NDLI: A New Intelligent Fusion Method of Multi-Dimensional Sensors and Its Application to Tribo-System Fault Diagnosis of Marine Diesel Engines

Content Provider	Springer Nature Link
Author	Li, Zhixiong Yan, Xinping Guo, Zhiwei Liu, Peng Yuan, Chengqing Peng, Zhongxiao
Copyright Year	2012
Abstract	Marine diesel engines, a critical component to provide power for entire ships, have been received and still need considerable attentions to ensure their safety operation. Vibration and wear debris analysis are currently the most popular techniques for diesel engine condition monitoring and fault diagnosis. However, they are usually used independently in practice, and limited work has been done to address the integration of data collected using the two techniques. To enhance early fault detections, a new fault diagnosis technique for the marine diesel engine has been proposed by the information fusion of the vibration and wear particle analyses in this paper. A new independent component analysis with reference algorithm (ICA-R) using the empirical mode decomposition based reference extraction scheme was adopted to identify the characteristic source signals of the engine vibration collected from multi-channel sensors. The advantage of this approach performed at a data fusion level is that the ICA-R can extract only the relevant source directly related to the engine fault features in one separation cycle via incorporating prior knowledge. The statistical values of the recovered source signals were then calculated. The above vibration features, along with the wear particle characteristics, were used as the feature vectors for the engine fault detection. Lastly, the improved simplified fuzzy ARTMAP (SFAM) was applied to integrate the distinctive features extracted from the two techniques at a decision level to detect faults in a supervised learning manner. Particularly, the immune particle swarm optimization was used to tune the vigilance parameter of the SFAM to improve the identification performance. The experimental tests were implemented on a diesel engine set-up to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have shown that distinguished fault features can be extracted and the fault identification accuracy is satisfactory. Moreover, the fault detection rate of the integration approach has been enhanced by 16.0 % or better when compared with using the two techniques separately.
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISSN	10238883
Journal	Tribology Letters
Volume Number	47
Issue Number	1
e-ISSN	15732711
Language	English
Publisher	Springer US
Publisher Date	2012-05-06
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Marine diesel engine Tribo-system Fault diagnosis Vibration analysis Wear debris analysis Physical Chemistry Tribology, Corrosion and Coatings Theoretical and Applied Mechanics Nanotechnology Surfaces and Interfaces, Thin Films
Content Type	Text
Resource Type	Article
Subject	Surfaces, Coatings and Films Mechanics of Materials Mechanical Engineering Surfaces and Interfaces

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