基于阳极电流波动的铝电解槽槽况诊断系统

李贺松; 殷小宝; 黄涌波; 丁立伟; 姜昌伟

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基于阳极电流波动的铝电解槽槽况诊断系统

更新时间：2025-12-10

- 基于阳极电流波动的铝电解槽槽况诊断系统
- CIESC Journal Issue 6, Pages: 1770-1777(2011)
- 作者机构：
  
  1中南大学能源科学与工程学院，2中铝沈阳铝镁设计研究院，3中铝公司广西分公司电解厂，4兖矿电铝公司,5能源高效清洁利用湖南省高校重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 June 2011，
  
  Published：05 June 2011
- 稿件说明：
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李贺松, 殷小宝, 黄涌波, et al. 基于阳极电流波动的铝电解槽槽况诊断系统[J]. CIESC Journal, 2011, (6): 1770-1777.
DOI：

李贺松, 殷小宝, 黄涌波, et al. 基于阳极电流波动的铝电解槽槽况诊断系统[J]. CIESC Journal, 2011, (6): 1770-1777. DOI：

摘要

Abstract

As the object of anodic current signal of 160 kA prebaked anode cells

these signals of different conditions were analyzed by the method of “spectrum  wavelet packet  neural network”.The results show that the anode current signals of the different status have different peak frequency value

so it is possible to extract energy characteristics vectors of anode current signals in different cell states using wavelet packets decomposition and wavelet packets reconstruction.According to the wavelet packet energy characteristics vectors extracted from anode current signals

diagnosis model based on BP neural network was established and verified.The simulation results show that the model of network identification is simple in construction

high accuracy in recognition

and convenient to realize online monitoring and realtime identification for the anode current signals of the different status.

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