SpringerLink
Log in

Parametric Study of Anomaly Detection Models for Defect Detection in Infrared Thermography

  • THERMAL METHODS
  • Published:
Russian Journal of Nondestructive Testing Aims and scope Submit manuscript

Abstract

In the current NDT 4.0 revolution, machine learning and artificial intelligence have emerged as the major enablers for non-destructive testing and evaluation (NDT&E) of industrial components. However, recent developments in active thermal NDT (TNDT) support its use as a practical method for checking a range of industrial components. Additionally, recent post-processing research in TNDT has developed several machine learning models to replace human interaction and offer automatic defect detection. However, the smaller area of the flaws and their related few thermal profiles than the wide sound area, leading to imbalanced datasets, make it difficult to train a supervised deep neural. Recently added to TNDT are anomaly detection models and one-class classifiers, both of which are commonly applied machine learning models to real-world issues. The accuracy and other important metrics in autonomous defect detection are influenced by the hyper-parameters of these models, such as contamination factor, volume of training data, and initialization parameter of the relevant model. The current paper investigates how initialization parameters affect these models' TNDT capabilities for automated flaw detection. Using quadratic frequency modulated thermal wave imaging (QFMTWI), a carbon fiber-reinforced polymer specimen with variously sized artificially produced back-holes at different depths is examined. A good hyper-parameter for automatic flaw identification is chosen after qualitatively comparing testing accuracy, precision, recall, F-score, and probability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price includes VAT (Brazil)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.

REFERENCES

  1. Aldrin, C. John, Intelligence augmentation and Human Machine Interface (HMI) best practices for NDT 4.0 reliability, ASNT Annu. Conf. (Las Vegas, 2019).

  2. Maldague, X.P.V., Theory and Practice of Infrared Technology for Nondestructive Testing, New York: Wiley, 2001.

    Google Scholar 

  3. Ciampa Francesco, Pooya Mahmoodi, Fulvio Pinto, and Michele Meo, Recent advances in active infrared thermography for nondestructive testing of aerospace components, Sensors, 2018, vol. 18, no. 2, p. 609.

    Article  ADS  Google Scholar 

  4. He Yunze, Baoyuan Deng, Hong** Wang, Liang Cheng, Ke Zhou, Siyuan Cai, and Francesco Ciampa, Infrared machine vision and infrared thermography with deep learning: A review, Infrared Phys. & Technol., 2021, vol. 116, p. 103754.

  5. Luo, Q., Gao, B., Woo, W.L., and Yang, Y., Temporal and spatial deep learning network for infrared thermal defect detection, NDT & E Int., 2019, vol. 108, p. 102164.

  6. Hu Bozhen, Bin Gao, Wai Lok Woo, Lingfeng Ruan, Jikun **, Yang Yang, and Yongjie Yu, A lightweight spatial and temporal multi-feature fusion network for defect detection, IEEE Trans. Imag. Process., 2020, vol. 30, pp. 472–486.

    Article  ADS  Google Scholar 

  7. Saeed Numan, Nelson King, Zafar Said, and Mohammed A. Omar, Automatic defects detection in CFRP thermograms, using convolutional neural networks and transfer learning, Infrared Phys. & Technol., 2019, vol. 102, p. 103048.

  8. Wei Ziang, Henrique Fernandes, Hans-Georg Herrmann, Jose Ricardo Tarpani, and Ahmad Osman, A deep learning method for the impact damage segmentation of curve-shaped CFRP specimens inspected by infrared thermography, Sensors, 2021, vol. 21, no. 2, p. 395.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fang Qiang, Clemente Ibarra-Castanedo, and Xavier Maldague, Automatic defects segmentation and identification by deep learning algorithm with pulsed thermography: Synthetic and experimental data, Big Data Cognit. Comput., 2021, vol. 5, no. 1, p. 9.

    Article  Google Scholar 

  10. Wei Ziang, Henrique Fernandes, Jose Ricardo Tarpani, Ahmad Osman, and Xavier Maldague, Stacked denoising autoencoder for infrared thermography image enhancement, 2021 IEEE 19th Int. Conf. Ind. Informatics (INDIN) (Palma de Mallorca, 2021), pp. 1–7.

  11. Cheng Liangliang and Mathias Kersemans, Dual-IRT-GAN: A defect-aware deep adversarial network to perform super-resolution tasks in infrared thermographic inspection, Compos. B Eng., 2022, p. 110309.

    Google Scholar 

  12. Tretout, H., David, D., Marin, J.Y., Dessendre, M., Couet, M., and Avenas-Payan, I., An evaluation of artificial neural networks applied to infrared thermography inspection of composite aerospace structures, NDT & E Int., 1996, vol. 6, no. 29, p. 392.

