基于多数据融合的电机故障诊断方法研究

电机作为各类电驱设备的主要动力装置,具有结构简单、控制方便、能效高、无污染等优点.在电机运行过程中,受载荷多变、零部件老化、散热条件差等影响,故障频发,进而降低电驱装置的工作效率和稳定性.此外,电机故障种类繁多,各故障的征兆与表现又极其相似,不同故障产生的原因也错综复杂,这极大地提高了电机故障诊断的难度.传统的电机故障...

Research on Motor Fault Diagnosis Method Based on Multi Data Fusion

As the main power equipment of all kinds of electric drive devices,motor has the advantages of simple structure,convenient control,high energy efficiency and no pollution.In the process of motor operation,affected by variable load,aging of parts and poor heat dissipation conditions,faults occur frequently,thus the working efficiency and stability of the electric drive device are reduced.In addition,there are many kinds of motor faults,the symptoms and performance of each fault are very similar,the causes of different faults are also complex,which greatly improve the difficulty of motor fault diagnosis.The traditional motor fault diagnosis process is mostly based on a single sensor signal,which has the problems such as large uncertainty and poor diagnosis accuracy.In order to overcome the above shortcomings,a motor fault diagnosis method based on multi-sensor parameter fusion was proposed.Based on vibration accelerometer and current sensor signal,combined with BP neural network algorithm and D-S evidence theory,the motor fault was accurately identified and the accuracy of motor fault diagnosis was improved.The structure framework of multi-sensor data fusion technology was briefly introduced.Based on the analysis of the typical fault mechanism of asynchronous motor,the multi-sensor data fusion motor fault diagnosis system based on BP neural network learning algorithm and D-S evidence theory was analyzed in detail,and the effectiveness of the proposed fault diagnosis method was verified by an example.The results show that the proposed multi data fusion motor fault diagnosis method can diagnose the motor fault type with high confidence.