Autoencoders for Anomaly Detection in Commercial Truck Gearshifts

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dc.contributor.authorImsirovic, Anes
dc.contributor.authorRokni, Arvin
dc.contributor.departmentChalmers tekniska högskola / Institutionen för elektrotekniksv
dc.contributor.examinerBrännström, Fredrik
dc.contributor.supervisorOlesen, Oscar
dc.contributor.supervisorLillskog, Simon
dc.contributor.supervisorBordbar, Alireza
dc.date.accessioned2024-09-23T13:30:36Z
dc.date.available2024-09-23T13:30:36Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractAbstract The following study revolves around implementing a machine learning model that supports engineers in analyzing vehicle sensor data at Volvo Trucks. The aim is to classify different gear shift types and to automate the detection of abnormal gear shifts making decisions more efficient and data-driven. By applying clustering- and classification algorithms, the model demonstrates how unlabeled field test data can be classified in diverse gear shift types. Furthermore, through evaluation and comparision of dense, convolutional and long short-term memory (LSTM) autoencoder (AE) neural networks, this study exhibits how abnormal sensor data within specific gear shift types can be detected. Obtained results indicate adequate gear shift classification with an overall accuracy of 98%. Furthermore, a comparison of the autoencoders in regard to the performance metrics accuracy, precision and recall concluded that the convolutional autoencoder outperformed the other architectures with scores above 95%. Although further fine-tuning of the implemented model is possible, findings indicate that it is feasible to develop and use machine learning models for classification and anomaly detection of gear shift data within heavy-duty trucks.
dc.identifier.coursecodeEENX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308775
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectKeywords: Machine Learning, Deep Neural Networks, Autoencoders, Sensor Data, Unsupervised, Clustering, Classification, Anomaly Detection, Truck Gear Shifts.
dc.titleAutoencoders for Anomaly Detection in Commercial Truck Gearshifts
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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