Diagnosis of working conditions of an aluminum reduction cell based on wavelet packets and fuzzy neural network

In this paper, fuzzy neural network is combined with wavelet packet analysis for diagnosis of working conditions of aluminum reduction cells. The sample data is pre-processed using best wavelet packet basis for the forecast and then an adaptive-network-based fuzzy inference system (ANFIS) is established for diagnosis of working conditions. The wavelet packet analysis was used to extract the characteristic of signal according to the frequency spectrum characteristics of voltage vibration signal of aluminum reduction cells. The signals were decomposed into eight frequency bands and the information pre-conditioned was used as an energy characteristic vector. The structure of ANFIS is given and the membership function is developed according to the actual situation. All simulated working conditions are emulated on 350 KA pre-baked aluminum reduction cells. The feasibility of this novel method is proved by the simulation results.