification of existing and new technologies for wheelset condition monitoring – Rep. for Task T607. Rep., Rail Safety and Standards Board Ltd, UK, July 2008.
19.
AlemiACormanFLodewijksG.Condition monitoring approaches for the detection of railway wheel defects. Proc IMechE, Part F: J Rail and Rapid Transit2017;
231: 961–981. DOI: 10.1177/0954409716656218.
20.
AsplundMGustafssonPNordmarkT, et al.
Reliability and measurement accuracy of a condition monitoring system in an extreme climate. A case study of automatic laser scanning of wheel profiles. Proc IMechE, Part F: J Rail and Rapid Transit2014;
228: 695–704. DOI: 10.1177/0954409714528485.
21.
MalyTSchweinzerH. New approaches of wayside temperature measurement for railway car fire detection. In: Sensors 2005, CA, USA, 30 October–3 November 2005, pp.794–797. New York: IEEE. DOI:10.1109/ICSENS.2005.1597819.
22.
KimMSOhSCKimGY, et al. Underbody component monitoring system of railway vehicles using the infra-red thermal images. In: International SoC design conference, Jeju, Korea, 3–6 November 2014, pp.222–223. New York: IEEE. DOI: 10.1109/ISOCC.2014.7087616.
23.
DeilamsalehyHHavensTCLautalaP, et al.
An automatic method for detecting sliding railway wheels and hot bearings using thermal imagery. Proc IMechE, Part F: J Rail and Rapid Transit2017;
231: 690–700. DOI: 10.1177/0954409716638703.
24.
EdwardsRBarkenCPLHartMJ, et al. Improving the efficiency and effectiveness of railcar safety appliance inspection using machine vision technology. In: Proceeding of the 2006 IEEE/ASME Joint Rail Conference, GA, USA, 4–6 April 2006, pp.81–89. New York: ASME.
25.
KilianKKilianMSokolikM. Using automated wayside systems for condition-based maintenance and critical events reporting. In: 5th IET international conference on railway condition monitoring, Derby, UK, 29–30 November 2011, pp.63–68. UK: IET. DOI: 10.1049/cp.2011.0606.
26.
KilianKKilianMMazurV, et al.
Rethinking reliability engineering using machine vision systems. Proc IMechE, Part F: J Rail and Rapid Transit2016;
230: 1006–1014.
27.
SchlakeBWTodorovicSEdwardsJR, et al.
Machine vision condition monitoring of heavy-axle load railcar structural underframe components. Proc IMechE, Part F: J Rail and Rapid Transit2010;
224: 499–511. DOI: 10.1243/09544097JRRT376.
28.
LiCWeiZXingJ.Online inspection system for the automatic detection of bolt defects on a freight train. Proc IMechE, Part F: J Rail and Rapid Transit2016;
230: 1213–1226. DOI: 10.1177/0954409715588119.
29.
LiuLZhouFHeY.Automated status inspection of fastening bolts on freight trains using a machine vision approach. Proc IMechE, Part F: J Rail and Rapid Transit2016;
230: 1629–1641. DOI: 10.1177/0954409715619603.
30.
HoTKLiuSYHoYT, et al. Signature analysis on wheel-rail interaction for rail defect defection. In: 4th IET international conference on railway condition monitoring, Derby, UK, 18 June–20 June 2008, pp.36–41. UK: IET. DOI: 10.1049/ic:20080342.
31.
LatchawAGageS.Listening out for trouble. Railway Gazette Int2016; Aug: 67–68.
32.
AllottaBGaburriGInnocentiA, et al. An innovative high speed weigh in motion system for railway vehicles. In: 2014 IEEE/ASME 10th international conference on mechatron and embedded systems and applications (MESA), Ancona, Italy, 10–12 September 2014, pp.150–156. New York: IEEE, ASME.
33.
CherkashinUMZakharovSMSemechkinAE.An overview of rolling stock and track monitoring systems and guidelines to provide safety of heavy and long train operation in the Russian Railways. In:Proc IMechE, Part F: J Rail and Rapid Transit2009;
223: 199–208. DOI:10.1243/09544097JRRT226.
34.
NenovNDimitrovEDodevN, et al. Sensors and microsystems of wayside monitoring system for rolling stock in motion. In: 39th international spring seminar on electronics technology (ISSE 2016), Pilsen, Czech Republic, 18 May–20 May 2016, pp.418–423. New York: IEEE.
35.
MittermayrPStephanidesJMaiczD. ARGOS – a decade of operational experience in wayside train monitoring. In: 5th IET Conf. on railway condition monitoring and non-destructive testing (RCM 2011), Derby, UK, 29–30 November 2011, pp.62–66. UK: IET. DOI: 10.1049/cp.2011.0605.
WolfGPPetersonFJ. Application and validation of automated truck performance measurement system. In: Proceeding of the 1998 IEEE/ASME joint rail conference, Philadelphia, USA, 16 April 1998, pp.127–134. New York: ASME. DOI: 10.1109/RRCON.1998.668048.
ShioyaAYamazakiKSemaK, et al. Kashatouchakukensa(FIIS)no jidoka ni tsuite (in Japanese). In: The 9th symposium on the use of cybernetics in the railway, Tokyo, Japan, 28–30 November 1972. pp.494–499. Tokyo: Japan Railway Cybernetics Association.
41.
ItonagaNYanoH.Shinkansen sharinjyotaihaaku shisutemu no kaihatsu (in Japanese). R&M2009;
6: 12–15.
42.
ShinodaNToyosawaYShionoK. Sharintoumen zenshu keijyo sokuteisouchi no kakuninjikke (in Japanese). In: The 52th symposium on the use of cybernetics in the railway, Osaka, Japan, 5–6 November 2015. pp.1–4. Tokyo: Japan Railway Cybernetics Association.
43.
MatsumotoK.Eigyosen ni okeru sharyousoukoujyotai no kanshi (in Japanese). R&M2002;
9: 4–8.
44.
UemuraYNakataKOhbaT, et al.
Shinkansendaisha jikubako ondokenchisouchi no kaihatsu (in Japanese). Jrea2013;
56: 37714–37717.
45.
KoyamaTUsudaTKawasakiK, et al.
Detecting methods of pantograph failure by using sensors installed on contact lines (in Japanese). Railway Tech Res Inst Rep2015;
29: 35–40.
46.
KoyamaTUsudaTIkedaM, et al.
Denshasen ni secchisitasensa ni yoru pantagurahusuriita no dantsukimamoukenchishuho no kaihatsu (in Japanese). Jrea2016;
59: 40865–40868.
47.
ShirotoriTFukazawaK. A method of the wayside bogie monitoring based on the internal yard running vibration (in Japanese). In: The 24th jointed railway technology symposium 2017 J-RAIL, Tokyo, Japan, 12–14 December 2017. paper no. S2-7-1. Niigata: JSCE.
48.
KoutakiGIjiriY.Sub-pixel object detection and benchmark (in Japanese) Ieice D2015;
J98–D: 1126–1141.
MamadaSSuzukiMSatoD, et al.
Detection method of damage of single link rubber by impact test (in Japanese). Trans Japan Soc Mech Eng Ser C2013;
79: 3399–3409.
