Zhao Jiangbin

Remote fault diagnostic model for tribological systems in marine diesel engine with two-level self-organizing map network

Many researches indicate that a great number of failures occur in the tribological system which will reduce the reliability of the marine diesel engine. Therefore, it is necessary to monitor the condition and identify the fault mode of the engine. In this paper, remote fault diagnostic technology is developed to take full advantage of the online oil monitoring system and the laboratory analysis for the tribological systems. To increase the efficiency of fault diagnosis, a two-level fault diagnostic model based on self-organizing map (SOM) was established with the oil parameters from the online oil system and the experimental data from the laboratory. Based on the component map of SOM network, the attributes of the feature vector in the second level were reduced to simplify the model and the trajectory of the samples was tracked during the application of the system. The diagnostic result indicates that the remote fault diagnosis technology benefits the full acquirement of the information reflecting the engine condition and the two level fault diagnostic model can be well applied in fault diagnosis for the tribological systems in marine diesel engine with satisfactory result.

Development of ME-GI dual-fuel engine fault diagnosis expert system based on self-learning ontology

In order to meet intelligent requirement of the expert system in the process of fault diagnosis, a fault diagnosis system architecture which based on self-learning ontology was proposed in this paper. The fault diagnosis knowledge structure was defined; the relevant structure ontology and core fault ontology were constructed. Based on design of data warehouse of fault diagnosis, the decision tree in machine learning and Apriori algorithm were used to acquire fault knowledge to realize ontology self-learning. Taking the Hydraulic Control System of ME-GI dual-fuel engine as a prototype, the Hydraulic Control System diagnosis expert system was developed based on self-learning ontology.