实验验证了神经网络用于火花点火发动机传感器冗余的目的

IF 1.1 Q3 TRANSPORTATION SCIENCE & TECHNOLOGY
E. Fornaro, Massimo Cardone, M. Terzo, S. Strano, C. Tordela
{"title":"实验验证了神经网络用于火花点火发动机传感器冗余的目的","authors":"E. Fornaro, Massimo Cardone, M. Terzo, S. Strano, C. Tordela","doi":"10.4271/03-17-02-0012","DOIUrl":null,"url":null,"abstract":"In the aeronautical field, aircraft reliability is strictly dependent on\n propulsion systems. Indeed, a reliable propulsion system ensures the safety of\n pilots and passengers and the possibility of making comfortable flights.\n Typically, on aircraft equipped with spark ignition (SI) engines, one of the\n principal requirements to make them reliable is the correct balancing between\n the intake air mass and fuel flows. Advances in the implementation of\n sophisticated control and estimation strategies on SI engines allow realizing\n engines with improved features in terms of performance, reducing pollution\n emissions, and fuel consumption. Approaches based on sensor redundancy are\n applied to improve the reliability in measurements of the manifold air pressure\n (MAP) and flow (MAF) to avoid issues related to possible faults of sensors vital\n for the correct functioning of SI engines. Model-based estimation techniques,\n based on the speed–density and alpha-speed methods for determining the MAF in\n engine control units, are employed to obtain sensor-less redundancy. The\n prediction of MAP and MAF, for sensors redundancy purposes, can be made through\n neural networks, allowing the avoidance of effects due to unmodeled dynamical\n behaviors. A sensor redundancy approach based on feedforward neural networks\n (FNNs) is proposed in this work for MAP and MAF prediction of a SI engine. The\n present work focuses on the possibility of estimating the physical quantities\n related to SI engines, such as the MAP and the MAF, fundamental for their\n monitoring using neural networks trained by means of a model-based approach\n avoiding expensive experimental tests for producing training data. A well-known\n intake manifold dynamical model (IMDM), parametrized based on the CMD 22\n aeronautical engine, is employed for generating synthetic training data in\n steady-state conditions functional for making the chosen FNNs able to predict\n both MAP and MAF even in transient behavior.\n\n \nThe MAP and MAF are predicted through two virtual sensors based on two\n independent FNNs, having the same inputs, constituted by the engine speed and\n the throttle angle. An experimental investigation based on an aircraft endurance\n test of two hours proposed by the European Aviation Safety Agency (EASA) has\n been made on a controlled and monitored CMD 22 engine for comparing the\n experimentally measured MAP and MAF with the predicted ones by the FNNs. The\n results demonstrate the suitability of the proposed approach for sensor\n redundancy purposes in SI engines to increase their reliability.","PeriodicalId":47948,"journal":{"name":"SAE International Journal of Engines","volume":"60 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Experimentally Validated Neural Networks for Sensors Redundancy\\n Purposes in Spark Ignition Engines\",\"authors\":\"E. Fornaro, Massimo Cardone, M. Terzo, S. Strano, C. Tordela\",\"doi\":\"10.4271/03-17-02-0012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the aeronautical field, aircraft reliability is strictly dependent on\\n propulsion systems. Indeed, a reliable propulsion system ensures the safety of\\n pilots and passengers and the possibility of making comfortable flights.\\n Typically, on aircraft equipped with spark ignition (SI) engines, one of the\\n principal requirements to make them reliable is the correct balancing between\\n the intake air mass and fuel flows. Advances in the implementation of\\n sophisticated control and estimation strategies on SI engines allow realizing\\n engines with improved features in terms of performance, reducing pollution\\n emissions, and fuel consumption. Approaches based on sensor redundancy are\\n applied to improve the reliability in measurements of the manifold air pressure\\n (MAP) and flow (MAF) to avoid issues related to possible faults of sensors vital\\n for the correct functioning of SI engines. Model-based estimation techniques,\\n based on the speed–density and alpha-speed methods for determining the MAF in\\n engine control units, are employed to obtain sensor-less redundancy. The\\n prediction of MAP and MAF, for sensors redundancy purposes, can be made through\\n neural networks, allowing the avoidance of effects due to unmodeled dynamical\\n behaviors. A sensor redundancy approach based on feedforward neural networks\\n (FNNs) is proposed in this work for MAP and MAF prediction of a SI engine. The\\n present work focuses on the possibility of estimating the physical quantities\\n related to SI engines, such as the MAP and the MAF, fundamental for their\\n monitoring using neural networks trained by means of a model-based approach\\n avoiding expensive experimental tests for producing training data. A well-known\\n intake manifold dynamical model (IMDM), parametrized based on the CMD 22\\n aeronautical engine, is employed for generating synthetic training data in\\n steady-state conditions functional for making the chosen FNNs able to predict\\n both MAP and MAF even in transient behavior.