Abstract
Maintenance of moving vehicles is quite challenging because they may disrupt the normal flow of transportation due to unexpected breakdowns, slowdowns and stoppages. In order to avoid stoppages and to minimize the downtime, maintenance and condition monitoring systems must be optimized. On one hand the condition monitoring on board should provide automatic failure detection, identification and localization together with a prognostic of the future failures. On the other hand maintenance logistics and product supportability must be also optimized since the onboard system should provide a suggestion of a repair shop that depends on location, cost and availability of spare parts, technicians’ skills and queuing time for repairs. However the vehicles are independent assets interacting among them within the traffic system and also interacting with the infrastructure (roads, rails etc.) seriously affected by weather, maintenance of infra, regulations etc. Therefore the proposed solution is to equip the vehicles with a context-aware system that monitors the condition and maintenance schedules of parts and alarm the driver of the parts that are in near to repair cycle. This system will perform risk analysis and will communicate with the cloud propose a decision of selection of repair shop on the location and path of vehicle depending on weather, road and traffic, cost and availability of spare parts at respective repair shops based on risk assessment and prediction. The information contained in the cloud will also communicate the workshop that will book time slot and block the necessary spare parts for the coming vehicle minimizing waiting time. This mechanism will help in reducing unexpected stoppages, vehicle degradation and efficient spare parts management combining in a successful way the workload of the workshops from both natural sources, the time based inspections and repairs together with the reactive maintenance coming from unexpected breakdown.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Price C, Price CJ (1999) Computer-based diagnostic systems. Springer, Heidelberg, pp 65–69
Gandolfo F, Mussa-Ivaldi FA, Bizzi E (1996) Motor learning by field approximation. Proc Natl Acad Sci 93(9):3843–3846
Fischhoff B, Slovic P, Lichtenstein S (1978) Fault trees: Sensitivity of estimated failure probabilities to problem representation. J Exp Psychol Hum Percept Perform 4(2):330
Galar D, Kumar U, Villarejo R, Johansson CA (2013) Hybrid prognosis for railway health assessment: an information fusion approach for PHM deployment. Chem Eng 33
Galar D, Thaduri A, Catelani M, Ciani L (2015) Context awareness for maintenance decision making: a diagnosis and prognosis approach. Measurement
Rizzoni G, Onori S, Rubagotti M (2009, June) Diagnosis and prognosis of automotive systems: motivations, history and some results. In: Proceedings of the 7th IFAC Symposium on fault detection, supervision and safety of technical processes (SAFEPROCESS’09)
Galar D, GuSTAFSON A, Tormos B, Berges L (2012) Maintenance decision making based on different types of data fusion Podejmowanie Decyzji Eksploatacyjnych W Oparciu O Fuzję Różnego Typu Danych. Eksploatacja i Niezawodnosc, Maint Reliab 14(2):135–144
Paipetis AS, Matikas TE, Aggelis DG, Van Hemelrijck D (eds) (2012) Emerging technologies in non-destructive testing V. CRC Press, Boca Raton
Galar D (2014) Context-driven maintenance: an eMaintenance approach. Manag Syst Prod Eng. http://wydawnictwo.panova.pl/pliki/15_2014/2014_03_05_GALAR.pdf
Labib AW (2004) A decision analysis model for maintenance policy selection using a CMMS. J Qual Maint Eng 10(3):191–202
Galar D, Palo M, Van Horenbeek A, Pintelon L (2012) Integration of disparate data sources to perform maintenance prognosis and optimal decision making. Insight-non-destructive testing and condition monitoring 54(8):440–445
Muller A, Marquez AC, Iung B (2008) On the concept of e-maintenance: review and current research. Reliab Eng Syst Saf 93(8):1165–1187
