More Web Proxy on the site http://driver.im/

article

Self-monitoring for maintenance of vehicle fleets

Authors:

Thorsteinn Rögnvaldsson,

Sławomir Nowaczyk,

Stefan Byttner,

Magnus SvenssonAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 32, Issue 2

Pages 344 - 384

https://doi.org/10.1007/s10618-017-0538-6

Published: 01 March 2018 Publication History

Abstract

An approach for intelligent monitoring of mobile cyberphysical systems is described, based on consensus among distributed self-organised agents. Its usefulness is experimentally demonstrated over a long-time case study in an example domain: a fleet of city buses. The proposed solution combines several techniques, allowing for life-long learning under computational and communication constraints. The presented work is a step towards autonomous knowledge discovery in a domain where data volumes are increasing, the complexity of systems is growing, and dedicating human experts to build fault detection and diagnostic models for all possible faults is not economically viable. The embedded, self-organised agents operate on-board the cyberphysical systems, modelling their states and communicating them wirelessly to a back-office application. Those models are subsequently compared against each other to find systems which deviate from the consensus. In this way the group (e.g., a fleet of vehicles) is used to provide a standard, or to describe normal behaviour, together with its expected variability under particular operating conditions. The intention is to detect faults without the need for human experts to anticipate them beforehand. This can be used to build up a knowledge base that accumulates over the life-time of the systems. The approach is demonstrated using data collected during regular operation of a city bus fleet over the period of almost 4 years.

References

[1]

Ackoff RL (1989) From data to wisdom. J Appl Syst Anal 16:3-9.

[2]

Alippi C, Roveri M, Trovò F (2012) A learning from models cognitive fault diagnosis system. In: Artificial neural networks and machine learning--ICANN 2012(Lecture notes in computer science), vol 7553. Springer, Berlin, pp 305-313.

[3]

Alippi C, Roveri M, Trovò F (2014) A self-building and cluster-based cognitive fault diagnosis system for sensor networks. IEEE Trans Neural Netw Learn Syst 25(6):1021-1032.

[4]

Blei DM, Jordan MI (2006) Variational inference for dirichlet process mixtures. Bayesian Anal 1(1):121-143.

[5]

Byttner S, Nowaczyk S, Prytz R, Rögnvaldsson T (2013) A field test with self-organized modeling for knowledge discovery in a fleet of city buses. In: Proceedings of 2013 IEEE international conference on mechatronics and automation (ICMA), pp 896-901.

[6]

Byttner S, Rögnvaldsson T, Svensson, M (2007) Modeling for vehicle fleet remote diagnostics. Technical paper 2007-01-4154, Society of Automotive Engineers (SAE).

[7]

Byttner S, Rögnvaldsson T, Svensson M (2011) Consensus self-organized models for fault detection (COSMO). Eng Appl Artif Intell 24:833-839.

Digital Library

[8]

Byttner S, Rögnvaldsson T, Svensson M, Bitar G, Chominsky W (2009) Networked vehicles for automated fault detection. In: Proceedings of IEEE international symposium on circuits and systems.

[9]

Cha S-H (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 1:300-307.

[10]

Chandola V, Banerjee A, Kumar V (2009) A nomaly detection: a survey. ACM Comput Surv 41:15:1-15:58.

Digital Library

[11]

Chen H, Ti¿o P, Rodan A, Yao X (2014) Learning in the model space for cognitive fault diagnosis. IEEE Trans Neural Netw Learn Syst 25(1):124-136.

[12]

Csiszár I, Shields PC (2004) Information theory and statistics: a tutorial. Found Trends¿ Commun Inf Theory 1:417-528.

[13]

D'Silva SH (2008) Diagnostics based on the statistical correlation of sensors. Technical paper 2008-01-0129, Society of Automotive Engineers (SAE).

[14]

Fan Y, Nowaczyk S, Rögnvaldsson T (2015a) Evaluation of self-organized approach for predicting compressor faults in a city bus fleet. Procedia Comput Sci 53:447-456.

