Degradation Assessment of Rolling Bearings Based on CMWRFDE and ATSNE

doi:10.3969/j.issn.1004-132X.2026.09.006

Abstract

Abstract:

To address the problem of accurately detecting the early degradation moment of rolling bearings， a degradation state assessment model was proposed based on CMWRFDE and ATSNE. First， the CMWRFDE algorithm was utilized to extract multi-scale degradation features from the collected vibration signals. Then， the ATSNE algorithm was employed to compress and reduce the dimensionality of the extracted multi-scale features， constructing a comprehensive characteristic indicator that accurately represents the performance changes of the rolling bearing. Finally， the performance degradation assessment result of the rolling bearing was obtained through a health threshold strategy based on Chebyshev's inequality. The results demonstrate that the proposed model accurately characterizes the performance degradation trend of rolling bearings， promptly detects the initial degradation moment，and enables precise assessment of the bearing's degradation state.

Key words: degradation state assessment, composite multi-scale weighted reverse fuzzy dispersion entropy （CMWRFDE）, augmented t-distributed stochastic neighborhood embedding（ATSNE）

摘要：

针对滚动轴承早期退化时刻难以准确检测的问题，提出了一种基于复合多尺度加权反向模糊散布熵（CMWRFDE）和增强t分布随机邻域嵌入（ATSNE）的退化状态评估模型。对采集到的振动信号利用CMWRFDE算法提取多尺度退化特征，然后利用ATSNE算法对提取到的多尺度特征进行压缩降维，构造出能准确表示滚动轴承性能变化的综合特性指标，最后通过切比雪夫不等式健康阈值策略得到滚动轴承的性能退化评估结果。结果表明，该模型能够准确描述滚动轴承性能退化规律，及时捕捉到初始退化时刻，从而实现对滚动轴承性能退化状态的准确评估。

关键词: 退化状态评估, 复合多尺度加权反向模糊散布熵, 增强t分布随机邻域嵌入

CLC Number:

TH17

WANG Xiaolong, FU Yuanlei, LUO Wenyu, GONG Zhaoyan, LIU Xuyihao, JIAO Jian, GUO Rui. Degradation Assessment of Rolling Bearings Based on CMWRFDE and ATSNE[J]. China Mechanical Engineering, 2026, 37(9): 2133-2140.

王晓龙, 伏元雷, 罗文宇, 宫兆岩, 刘徐祎昊, 焦健, 郭睿. 基于复合多尺度加权反向模糊散布熵和增强t分布随机邻域嵌入的滚动轴承退化评估[J]. 中国机械工程, 2026, 37(9): 2133-2140.

Figures/Tables 13

Fig.1 Composite coarse graining schematic diagram

Fig.2 Time domain waveforms of simulated signals under different signal to noise ratios

Fig.3 Entropy curves of different algorithms under various signal to noise ratios

Fig.4 Correlation coefficients of different algorithms under various signal to noise ratios

Fig.5 Comparison of damage variation sensitivity among different algorithms

Fig.6 Assessment process of rolling bearing degradation

Fig.7 Linear kurtosis and root mean squarefluctuation curves

Fig.8 CMWRFDE extraction results across multiple scale factors

Fig.9 Average value of composite index across different scale factors

Fig.10 The degradation state curve

Fig.11 CYCBD analysis results of the 58th sample

Fig.12 Degradation assessment results of different models

Tab.1 The comparison of degradation assessment results of different models

模型	初始退化点	是否误报、延报	是否持续报警
CMFDE+t-SNE	73	是	否
CMSE+ATSNE	63	是	否
CMPE+SPCA	65	是	否
CMWRFDE+ATSNE	58	否	否

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