Wrong Injection Detection in a Small Diesel Engine, a Machine Learning Approach

Abstract

In the last ten years, Machine Learning (ML) and Artificial Intelligence (AI) have overwhelmed every engineering research branch finding a broad variety of applications; anomaly detection and anomaly classification are two of the topics that have benefited mostly by data-driven methods’ insights. On the other side, in the small diesel engine domain, the current trend is to lean on traditional anomaly detection/classification procedures and do not foster the use of AI. The goal of this work is to detect anomalies in the in-cylinders injectors of a small diesel engine as soon as a wrong quantity of fuel is inputted into one or more cylinders by means of ML approaches. Part of the analysis aim to understand which measurements are the most relevant for the detection and to compare different techniques to select the most suitable one. Furthermore, a condition-based methodology for maintenance is proposed. After a brief review of the state-of-the-art, the case study scenario is presented grouping sensors accordingly to their degree of accessibility; then, the implemented techniques are explained, and results are discussed.