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}
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.