基于联合定位密集网络的变载齿轮箱故障特征提取

doi:10.16265/j.cnki.issn1003-3033.2024.10.1296

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (10): 166-173.doi: 10.16265/j.cnki.issn1003-3033.2024.10.1296

基于联合定位密集网络的变载齿轮箱故障特征提取

樊晓萱¹^,²(), 段礼祥¹^,²^,^**(), 张娜¹^,², 李兴涛³, 蒋璐朦³

¹ 中国石油大学(北京) 安全与海洋工程学院,北京 102249
² 应急管理部油气生产安全与应急技术重点实验室,北京 102249
³ 中国石油国际勘探开发有限公司,北京 100034

收稿日期:2024-06-15 修回日期:2024-08-21 出版日期:2024-10-28
通信作者:
^** 段礼祥(1969—),男,四川泸州人,博士,教授,主要从事安全监测与智能诊断工程方面的研究。E-mail:duanlx@cup.edu.cn。
作者简介:
樊晓萱 (2000—),女,湖南益阳人,博士研究生,研究方向为安全大数据与人工智能。E-mail:2018010731@student.cup.edu.cn。
基金资助:
中石油战略合作科技专项(ZLZX2020-05-02)

Conjoint localization dense networks for fault feature extraction of variable load gearbox

FAN Xiaoxuan¹^,²(), DUAN Lixiang¹^,²^,^**(), ZHANG Na¹^,², LI Xingtao³, JIANG Lumeng³

¹ College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
² Key Laboratory of Oil and Gas Production Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
³ China National Oil and Gas Exploration and Development Co., Ltd., Beijing 100034, China

Received:2024-06-15 Revised:2024-08-21 Published:2024-10-28

摘要/Abstract

摘要：

为解决变载齿轮箱故障诊断中因冗余特征而导致的脉冲信号提取难题,提出一种基于注意力模块(CAM)的脉冲特征提取方法。首先,设计联合定位CAM,包括2个阶段:第1阶段使用多层感知机建模通道依赖关系,增强与故障相关的关键通道特征;第2阶段通过卷积层学习与故障相关的信号段,结合2个阶段重新校准特征,聚焦关键脉冲特征;然后,基于CAM构建联合定位密集网络(CLDN)的变载齿轮箱故障特征提取方法,CLDN通过自适应地重新校准每一层的特征,进一步提高对脉冲信号的学习和表征能力;最后,将提取到的特征输入Softmax分类器,验证所提方法的特征提取效果。结果表明: 相比于Self-Attention等4种注意力机制,CAM的准确率平均提升3.8%,可实现脉冲特征的准确定位;相比于ResNet34等7种诊断方法,CLDN的准确率提升3.7%~14.6%,显著增强故障特征的提取效果。

关键词: 联合定位密集网络(CLDN), 变载齿轮箱, 故障诊断, 特征提取, 注意力模块(CAM)

Abstract:

To address the challenge of extracting pulse signals in fault diagnosis of variable-load gearbox caused by redundant features, a pulse feature extraction method based on CAM was proposed. First, a CAM was designed, which consisted of two stages. In the first stage, a multilayer perceptron was used to simulate the channel dependencies and enhanced the important channel features related to faults. In the second stage, the convolutional layers were employed to learn signal segments related to faults. By recalibrating the features in two stages, the module focused on the critical pulse features. Next, based on CAM, this study proposed a CLDN method for extracting fault features in variable-load gearboxes. CLDN further improved the learning and representation of impulse signals by adaptively recalibrating the features at each layer. Finally, the extracted features were fed into a Softmax classifier to validate the feature extraction effect of the proposed method. The results show that CAM's accuracy is on average 3.8% higher than 4 attention mechanisms like Self-Attention, achieving accurate localization of impulse features. Compared with 7 diagnostic methods such as ResNet34, the accuracy of CLDN is 3.7% to 14.6% higher, which significantly enhances the extraction of fault features.

Key words: conjoint localization dense networks (CLDN), variable-load gearbox, fault diagnosis, feature extraction, conjoint attention module(CAM)

中图分类号:

X941

樊晓萱, 段礼祥, 张娜, 李兴涛, 蒋璐朦. 基于联合定位密集网络的变载齿轮箱故障特征提取[J]. 中国安全科学学报, 2024, 34(10): 166-173.

FAN Xiaoxuan, DUAN Lixiang, ZHANG Na, LI Xingtao, JIANG Lumeng. Conjoint localization dense networks for fault feature extraction of variable load gearbox[J]. China Safety Science Journal, 2024, 34(10): 166-173.

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网络	各类故障状态诊断精度					总体诊断精度
网络	正常	剥落	点蚀	断齿	缺齿	总体诊断精度
DenseNet	92.22	100.00	96.67	88.89	100.00	95.56
DenseNet-FAM	97.78	100.00	96.67	85.56	100.00	96.00
DenseNet-SAM	94.44	100.00	96.67	97.78	100.00	97.78
CLDN	100.00	95.56	100.00	98.89	100.00	98.89

信噪比/dB	各网络诊断精度
信噪比/dB	CNN	ResNet34	DenseNet	Self-Attention	CBAM	ECA	No-local	CLDN
1	89.11	96	97.11	94.89	96.67	98.22	97.78	98.67
0	87.33	94.89	96.67	92.89	92.44	98	94.22	98.44
-1	75.78	94.67	95.56	91.11	92.45	96.67	92.89	98.89
平均值	84.07	95.19	96.45	92.96	93.85	97.63	94.96	98.67

故障类型	负载/kW×转速/ (r·min^-1)	训练	验证	测试	故障标签
正常	2.250×1 730	140	30	30	0
内圈故障IR1	0×1 797	140	30	30	1
内圈故障IR2		140	30	30	2
内圈故障IR3		140	30	30	3
外圈故障OR1	0.735×1 772	140	30	30	4
外圈故障OR2		140	30	30	5
外圈故障OR3		140	30	30	6
滚动体故障BA1	1.470×1 750	140	30	30	7
滚动体故障BA2		140	30	30	8
滚动体故障BA3		140	30	30	9

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基于联合定位密集网络的变载齿轮箱故障特征提取

Conjoint localization dense networks for fault feature extraction of variable load gearbox

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