Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (2): 494-502.doi: 10.13229/j.cnki.jdxbgxb.20230530

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Rolling bearing fault diagnosis method via wavelet packet logarithmic-energy map

Na WANG1,2(),Yue-lei CUI1,Yang LI1,Zi-cong WANG1   

  1. 1.School of Control Science and Engineering，Tiangong University，Tianjin 300387，China
    2.Key Laboratory of Intelligent Control of Electrical Equipment，Tianjin 300387，China
  • Received:2023-05-27 Online:2025-02-01 Published:2025-04-16

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Abstract:

For the fault diagnosis on rolling bearing， a method via wavelet packet logarithmic-energy map is proposed. Firstly， a new wavelet packet node logarithmic energy formula is improved and presented to overcome the complexity and subjectivity of parameters in the traditional ones. Thus the high-frequency faults and the low-frequency faults are easily identified. As a result， the initial time-frequency features are extracted adequately. Secondly， the idea of Gramian angular summation field is used to transform the features from one-dimension data to two-dimension picture. Therefore the features via wavelet packet logarithmic-energy map are constructed. In them， the space information among the adjacent features are considered further. So the optimization for the initial time-frequency features is completed and their significance are increased. On this basis， the residual network is applied to enhance the accuracy of the presented approach. Finally， the higher accuracy of diagnosis and the greater generalization ability of the proposed method is verified by the standard rolling bearing data set of Case Western Reserve University.

Key words: fault diagnosis, feature extraction, rolling bearing, Gramian angular summation field, wavelet packet logarithmic-energy map, residual network

CLC Number: 

  • TH113.1

Fig.1

Time domain waveform of four state signals"

Fig.2

Schematic diagram of four-layer wavelet packet decomposition"

Fig.3

Schematic diagram of mapping one-dimensional logarithmic energy in Gram angle and field to two-dimensional image"

Fig.4

Basic structure of residual block"

Fig.5

Basic structure of residual network"

Table 1

Data parameters of rolling bearing"

序号类型工况损伤程度/mm损伤位置数量
1OR20.355 63点钟100
0.533 46点钟100
2IR20.355 6—100
0.533 4—100
3B20.355 6—100
0.533 4—100
4N2——200

Fig.6

Comparison of features extraction effect between the logarithmic energy maps and the ordinary energy maps"

Table 2

Structural parameters of residual network"

网络层输入通道输出通道卷积核大小步长
Conv2d1165*51
ReLU1616——
MaxPool2d16162*2—
Conv2d（RB1）16163*3—
ReLU（RB1）1616——
Conv2d（RB1）16163*31
ReLU（RB1）1616——
Conv2d16325*51
ReLU3232——
MaxPool2d32322*2—
Conv2d（RB2）32323*31
ReLU（RB2）3232——
Conv2d（RB2）32323*31
ReLU（RB2）3232——
Linear321——

Fig.7

Comparison of training accuracy among CNN， E-CNN， EI-Res and LEI-Res models"

Fig.8

Comparison of training loss among CNN， E-CNN， EI-Res and LEI-Res models"

Table 3

Comparison of diagnosis accuracy and computation complexity on test dataset among CNN， E-CNN， EI-Res and LEI-Res models"

方法CNNE-CNNEI-ResLEI-Res
测试准确率/%92.0896.6699.16100
运行时间/s48.7445.9157.1056.88
时间复杂度O（105）O（103）O（106）O（106）
空间复杂度O（104）O（102）O（104）O（104）

Table 4

Ablation experiments"

