Maximum correlation Pearson correlation coefficient deconvolution bearing fault diagnosis algorithm based on generalized spherical coordinate transformation

YANG Gang; CHENG Lei; XU Wuyi; DENG Qin; KANG Haoming

doi:10.16579/j.issn.1001.9669.2026.06.006

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Maximum correlation Pearson correlation coefficient deconvolution bearing fault diagnosis algorithm based on generalized spherical coordinate transformation

·Vibration·Noise·Monitoring·Diagnosis· | 更新时间：2026-06-16

- Maximum correlation Pearson correlation coefficient deconvolution bearing fault diagnosis algorithm based on generalized spherical coordinate transformation
- Journal of Mechanical Strength Vol. 48, Issue 6, Pages: 52-61(2026)
- 作者机构：
  
  1.西南交通大学机械工程学院，成都 610036
  2.西南交通大学唐山研究院，唐山 063000
- 作者简介：
  
  YANG Gang, E-mail: yanggang@swjtu.cn
- 基金信息：
  
  National Key R&D Program of China(2020YFB1200300ZL);Sichuan Provincial Key R&D Project(2023YFG0063)
- DOI：10.16579/j.issn.1001.9669.2026.06.006
  CLC： TH133.33
- Received：13 June 2024，
  
  Revised：2024-09-02，
  
  Published：15 June 2026
- 稿件说明：
移动端阅览
杨岗，成雷，徐五一，等. 基于广义球面坐标变换的最大相关皮尔逊相关系数解卷积轴承故障诊断算法［J］. 机械强度，2026，48（6）：52-61.

YANG Gang，CHENG Lei，XU Wuyi，et al. Maximum correlation Pearson correlation coefficient deconvolution bearing fault diagnosis algorithm based on generalized spherical coordinate transformation［J］. Journal of Mechanical Strength，2026，48（6）：52-61.
杨岗，成雷，徐五一，等. 基于广义球面坐标变换的最大相关皮尔逊相关系数解卷积轴承故障诊断算法［J］. 机械强度，2026，48（6）：52-61. DOI： 10.16579/j.issn.1001.9669.2026.06.006.

YANG Gang，CHENG Lei，XU Wuyi，et al. Maximum correlation Pearson correlation coefficient deconvolution bearing fault diagnosis algorithm based on generalized spherical coordinate transformation［J］. Journal of Mechanical Strength，2026，48（6）：52-61. DOI： 10.16579/j.issn.1001.9669.2026.06.006.

摘要

目的

最大相关皮尔逊相关系数解卷积（Maximum Correlation Pearson Correlation Coefficient Deconvolution

MCPCCD）算法能够在强背景噪声下增强轴承故障脉冲的幅频特性，同时还能保留其相频特性。然而，该算法在受到随机冲击和谐波干扰的情况下极易失效，且该算法的性能受限于先验解卷积周期的精度。针对这些问题，提出一种改进的MCPCCD方法。

方法

首先，通过广义球面坐标变换，将解卷积滤波器系数投影到

-1维、半径为1的球面上，从而缩小搜索空间；其次，采用麻雀搜索算法搜索最优滤波器系数，进行信号滤波；最后，利用仿真信号和高速列车牵引电动机轴承实测信号进行算法验证。

结果

结果表明，所提算法解决了MCPCCD算法在强干扰下容易失效的问题，并且在解卷积周期出现偏差时，算法也具有良好的性能。而对比研究的结果表明，相比于最大相关峭度解卷积算法和经验模态分解算法，所提算法具有更好的抗干扰能力。

Abstract

Objective

The maximum correlation Pearson correlation coefficient deconvolution (MCPCCD) algorithm can enhance the amplitude-frequency characteristics of bearing fault impulses under strong background noise while preserving their phase-frequency characteristics. However

the algorithm is prone to failure under the interference of random impacts and harmonic components

and its performance is limited by the accuracy of the prior deconvolution period. To address these issues

an improved MCPCCD method was proposed.

Methods

Firstly

the coefficients of the deconvolution filter were projected onto an

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1.52400005

2.28600001

1 dimensional sphere with a radius of 1 via generalized spherical coordinate transformation to narrow the search space. Secondly

the sparrow search algorithm was adopted to optimize the filter coefficients for signal filtering. Finally

simulated signals and measured bearing signals of high-speed train traction motors were employed to verify the proposed algorithm.

Results

The results show that the proposed algorithm solves the failure problem of the original MCPCCD algorithm under strong interference and maintains satisfactory performance even when the deconvolution period deviates. The results of the comparative studies demonstrate that the proposed algorithm possesses better anti-interference capability than the maximum correlated kurtosis deconvolution algorithm and the empirical mode decomposition algorithm.

关键词

Keywords

references

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