University of Sussex
Trimby, Stuart.pdf (5.82 MB)

Engine cylinder pressure reconstruction using crank kinematics, block vibrations, and time-delay neural networks

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posted on 2023-06-09, 01:25 authored by Stuart Trimby
Time-delay feed-forward Artificial Neural Networks are examined for gasoline engine cylinder pressure reconstruction using both measured crank kinematics obtained from a shaft encoder, and measured engine cylinder block vibrations obtained from a production knock sensor. Initially, the study focuses on the information content associated with measured data, which is considered to be of equal importance to the particular network architecture and the training methodology. Several hypotheses are constructed, which when tested, reveal the influence of the data information content on the reconstruction potential and limitations. These hypotheses are tested on real data from a 3-cylinder (DISI) engine. Three distinct ideas emerge through this testing process, which are combined to produce a single pressure reconstruction methodology. Reconstruction results obtained via this methodology, applied to crank kinematics associated with steady-state engine operation, show a marked improvement over previously published reconstruction accuracy. Moreover, in steady-state engine operation, the application of this methodology to acceleration measurements of cylinder block vibration, obtained from a knock sensor, show very significant improvements over previous attempts. But the direct application of this same reconstruction methodology to transient engine operation, proves to be problematic. However, a novel generalisation of the approach in the form of a time-dependent feed-forward neural network is proposed and the required adaptation made to the use of the Levenberg-Marquardt training algorithm. This time-dependent approach has been tested under limited transient conditions and shown in the thesis to give good results, therefore offering considerable potential for use with real engine operation. Overall, the thesis shows that by careful processing of measured engine data, standard neural network architectures and standard training algorithms can be used to reconstruct engine cylinder pressure.


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University of Sussex

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