Fault Diagnosis Method for Rolling Bearings Based on WP-TRP

doi:10.12068/j.issn.1005-3026.2025.20239058

Abstract

Abstract:

In fault diagnosis， the traditional time-frequency domain methods are easily affected by subjective factors while being used for feature extraction， so that the redundancy emerges. Deep learning algorithm is highly dependent on training data and has computation complexity. Fault diagnosis method for rolling bearings based on wavelet packet-thresholdless recurrence plot （WP-TRP）is proposed by combining time with frequency domains. Firstly， the decreasing information entropy criterion is developed to overcome the subjectivity of wavelet packet decomposition for acquisition of more accurate time-frequency feature. On this basis， the idea of thresholdless recurrence plot is introduced to extract the initial time domain feature. Moreover， by adopting the singular value decomposition to decrease the redundant feature， the computational efficiency can be increased. Secondly， the marine predator algorithm is introduced to obtain the optimal parameters of supporting vector machine， by which the more accurate classification can be realized. Finally， the effectiveness of the presented method is verified by using the simulation on the benchmark rolling bearing datasets.

Key words: fault diagnosis, wavelet packet decomposition, information entropy, thresholdless recurrence plot（TRP）, singular value decomposition, marine predator algorithm

CLC Number:

TP 277

Na WANG, Yue-lei CUI, Liang LUO, Zi-cong WANG. Fault Diagnosis Method for Rolling Bearings Based on WP-TRP[J]. Journal of Northeastern University(Natural Science), 2025, 46(3): 20-27.

Figures/Tables 7

References 18

1	侯东晓，穆金涛，方成，等. 基于GADF与引入迁移学习的ResNet34对变速轴承的故障诊断［J］. 东北大学学报（自然科学版）， 2022， 43（3）： 383-389.
	Hou Dong-xiao， Mu Jin-tao， Fang Cheng， et al. Fault diagnosis of variable speed bearings based on GADF and ResNet34 introduced transfer learning［J］. Journal of Northeastern University （Natural Science）， 2022， 43（3）： 383-389.
2	Yan R Q， Shang Z G， Xu H， et al. Wavelet transform for rotary machine fault diagnosis：10 years revisited［J］. Mechanical Systems and Signal Processing， 2023， 200（5）： 110545.
3	Sun Y J， Li S H， Wang X H. Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image［J］. Measurement， 2021， 176（17）： 109100.
4	Ye M Y， Yan X A， Chen N， et al. Intelligent fault diagnosis of rolling bearing using variational mode extraction and improved one-dimensional convolutional neural network［J］. Applied Acoustics， 2023， 202： 109143.
5	Sun Y K， Cao Y， Li P. Fault diagnosis for train plug door using weighted fractional wavelet packet decomposition energy entropy［J］. Accident Analysis & Prevention， 2022， 166： 106549.
6	周奇才，沈鹤鸿，赵炯，等. 基于改进堆叠式循环神经网络的轴承故障诊断［J］. 同济大学学报（自然科学版）， 2019， 47（10）： 1500-1507.
	Zhou Qi-cai， Shen He-hong， Zhao Jiong， et al. Bearing fault diagnosis based on improved stacked recurrent neural network［J］. Journal of Tongji University （Natural Science）， 2019， 47（10）： 1500-1507.
7	Liang H P， Cao J， Zhao X Q. Average descent rate singular value decomposition and two-dimensional residual neural network for fault diagnosis of rotating machinery［J］. IEEE Transactions on Instrumentation and Measurement， 2022， 71： 3512616.
8	张龙，胡燕青，赵丽娟，等. 采用递归图编码技术与残差网络的滚动轴承故障诊断［J］. 西安交通大学学报， 2023， 57（2）： 110-120.
	Zhang Long， Hu Yan-qing， Zhao Li-juan， et al. Fault diagnosis of rolling bearings using recurrence plot coding technique and residual network［J］. Journal of Xi’an Jiaotong University， 2023， 57（2）： 110-120.
9	武明泽. 基于小波包分解和FPA-SVM的动车组轴箱轴承故障诊断研究［D］. 北京：北京交通大学， 2021.
	Wu Ming-ze. Research on fault diagnosis of EMU axle box bearing based on wavelet packet decomposition and FPA-SVM［D］. Beijing： Beijing Jiaotong University， 2021.
10	Han T， Liu C， Yang W G， et al. Deep transfer network with joint distribution adaptation： a new intelligent fault diagnosis framework for industry application［J］. ISA Transactions， 2020， 97： 269-281.
11	刘涛，梁成玉. 信息熵融合的PSO-SVC涡旋压缩机故障诊断［J］. 振动、测试与诊断， 2022， 42（1）： 141-147，200.
	Liu Tao， Liang Cheng-yu. PSO-SVC fault diagnosis of scroll compressor based on information entropy fusion［J］. Journal of Vibration， Measurement & Diagnosis， 2022，42（1）： 141-147，200.
12	Kok T L， Aldrich C， Zabiri H， et al. Application of unthresholded recurrence plots and texture analysis for industrial loops with faulty valves［J］. Soft Computing， 2022， 26（19）： 10477-10492.
13	金江涛，许子非，李春，等. 基于深度学习与混沌特征融合的滚动轴承故障诊断［J］. 控制理论与应用， 2022， 39（1）： 109-116.
	Jin Jiang-tao， Xu Zi-fei， Li Chun， et al. Rolling bearing fault diagnosis based on deep learning and chaotic feature fusion［J］. Control Theory & Applications， 2022， 39（1）： 109-116.
14	Cui L L， Liu Y H， Zhao D Z. Adaptive singular value decomposition for bearing fault diagnosis under strong noise interference［J］. Measurement Science and Technology， 2022， 33（9）： 095002.
15	郭明军，李伟光，杨期江，等. 深度卷积神经网络在滑动轴承转子轴心轨迹识别中的应用［J］. 振动与冲击， 2021， 40（3）： 233-239， 283.
	Guo Ming-jun， Li Wei-guang， Yang Qi-jiang，et al. Application of deep convolution neural network in identification of journal bearing rotor center orbit［J］. Journal of Vibration and Shock， 2021， 40（3）： 233-239， 283.
16	Wang Z Y， Yao L G， Chen G， et al. Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals［J］. ISA Transactions， 2021， 114： 470-484.
17	Faramarzi A， Heidarinejad M， Mirjalili S， et al. Marine predators algorithm： a nature-inspired metaheuristic［J］. Expert Systems with Applications， 2020， 152（4）： 113377.
18	司莉，毕贵红，魏永刚，等. 基于RQA与SVM的声发射信号检测识别方法［J］. 振动与冲击， 2016， 35（2）： 97-103， 123.
	Si Li， Bi Gui-hong， Wei Yong-gang， et al. Detection and identification of acoustic emission signals based on recurrence quantification analysis and support vector machines［J］. Journal of Vibration and Shock， 2016， 35（2）： 97-103， 123.