[15]

Fan Y, Nowaczyk S, Rögnvaldsson T (2015b) Incorporating expert knowledge into a self-organized approach for predicting compressor faults in a city bus fleet. Front Artif Intell Appl 278:58-67.

[16]

Fan Y, Nowaczyk S, Rögnvaldsson T, Antonelo EA (2016) Predicting air compressor failures with echo state networks. In: PHME 2016: proceedings of the Tthird European conference of the prognostics and health management society 2016. PHM Society, pp 568-578.

[17]

Filev DP, Chinnam RB, Tseng F, Baruah P (2010) An industrial strength novelty detection framework for autonomous equipment monitoring and diagnostics. IEEE Trans Ind Inform 6:767-779.

[18]

Filev DP, Tseng F (2006) Real time novelty detection modeling for machine health prognostics. In: Annual meeting of the North American fuzzy information processing society NAFIPS, IEEE Press.

[19]

Gogoi P, Bhattacharyya D, Borah B, Kalita JK (2011) A survey of outlier detection methods in network anomaly identification. Comput J 54:570-588.

Digital Library

[20]

Gupta M, Gao J, Aggarwal CC, Han J (2013) Outlier detection for temporal data: a survey. IEEE Trans Knowl Data Eng 25:1-20.

[21]

Hansson J, Svensson M, Rögnvaldsson T, Byttner S (2008) Remote diagnosis modelling. U.S. Patent 8,543,282 filed May 12, 2008, and issued Sept 24, 2013.

[22]

Hines J, Garvey D, Seibert R, Usynin A (2008a) Technical review of on-line monitoring techniques for performance assessment. In: Volume 2: theoretical issues. Technical review NUREG/CR-6895, vol 2. U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, Washington, DC 20555-0001.

[23]

Hines J, Garvey J, Garvey DR, Seibert R (2008b) Technical review of on-line monitoring techniques for performance assessment. In: Volume 3: limiting case studies. Technical review NUREG/CR-6895, vol 3. U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, Washington, DC 20555-0001.

[24]

Hines J, Seibert R (2006) Technical review of on-line monitoring techniques for performance assessment. In: Volume 1: state-of-the-art. Technical review NUREG/CR-6895. U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, Washington, DC 20555-0001.

[25]

Isermann R (2006) Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer, Heidelberg.

[26]

Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20:1483-1510.

[27]

Kargupta H, Bhargava R, Liu K, Powers M, Blair P, Bushra S, Dull J (2004) VEDAS: a mobile and distributed data stream mining system for real-time vehicle monitoring. In: Fourth international conference on data mining.

[28]

Kargupta H, Gilligan M, Puttagunta V, Sarkar K, Klein M, Lenzi N, Johnson D (2010) MineFleet®: the vehicle data stream mining system for ubiquitous environments (Lecture Notes in Computer Science), vol 6202. Springer, pp 235-254.

[29]

Kargupta H, Puttagunta V, Klein M, Sarkar K (2007) On-board vehicle data stream monitoring using minefleet and fast resource constrained monitoring of correlation matrices. New Gener Comput 25:5-32.

Digital Library

[30]

Karlsson N, Svensson M, Nowaczyk S, Byttner S, Prytz R, Rögnvaldsson T (2013) A method for monitoring the operation of a sensor. U.S. Patent 20,160,232,723 filed Oct 4, 2013.

[31]

Lapira ER (2012) Fault detection in a network of similar machines using clustering approach. Ph.D. thesis, University of Cincinnati.

[32]

Lapira ER, Al-Atat H, Lee J (2011) Turbine-to-turbine prognostics technique for wind farms. U.S. Patent 8,924,162 filed Nov 12, 2012, and issued Dec 30, 2014.

[33]

Laxhammar R (2014) Conformal anomaly detection. Ph.D. thesis, University of Skövde.

[34]

Ma J, Jiang J (2011) Applications of fault detection and diagnosis methods in nuclear power plants: a review. Prog Nucl Energy 53:255-266.

[35]

McClelland JL, Rumelhart DE (eds) (1988) Explorations in parallel distributed processing: a handbook of models, programs, and exercises. MIT Press, Cambridge.