Van Horenbeek A, Pintelon L, Galar D Integration of disparate data sources to perform maintenance prognosis and optimal decision making. http://pure.ltu.se/portal/files/40110675/317_Horenbeek.pdf
Bjorling SE, Baglee D, Galar D, Singh S (2013) Maintenance knowledge management with fusion of CMMS and CM
Nandi S, Toliyat HA, Li X (2005) Condition monitoring and fault diagnosis of electrical motors-a review. IEEE Trans Energy Convers 20(4):719–729
Schilit BN, Theimer MM (1994) Disseminating active map information to mobile hosts. IEEE Netw 8(5):22–32
Schilit B, Adams N, Want R (1994, December) Context-aware computing applications. In: WMCSA 1994. First workshop on mobile computing systems and applications, 1994. IEEE, pp 85–90
Thaduri A, Kumar U, Verma AK Computational intelligence framework for context-aware decision making. Int J Syst Assur Eng Manag 1–12
Chen H, Finin T, Joshi A (2003) An intelligent broker architecture for context-aware systems. PhD proposal in computer science, University of Maryland, Baltimore, USA
Nixon M, Keyes M, Schleiss T, Gudaz J, Belvins T (2001) U.S. Patent Application 09/953,811
Remboski D, Brooks K, Canavan P, Douros K, Gardner J, Gardner R, Hurwitz J, Leivian R, Nagel J, Wheatley D, Wood C (2001) U.S. Patent Application 09/976,974
Bottazzi D, Corradi A, Montanari R (2006) Context-aware middleware solutions for anytime and anywhere emergency assistance to elderly people. IEEE Commun Mag 44(4):82–90
Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3):15
Lane TD (2000) Machine learning techniques for the computer security domain of anomaly detection
Ye N, Chen Q (2001) An anomaly detection technique based on a chi-square statistic for detecting intrusions into information systems. Qual Reliab Eng Int 17(2):105–112
George J, Crassidis J, Singh T, Fosbury AM (2011) Anomaly detection using context-aided target tracking. J Adv Inf Fusion 6(1):39–56
Galar D, Wandt K, Karim R, Berges L (2012) The evolution from e (lectronic) maintenance to i (ntelligent) maintenance. Insight-Non-Destr Test Cond Monit 54(8):446–455
Wilmering TJ, Ramesh AV (2005, March) Assessing the impact of health management approaches on system total cost of ownership. In: 2005 IEEE Aerospace conference. IEEE, pp 3910–3920
de Novaes Kucinskis F, Ferreira MGV (2008) An Internal State Inference Service for onboard diagnosis, prognosis and contingency planning applications
Luo J, Pattipati KR, Qiao L, Chigusa S (2007) An integrated diagnostic development process for automotive engine control systems. IEEE Trans Syst, Man Cybern Part C: Appl Rev 37(6):1163–1173
Byington CS, Kalgren PW, Dunkin BK, Donovan BP (2004, March) Advanced diagnostic/prognostic reasoning and evidence transformation techniques for improved avionics maintenance. In: 2004 IEEE Aerospace conference, 2004. Proceedings, vol 5. IEEE
Sankavaram C, Pattipati B, Kodali A, Pattipati K, Azam M, Kumar S, Pecht M (2009, August) Model-based and data-driven prognosis of automotive and electronic systems. In: IEEE International conference on automation science and engineering, 2009. CASE 2009. IEEE, pp 96–101
Scudder GD (1985) An evaluation of overtime policies for a repair shop. J Oper Manag 6(1):87–98
Scudder GD, Hausman WH (1982) Spares stocking policies for repairable items with dependent repair times. Naval Res Logist Q 29(2):303–322
Subramaniam V, Raheja AS (2003) mAOR: A heuristic-based reactive repair mechanism for job shop schedules. Int J Adv Manuf Technol 22(9–10):669–680
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Thaduri, A., Galar, D., Kumar, U., Verma, A.K. (2016). Context-Based Maintenance and Repair Shop Suggestion for a Moving Vehicle. In: Kumar, U., Ahmadi, A., Verma, A., Varde, P. (eds) Current Trends in Reliability, Availability, Maintainability and Safety. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-23597-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-23597-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23596-7
Online ISBN: 978-3-319-23597-4
eBook Packages: EngineeringEngineering (R0)