    Google Scholar 

  13. Vijaya Lakshmi, A., Gopi tilak, V., Muzammil M. Parvez, Subhani, S.K., and Ghali, V.S., Artificial neural networks based quantitative evaluation of subsurface anomalies in quadratic frequency modulated thermal wave imaging, Infrared Phys. Technol., 2019, vol. 97, pp. 108–115.

    Article  ADS  Google Scholar 

  14. Lakshmi A. Vijaya, Ghali, V.S., Subhani, S.K., and Naik R. Baloji, Automated quantitative subsurface evaluation of fiber reinforced polymers, Infrared Phys. & Technol., 2020, vol. 110, p. 103456.

  15. Vesala, G.T., Ghali, V.S., Vijaya Lakshmi, A., and Naik, R.B., Deep and handcrafted feature fusion for automatic defect detection in quadratic frequency modulated thermal wave imaging, Russ. J. Nondestr. Test., 2021, vol. 57, pp. 476–485.

    Article  Google Scholar 

  16. Liu Lishuai, Chenjun Guo, Yanxun **ang, Yanxin Tu, Liming Wang, and Fu-Zhen Xuan, A semi-supervised learning framework for recognition and classification of defects in transient thermography detection, IEEE Trans. Ind. Informatics, 2021, vol. 18, no. 4, pp. 2632–2640.

    Article  Google Scholar 

  17. Morelli Davide, Roberto Marani, Ester D’Accardi, Davide Palumbo, Umberto Galietti, and Tiziana D’Orazio, A convolution residual network for heating-invariant defect segmentation in composite materials inspected by lock-in thermography, IEEE Trans. Instrum. Meas., 2021, vol. 70, pp. 1–14.

    Google Scholar 

  18. Tilak V. Gopi, Ghali, V.S., Vijaya Lakshmi, A., Suresh, B., and Naik, R.B., Proximity based automatic defect detection in quadratic frequency modulated thermal wave imaging, Infrared Phys. & Technol., 2021, vol. 114, p. 103674.

  19. Vesala, G.T., Ghali, V.S., Rama Sastry, D.V.A., and Naik, R.B., Deep anomaly detection model for composite inspection in quadratic frequency modulated thermal wave imaging, NDT & E Int., 2022, vol. 132, p. 102710.

  20. Munir Mohsin, Muhammad Ali Chattha, Andreas Dengel, and Sheraz Ahmed, A comparative analysis of traditional and deep learning-based anomaly detection methods for streaming data, 2019 18th IEEE Int. Conf. Mach. Learn. Appl. (ICMLA) (Boca Raton, 2019), pp. 561–566.

  21. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., and Williamson, R.C., Estimating the support of a high-dimensional distribution, Neural Comput., 2001, vol. 13, no. 7, pp. 1443–1471.

    Article  PubMed  Google Scholar 

  22. Liu Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou, Isolation forest, 2008 8th IEEE Int. Conf. Data Min. (Pisa, 2008), pp. 413–422.

  23. Breunig Markus M., Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander, LOF: identifying density-based local outliers, Proc. 2000 ACM SIGMOD Int. Conf. Manag. Data (Dallas, 2000), pp. 93–104.

Download references

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, School of Engineering, Mallareddy University, 500004, Telangana, Hyderabad, India

    G. T. Vesala

  2. Infrared Imaging Center, Department of ECE, Koneru Lakshmaiah Educational Foundation, Andhra Pradesh, Vaddeswaram, India

    V. S. Ghali, Y. Naga Prasanthi & B. Suresh

  3. Department of Electronics and Communication Engineering, Dhanekula Institute of Engineering and Technology, Ganguru, A.P, India

    Y. Naga Prasanthi

Authors
  1. G. T. Vesala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. S. Ghali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Y. Naga Prasanthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B. Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. T. Vesala.

Ethics declarations

CONFLICT OF INTEREST

The authors of this work declare that they have no conflicts of interest.

ABBREVIATIONS

NDT&E

Non-destructive testing and evaluation

TNDT

Thermal non-destructive testing

QFMTWI

Quadratic frequency modulated thermal wave imaging

ML

Machine learning

DL

Deep learning

CNN

Convolution neural network

LSTM

Long-short-term memory

RCNN

Region based convolution neural network

YOLO

You only look once

ADM

Anomaly detection model

CFRP

Carbon fiber reinforced polymer

OCSVM

One class support vector machine

IF

Isolation forest

LOF

Local outlier factor

TP

True positive

TN

True negative

FP

False positive

FN

False negative

POD

Probability of detection

SNR

Signal-to-noise ratio

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vesala, G.T., Ghali, V.S., Prasanthi, Y.N. et al. Parametric Study of Anomaly Detection Models for Defect Detection in Infrared Thermography. Russ J Nondestruct Test 59, 1259–1271 (2023). https://doi.org/10.1134/S1061830923600600

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1061830923600600

Keywords:

Search

Navigation