An Anomaly Detection Study on Automotive Sensor Data Time Series for Vehicle Applications

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dc.contributor.authorDerse C.
dc.contributor.authorEl Baghdadi M.
dc.contributor.authorHegazy O.
dc.contributor.authorSensoz U.
dc.contributor.authorGezer H.N.
dc.contributor.authorNil M.
dc.date.accessioned2024-07-22T08:06:01Z
dc.date.available2024-07-22T08:06:01Z
dc.date.issued2021
dc.description.abstractAnomaly detection in automotive systems has been a strong challenge: first, during the development phase, then after the manufacturing approval in ramp-up production and finally during the vehicles life cycle management. The numerous sensors positioned inside a vehicle generate more than a gigabyte of data at each second timeframe. These sensors are connected through the vehicle network, which comprises Electronic Control Units (ECUs) and Controller Area Network (CAN) buses. Each ECU gets input from its sensors, executes specific instructions and aims to monitor the vehicle's normal state detecting any irregular action corresponding to its observed behavior. The aggregator of all sensor data and control actions detects the anomalies in vehicle systems, which poses a multi-source big data problem. Detecting anomalies during manufacturing has turned out to be another research challenge after the introduction of Industry 4.0. This paper presents a performance comparison of different anomaly detection algorithms on time series originating from automotive sensor data. Interquartile range, isolation forest, particle swarm optimization and k-means clustering algorithms are used to detect outlier data in the study. © 2021 IEEE.
dc.identifier.DOI-ID10.1109/EVER52347.2021.9456629
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/13349
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectControl system synthesis
dc.subjectControllers
dc.subjectEcology
dc.subjectIndustrial research
dc.subjectK-means clustering
dc.subjectLife cycle
dc.subjectManufacture
dc.subjectParticle swarm optimization (PSO)
dc.subjectTime series
dc.subjectTransducers
dc.subjectVehicles
dc.subjectAnomaly-detection algorithms
dc.subjectController-area-network bus
dc.subjectElectronic control units
dc.subjectInter quartile ranges
dc.subjectLife-cycle management
dc.subjectPerformance comparison
dc.subjectSpecific instruction
dc.subjectVehicle applications
dc.subjectAnomaly detection
dc.titleAn Anomaly Detection Study on Automotive Sensor Data Time Series for Vehicle Applications
dc.typeConference paper

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