基于多尺度特征能量-核极限学习机的双循环流化床气流堵塞故障智能诊断

杨新; 麻哲瑞; 申赫男; 陈鸿伟

doi:10.11949/0438-1157.20181334

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基于多尺度特征能量-核极限学习机的双循环流化床气流堵塞故障智能诊断

过程系统工程 | 更新时间：2026-07-24

- 基于多尺度特征能量-核极限学习机的双循环流化床气流堵塞故障智能诊断
- Fault diagnosis of airflow jamming fault in double circulating fluidized bed based on multi-scale feature energy and KELM
- 化工学报 2019年70卷第7期页码：2616-2625
- 作者机构：
  
  1.河北工程大学水利水电学院，河北邯郸 056002
  2.华北电力大学电站设备状态监测与控制教育部重点实验室，河北保定 071000
- 作者简介：
  
  杨新（1987—），男，博士，讲师，yangxin890322@126.com
  麻哲瑞（1995—），男，博士研究生，mazherui1995@126.com
- 基金信息：
  
  河北省青年基金项目(QN2016204)
- DOI：10.11949/0438-1157.20181334
  中图分类号： TK 229
- 收稿：2018-11-15，
  
  修回：2019-03-18，
  
  网络首发：2026-07-24，
  
  纸质出版：2019-07-05
- 稿件说明：
移动端阅览
杨新, 麻哲瑞, 申赫男, 陈鸿伟. 基于多尺度特征能量-核极限学习机的双循环流化床气流堵塞故障智能诊断[J]. 化工学报, 2019, 70(7): 2616-2625

YANG Xin, MA Zherui, SHEN Henan, CHEN Hongwei. Fault diagnosis of airflow jamming fault in double circulating fluidized bed based on multi-scale feature energy and KELM[J]. CIESC Journal, 2019, 70(7): 2616-2625
杨新, 麻哲瑞, 申赫男, 陈鸿伟. 基于多尺度特征能量-核极限学习机的双循环流化床气流堵塞故障智能诊断[J]. 化工学报, 2019, 70(7): 2616-2625 DOI： 10.11949/0438-1157.20181334.

YANG Xin, MA Zherui, SHEN Henan, CHEN Hongwei. Fault diagnosis of airflow jamming fault in double circulating fluidized bed based on multi-scale feature energy and KELM[J]. CIESC Journal, 2019, 70(7): 2616-2625 DOI： 10.11949/0438-1157.20181334.

摘要

为减轻双循环流化床结块与堵塞故障对生物质气化反应的负面影响，提出基于多尺度特征能量-核极限学习机(kernel extreme learning machine

KELM)的故障诊断模型。首先对故障状态下压力信号采用小波分解获得多尺度信号，然后提取各尺度特征能量作为特征向量，最后将其输入经遗传算法优化的核极限学习智能诊断模型，实现双循环流化床气流堵塞故障的智能诊断。通过对公开的轴承故障数据集和双循环流化床冷态实验系统数据的分类识别分析，并与基于变分模态分解和样本熵特征提取的KELM诊断模型进行比较，结果表明：本模型具有较高的故障诊断精度(82.5%)，能够有效提取故障特征，用于双循环流化床气流堵塞的高效分类识别。

Abstract

In order to alleviate the negative impact of dual circulating fluidized bed agglomeration and plugging failure on biomass gasification reaction

a fault diagnosis model based on multi-scale feature energy-kernel extreme learning machine (KELM) was proposed. Firstly

wavelet decomposition is used to obtain the multi-scale signal in the fault state

then the feature energy of each scale is extracted as the feature vector

and finally input into the nuclear limit learning intelligent diagnosis model optimized by genetic algorithm to realize the intelligent diagnosis of dual circulating fluidized bed airflow blockage faults. Through the classification and analysis of the open bearing fault data set and the dual-circulating fluidized bed cold experimental system data

and compared with the KELM diagnostic model based on variational mode decomposition and sample entropy feature extraction

the results show that the model has higher fault diagnosis accuracy (82.5%)

which can effectively extract fault characteristics and be used for efficient classification and identification of dual circulating fluidized bed airflow blockage.

关键词

Keywords

references

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