Application Of Intelligent Fault Diagnosis Technology In Intermediate Frequency Smelting

In recent years, intermediate frequency smelting technology as an emerging technology has been widely used in quenching, melting, heat penetrating industrial areas. As the automation levels rising, reliable, stable and safe operation is becoming more and more important to intermediate frequency smelting system.The fault diagnosis of intermediate frequency smelting system is researched by monitoring main circuit voltage signals. The short-circuited fault signals of converter are random and unstable. Fourier Transform can't meet the unsteady signal feature vector extraction requirements. At the same time, establishing a mathematical model of inverter fault is more complex. For this reason, the fault diagnosis method of inverter based on wavelet packet analysis and PSO neural network is put forward in this paper. Wavelet packet is used to extract the feature vector from fault signals. The PSO neural network is used to identify the feature vector and output the corresponding fault type of the system.First, the method of intermediate frequency smelting fault diagnosis obtains corresponding fault waveform through MATLAB software to build a simulation model.Secondly, when circuit in fault conditions, energy value of the voltage test point is different in the same band. This is the so-called law of conservation of energy. If a band energy is low ,another band energy may be high. Anyhow, various changes of band energy embody the fault characteristics. The algorithm of wavelet packet strikes the energy of frequency band and gains the open circuit fault characteristics from various changes in each energy frequency band. Finally, this paper focuses on testing five types of short-circuit fault state of the converter: no-power tube failure, single power tube failure, two power tube failure in the same bridge arm, two power tube failure in the same half bridge arm, cross two power tube failure. After finishing feature extraction, PSO neural network is established by setting initial value and training neural network. The algorithms in this paper are realized with MATLAB software.The simulation experiment verifies the accuracy and validity of the fault diagnosis algorithm. The samples and testing signals are collected separately by simulating. Fault feature vectors are obtained by wavelet packet analysis which are off-line analysis. Neural network based on PSO is trained by sample eigenvectors, and tested by testing eigenvectors after the successful training. Test results show that the output fault type accords with the corresponding actual fault state of the test signals. The simulation experiment results prove that PSO neural network intelligent fault diagnosis method is practicable and has the advantage of fast convergence and high accuracy...