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A Weld Quality Classification Approach Based on Local Mean Decomposition and Deep Belief Network

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Abstract

Weld quality directly affects the performance and reliability of structural parts and can be critically ensured by online monitoring. In this study, local mean decomposition (LMD), an adaptive time-frequency analysis is used to analyze the current signals of welding, and then combined with deep belief network (DBN) to classify the weld quality. Firstly, the current signals are decomposed by LMD into a series of product functions. Each product function is a complex signal, and its complexity is calculated by multi-scale entropy to select the most relevant product function with weld quality. Finally, DBN is applied to classify weld quality into four types. This method has a higher classification and recognition rate compared with principal component analysis and extreme learning machine classification. Thus, LMD is a potentially effective method to diagnose weld quality.

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References

  1. H. Sebestova, H. Chmelickova, L. Nozka and J. Moudry, Non-Destructive Real Time Monitoring of the Laser Welding Process, J. Mater. Eng. Perform., 2012, 21(5), p 764–769.

    Article  CAS  Google Scholar 

  2. Y. Zhang, H. Luo, J. Li, J. Lv, Z. Zhang and Y. Ma, An Integrated Processing Method for Fatigue Damage Identification in a Steel Structure Based on Acoustic Emission Signals, J. Mater. Eng. Perform., 2017, 26(4), p 1784–1791.

    Article  CAS  Google Scholar 

  3. J. Fang and K. Wang, Weld Pool Image Segmentation of Hump Formation Based on Fuzzy C-Means and Chan-Vese Model, J. Mater. Eng. Perform., 2019, 28(7), p 4467–4476.

    Article  CAS  Google Scholar 

  4. K. Luksa, Influence of Weld Imperfection on Short Circuit GMA Welding Arc Stability, J. Mater. Process. Technol., 2006, 175(1–3), p 285–290.

    Article  CAS  Google Scholar 

  5. S. Pal, S.K. Pal and A.K. Samantaray, Prediction of the Quality of Pulsed Metal Inert Gas Welding Using Statistical Parameters of Arc Signals in Artificial Neural Network, Int. J. Comput. Integr. Manuf., 2010, 23(5), p 453–465.

    Article  Google Scholar 

  6. A. Sumesh, B.B. Nair, K. Rameshkumar, A. Santhakumari, A. Raja and K. Mohandas, Decision Tree Based Weld Defect Classification Using Current and Voltage Signatures in GMAW Process, Mater. Today Proc., 2018, 5(2), p 8354–8363.

    Article  Google Scholar 

  7. Z. Zhang, X. Chen, H. Chen, J. Zhong and S. Chen, Online Welding Quality Monitoring Based on Feature Extraction of Arc Voltage Signal, Int. J. Adv. Manuf. Technol., 2014, 70(9–12), p 1661–1671.

    Article  Google Scholar 

  8. K. He, Z. Zhang, S. Xiao and X. Li, Feature Extraction of AC Square Wave SAW Arc Characteristics Using Improved Hilbert-Huang Transformation and Energy Entropy, Measurement, 2013, 46(4), p 1385–1392.

    Article  Google Scholar 

  9. Y. Huang, D. Wu, Z. Zhang, H. Chen and S. Chen, EMD-Based Pulsed TIG Welding Process Porosity Defect Detection and Defect Diagnosis Using GA-SVM, J. Mater. Process. Technol., 2017, 239, p 92–102.

    Article  CAS  Google Scholar 

  10. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.-C. Yen, C.C. Tung and H.H. Liu, The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci., 1971, 1998(454), p 903–995.

    Google Scholar 

  11. Z. Wu and N.E. Huang, Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method, Adv. Adapt. Data Anal., 2009, 1(01), p 1–41.

    Article  Google Scholar 

  12. J.S. Smith, The Local Mean Decomposition and Its Application to EEG Perception Data, J. R. Soc. Interface, 2005, 2(5), p 443–454.

    Article  Google Scholar 

  13. W.Y. Liu, W.H. Zhang, J.G. Han and G.F. Wang, A New Wind Turbine Fault Diagnosis Method Based on the Local Mean Decomposition, Renew. Energy, 2012, 48, p 411–415.

    Article  Google Scholar 

  14. J. Sun, Q. Xiao, J. Wen and Y. Zhang, Natural Gas Pipeline Leak Aperture Identification and Location Based on Local Mean Decomposition Analysis, Measurement, 2016, 79, p 147–157.

    Article  Google Scholar 

  15. J.S. Richman and M.J. Randall, Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy, Am. J. Physiol. Hear. Circ. Physiol., 2000, 278(6), p H2039.

    Article  CAS  Google Scholar 

  16. Y. Li, L. Wang, X. Li and X. Yang, A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Duffing Chaotic Oscillator, and Weighted-Permutation Entropy, Entropy, 2019, 21(5), p 507.

