Bennett, C, Dunne, J F, Trimby, S and Richardson, D (2017) Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks. Mechanical Systems and Signal Processing, 85. pp. 126-145. ISSN 0888-3270

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Abstract

A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 litre, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified during RAGD training, acceptably accurate cylinder pressures, and especially accurate location-of-peak-pressure, can be reconstructed robustly under generalisation conditions, making it the most practical NARX configuration and recurrent training methodology for use on production engines.

Item Type:	Article
Keywords:	Neural network, NARX, recurrent training, IC engine, gasoline, cylinder pressure, crank kinematics.
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
Research Centres and Groups:	Dynamics, Control and Vehicle Research Group
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ0170 Mechanics applied to machinery. Dynamics T Technology > TJ Mechanical engineering and machinery > TJ0255 Heat engines
Depositing User:	Julian Dunne
Date Deposited:	25 Sep 2016 12:11
Last Modified:	19 Aug 2017 15:32
URI:	http://srodev.sussex.ac.uk/id/eprint/63580

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