Deep feature analysis and extraction on Logged Vehicle data for the task of predictive maintenance

From ISLAB/CAISR
Title Deep feature analysis and extraction on Logged Vehicle data for the task of predictive maintenance
Summary This project is about applying supervised/unsupervised methods of feature selection on Logged Vehicle data (LVD) from Volvo trucks and investigate the contribution in model construction for different predictive maintenance tasks
Keywords
TimeFrame
References • Doquet, Guillaume, and Michele Sebag. "Agnostic feature selection." The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2019

• Prytz, Rune, et al. "Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data." Engineering applications of artificial intelligence 41 (2015): 139-150.

Prerequisites Machine Learning
Author
Supervisor Mahmoud Rahat, Sławomir Nowaczyk
Level
Status Open

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This project is about applying supervised/unsupervised methods of feature selection on Logged Vehicle data (LVD) from Volvo trucks and investigate the contribution in model construction for different predictive maintenance tasks. The LVD dataset is collected by storing aggregated vehicle statistics (parameters) such as average vehicle speed, total fuel consumption, and so on. These parameters provide information about truck usage and its current condition. Though, the data is very noisy, often inaccurate and contains many redundant, and uninformative features, which can be identified and removed without incurring much loss of information.