相邻分解层	数据/个
第1层、第2层	224
第2层、第3层	486
第3层、第4层	290

相邻分解层	数据/个
第1层、第2层	224
第2层、第3层	486
第3层、第4层	290

方法	训练正确率/%	测试正确率/%	运行时间/s
RP-SVD-SVM	73.00	70.70	112.78
TRP-SVD-SVM	92.71	89.67	86.65
RQA-SVM^[18]	97.71	94.00	91.91
TRP-CNN	100.00	97.60	219.56
WP-TRP	100.00	100.00	10.66

方法	训练正确率/%	测试正确率/%	运行时间/s
RP-SVD-SVM	73.00	70.70	112.78
TRP-SVD-SVM	92.71	89.67	86.65
RQA-SVM^[18]	97.71	94.00	91.91
TRP-CNN	100.00	97.60	219.56
WP-TRP	100.00	100.00	10.66

故障类型序号	RP-SVD-SVM	TRP-SVD-SVM	RQA-SVM	TRP-CNN	WP-TRP
1	100.0	100.0	100.0	100.0	100.0
2	63.6	100.0	88.9	100.0	100.0
3	100.0	100.0	100.0	100.0	100.0
4	32.3	63.0	74.1	100.0	100.0
5	95.7	100.0	96.6	100.0	100.0
6	72.0	100.0	100.0	100.0	100.0
7	69.0	78.8	100.0	100.0	100.0
8	65.5	80.0	82.9	97.6	100.0
9	33.3	68.0	100.0	100.0	100.0
10	86.2	100.0	100.0	100.0	100.0