RESEARCH PAPER
A New Lightweight Image Coding Method and Its Application in DC-DC Converter Parametric Fault Diagnosis
1
Xidian University, Xian 710126, China, China
2
School of Electromechanical Engineering, Guangdong Poly-technic Nomal University, Guangzhou, China
3
School of Electrical Engineering, Beijing Jiaotong University, China
These authors had equal contribution to this work
Submission date: 2024-11-14
Final revision date: 2025-03-01
Acceptance date: 2025-12-26
Online publication date: 2025-12-31
Publication date: 2025-12-31
Corresponding author
HIGHLIGHTS
- Use the experimental setting directly reflect the degradation process.
- A lightweight fault coding method is constructed.
- Reduced diagnosis time by 30%.
KEYWORDS
TOPICS
ABSTRACT
A novel data encoding method that integrates Variational Mode Decomposition with a new lightweight image coding technique is proposed and applied to the parametric fault diagnosis of DC-DC converters. This approach addresses the limitations of existing fault diagnosis methods, specifically the low diagnostic accuracy and poor noise resistance resulting from inadequate feature extraction. Initially, the parameter fault data of the DC-DC converter is decomposed into multiple modal components using the VMD method. Subsequently, these modal components are processed through Parameter-Weighted Trigonometric Difference coding to emphasize fault features. The enhanced features are then transformed into grayscale images, converting them into two-dimensional datasets for training the diagnostic model. Experimental results demonstrate that the proposed VMD-PWTDIC method achieves a diagnostic accuracy of 99.37%, which is 58.67% higher than four other comparative methods. Furthermore, compared to other methods, the proposed method reduces processing time by an average of 10.25 seconds.
REFERENCES (34)
1.
T. Zhang, T. Li, A novel approach of analog circuit fault diagnosis utilizing RFT noise estimation[J], Analog Integrated Circuits and Signal Processing, Vol.98, pp.517-526, March. 2019. https://doi.org/10.1007/s10470....
2.
J. Zhen, Z.B. Liu, Z.W. Huang, et al. DC-DC Buck circuit fault diagnosis with insufficient state data based on deep model and transfer strategy[J], Expert Systems with Applications, Vol.213, Part A, pp.118918, March. 2023. https://doi.org/10.1016/j.eswa....
3.
Q. Sun, Y. Wang, and Y. Jiang, A novel fault diagnostic approach for DC–DC converters based on CSA-DBN[J], IEEE Access, Vol.6, pp.6273-6285, 2018. https://doi.org/10.1109/ACCESS....
4.
Q.Z. Zhao, Y. Li, and S.Tian, et al. Condition Monitoring and Fault Handling Method Based on Big Data Analysis of Intelligent Distribution Network[J], Power System Technology, Vol.40, no.03, pp.774-780, 2016.
5.
D. Binu, B.S. Kariyappa, A survey on fault diagnosis of analog circuits: Taxonomy and state of the art[J], AEU - International Journal of Electronics and Communications, Vol.73, pp.68-83,2017. https://doi.org/10.1016/j.aeue....
6.
Y. Fu, Z. Gao, H. Wu, et al. Data-driven fault classification for non-inverting buck-boost DC–DC power converters based on expectation maximisation principal component analysis and support vector machine approaches[C], 2021 IEEE 1st International Power Electronics and Application Symposium (PEAS), pp.1-6, 2021. https://doi.org/10.1109/PEAS53....
7.
Q. Xue , B.Y. Xu , C.B. He, et al., Feature extraction using hierarchical dispersion entropy for rolling bearing fault diagnosis[J], IEEE Transactions on Instrumentation and Measurement, Vol.70, pp.1-11, Jun. 2021. https://doi.org/10.1109/TIM.20....
8.
Y. Jiang, L. Xia, and J. Zhang, A Fault Feature Extraction Method for DC–DC Converters Based on Automatic Hyperparameter-Optimized 1-D Convolution and Long Short-Term Memory Neural Networks[J], IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol.10, no.4, pp.4703-4714, Aug. 2022. https://doi.org/10.1109/JESTPE....
9.
X.M. Tao, C. Ren, Y.K. Wu, et al. Bearings fault detection using wavelet transform and generalized Gaussian density modeling[J], Measurement, Vol.155, PP.107557, 2020. https://doi.org/10.1016/j.meas....
10.
C. Liu, F. Zhuo and F. Wang, Fault Diagnosis of Commutation Failure Using Wavelet Transform and Wavelet Neural Network in HVDC Transmission System[J]. IEEE Transactions on Instrumentation and Measurement, Vol.70, pp.1-8, 2021. https://doi.org/10.1109/TIM.20....
11.
H. Li, X. Wu, T. Liu, et al. Composite fault diagnosis for rolling bearing based on parameter-optimized VMD[J], Measurement, Vol.201, pp.11637, 2022. https://doi.org/10.1016/j.meas....
12.
Z. Jin, D.Q. He, Z.X. Wei, Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN[J], Engineering Applications of Artificial Intelligence, Vol.110, PP.104713, 2022.
13.
X. Qi, C.F. Hong, T. Ye, et al. Frequency reconstruction oriented EMD-LSTM-AM based surface temperature prediction for lithium-ion battery[J], Journal of Energy Storage, Vol.84, Part.B, PP.111001, 2024.
14.
A. Redekar, H.S. Dhiman, D. Deb, et al. On reliability enhancement of solar PV arrays using hybrid SVR for soiling forecasting based on WT and EMD decomposition methods[J], Ain Shams Engineering Journal, Vol.15, Issue.6, pp.102716, 2024.
