小型柴油机错喷检测:一种机器学习方法

Piero Danti, Ryota Minamino, G. Vichi
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摘要

在过去的十年里,机器学习(ML)和人工智能(AI)已经压倒了每一个工程研究分支,找到了各种各样的应用;异常检测和异常分类是两个主要受益于数据驱动方法洞察力的主题。另一方面,在小型柴油机领域,目前的趋势是依靠传统的异常检测/分类程序,而不促进人工智能的使用。这项工作的目标是通过ML方法在一个或多个气缸中输入错误数量的燃料时,检测小型柴油机气缸内喷油器的异常情况。分析的部分目的是了解哪些测量与检测最相关,并比较不同的技术以选择最合适的技术。此外,还提出了一种基于状态的维修方法。在简要回顾了最新技术之后,案例研究场景根据传感器的可访问程度对其进行分组;然后对实现的技术进行了说明,并对结果进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wrong Injection Detection in a Small Diesel Engine, a Machine Learning Approach
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.
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