Fault Diagnosis of Planetary Gearbox based on 1-DCNN

Xuanyi Xue; Xinyu Pang

doi:10.16578/j.issn.1004.2539.2020.11.021

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Fault Diagnosis of Planetary Gearbox based on 1-DCNN

Test·Analysis | 更新时间：2022-11-10

- Fault Diagnosis of Planetary Gearbox based on 1-DCNN
- Journal of Mechanical Transmission Vol. 44, Issue 11, Pages: 127-133(2020)
- 作者机构：
  
  1.太原理工大学机械与运载工程学院，山西太原 030024
  2.煤矿综采装备山西省重点实验室，山西太原 030024
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2020.11.021
  CLC：
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薛璇怡,庞新宇.基于1-DCNN的行星齿轮箱故障诊断[J].机械传动,2020,44(11):127-133.

Xue Xuanyi,Pang Xinyu.Fault Diagnosis of Planetary Gearbox based on 1-DCNN[J].Journal of Mechanical Transmission,2020,44(11):127-133.
薛璇怡,庞新宇.基于1-DCNN的行星齿轮箱故障诊断[J].机械传动,2020,44(11):127-133. DOI： 10.16578/j.issn.1004.2539.2020.11.021.

Xue Xuanyi,Pang Xinyu.Fault Diagnosis of Planetary Gearbox based on 1-DCNN[J].Journal of Mechanical Transmission,2020,44(11):127-133. DOI： 10.16578/j.issn.1004.2539.2020.11.021.

摘要

传统的机器学习方法在行星齿轮箱故障诊断方面存在识别率低、特征提取操作繁琐等问题。为提高行星齿轮箱的诊断效率，提出基于一维深度卷积神经网络（One-dimensional deep convolutional neural network，1-DCNN）的故障诊断方法，将原始信号直接输入到网络中进行诊断。通过对行星齿轮箱行星轮5种故障信号进行训练验证，精度可达100%，且在诊断精度和效率上优于其他常用算法。

Abstract

Traditional machine learning methods have disadvantages such as low recognition rate and complicated feature extraction operations in the planetary gearbox fault diagnosis. In order to improve the diagnosis efficiency of planetary gearboxes， a fault diagnosis method based on one-dimensional deep convolutional neural network （1-DCNN） is proposed， and the original signals are directly input to the network for diagnosis. The accuracy of diagnosing five kinds of fault signals of planetary gear of planetary gear box can reach 100%， and the diagnostic accuracy and efficiency are better than other commonly used algorithms.

关键词

1-DCNN智能诊断特征提取行星齿轮箱

Keywords

1-DCNN intelligent diagnosisFeature extractionPlanetary gearbox

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