[36]

Patcha A, Park J-M (2007) An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput Netw 51:3448-3470.

Digital Library

[37]

Pele O (2011) Distance functions: theory, algorithms and applications. Ph.D. thesis, The Hebrew University of Jerusalem.

[38]

Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol 50:297-313.

[39]

Pimentel MA, Clifton DA, Clifton L, Tarassenko L (2014) A review of novelty detection. Signal Process 99:215-249.

Digital Library

[40]

Prytz R, Nowaczyk S, Rgnvaldsson T, Byttner S (2015) Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Eng Appl Artif Intell 41:139-150.

Digital Library

[41]

Quevedo J, Chen H, Cugueró MÀ, Ti¿o P, Puig V, Garciá D, Sarrate R, Yao X (2014) Combining learning in model space fault diagnosis with data validation/reconstruction: application to the Barcelona water network. Eng Appl Artif Intell 30:18-29.

Digital Library

[42]

Rajagopalan S, Wang Y-Y, Feldmann S (2011) Offset and slow response diagnostic methods for NOx sensors in vehicle exhaust treatment applications. U.S. Patent 8,930,121 filed Apr 7, 2011, and issued Jan 6, 2015.

[43]

Riemer M (2013a) Days out of service: the silent profit-killer--why fleet financial and executive management should care more about service & repair. White paper, DECISIV.

[44]

Riemer M (2013b) Service relationship management--driving uptime in commercial vehicle maintenance and repair. White paper, DECISIV.

[45]

Rögnvaldsson T, Norrman H, Byttner S, Järpe E (2014) Estimating p-values for deviation detection. In: 2014 IEEE eighth international conference on self-adaptive and self-organizing systems. IEEE Computer Society, pp 100-109.

[46]

Rowley J (2007) The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci 33:163-180.

Digital Library

[47]

Rubner Y, Tomasi C, Guibas L (2000) The earth mover's distance as a metric for image retrieval. Int J Comput Vis 40:99-121.

Digital Library

[48]

Sodemann AA, Ross MP, Borghetti BJ (2012) A review of anomaly detection in automated surveillance. IEEE Trans Syst Man Cybern 42:1257-1272.

Digital Library

[49]

Theissler A (2017) Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl Based Syst 123:163-173.

Digital Library

[50]

Vachkov G (2006) Intelligent data analysis for performance evaluation and fault diagnosis in complex systems. In: Proceedings of the IEEE international conference on fuzzy systems, 2006. IEEE Press, pp 6322-6329.

[51]

Vovk V, Gammerman A, Shafer G (2005) Algorithmic learning in a random world. Springer, New York.

[52]

Xie M, Han S, Tian B, Parvin S (2011) A nomaly detection in wireless sensor networks: a survey. J Netw Comput Appl 34:1302-1325.

Digital Library

[53]

Zhang J (2013) Advancements of outlier detection: a survey. ICST Trans Scalable Inf Syst 13:e2:1-e2:26.

[54]

Zhang Y, Gantt GW Jr, Rychlinski MJ, Edwards RM, Correia JJ, Wolf CE (2009) Connected vehicle diagnostics and prognostics, concept, and initial practice. IEEE Trans Reliab 58:286-294.

[55]

Zimek A, Schubert E, Kriegel HP (2012) A survey on unsupervised outlier detection in high-dimensional numerical data. Stat Anal Data Min 5:363-378.

Digital Library

Cited By

Killeen PKiringa IYeap T(2022)Unsupervised Dynamic Sensor Selection for IoT-Based Predictive Maintenance of a Fleet of Public Transport BusesACM Transactions on Internet of Things10.1145/35309913:3(1-36)Online publication date: 19-Jul-2022
https://dl.acm.org/doi/10.1145/3530991
Giannoulidis AGounaris A(2022)A context-aware unsupervised predictive maintenance solution for fleet managementJournal of Intelligent Information Systems10.1007/s10844-022-00744-260:2(521-547)Online publication date: 17-Sep-2022
https://dl.acm.org/doi/10.1007/s10844-022-00744-2
Davari NPashami SVeloso BNowaczyk SFan YPereira PRibeiro RGama J(2022)A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data SetAdvances in Intelligent Data Analysis XX10.1007/978-3-031-01333-1_4(39-52)Online publication date: 20-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-01333-1_4
Show More Cited By

