Abnormal combustion is an important factor in the development process of hydrogen engine and it mainly includes pre-ignition, backfire and knocking, among which pre-ignition has the most serious impact on hydrogen engine. In this paper, it is divided into four types: normal combustion, slight pre-ignition, moderate pre-ignition and severe pre-ignition according to different crankshaft rotation angles. In order to identify different combustion types, this paper proposes a fault diagnosis model based on the fusion of SOM neural network and Multi-Agent System (SOM-MAS). Firstly, different combustion types are identified by SOM. Secondly, the abnormal combustion is tracked and located mainly through the Multi-Agent System, and the location of the abnormality is identified. Finally, based on 44 sets of pressure data samples collected from the in-cylinder combustion of a hydrogen engine on the experimental bench, different combustion types were diagnosed and identified, and the location of abnormal combustion faults was tracked, which verifies the effectiveness of the proposed method shows that the method has certain feasibility and superiority for the diagnosis of hydrogen engine pre-ignition.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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