Date of Award


Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Mechanical Engineering (MS)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Jeffrey D. Naber

Advisor 2

Jason R. Blough

Committee Member 1

Wayne W. Weaver


As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control – effective control systems require a feedback signal. Indicated mean effective pressure (IMEP) is a useful feedback signal for automotive control but is costly to measure directly.

Successful machine learning based sensor fusion requires effective feature extraction and model creation. Through a multistage application of machine learning to both the feature extraction process and the IMEP estimation process we are able to efficiently extract the useful data from angle-domain sensors and supply them to an estimation model.

Phasing exhaust and intake pressure signals with their respective cam shaft timing is shown to improve the correlation between pressure pulse phase and IMEP. Incremental improvements in preprocessing and network structure allow the model to estimate cycle-cycle variations in IMEP with an accuracy of 16 kPa rms error using a combination of existing and proposed cost-effective sensors.