\\n\\n \\nThe MAP and MAF are predicted through two virtual sensors based on two\\n independent FNNs, having the same inputs, constituted by the engine speed and\\n the throttle angle. An experimental investigation based on an aircraft endurance\\n test of two hours proposed by the European Aviation Safety Agency (EASA) has\\n been made on a controlled and monitored CMD 22 engine for comparing the\\n experimentally measured MAP and MAF with the predicted ones by the FNNs. The\\n results demonstrate the suitability of the proposed approach for sensor\\n redundancy purposes in SI engines to increase their reliability.\",\"PeriodicalId\":47948,\"journal\":{\"name\":\"SAE International Journal of Engines\",\"volume\":\"60 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SAE International Journal of Engines\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4271/03-17-02-0012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAE International Journal of Engines","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4271/03-17-02-0012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

在航空领域,飞机的可靠性严格依赖于推进系统。事实上,一个可靠的推进系统可以确保飞行员和乘客的安全,并有可能使飞行舒适。通常,在配备火花点火(SI)发动机的飞机上,使其可靠的主要要求之一是进气质量和燃油流量之间的正确平衡。在SI发动机上实施复杂控制和估计策略的进展允许实现发动机在性能,减少污染排放和燃料消耗方面具有改进的功能。基于传感器冗余的方法被应用于提高流道空气压力(MAP)和流量(MAF)测量的可靠性,以避免与传感器可能出现的故障相关的问题,这些故障对SI发动机的正确运行至关重要。在确定发动机控制单元MAF的速度-密度和α -速度方法的基础上,采用基于模型的估计技术来获得无传感器冗余。出于传感器冗余的目的,MAP和MAF的预测可以通过神经网络进行,从而避免由于未建模的动态行为而产生的影响。本文提出了一种基于前馈神经网络(fnn)的传感器冗余方法,用于SI发动机的MAP和MAF预测。目前的工作重点是估计与SI引擎(如MAP和MAF)相关的物理量的可能性,这是使用基于模型的方法训练的神经网络进行监测的基础,避免了为产生训练数据而进行昂贵的实验测试。基于CMD - 22航空发动机参数化的进气歧管动力学模型(IMDM)用于生成稳态条件下的综合训练数据,使所选的fnn在瞬态行为下也能预测MAP和MAF。MAP和MAF是通过基于两个独立的fnn的虚拟传感器来预测的,它们具有相同的输入,由发动机转速和油门角组成。以欧洲航空安全局(EASA)提出的2小时飞机续航测试为基础,在一台受控和监测的CMD - 22发动机上进行了实验研究,将实验测量的MAP和MAF与fnn预测的MAP和MAF进行了比较。结果表明,该方法适用于SI引擎的传感器冗余目的,以提高其可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experimentally Validated Neural Networks for Sensors Redundancy Purposes in Spark Ignition Engines
In the aeronautical field, aircraft reliability is strictly dependent on propulsion systems. Indeed, a reliable propulsion system ensures the safety of pilots and passengers and the possibility of making comfortable flights. Typically, on aircraft equipped with spark ignition (SI) engines, one of the principal requirements to make them reliable is the correct balancing between the intake air mass and fuel flows. Advances in the implementation of sophisticated control and estimation strategies on SI engines allow realizing engines with improved features in terms of performance, reducing pollution emissions, and fuel consumption. Approaches based on sensor redundancy are applied to improve the reliability in measurements of the manifold air pressure (MAP) and flow (MAF) to avoid issues related to possible faults of sensors vital for the correct functioning of SI engines. Model-based estimation techniques, based on the speed–density and alpha-speed methods for determining the MAF in engine control units, are employed to obtain sensor-less redundancy. The prediction of MAP and MAF, for sensors redundancy purposes, can be made through neural networks, allowing the avoidance of effects due to unmodeled dynamical behaviors. A sensor redundancy approach based on feedforward neural networks (FNNs) is proposed in this work for MAP and MAF prediction of a SI engine. The present work focuses on the possibility of estimating the physical quantities related to SI engines, such as the MAP and the MAF, fundamental for their monitoring using neural networks trained by means of a model-based approach avoiding expensive experimental tests for producing training data. A well-known intake manifold dynamical model (IMDM), parametrized based on the CMD 22 aeronautical engine, is employed for generating synthetic training data in steady-state conditions functional for making the chosen FNNs able to predict both MAP and MAF even in transient behavior. The MAP and MAF are predicted through two virtual sensors based on two independent FNNs, having the same inputs, constituted by the engine speed and the throttle angle. An experimental investigation based on an aircraft endurance test of two hours proposed by the European Aviation Safety Agency (EASA) has been made on a controlled and monitored CMD 22 engine for comparing the experimentally measured MAP and MAF with the predicted ones by the FNNs. The results demonstrate the suitability of the proposed approach for sensor redundancy purposes in SI engines to increase their reliability.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
SAE International Journal of Engines
SAE International Journal of Engines TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
2.70
自引率
8.30%
发文量
38
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信