 Fault detection in an engine by fusing information from multivibration sensorsRuili Zeng, Lingling Zhang, Jianmin Mei, Hong Shen and Huimin Zhao International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 7, 1550147717719057 Abstract: 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. Keywords: Multisensor information fusion; fault detection; support vector data description; Dempster–Shafer evidence theory (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:7:p:1550147717719057 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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