Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles

Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles

This work focused on demonstrating the capability of unsupervised machine learning techniques in detecting impending anomalies by extracting hidden trends in the datasets of fuel economy and emissions of light-duty vehicles (LDVs), which consist of cars and light-duty trucks. This case study used th...

Full description

Bibliographic Details
Main Authors: Dhan Lord B. Fortela, Ashton C. Fremin, Wayne Sharp, Ashley P. Mikolajczyk, Emmanuel Revellame, William Holmes, Rafael Hernandez, Mark Zappi
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Clean Technologies
Subjects:
machine learning
fuel economy
vehicle emissions
Online Access:https://www.mdpi.com/2571-8797/5/1/21

Internet

https://www.mdpi.com/2571-8797/5/1/21

Similar Items