Application and Research of Multi-Sensor Data Fusion for the Earlier Period Diagnosis in Induction Motors

Abstract:

The multi-sensor data fusion widely applied to military and industry areas is a new technique developed in recent years. It can avoid the limitations of a single sensor and obtain more information, improving the recognition ability. This paper analyzes the uncertainties in the traditional earlier period diagnosis in induction motors based on the single parameter and introduces the idea of using multi-sensor data fusion to handle these uncertainties. In a fusion system several parameters will be fused according to the D-S evidential fusion algorithm in order to identify accurately the earlier period faults of the induction motors. Practical diagnostic examples show that the fault diagnostic accuracy and confidence are markedly promoted by using the multi-sensor data fusion.

• Periodical: Advanced Materials Research (Volumes 706-708)
• Pages: 644-649
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.706-708.644
• Online since: June 2013
• Authors: Shu Ying Li, Mu Qin Tian, Lei Xue
• Keywords: Dempster-Shafer (D-S) Evidential Theory, Earlier Period Diagnosis, Multi-Sensor Data Fusion

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