文章基本信息

标题：Fault detection in an engine by fusing information from multivibration sensors
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作者：Ruili Zeng ; Lingling Zhang ; Jianmin Mei 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2017
卷号：13
期号：7
页码：1
DOI：10.1177/1550147717719057
出版社：Hindawi Publishing Corporation
摘要：Fault detection based on the vibration signal of an engine is an effective non-disassembly method for engine diagnosis because a vibration signal includes a lot of information about the condition of the engine. To obtain multi-information for this article, three vibration sensors were placed at different test points to collect vibration information about the engine operating process. A method combining support vector data description and Dempster–Shafer evidence theory was developed for engine fault detection, where support vector data description is used to recognize the data from a single sensor and Dempster–Shafer evidence theory is used to classify the information from the three vibration sensors in detail. The experimental results show that the fault detection accuracy using three sensors is higher than using a single sensor. The multi-complementary sensor information can be adopted in the proposed method, which will increase the reliability of fault detection and reduce uncertainty in the recognition of a fault.
关键词：Multisensor information fusion; fault detection; support vector data description; Dempster–Shafer evidence theory