Recommendations

Development and Field Trial of a Driver Assistance System to Encourage Eco-Driving in Light Commercial Vehicle Fleets

Driver training schemes and eco-driving techniques can reduce fuel consumption by 10%, but their effectiveness depends on the willingness of drivers to change their behavior, and changes may be short lived. Onboard driver assistance systems have been ...
Monitoring vehicle outliers based on clustering technique

Display Omitted Develop a novel framework for monitoring vehicle outliers caused by complex vehicle states.Suggest techniques of clustering vehicle data needed in abnormal state monitoring.Show an approach identifying characteristics of clusters by ...
Quick and Autonomous Platoon Maintenance in Vehicle Dynamics For Distributed Vehicle Platoon Networks
IoTDI '17: Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

Platoon systems, as a type of adaptive cruise control systems, will play a significant role to improve travel experience and roadway safety. The stability of a platoon system is crucial so that each vehicle maintains a safety distance from its ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 32, Issue 2

March 2018

273 pages

ISSN:1384-5810

Issue’s Table of Contents

Copyright © Copyright © 2018 The Author(s).

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2018

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Killeen PKiringa IYeap T(2022)Unsupervised Dynamic Sensor Selection for IoT-Based Predictive Maintenance of a Fleet of Public Transport BusesACM Transactions on Internet of Things10.1145/35309913:3(1-36)Online publication date: 19-Jul-2022
https://dl.acm.org/doi/10.1145/3530991
Giannoulidis AGounaris A(2022)A context-aware unsupervised predictive maintenance solution for fleet managementJournal of Intelligent Information Systems10.1007/s10844-022-00744-260:2(521-547)Online publication date: 17-Sep-2022
https://dl.acm.org/doi/10.1007/s10844-022-00744-2
Davari NPashami SVeloso BNowaczyk SFan YPereira PRibeiro RGama J(2022)A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data SetAdvances in Intelligent Data Analysis XX10.1007/978-3-031-01333-1_4(39-52)Online publication date: 20-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-01333-1_4
Ferreira DScholz TPrytz R(2020)Importance Weighting of Diagnostic Trouble Codes for Anomaly DetectionMachine Learning, Optimization, and Data Science10.1007/978-3-030-64583-0_37(410-421)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-64583-0_37
Calikus EFan YNowaczyk SSant'Anna A(2019)Interactive-COSMOProceedings of the Workshop on Interactive Data Mining10.1145/3304079.3310289(1-9)Online publication date: 15-Feb-2019
https://dl.acm.org/doi/10.1145/3304079.3310289
Pirasteh PNowaczyk SPashami SLöwenadler MThunberg KYdreskog HBerck P(2019)Interactive feature extraction for diagnostic trouble codes in predictive maintenanceProceedings of the Workshop on Interactive Data Mining10.1145/3304079.3310288(1-10)Online publication date: 15-Feb-2019
https://dl.acm.org/doi/10.1145/3304079.3310288
Maroli JÖzgüner ÜRedmill K(2019)A Framework for Automated Collaborative Fault Detection in Large-Scale Vehicle Networks2019 IEEE Intelligent Vehicles Symposium (IV)10.1109/IVS.2019.8814176(1923-1927)Online publication date: 9-Jun-2019
https://dl.acm.org/doi/10.1109/IVS.2019.8814176
Khoshkangini RPashami SNowaczyk S(2019)Warranty Claim Rate Prediction Using Logged Vehicle DataProgress in Artificial Intelligence10.1007/978-3-030-30241-2_55(663-674)Online publication date: 3-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30241-2_55
Nowaczyk SSant’Anna ACalikus EFan Y(2018)Monitoring Equipment Operation Through Model and Event DiscoveryIntelligent Data Engineering and Automated Learning – IDEAL 201810.1007/978-3-030-03496-2_6(41-53)Online publication date: 21-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-03496-2_6

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents