Early Fault Detection for Rolling Bearings Based on One-dimensional Structure Graph Entropy

doi:10.3969/j.issn.1004-132X.2026.03.014

Abstract

Abstract:

To address the challenges of accurately identifying early faults in rolling bearings， a fault detection method was proposed based on one-dimensional structural graph entropy. A graph model was developed to transform time-series data into spatial structures， enabling effective extraction of bearing condition features. A complete graph model of signal short-time power spectrum was construtured， and the complexity changing rules of time-frequency energy distribution were captured. Leveraging the ability of entropy to describe signal nonlinearity， a one-dimensional structural graph entropy measure was defined to quantify the variations in complexity of model structure， whose mean value served as health indicator for assessing the condition of the bearings. Theoretical explanations and numerical analyses demonstrated the discriminative mechanism of health indicators regarding operating states. Additionally， an adaptive detection method was developed based on the characteristics of this health indicator. The method was experimentally validated on XJTU-SY， IMS， PHM， and pulp mill datasets. Results show that the method may accurately identify fault conditions without any parametric adjustments. When compared with methods such as mean square value， synchronized pseudo-velocity corrected mean square value， variance， and kurtosis， the proposed health indicator shows superior robustness and trend-tracking performance.

Key words: rolling bearing, early fault detection, graph model, one-dimensional structure graph entropy

摘要：

针对滚动轴承早期故障难以准确识别问题，提出了一种基于一维结构图熵的故障检测方法。设计了一种将时间序列重构为空间结构的图模型，能够有效提取轴承状态特征。通过对信号短时功率谱进行完全图建模，获取了时频能量分布复杂性变化特性。利用熵对信号非线性描述的优点，定义一维结构图熵度量模型结构的复杂性变化，并将其均值作为健康指标来评估轴承健康状态。理论解释和数值化分析了健康指标对运行状态的区分机制，并根据特点设计了自适应检测方法。该方法分别在XJTU-SY、IMS、PHM数据集以及纸浆工厂数据集上进行实验验证，结果显示该方法无需任何参数调整即可准确识别故障状态。与均方值、同步伪速度校正均方值、方差、峰度等方法相比，所述健康指标具有更好的鲁棒性和趋势性。

关键词: 滚动轴承, 早期故障检测, 图模型, 一维结构图熵

CLC Number:

TH133.33

LI Ke, WANG Mengjun, YUAN Maojun, ZHANG Hongshuo, YUAN Keyan, LU Guoliang. Early Fault Detection for Rolling Bearings Based on One-dimensional Structure Graph Entropy[J]. China Mechanical Engineering, 2026, 37(3): 645-655.

李科, 王梦君, 袁茂军, 张宏硕, 袁科研, 卢国梁. 基于一维结构图熵的滚动轴承早期故障检测[J]. 中国机械工程, 2026, 37(3): 645-655.

Figures/Tables 22

References 34

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数据集名称	数据名称	工况	失效位置	样本数量
XJTU-SY	Bearing1-1	转速2100 r/min径向力12 kN	外圈	123
	Bearing1-2		外圈	161
	Bearing1-3		外圈	158
	Bearing1-4		保持架	122
	Bearing1-5		内圈、外圈	52
	Bearing2-1	转速2250 r/min径向力11 kN	内圈	491
	Bearing2-2		外圈	161
	Bearing2-3		保持架	533
	Bearing2-4		外圈	42
	Bearing2-5		外圈	339
	Bearing3-1	转速2400 r/min径向力10 kN	外圈	2538
	Bearing3-2		内圈、外圈、滚动体、保持架	2496
	Bearing3-3		内圈	371
	Bearing3-4		内圈	1515
	Bearing3-5		外圈	114
IMS	Dataset 1	转速2000 r/min 径向力26.67 kN	轴承3内圈；轴承4滚动体	2156
	Dataset 2		轴承1外圈	948
	Dataset 3		轴承3外圈	6324

数据集名称	数据名称	工况	失效位置	样本数量
XJTU-SY	Bearing1-1	转速2100 r/min径向力12 kN	外圈	123
	Bearing1-2		外圈	161
	Bearing1-3		外圈	158
	Bearing1-4		保持架	122
	Bearing1-5		内圈、外圈	52
	Bearing2-1	转速2250 r/min径向力11 kN	内圈	491
	Bearing2-2		外圈	161
	Bearing2-3		保持架	533
	Bearing2-4		外圈	42
	Bearing2-5		外圈	339
	Bearing3-1	转速2400 r/min径向力10 kN	外圈	2538
	Bearing3-2		内圈、外圈、滚动体、保持架	2496
	Bearing3-3		内圈	371
	Bearing3-4		内圈	1515
	Bearing3-5		外圈	114
IMS	Dataset 1	转速2000 r/min 径向力26.67 kN	轴承3内圈；轴承4滚动体	2156
	Dataset 2		轴承1外圈	948
	Dataset 3		轴承3外圈	6324

数据名称	故障预警窗位置	故障样本序号	故障时间/s
Bearing 1-1	512	79	4842
Bearing 1-2	270	42	2574
Bearing 1-3	478	73	4474
Bearing 1-4	797	122	7477
Bearing 1-5	231	36	2207
Bearing 2-1	2977	455	27 883
Bearing 2-2	304	47	2881
Bearing 2-3	1987	304	18 630
Bearing 2-4	198	31	1890
Bearing 2-5	958	147	9009
Bearing 3-1	15 647	2388	146 337
Bearing 3-2	16 357	2086	127 831
Bearing 3-3	2248	344	21 081
Bearing 3-4	9318	1422	87 141
Bearing 3-5	53	9	552

数据名称	故障预警窗位置	故障样本序号	故障时间/s
Bearing 1-1	512	79	4842
Bearing 1-2	270	42	2574
Bearing 1-3	478	73	4474
Bearing 1-4	797	122	7477
Bearing 1-5	231	36	2207
Bearing 2-1	2977	455	27 883
Bearing 2-2	304	47	2881
Bearing 2-3	1987	304	18 630
Bearing 2-4	198	31	1890
Bearing 2-5	958	147	9009
Bearing 3-1	15 647	2388	146 337
Bearing 3-2	16 357	2086	127 831
Bearing 3-3	2248	344	21 081
Bearing 3-4	9318	1422	87 141
Bearing 3-5	53	9	552

数据名称	故障预警窗位置	故障样本序号	故障时间/min
Dataset 1	8028	2155	21 550
Dataset 2	2872	439	4390
Dataset 3	25 283	6173	61 730