15.
P. Zou, B.C. Hou, J. Lei, et al. Bearing Fault Diagnosis Method Based on EEMD and LSTM[J], International Journal of Computers Communications & Control, Vol.15, no.1, 2020. https://doi.org/10.15837/ijccc....
16.
T. Zhong, J.F. Qu, X.Y. Fang, et al. The intermittent fault diagnosis of analog circuits based on EEMD-DBN[J], Neurocomputing, Vol.436, pp.74-91, 2021. https://doi.org/10.1016/j.neuc....
17.
R. S. Kumar, I. G. C. Raj, I. Alhamrouni, et al. A combined HT and ANN based early broken bar fault diagnosis approach for IFOC fed induction motor drive[J], Alexandria Engineering Journal, Vol.66, pp.15-30, 2023. https://doi.org/10.1016/j.aej.....
18.
X.W. Wang, Y.H. Zhao, Z.H. Wang, et al. An ultrafast and robust structural damage identification framework enabled by an optimized extreme learning machine[J], Mechanical Systems and Signal Processing, Volume 216, 2024, 111509, ISSN 0888-3270, https://doi.org/10.1016/j.ymss....
19.
Y.Y. Zhao, Y.G. He, Z.K. Xing, et al., Multibranch 1-D CNN Based on Attention Mechanism for the DAB Converter Fault Diagnosis[J], IEEE Transactions on Instrumentation and Measurement, Vol.71, pp.1-12, 2022. https://doi.org/10.1109/TIM.20....
20.
Z. Jia, Z.F. Li, K. Zhao, et al. CNN-DBLSTM: A long-term remaining life prediction framework for lithium-ion battery with small number of samples[J], Journal of Energy Storage, Volume 97, Part B, 2024, 112947, ISSN 2352-152X, https://doi.org/10.1016/j.est.....
21.
B.Y. Song, Y. Liu, J.Z. Fang, et al. An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples[J], Neurocomputing, Vol.574, pp.127284, 2024.
22.
Z.J. Zhang, L. Chen, C.L. Zhang, et al. GMA-DRSNs: A novel fault diagnosis method with global multi-attention deep residual shrinkage networks[J], Measurement, Vol. 196, 2022, https://doi.org/10.1016/j.meas....
23.
J.Y. Tong, S.Y. Tang, Y. Wu, et al. A fault diagnosis method of rolling bearing based on improved deep residual shrinkage networks, Measuremen[J]t, Vol. 206, 2023, https://doi.org/10.1016/j.meas....
24.
Z. Jia, S.D. Wang, K. Zhao, et al. An efficient diagnostic strategy for intermittent faults in electronic circuit systems by enhancing and locating local features of faults, Measurement science and technology, Vol.35, no.3. 2024, doi: 10.1088/1361-6501/ad156f.
25.
S.D. Wang, Z. Jia, Z.B. Liu, et al, Self-supervised contrast learning based UAV fault detection and interpretation with spatial–temporal information of multivariate flight data[J], Expert Systems with Applications, Vol.267, pp.126156, 2025,.
26.
J. Yang, J. Liu, J. Xie, et al. Conditional GAN and 2-D CNN for Bearing Fault Diagnosis With Small Samples[J], IEEE Transactions on Instrumentation and Measurement, Vol.70, pp.1-12, 2021. https://doi.org/10.1109/TIM.20....
27.
P. Wang, D.Q. Li, H. Wang, Rolling bearing fault diagnosis algorithm based improved alternating transfer learning, Journal of vibration and shock[J], Journal of Vibration and Shock, Vol.43, no.5, pp.239-249, Jun. 2024.
28.
L. Xue, C. Lei, M. Jiao, et al. Rolling Bearing Fault Diagnosis Method Based on Self-Calibrated Coordinate Attention Mechanism and Multi-Scale Convolutional Neural Network Under Small Samples[J], IEEE Sensors Journal, Vol.23, no.9, pp.10206-10214, May. 2023. https://doi.org/10.1109/JSEN.2....
29.
J. Song, X. Wu, L. Qian, et al. PMSLM Eccentricity Fault Diagnosis Based on Deep Feature Fusion of Stray Magnetic Field Signals[J], IEEE Transactions on Instrumentation and Measurement, Vol.73, pp. 1-12, 2024. https://doi.org/10.1109/TIM.20....
30.
Y.K. Zhou, R. Zhu, H. Zhao, et al. Influence of noise on wear fault diagnosis based on recurrence plot[J], Measurement, Vol.205, pp.112158, 2022.
31.
X. Liu, L. Xia, J. Shi, et al. A Fault Diagnosis Method of Rolling Bearing Based on Improved Recurrence Plot and Convolutional Neural Network[J], IEEE Sensors Journal, Vol.23, no.10, pp.10767-10775, May. 2023. https://doi.org/10.1109/JSEN.2....
32.
X.C. Luan, Z.P. Zhang, Y.D. Shan, et al, Main bearing fault feature enchancement method based on optimal variational mode decomposition and computational order analysis[J], Journal of propulsion technology, pp.1-13, Jun. 2024,.
33.
T. Guo, L.P. Wu, C.J. Wang, et al. Damage detection in a novel deep-learning framework: a robust method for feature extraction[J], Struct. Health Monit. Vol.19, no.2, pp.424–442, 2020. https://doi.org/10.1177/147592....
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
