Research On Fault Diagnosis Algorithm Of Subway Auxiliary Inverter

As the development of technology, Industrial department own a batch of intelligent production equipment with strong capability and advanced technology. The degree of intelligent equipment industry has become an importance marking which measures industry modernization degree. It is an important approach to train and opening-up strategic emerging industries. However, with the device and the industrial environment more and more complicated, the methods formerly used in fault diagnosis could no longer satisfy the ever increasing demand. Hence, on the basis of the subway auxiliary inverter fault diagnosis, this paper research possible fault types such as frequency translation, voltage fluctuation and impulsive transient.This paper can be divided into five chapters. In the first chapter, the origin, engineering background and significance of the subject are introduced. The second chapter includes some common analysis methods of signal, their advantages and disadvantages were analyzed objectively. The 3rd, 4th and 5th chapters are the main content of this paper, in these chapters, the author design the fault diagnosis simulation experiment of auxiliary inverter of metro. The main thread of this paper is listed below:The 3rd chapter introduces EMD, EEM and LMD firstly. Using EMD, EEMD and LMD carry on the decomposition of original signal of frequency translation, voltage fluctuation and impulsive transient, and get different characteristic vector of signal by energy moment and approximate entropy.The 4th chapter introduces an optimized algorithm for identifying fault types based on QPSO and LSSVM. The 5th chapter introduces an optimized algorithm for identifying fault types based on GA and BP neural network. This paper set the contrast experiment, through experiments prove, the two optimization algorithms can identify fault types more effectively and more accurate than the standard algorithms.