Patrick Killeen
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Abstract
In recent years, big data produced by the Internet of Things has enabled new kinds of useful applications. One such application is monitoring a fleet of vehicles in real time to predict their remaining useful life. The consensus self-organized models (COSMO) approach is an example of a predictive maintenance system. The present work proposes a novel Internet of Things based architecture for predictive maintenance that consists of three primary nodes: the vehicle node, the server leader node, and the root node, which enable on-board vehicle data processing, heavy-duty data processing, and fleet administration, respectively. A minimally viable prototype of the proposed architecture was implemented and deployed to a local bus garage in Gatineau, Canada.
The present work proposes improved consensus self-organized models (ICOSMO), a fleet-wide unsupervised dynamic sensor selection algorithm. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a hybrid bus was used to generate synthetic data in the simulations. Simulation results that compared the performance of the COSMO and ICOSMO approaches revealed that in general ICOSMO improves the average area under the curve of COSMO by approximately 1.5% when using the Cosine distance and 0.6% when using Hellinger distance.
- [1] . 2015. Cloud-based driver monitoring and vehicle diagnostic with OBD2 telematics. In Proceedings of the 2015 15th International Conference on Electro/Information Technology (EIT’15).Google Scholar
Cross Ref
- [2] . 2014. Fog computing: A platform for Internet of Things and Analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments, Nik Bessis and Ciprian Dobre (Eds.). Studies in Computational Intelligence, Vol. 546. Springer, 169–186.Google Scholar
- [3] . 2014. A Prognostic and Data Fusion Based Approach to Validating Automotive Electronics. SAE Technical Paper 2014-01-0724. SAE.Google Scholar
- [4] . 2018. An IoT Gateway Middleware for Interoperability in SDN Managed Internet of Things. Ph.D. Dissertation. Carleton University.Google Scholar
- [5] . 2018. Predictive Maintenance Framework for a Vehicular IoT Gateway Node Using Active Database Rules. Master’s Thesis. University of Ottawa. https://ruor.uottawa.ca/handle/10393/38568.Google Scholar
- [6] . 2013. A field test with self-organized modeling for knowledge discovery in a fleet of city buses. In Proceedings of the 2013 IEEE International Conference on Mechatronics and Automation.Google ScholarCross Ref
- [7] . 2011. Consensus self-organized models for fault detection (COSMO). Engineering Applications of Artificial Intelligence 24, 5 (2011), 833–839.Google ScholarDigital Library
- [8] . 2019. Interactive-COSMO: Consensus self-organized models for fault detection with expert feedback. In Proceedings of the Workshop on Interactive Data Mining. 1–9.Google Scholar
- [9] . 2008. Taxonomy of nominal type histogram distance measures. In Proceedings of the American Conference on Applied Mathematics (MATH’08). 325–330.Google Scholar
- [10] . 2020. A Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus. Master’s Thesis. University of Ottawa. https://ruor.uottawa.ca/handle/10393/40661.Google Scholar
- [11] . 2020. A system for managing and processing industrial sensor data: SMS. In Proceedings of the 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP’20). IEEE, Los Alamitos, CA, 213–220.Google Scholar
- [12] . 2010. Multiple kernel learning for heterogeneous anomaly detection: Algorithm and aviation safety case study. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 47–56.Google ScholarDigital Library
- [13] . 2014. An IoT gateway centric architecture to provide novel M2M services. In Proceedings of the 2014 IEEE World Forum on Internet of Things (WF-IoT’14). IEEE, Los Alamitos, CA, 514–519.Google Scholar
- [14] . 2018. Eclipse Mosquitto Home Page. Retrieved December 14, 2021 from https://mosquitto.org/.Google Scholar
- [15] . 2020. Transfer learning for remaining useful life prediction based on consensus self-organizing models. Reliability Engineering & System Safety 203 (2020), 107098. Google ScholarCross Ref
- [16] . 2018. On monitoring heat-pumps with a group-based conformal anomaly detection approach. In Proceedings of the 2018 Internal Conference on Data Science (ICDATA’18).63–69.Google Scholar
- [17] . 2020. Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach. Engineering Applications of Artificial Intelligence 90 (2020), 103492. Google ScholarDigital Library
- [18] . 2017. Using telemetry for maintenance of special military vehicles. In Proceedings of the International Conference on Modelling and Simulation for Autonomous Systems. 392–401.Google Scholar
- [19] . 2010. Knowledge Discovery from Data Streams. Chapman & Hall/CRC, Boca Raton, FL.Google ScholarCross Ref
- [20] . 2015. A comprehensive framework of factory-to-factory dynamic fleet-level prognostics and operation management for geographically distributed assets. In Proceedings of the 2015 IEEE International Conference on Automation Science and Engineering (CASE’15). IEEE, Los Alamitos, CA, 225–230.Google Scholar
- [21] . 2006. On-board vehicle data stream monitoring using MineFleet and fast resource constrained monitoring of correlation matrices. New Generation Computing 25, 1 (2006), 5–32.Google ScholarDigital Library
- [22] . 2010. MineFleet®: An overview of a widely adopted distributed vehicle performance data mining system. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 37–46.Google Scholar
- [23] . 2015. Fundamentals of Machine Learning for PredictiveAnalytics:Algorithms, Worked Examples, and Case Studies. MIT Press, Cambridge, MA.Google Scholar
- [24] . 2020. Knowledge-Based Predictive Maintenance for Fleet Management. Master’s Thesis. University of Ottawa. https://ruor.uottawa.ca/handle/10393/40086.Google Scholar
- [25] . 2019. IoT-based predictive maintenance for fleet management. Procedia Computer Science 151 (2019), 607–613.Google ScholarDigital Library
- [26] . 2018. An AHP-Based Evaluation of Real-Time Stream Processing Technologies in IoT.