方法CNNCNN+RB1CNN+RB2CNN+RB1+RB2
测试准确率/%99.58100100100
测试损失值0.010 20.004 20.004 70.001 3
运行时间/s54.9156.2554.8156.88
1 陈晓雷, 孙永峰, 李策, 等. 基于卷积神经网络和双向长短期记忆的稳定抗噪声滚动轴承故障诊断[J]. 吉林大学学报: 工学版, 2022, 52(2): 296-309.
Chen Xiao-lei, Sun Yong-feng, Li Ce, et al. Stable anti-noise fault diagnosis of rolling bearing based on CNN-BiLSTM[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(2): 296-309.
2 江星星, 彭德民, 沈长青, 等. 快速固有成分滤波特征融合的轴承故障诊断方法[J]. 机械工程学报, 2022, 58(22): 129-139.
Jiang Xing-xing, Peng De-min, Shen Chang-qing, et al. Feature fusion of fast intrinsic component filtering for bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2022, 58(22): 129-139.
3 刘新厂. 含齿根裂纹的机车齿轮传动系统振动特性与故障诊断方法研究[D]. 成都: 西南交通大学机械工程学院, 2022.
Liu Xin-chang. Study on vibration characteristics and fault diagnosis method of locomotive gear transmission system with root crack[D]. Chengdu: School of Mechanical Engineering, Southwest Jiaotong University, 2022.
4 吴彬云. 考虑变转速工况的转子动平衡故障诊断方法研究及应用[D]. 厦门: 厦门大学机械工程学院, 2022.
Wu Bin-yun. Research and application of rotor dynamic balance fault diagnosis method considering variable speed condition[D]. Xiamen: School of Mechanical Engineering, Xiamen University, 2022.
5 王泽坤, 贾彦, 许瑾, 等. 风电机组叶片桨距角安装偏差故障诊断[J]. 动力工程学报, 2022, 42(2): 138-143.
Wang Ze-kun, Jia Yan, Xu Jin, et al. Fault diagnosis of blade pitch angle installation deviation of wind turbine[J]. Journal of Chinese Society of Power Engineering, 2022, 42(2): 138-143.
6 张宗振, 王金瑞, 韩宝坤, 等. 非线性稀疏盲解卷积的轴承早期故障诊断方法[J]. 机械工程学报, 2023, 59(16): 157-166.
Zhang Zong-zhen, Wang Jin-rui, Han Bao-kun, et al. Early stage fault diagnosis method of bearings based on nonlinear sparse blind deconvolution[J]. Journal of Mechanical Engineering, 2023,59(16): 157-166.
7 Zhou H, Yan P, Yuan Y F, et al. Denoising the hob vibration signal using improved complete ensemble empirical mode decomposition with adaptive noise and noise quantization strategies[J]. ISA Transactions, 2022, 131: 715-735.
8 时培明, 范雅斐, 伊思颖, 等. HVD小波包降噪编码深度学习的风电机组智能诊断研究[J]. 振动与冲击, 2022, 41(12): 196-201.
Shi Pei-ming, Fan Ya-fei, Yi Si-ying, et al. A study on wind turbine intelligent diagnosis based on HVD wavelet packet de noising coding deep learning[J]. Journal of Vibration and Shock, 2022, 41(12): 196-201.
9 皮骏, 刘鹏, 马圣, 等. 基于MGA-BP网络的航空轴承故障诊断[J]. 振动、测试与诊断, 2020, 40(2): 381-388.
Pi Jun, Liu Peng, Ma Sheng, et al. Aero-engine bearing fault diagnosis based on MGA-BP neural network[J]. Journal of Vibration, Measurement & Diagnosis, 2020, 40(2): 381-388.
10 Zhou Q C, Shen H H, Zhao J, et al. Bearing fault diagnosis based on improved stacked recurrent neural network[J]. Journal of Tongji University, 2019, 47(10): 1500-1507.
11 王兴, 张晗, 朱家正, 等. 多头注意力驱动的航空高速轴承故障诊断方法[J]. 振动与冲击, 2023, 42(4): 295-305.
Wang Xing, Zhang Han, Zhu Jia-zheng, et al. A fault diagnosis method for aviation high-speed bearings driven by multi-head attention[J]. Journal of Vibration and Shock, 2023, 42(4): 295-305.
12 刘颖, 陶建峰, 黄武涛, 等. 小波包能量与CNN相结合的滚动轴承故障诊断方法[J]. 机械设计与制造, 2021(11): 127-131.
Liu Ying, Tao Jian-feng, Huang Wu-tao, et al. Rolling bearing fault diagnosis method based on the combination of wavelet packet energy and CNN[J]. Machinery Design & Manufacture, 2021(11): 127-131.
13 古莹奎, 吴宽, 李成, 等. 基于格拉姆角场和迁移深度残差神经网络的滚动轴承故障诊断[J]. 振动与冲击, 2022, 41(21): 228-237.
Gu Ying-kui, Wu Kuan, Li Cheng, et al. Rolling bearing fault diagnosis based on Gram angle field and transfer deep residual neural network[J]. Journal of Vibration and Shock, 2022, 41(21): 228-237.
14 Liu J Y, Wang X S, Wu S J, et al. Wind turbine fault detection based on deep residual networks[J]. Expert Systems with Applications, 2023, 213: 1-13.
15 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.
16 Zhao M H, Fu X Y, Zhang Y J, et al. Highly imbalanced fault diagnosis of mechanical systems based onwavelet packet distortion and convolutional neural networks[J]. Advanced Engineering Informatics, 2022, 51: 1-10.
17 Yu X, Liang Z T, Wang Y J, et al. A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions[J]. Measurement, 2022, 201: 1-14.
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