Research on Fault Diagnosis Method of Planetary Gearboxes Based on DPD-1DCNN

Zhang Bowen; Pang Xinyu; Guan Chongyang

doi:10.16578/j.issn.1004.2539.2023.03.016

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Research on Fault Diagnosis Method of Planetary Gearboxes Based on DPD-1DCNN

Development·Application | 更新时间：2023-03-29

- Research on Fault Diagnosis Method of Planetary Gearboxes Based on DPD-1DCNN
- Journal of Mechanical Transmission Vol. 47, Issue 3, Pages: 113-119(2023)
- 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.1004.2539.2023.03.016
  CLC：
- Published：15 March 2023，
  
  Received：01 March 2022，
  
  Revised：14 May 2022，
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张搏文,庞新宇,关重阳.基于DPD-1DCNN的行星齿轮箱故障诊断方法研究[J].机械传动,2023,47(03):113-119.

Zhang Bowen,Pang Xinyu,Guan Chongyang.Research on Fault Diagnosis Method of Planetary Gearboxes Based on DPD-1DCNN[J].Journal of Mechanical Transmission,2023,47(03):113-119.
张搏文,庞新宇,关重阳.基于DPD-1DCNN的行星齿轮箱故障诊断方法研究[J].机械传动,2023,47(03):113-119. DOI： 10.16578/j.issn.1004.2539.2023.03.016.

Zhang Bowen,Pang Xinyu,Guan Chongyang.Research on Fault Diagnosis Method of Planetary Gearboxes Based on DPD-1DCNN[J].Journal of Mechanical Transmission,2023,47(03):113-119. DOI： 10.16578/j.issn.1004.2539.2023.03.016.

摘要

基于数据驱动的故障诊断方法已被广泛应用于旋转机械零部件故障诊断领域。目前，大多数诊断方法主要依赖于定长数据分割产生的大量数据，但分割的数据通常为短周期的小片段信号，而实际长周期冗余信号由于数据尺度不匹配，无法直接作为测试样本进行故障识别。针对以上不足，提出了一种新的基于数据概率密度与一维卷积神经网络（Data Probability Density and One-Dimensional Convolutional Neural Network，DPD-1DCNN）的故障诊断方法，其具有两个特点：①提取信号的密度特征可抵抗数据的冗余；②适应不同长度的冗余信号可作为诊断模型的输入。该方法采用DDS试验台产生的行星齿轮箱故障数据进行了验证；其在保证高诊断精度的同时，又增强了诊断模型的适应性。

Abstract

Data-driven fault diagnosis methods have been widely used in the field of fault diagnosis of rotating machinery components. However

most of the current research methods mainly rely on a large amount of data generated by fixed-length data segmentation. The segmented data is usually a short-period small segment signal

and the actual long-period redundant signal cannot be directly used as a test sample for fault identification. In view of the above shortcomings

a new fault diagnosis method based on data probability density and one-dimensional convolutional neural network (DPD-1DCNN) is proposed. It has two characteristics: ①the density feature of the extracted signal resists the redundancy of the data; ②adapt redundant signals of different lengths as input to the diagnostic model. The method is verified on the planetary gearbox fault data generated by the DDS test bench

which not only ensures high diagnostic accuracy

but also enhances the adaptability of the diagnostic model.

关键词

行星齿轮箱数据概率密度一维卷积神经网络故障诊断

Keywords

Planetary gearboxData probability density1DCNNFault Diagnosis

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