Technical Report . University of Ottawa. https://www.mudlakebiodiversity.ca/papers/ahp-based-evaluation-iot-2018.pdf.Google Scholar - [27] . 2010. Parameter selection for health monitoring of electronic products. Microelectronics Reliability 50, 2 (2010), 161–168.Google Scholar
- [28] . 2012. Fault Detection in a Network of Similar Machines Using Clustering Approach. Ph.D. Dissertation. University of Cincinnati.Google Scholar
- [29] . 2018. LoRaWAN Coverage for LATAM and Asia-Pacific on Its IoT Sensor Platform. Retrieved November 20, 2018 from http://www.libelium.com/libelium-expands-lorawan-coverage-for-latam-and-asia-pacific-on-its-iot-sensor-platform/?utm_source=NewsletterLB&utm_medium=Email&utm_campaign=NLB-301018.Google Scholar
- [30] . 2018. Cyber-Physical System Augmented Prognostics and Health Management for Fleet-Based Systems. Ph.D. Dissertation. University of Cincinnati.Google Scholar
- [31] . 2015. Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine. Microelectronics Reliability 55 (2015), 2092–2096. Google ScholarCross Ref
- [32] . 2019. Unsupervised fault detection in varying operating conditions. In Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management (ICPHM’19). IEEE, Los Alamitos, CA, 1–10.Google Scholar
- [33] . 2020. Intelligent anomaly detection of machine tools based on mean shift clustering. Procedia CIRP 93 (2020), 1448–1453.Google Scholar
- [34] . 2018. Monitoring equipment operation through model and event discovery. In Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning. 41–53.Google Scholar
- [35] . 2016. Smart Cities and Homes: Key Enabling Technologies. Morgan Kaufmann.Google Scholar
- [36] . 2021. Home | OC Transpo. Retrieved December 7, 2021 from https://www.octranspo.com/.Google Scholar
- [37] . 2020. Distributed Collaborative Prognostics. Ph.D. Dissertation. University of Cambridge.Google Scholar
- [38] . 2019. An industrial multi agent system for real-time distributed collaborative prognostics. Engineering Applications of Artificial Intelligence 85 (2019), 590–606.Google Scholar
- [39] . 2019. Multi-agent system architectures for collaborative prognostics. Journal of Intelligent Manufacturing 30, 8 (2019), 2999–3013.Google Scholar
- [40] . 2015. Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Engineering Applications of Artificial Intelligence 41 (2015), 139–150. Google ScholarDigital Library
- [41] . 2020. A six-layer architecture for the digital twin: A manufacturing case study implementation. Journal of Intelligent Manufacturing 31 (2020), 1383–1402.Google Scholar
- [42] . 2014. Estimating p-values for deviation detection. In Proceedings of the 2014 IEEE 8th International Conference on Self-Adaptive and Self-Organizing Systems (SASO’14). IEEE, Los Alamitos, CA, 100–109.Google ScholarDigital Library
- [43] . 2018. Self-monitoring for maintenance of vehicle fleets. Data Mining and Knowledge Discovery 32, 2 (March 2018), 344–384.Google ScholarDigital Library
- [44] . 2021. STO | Société de Transport d l’Outaouais. Retrieved December 7, 2021 from http://www.sto.ca/.Google Scholar
- [45] . 2017. J1939 Digital Annex October 2017. Retrieved April 18, 2022 from https://www.sae.org/standards/content/j1939da_201710/.Google Scholar
- [46] . 2008. Self-organizing maps for automatic fault detection in a vehicle cooling system. In Proceedings of the 2008 4th International IEEE Conference Intelligent Systems.Google ScholarCross Ref
- [47] . 2016. Evaluation of micro-flaws in metallic material based on a self-organized data-driven approach. In Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM’16). IEEE, Los Alamitos, CA, 1–5.Google Scholar
- [48] . 2014. Performance evaluation of MQTT and CoAP via a common middleware. In Proceedings of the 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks, and Information Processing (ISSNIP’14). IEEE, Los Alamitos, CA, 1–6.Google Scholar
- [49] . 2020. Streaming analytics in edge-cloud environment for logistics processes. In Proceedings of the IFIP International Conference on Advances in Production Management Systems. 245–253.Google Scholar
- [50] . 2016. Fog-enabled architecture for data-driven cyber-manufacturing systems. In Proceedings of the International Manufacturing Science and Engineering Conference, Vol. 49903.Google Scholar
- [51] . 2020. Secured System Architecture for the Internet of Things Using a Two Factor Authentication Protocol. Ph.D. Dissertation. University of Ottawa.Google Scholar
- [52] . 2012. Peer-to-peer collaborative vehicle health management—The concept and an initial study. In Proceedings of the Annual Conference of the Prognostics and Health Management Society.Google Scholar
- [53] . 2009. Connected vehicle diagnostics and prognostics, concept, and initial practice. IEEE Transactions on Reliability 58, 2 (2009), 286–294.Google ScholarCross Ref
- [54] . 2012. A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining 5, 5 (2012), 363–387.Google ScholarDigital Library
Index Terms
- Unsupervised Dynamic Sensor Selection for IoT-Based Predictive Maintenance of a Fleet of Public Transport Buses
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