No abstract

In order to reflect the motor from various aspects and realize the motor system state failure mode automatic identification and accurate diagnosis, neural network combined with the D-S evidence theory to form the motor fault diagnosis system. In data fusion level, fault characteristic is classified; and then the fault feature is extracted by the BP neural network and the local fault of the motor is diagnosed, as a result, the independent evidence is obtained; at last the D-S evidence theory fusion algorithm is used on the evidence to achieve the fault of the motor accurate diagnosis.Broken test proved that the diagnosis system improves the motor of the fault diagnosis of accuracy, and can meet the needs of real-time diagnosis. The diagnostic test proved that the diagnosis system improves the accuracy of motor fault diagnosis, and can satisfy the diagnosis in real-time.

A problem is aroused in multi-classifier system that normally each of the classifiers is considered equally important in evidences’ combination, which gone against with the knowledge that different classifier has various performance due to diversity of classifiers. Therefore, how to determine the weights of individual classifier in order to get more accurate results becomes a question need to be solved. An optimal weight learning method is presented in this paper. First, the training samples are respectively input into the multi-classifier system based on Dempster-Shafer theory in order to obtain the output vector. Then the error is calculated by means of figuring up the distance between the output vector and class vector of corresponding training sample, and the objective function is defined as mean-square error of all the training samples. The optimal weight vector is obtained by means of minimizing the objective function. Finally, new samples are classified according to the optimal weight vector. The effectiveness of this method is illustrated by the UCI standard data set and electric actuator fault diagnostic experiment.

This paper introduces the design and simulation of Photovoltaic DC/DC transform circuit and single-phase DC/AC inverter, the parameters are introduced, and the simulation results are analyzed.

The non-stationary signal is classified into the abrupt singular signal and the gradual singular signal. Because the fault of closed-loop control system is difficult to detect the initial moment when the fault happened, and often there is a more serious consequences once the fault was detected. A new method is proposed which can predict whether the fault happened through the trend signal of a dynamic system. The trend signal was got by the wavelet transform of the detected signal. In order to verify the effectiveness of the method, the leak fault was installed in the process control water tank experiment system, and the signal of liquid level and flow was collected. The gradual leak fault was found through the trend signal in low frequency which was got by the wavelet multi-scale resolution, so the practicability of the proposed method was proved.