Collaborative Sparse Low Rank Regularization for Aero-Engine Bearing Fault Diagnosis

ZHANG Han; WANG Xing; TIAN Yi; LIN Jianbo; DU Zhaohui

doi:10.7652/xjtuxb202111006

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Collaborative Sparse Low Rank Regularization for Aero-Engine Bearing Fault Diagnosis

更新时间：2025-07-09

- Collaborative Sparse Low Rank Regularization for Aero-Engine Bearing Fault Diagnosis
- Vol. 55, Issue 11, Pages: 46-58(2021)
- 作者机构：
  
  1. 长安大学工程机械学院,西安,710064
  2. 长安大学道路施工技术与装备教育部重点实验室,西安,710064
  3. 西北工业大学航海学院,西安,710072
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202111006
  CLC： TP206.3;TH133.3
- Online First：10 November 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
ZHANG Han, WANG Xing, TIAN Yi, et al. Collaborative Sparse Low Rank Regularization for Aero-Engine Bearing Fault Diagnosis[J]. 2021, 55(11): 46-58.
DOI：

ZHANG Han, WANG Xing, TIAN Yi, et al. Collaborative Sparse Low Rank Regularization for Aero-Engine Bearing Fault Diagnosis[J]. 2021, 55(11): 46-58. DOI： 10.7652/xjtuxb202111006.

摘要

航空发动机振动测点少

主轴轴承微弱的故障特征信息被多源非高斯噪声干扰所淹没

难以有效诊断。针对该问题

首先分析了整机振动信号源成分的表征机制

揭示了故障特征信号在特定二维变换空间的低秩先验以及谐波干扰信号在频域的稀疏先验

进而分别构建了特征信息的空域低秩正则函数和谐波干扰信号的谱域稀疏正则函数

通过协同空域和谱域的两类正则函数

提出了稀疏低秩协同正则优化算法。所提算法基于故障信号和干扰信号在不同变换空间的结构差异性

将两类信号分别在两个完全不耦合的空间进行表示和正则

解决了目前稀疏分解算法难以构造高度不耦合字典的瓶颈问题。仿真分析表明

所提算法可实现冲击特征、谐波干扰信号和高斯噪声这3种成分的解耦

从而可靠提取轴承微弱的冲击故障模式。两组航空轴承实验表明

所提算法不仅可实现运行转速为18 000 r/min、剥落面积为1.0 mm

的航空轴承故障诊断

并且可有效识别加速疲劳寿命实验中轴承故障萌生初期的特征信息。

Abstract

It is very difficult to detect weak signatures for the early fault diagnosis of aero-engine bearings due to fewer vibration transducers and stronger non-Gaussian noises. Therefore

prior knowledges of latent subcomponents of aero-engine vibration signals are investigated

then the low rank pattern of the fault features in the tailored two-dimensional transformation space and the sparse structure of harmonic interference signals in the frequency domain are revealed. Moreover

the low-rank regularization in spatial domain and the sparse regularization in spectral domain are establis

hed. Based on the two regularization priors

a collaborative sparse low-rank model(CSLM)is proposed. The CSLM emphatically exploits structural differences of fault signals and interference signals in different transformation spaces

and further describes these differences in two completely uncoupled spaces

which provides a favorable way to construct highly incoherent dictionaries for sparse decomposition strategy. The simulation results verify that the proposed method can decouple the hidden impulsive features from strong harmonic interferences and noises

and meanwhile reliably discover fault sources. Two aero-engine bearing experiments indicate that the CSLM can effectively detect the fault patterns of aero-engine bearing with spalling area of 1.0 mm

at running speed up to 18 000 r/min

and reliably identify the weak characteristic information of bearing faults even in the initial stage of an accelerated fatigue life cycle experiment.

关键词

Keywords

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