| Title | Research on fusion diagnosis method of thermal fault of Marine diesel engine |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Zhong, Guo-qiang, Wang, Huai-yu, Zheng, Shuai, JIA, Bao-zhu |
| Conference Name | 2019 Chinese Automation Congress (CAC) |
| Keywords | AVL BOOST software, cyber physical systems, Data models, diesel engines, environmental noise, fault diagnosis, genetic algorithm, genetic algorithms, human factors, information fusion, intelligent fault fusion diagnosis method, marine diesel engine, marine diesel engine thermal fault, Marine vehicles, Mathematical model, mechanical engineering computing, Metrics, multiple fault diagnosis, multiple fault diagnosis models, pubcrawl, Resiliency, single sensor data, support vector machine, Support vector machines, Temperature measurement |
| Abstract | In order to avoid the situation that the diagnosis model based on single sensor data is easily disturbed by environmental noise and the diagnosis accuracy is low, an intelligent fault fusion diagnosis method for marine diesel engine is proposed. Firstly, the support vector machine which is optimized by genetic algorithm is used to learn the fault sample data from different sensors, then multiple fault diagnosis models and results can be got. After that, multiple groups of diagnosis results are taken as evidence bodies and fused by evidence theory to obtain more accurate diagnosis results. By analyzing the sample data obtained from the fault simulation experiment of marine diesel engine based on AVL BOOST software, the proposed method can improve the fault diagnosis accuracy of marine diesel engine and reduce the uncertainty value of diagnosis results. |
| DOI | 10.1109/CAC48633.2019.8996760 |
| Citation Key | zhong_research_2019 |
Research on fusion diagnosis method of thermal fault of Marine diesel engine