The Application of the Equipment Fault Diagnosis based on Modified Elman Neural Network
The aluminum electrolysis cell is the most important equipment in electrolytic process, which has many types of fault and high occurrence rate. So, it is a high energy consumption process and the process control is very difficult, which reduce the production and quality of the aluminum and waste a lot of electricity energy. Therefore, this paper proposes an equipment fault diagnosis method based on modified output feedback wavelet Elman neural network. This fault diagnosis model adopts wavelet function, with wavelet expansion coefficient and translation coefficient, which results in the guarantee of the speed and accuracy, avoiding falling into local optimal values, and improving the rate of fault diagnosis. Simulation results prove the effectiveness of this method.
fault diagnosis aluminum equipment Elman neural network wavelet fuction
Jiejia Li Hao Wu Jinxiang Pian
School of Information and Control Engineering, Shenyang Jianzhu University Shenyang, China
国际会议
哈尔滨
英文
4135-4137
2011-08-12（万方平台首次上网日期，不代表论文的发表时间）