Abstract: Addressing the limitations of traditional asynchronous motor fault diagnosis methods, this paper proposes a fault diagnosis method based on information fusion using DS evidence theory, building upon a study of the characteristics and requirements of asynchronous motor fault diagnosis. This method constructs theoretical evidence from collected characteristic information such as voltage, current, and winding temperature of the asynchronous motor. Using specific decision rules, it selects the maximum hypothesis under the fused evidence to determine the system state and identify faults. Simulation experiments were conducted on the diagnostic system constructed using this method, demonstrating its rapid and effective fault diagnosis. Keywords: fault diagnosis; information fusion; evidence theory[b][align=center]APPLICATION OF INFORMATION FUSION TECHNOLOGY IN FAULT DIAGONOSIS FOR ASYNCHRONOUS MOTOR Zhang Lian Yu Chengbo Liu Shuxi Chen Hongyan[/align][/b] Abastract: Aiming at limitation of the traditional method of fault diagnosis for asynchronous motor, based on the research of the characteristics and demands of fault diagnosis for asynchronous motor, this paper puts forward a fault diagnosis method base on DS evidence theory information fusion. This method uses characteristic information of asynchronous motor such as voltage, current, winding temperature, and so on, to form the evidences in the theory. It adopts some decision-making rule, chooses maximal suppose under fusion evidence, judge the state of system, and recognize the fault of system. 0 Introduction Asynchronous motors are widely used in various industries and play a very important role in national economic construction. However, due to the complex operating environment, asynchronous motors are affected by various factors such as electricity, heat, mechanics, and the surrounding environment during operation, resulting in various faults that cause serious damage and malignant accidents. These fault types are diverse and their relationships are complex, which makes it difficult to effectively and quickly diagnose motor faults. At present, asynchronous motor fault diagnosis technology has developed from the traditional offline method to the modern online diagnosis method, and there are more and more new theories and methods for fault diagnosis [1]. This paper uses the DS evidence theory to predict, analyze, judge, and determine the nature and type of faults that may occur or have already occurred based on the various voltage, current, winding temperature and other characteristic information of the asynchronous motor collected, combined with known parameters, structural characteristics and ambient temperature. This method can not only improve the utilization rate of information and make comprehensive use of the information provided by various sensors, but also obtain more information in less time, which greatly improves the identification efficiency of the system. For details, please click: Application of Information Fusion in Asynchronous Motor Fault Diagnosis