Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine

FU Qiang; HU Dong; YANG Tongliang; LUO Guoqing; TAN Weimin

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Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine

更新时间：2024-12-16

- Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine
- Journal of Mechanical Strength Pages: 1-9(2024)
- 作者机构：
  
  西南大学工程技术学院，重庆 400100
- 作者简介：
  
  TAN Weimin, E-mail:17742832901@163.com
- 基金信息：
  
  Chongqing Overseas Students Returning Home Entrepreneurship and Innovation Support Program(cx2019123)
- DOI：
  CLC： TH133
- Published Online：16 December 2024，
  
  Received：18 October 2023，
  
  Revised：06 December 2023，
- 稿件说明：
移动端阅览
付强，胡东，杨童亮等.基于改进压缩感知与深度多核极限学习机的轴承故障诊断方法［J］.机械强度，DOI：10.16579/j.issn.1001.9669.XXXX.XX.001.

FU Qiang，HU Dong，YANG Tongliang，et al.Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine［J］.Journal of Mechanical Strength，DOI：10.16579/j.issn.1001.9669.XXXX.XX.001.
付强，胡东，杨童亮等.基于改进压缩感知与深度多核极限学习机的轴承故障诊断方法［J］.机械强度，DOI：10.16579/j.issn.1001.9669.XXXX.XX.001. DOI：

FU Qiang，HU Dong，YANG Tongliang，et al.Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine［J］.Journal of Mechanical Strength，DOI：10.16579/j.issn.1001.9669.XXXX.XX.001. DOI：

摘要

目的

针对传统轴承故障诊断采样数据量大、诊断时间长和故障特征选择主观性强等问题，基于压缩感知（Compressed Sensing，CS）和深度多核极限学习机（Deep Multi-Kernel Extreme Learning Machine，D-MKELM）理论，提出了CS-DMKELM滚动轴承智能诊断模型。

方法

首先，对变换域信号阈值处理得到稀疏信号，使用高斯随机矩阵作为测量矩阵，对处理后的数据进行压缩；其次，使用压缩后的数据作为DMKELM的输入信号，利用粒子群优化（Particle Swarm Optimization，PSO）算法对关键参数进行优化，实现故障的智能诊断。

结果

结果表明，所提方法可使用较少的轴承诊断数据，利用DMKELM从少量测量信号中自动提取轴承的特征信息，实现了轴承的快速故障诊断。在诊断时间0.55 s的情况下，最终识别准确率可达99.29%。所提方法不仅诊断时间更短，而且诊断精度较高，为处理海量轴承数据的故障诊断提供了新的方法。

Abstract

Objective

In response to challenges such as large sampling data

extended diagnosis time

and subjective fault feature selection in traditional bearing fault diagnosis

a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.

Methods

Firstly

sparse signals were obtained through threshold processing of transformed domain signals. A Gaussian random matrix was employed as the measurement matrix to compress the processed data. Subsequently

the compressed data was used as the input signal for the D-MKELM. Particle swarm optimization(PSO) algorithm was applied to optimize critical parameters

enabling intelligent fault diagnosis.

Results

Results demonstrate that the proposed method

using only a small amount of bearing diagnostic data

automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM. The proposed method enables rapid fault diagnosis of bearings. With a diagnostic time of 0.55 s

a final recognition accuracy of 99.29% was achieved. The proposed method reduces diagnostic time and exhibits high diagnostic accuracy

providing a new approach for handling massive bearing data in fault diagnosis.

关键词

压缩感知轴承核函数极限学习机故障诊断

Keywords

Compressed sensingBearingKernel functionExtreme learning machineFault diagnosis

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