Aluminum electrolysis fault diagnosis research based on principal component analysis

作者：Li Jiejia^*; Han Xiaoyan; Zhou Peng

来源：Information Technology Journal, 2013, 12(23): 7076-7082.

DOI：10.3923/itj.2013.7076.7082

• Aluminum electrolysis
• Decision fusion
• Fault feature
• Improved BP network
• Improved BP neural network
• Large amounts
• Multiple faults diagnosis
• Neural network fault diagnosis

According to the characteristics of the aluminum electrolysis fault, the principal component analysis and improved BP network are used for extracting fault feature, the improved BP neural network can extract the fault feature and can also be used as preliminary diagnosis of the fault, thereby electrolytic method for multiple faults diagnosis of three neural networks aluminum is taken use, this method analyzes the deficiency of single neural network and two stage neural network fault diagnosis and in order to form the decision fusion network, the wavelet analysis and neural network are combined organically. The simulation results show that: the aluminum electrolysis fault diagnosis resrarch based on principal component analysis has the characteristic of large amount of ault forecast.

• 出版日期2013