Modeling The Stable Operating Envelope For Partially Stable Combustion Engines Using Class Imbalance Learning

TL;DR

This paper employs machine learning techniques, including cost-sensitive algorithms, to accurately model the narrow stable operating envelope of HCCI engines from experimental data, addressing class imbalance issues.

Contribution

It introduces a novel application of cost-sensitive learning methods to model the HCCI stable operating boundary directly from data, improving stability prediction accuracy.

Findings

Cost-sensitive SVM and ELM outperform traditional methods.

Models effectively predict HCCI instability from sensor data.

Approaches handle class imbalance in engine stability data.

Abstract

Advanced combustion technologies such as homogeneous charge compression ignition (HCCI) engines have a narrow stable operating region defined by complex control strategies such as exhaust gas recirculation (EGR) and variable valve timing among others. For such systems, it is important to identify the operating envelope or the boundary of stable operation for diagnostics and control purposes. Obtaining a good model of the operating envelope using physics becomes intractable owing to engine transient effects. In this paper, a machine learning based approach is employed to identify the stable operating boundary of HCCI combustion directly from experimental data. Owing to imbalance in class proportions in the data, two approaches are considered. A re-sampling (under-sampling, over-sampling) based approach is used to develop models using existing algorithms while a cost-sensitive approach is used to modify the learning algorithm without modifying the data set. Support vector machines and recently developed extreme learning machines are used for model development and results compared against linear classification methods show that cost-sensitive versions of ELM and SVM algorithms are well suited to model the HCCI operating envelope. The prediction results indicate that the models have the potential to be used for predicting HCCI instability based on sensor measurement history.