Projekteja vuodessa
Abstrakti
In this work neural network models are used to reconstruct incylinder pressure from a vibration signal measured from the engine surface by a lowcost accelerometer. Using accelerometers to capture engine combustion is a costeffective approach due to their low price and flexibility. The paper describes a virtual sensor that reconstructs the incylinder pressure and some of its key parameters by using the engine vibration data as input. The vibration and cylinder pressure data have been processed before the neural network model training. Additionally, the correlation between the vibration and incylinder pressure data is analyzed to show that the vibration signal is a good input to model the cylinder pressure.The approach is validated on a RON95 single cylinder research engine realizing homogeneous charge compression ignition (HCCI). The experimental matrix covers multiple load/rpm steadystate operating points with different start of injection and lambda setpoints. A radial basis function (RBF) neural network model was first trained with a series of two operating points at low loads with data of 1000 consecutive combustion cycles, to build the needed nonlinear mapping. The results show that the developed neural network model is capable of reconstructing incylinder pressure at low loads with good accuracy. The error for combustion parameter such as maximum cylinder pressure did not exceed 5%. The approach is further validated with another series of operating points consisting of both low loads and high loads. However, the results in this case deteriorated. Changing the neural network model to generalized regression (GR) improved the incylinder pressure reconstruction quality. The performance of the models was also considered in terms of combustion parameters, such as maximum pressure and mass burned fraction. The paper concludes that vibration signal carries sufficient information to estimate combustion parameters independently on the engine platform or combustion concept.
Alkuperäiskieli  Englanti 

Sivumäärä  5 
Julkaisu  SAE Technical Papers 
DOI  pysyväislinkit  
Tila  Julkaistu  30 elok. 2022 
OKMjulkaisutyyppi  A4 Artikkeli konferenssijulkaisussa 
Tapahtuma  SAE International Powertrains, Fuels and Lubricants Meeting  Krakow, Puola Kesto: 6 syysk. 2022 → 8 syysk. 2022 
Sormenjälki
Sukella tutkimusaiheisiin 'A Neural Network Approach for Reconstructing InCylinder Pressure from Engine Vibration Data'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.Projektit
 2 Päättynyt

CPT Zenger: Clean Propulsion Technologies
Zenger, K. (Vastuullinen tutkija)
01/02/2021 → 31/12/2023
Projekti: Business Finland: Strategic centres for science, technology and innovation (SHOK)

: CPT Zenger
Zenger, K. (Vastuullinen tutkija), Nguyen Khac, H. (Projektin jäsen) & Modabberian, A. (Projektin jäsen)
01/02/2021 → 31/12/2023
Projekti: Business Finland: Strategic centres for science, technology and innovation (SHOK)