[1]段耀斌,王城宇,张国谋,等.基于注意力机制的多向传感器数据融合齿轮箱故障诊断方法[J].机械与电子,2025,(07):3-8.
　DUAN Yaobin,WANG Chengyu,ZHANG Guomou,et al.Fault Diagnosis Method for Gearbox Based on Attention Mechanism and Multi-directional Sensor Data Fusion[J].Machinery & Electronics,2025,(07):3-8.
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基于注意力机制的多向传感器数据融合齿轮箱故障诊断方法()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年07期
页码:
3-8
栏目:
研究与设计
出版日期:
2025-07-27

文章信息/Info

Title:
Fault Diagnosis Method for Gearbox Based on Attention Mechanism and Multi-directional Sensor Data Fusion
文章编号:
1001-2257 ( 2025 ) 0- 0003-06
作者:
段耀斌 1 ; 王城宇 2 ; 张国谋 1 ; 王志峰 1 ; 徐伯梁 1 ; 万书亭 2
1. 华电新疆五彩湾北一发电有限公司,新疆 昌吉 831700 ;
2. 华北电力大学河北省电力机械装备健康维护与失效预防重点实验室,河北 保定 071003
Author(s):
DUAN Yaobin1 ;  WANG Chengyu2 ;  ZHANG Guomou1 ;  WANG Zhifeng1 ;  XU Boliang1 ;  WAN Shuting2
( 1.Huadian Xinjiang Wucaiwan Power Generation Co. , Ltd. , Changji 831700 , China ;
?2.Hebei Key Laboratory of Electric Machinery Health Maintenance and Failure Prevention , North China Electric Power University , Baoding 071003 , China )
关键词:
齿轮箱; 故障诊断; 格拉姆角场; 多向传感器; 注意力机制
Keywords:
gearbox ;  fault diagnosis ;  Gramian angular summation ;  multi-sensor ;  attention mechanism
分类号:
TH132.41 ;TP18
文献标志码:
A
摘要:
针对单个传感器故障诊断中信息不足引起齿轮箱诊断准确度降低的问题,提出了一种结合注意力机制的多传感器数据融合方法,用于提高齿轮箱故障诊断的效果。首先,依据振动信号对随机脉冲的敏感性,采用综合悬崖熵度量作为权重标准,对多向传感器采集的振动信号进行融合,从而实现了信息的互补与有效融合。然后,构建了一种基于 2DCNN 的轻量级 CA GAPNet 故障诊断模型,将融合后的信号转换为 GAF 图像,作为模型的输入,最终实现了齿轮箱故障的高效诊断。实验结果表明,与单一传感器和其他常用算法相比,所提方法在齿轮箱故障诊断中展现了更为优越的诊断性能。
Abstract:
To address the issue of reduced fault diagnosis accuracy in gearbox systems due to information loss from using a single sensor , this paper proposes a multi-sensor data fusion fault diagnosis method for gearboxes based on an attention mechanism.First , based on the sensitivity of vibration signals to random pulses , the comprehensive cliff entropy measure is used as a weighting standard to fuse vibration signals collected from multiple sensors , achieving information complementarity and fusion.Then , a light-weight CA-GAPNet fault diagnosis model based on 2DCNN is constructed , and the fused signals are converted into GAF images as input to the model , ultimately achieving efficient fault diagnosis of the gearbox. Experimental results show that , compared with single-sensor and other commonly used algorithms , the proposed method demonstrates superior diagnostic performance in gearbox fault diagnosis.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2024-12-29
基金项目:国家自然科学基金资助项目( 52275109 );河北省自然科学基金资助项目( E2022502007 )
作者简介:段耀斌 ( 1986- ),男,新疆乌鲁木齐人,工程师,研究方向为燃煤电厂输煤系统智能化;万书亭 ( 1970- ),男,山西长治人,博士,教授,博士研究生导师,研究方向为燃煤电厂输煤系统智能化,通信作者, E-mail : 52450809@ncepu.edu.cn 。
更新日期/Last Update: 2025-08-28