    Article  Google Scholar 

  17. Y. Li, X. Gao and L. Wang, Reverse Dispersion Entropy: A New Complexity Measure for Sensor Signal, Sensors, 2019, 19(23), p 5203.

    Article  Google Scholar 

  18. M. Costa, A.L. Goldberger and C.-K. Peng, Multiscale Entropy Analysis of Biological Signals, Phys. Rev. E, 2005, 71(2), p 21906.

    Article  Google Scholar 

  19. L. Zhang, G. Xiong, H. Liu, H. Zou and W. Guo, Bearing Fault Diagnosis Using Multi-Scale Entropy and Adaptive Neuro-Fuzzy Inference, Expert Syst. Appl., 2010, 37(8), p 6077–6085.

    Article  Google Scholar 

  20. S. Aouabdi, N. Boutasseta, M. Taibi and S. Bouras, Using Multi-Scale Entropy and Principal Component Analysis to Monitor Gears Degradation via the Motor Current Signature Analysis, Mech. Syst. Signal Process., 2017, 90, p 298–316.

    Article  Google Scholar 

  21. S. Begum, S. Barua, R. Filla and M.U. Ahmed, Classification of Physiological Signals for Wheel Loader Operators Using Multi-Scale Entropy Analysis and Case-Based Reasoning, Expert Syst. Appl., 2014, 41(2), p 295–305.

    Article  Google Scholar 

  22. S. Yin, C. Yang, J. Zhang and Y. Jiang, A Data-Driven Learning Approach for Nonlinear Process Monitoring Based on Available Sensing Measurements, IEEE Trans. Ind. Electron., 2016, 64(1), p 643–653.

    Article  Google Scholar 

  23. Y. Jiang, S. Yin and O. Kaynak, Optimized Design of Parity Relation Based Residual Generator for Fault Detection: Data-Driven Approaches, IEEE Trans. Ind. Informatics., 2020, 17, p 1449–1458.

    Article  Google Scholar 

  24. S. Shin, C. Jin, J. Yu and S. Rhee, Real-Time Detection of Weld Defects for Automated Welding Process Base on Deep Neural Network, Metals (Basel), 2020, 10(3), p 389.

    Article  CAS  Google Scholar 

  25. L. Yin, J. Wang, H. Hu, S. Han and Y. Zhang, Prediction of Weld Formation in 5083 Aluminum Alloy by Twin-Wire CMT Welding Based on Deep Learning, Weld. World., 2019, 63, p 947–955.

    Article  CAS  Google Scholar 

  26. G.E. Hinton, S. Osindero and Y.-W. Teh, A Fast Learning Algorithm for Deep Belief Nets, Neural Comput., 2006, 18(7), p 1527–1554.

    Article  Google Scholar 

  27. Z. Chen and W. Li, Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network, IEEE Trans. Instrum. Meas., 2017, 66, p 1693–1702.

    Article  Google Scholar 

  28. H. Zhao, H. Liu, J. Xu, C. Guo and W. Deng, Research on a Fault Diagnosis Method of Rolling Bearings Using Variation Mode Decomposition and Deep Belief Network, J. Mech. Sci. Technol., 2019, 33, p 4165–4172.

    Article  Google Scholar 

  29. G. Zhao, X. Liu, B. Zhang, Y. Liu, G. Niu and C. Hu, A Novel Approach for Analog Circuit Fault Diagnosis Based on Deep Belief Network, Measurement, 2018, 121, p 170–178.

    Article  Google Scholar 

  30. Y. Huang, S. Li, J. Li, H. Chen, L. Yang and S. Chen, Spectral Diagnosis and Defects Prediction Based on ELM During the GTAW of Al Alloys, Measurement, 2019, 136, p 405–414.

    Article  Google Scholar 

  31. S. Chen, M. Wu and S. Zhao, Analog Circuit Fault Diagnosis Based on PCA and ELM, Comput. Eng. Appl., 2015, 1, p 509–513.

    Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant Nos. 51805266 and 51905273) and Natural Science Foundation of Jiangsu Province (BK20200497 and BK20190472).

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Authors and Affiliations

  1. School of Material Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yong Huang, Xiaowei Wang, Dongqing Yang, Lei Wang, Jieren Gu, Xiaoyong Zhang & Kehong Wang

  2. Key Laboratory of Controlled Arc Intelligent Additive Manufacturing, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yong Huang, Xiaowei Wang, Dongqing Yang, Lei Wang, Jieren Gu, Xiaoyong Zhang & Kehong Wang

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  1. Yong Huang
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  2. Xiaowei Wang
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Correspondence to Dongqing Yang.

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Huang, Y., Wang, X., Yang, D. et al. A Weld Quality Classification Approach Based on Local Mean Decomposition and Deep Belief Network. J. of Materi Eng and Perform 30, 2229–2237 (2021). https://doi.org/10.1007/s11665-021-05495-9

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  • DOI: https://doi.org/10.1007/s11665-021-05495-9

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