Fault diagnosis method of petrochemical air compressor based on deep belief network

LU Chunyan; LI Wei

doi:10.11949/j.issn.0438-1157.20181357

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Fault diagnosis method of petrochemical air compressor based on deep belief network

Process system engineering | 更新时间：2026-07-24

- Fault diagnosis method of petrochemical air compressor based on deep belief network
- CIESC Journal Vol. 70, Issue 2, Pages: 757-763(2019)
- 作者机构：
  
  1.兰州理工大学电气工程与信息工程学院，甘肃兰州 730050
  2.甘肃省工业过程先进控制重点实验室，甘肃兰州 730050
  3.兰州理工大学国家级电气与控制工程实验教学中心，甘肃兰州 730050
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20181357
  CLC： TP273
- Received：16 November 2018，
  
  Revised：2018-11-26，
  
  Online First：16 June 2020，
  
  Published：05 February 2019
- 稿件说明：
移动端阅览
鲁春燕, 李炜. 基于深度置信网络的炼化空压机故障诊断方法[J]. 化工学报, 2019, 70(2): 757-763

LU Chunyan, LI Wei. Fault diagnosis method of petrochemical air compressor based on deep belief network[J]. CIESC Journal, 2019, 70(2): 757-763
鲁春燕, 李炜. 基于深度置信网络的炼化空压机故障诊断方法[J]. 化工学报, 2019, 70(2): 757-763 DOI： 10.11949/j.issn.0438-1157.20181357.

LU Chunyan, LI Wei. Fault diagnosis method of petrochemical air compressor based on deep belief network[J]. CIESC Journal, 2019, 70(2): 757-763 DOI： 10.11949/j.issn.0438-1157.20181357.

摘要

针对炼化空压机故障诊断中故障机理复杂、先验知识欠缺，且传统浅层神经网络诊断精度不高等问题，结合深度置信网络（DBN）在特征提取和处理非线性数据等方面的优势，提出一种基于DBN的炼化空压机故障诊断方法。该方法利用空压机状态监测实测数据，实现训练网络的无监督特征学习和有监督微调，构建空压机故障的深层网络模型，从而实现对空压机故障类型的有效智能诊断。为评估该方法的有效性，与传统的故障诊断方法进行了比较研究，结果表明，该方法的诊断精度优于传统的故障诊断方法，且稳定性更好。

Abstract

According to the complexity of fault mechanism

the lack of prior knowledge

and the low diagnosis precision of traditional shallow layer neural network for the fault diagnosis of petrochemical air compressor

a kind of petrochemical air compressor fault diagnosis method is put forward based on the deep belief network because of its advantage in feature extraction and nonlinear data processing. By using state monitoring data of the air compressor

the method realizes the unsupervised characteristics learning and supervised fine-tuning of training network

constructs the deep network model of the air compressor fault

thus achieving the effective intelligent diagnosis for fault types of the air compressor. The effectiveness of the method is compared with the traditional fault diagnosis method. The results show that the diagnostic accuracy of the method is better than the traditional fault diagnosis method and the stability is better.

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references

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