Concise convolutional neural network model for fault detection

Main Article Content

Muhammad Al Firdausi
Shafiq Ahmad


Fault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the era of big data and the fourth industrial revolution (Industry 4.0). Say, a working condition of bearing can be monitored and then any fault can be detected using the vibration analysis of bearing acceleration data. Most of the published works are presented based upon the knowledge of signal processing in which the result depends heavily on feature extraction. It becomes a challenge then to apply a machine learning algorithm directly to the raw acceleration data as it has been successfully applied to raw data in other science and engineering domains. In this article, a concise Convolutional Neural Networks-based deep learning model is proposed for bearing fault detection. The proposed model was concise with 98% less number of parameters compared to other well-known models. It produced 21.21% and 7.03% better accuracy and fault detection rate, respectively. The model was also tested in different operating parameter environments and still gave an excellent result. Since the proposed concise architecture of the model needed short training time, it is deemed suitable for application on manufacturing floor where the pace of production moves fast and the change of the production machine configuration likely occurs.


Download data is not yet available.

Article Details

How to Cite
Al Firdausi, M., & Ahmad, S. (2022). Concise convolutional neural network model for fault detection. Communications in Science and Technology, 7(1), 62-72.


1. O. Janssens, V. Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Ver-stockt, R. V. d. Walle, and S. V. Hoecke, Convolutional Neural Network Based Fault Detection for Rotating Machinery, J. Sound Vib. 377 (2016) 331-345.

2. L. Wen, X. Li, L. Gao, and Y. Zhang, A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method, IEEE Trans. Ind. Electron. 65 (2018) 5990–5998.

3. C. Lu, Y. Wang, M. Ragulskis, and Y. Cheng, Fault Diagnosis for Rotating Machinery: A Method based on Image Processing, PloS one, 11 (2016) 1-22.

4. J. Tao, Y. L. Liu, and D. L. Yang, Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion, Shock Vib. 2016 (2016) 1-9.

5. Z. Y. Chen and W. H. Li, Multisensor Feature Fusion for Bearing Fault Di-agnosis Using Sparse Autoencoder and Deep Belief Network, IEEE Trans Instrum Meas. 66 (2017) 1693–1702.

6. R. Zhao, D. Wang, R. Yan, K. Mao, F. Shen, and J. Wang, Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks, IEEE Trans. Ind. Electron. 65 (2018) 1539–1548.

7. F. Jia, Y. G. Lei, J. Lin, X. Zhou, and N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of ro-tating machinery with massive data, Mech Syst Signal Process. 72–73 (2016) 303–315.

8. S.-Y. Shao, W.-J. Sun, R.-Q. Yan, P. Wang, and R. X. Gao, A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing, Chin. J. Mech. Eng. 30 (2017) 1347–1356.

9. K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybernetics, 36, (1980) 193–202.

10. Y. Le Cun, L. D. Jackel, B. Boser, J. S. Denker, H. P. Graf, I. Guyon, D. Henderson, R. E. Howard and W. Hubbard, Handwritten digit recognition: applications of neural network chips and automatic learning, IEEE Commun. Mag. 27 (1989) 41–46.

11. A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Adv Neural Inf Process Syst. 25 (2012) 1-9.

12. C. Szegedy, et al., Going Deeper with Convolutions, Proc. IEEE Conf. Com-put. Vis. Pattern Recognit. Boston, MA, USA, 2014, pp. 1-9.

13. K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations. San Diego, CA, USA, 2015, pp. 1-14.

14. K. He, et al., Deep Residual Learning for Image Recognition. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Las Vegas, NV, USA, 2015, pp. 770-778.

15. X. Shi, Y. Cheng, B. Zhang, and H. Zhang, Intelligent fault diagnosis of bearings based on feature model and Alexnet neural network, Int. Conf. Progn. Health Manag. Detroit, MI, USA, 2020, pp. 1–6.

16. L. Wen, X. Li, and L. Gao, A transfer convolutional neural network for fault diagnosis based on ResNet-50, Neural Comput. Applic. 32 (2020) 6111–6124.

17. T. Harris, Boca Raton, USA: John Wiley Sons, Inc., 2001.

18. C. Lessmeier, J. K. Kimotho, D. Zimmer, and W. Sextro, Condition Monitor-ing of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification, PHM Society European Conference. 3 (2016) 1-17.

19. K. He, et al., Delving Deep into Rectifiers: Surpassing Human-Level Per-formance on ImageNet Classification, Proc. IEEE Int. Conf. Comput. Vis., Santiago, Chile. 2015, pp. 1026-1034.

20. V. Suarez´-Paniagua and I. Segura-Bedmar, Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction, BMC Bioinformatics. 19 (2018) 39-47.

21. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. USA: MIT Press, 2016.

22. R. Grosse, CSC321 Lecture 8 Optimization. Canada: CS University of Toronto, 2021.

23. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn Res. 15 (2014) 1929–1958.

24. R. Magar, L. Ghule, J. Li, Y. Zhao, and A. B. Farimani, FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification. IEEE Ac-cess, 9 (2021) 25189–25199.

25. Z. Zhang and M. R. Sabuncu, Generalized Cross Entropy Loss for Train-ing Deep Neural Networks with Noisy Labels, 32nd Int. Conf. Neural Inf. Process. Syst., NY, USA. 2018, pp. 8792–8802.

26. X. Xue and J. Zhou, A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery, ISA Trans. 66 (2017) 284–295.

27. D. G. Altman and J. M. Bland, Diagnostic tests. 1: Sensitivity and speci-ficity, BMJ-Brit. Med. J. 308 (1994) 1552.

28. L. Hou, R. Jiang, Y. Tan, and J. Zhang, Input feature mappings-based deep residual networks for fault diagnosis of rolling element bearing with complicated dataset, IEEE Access. 8 (2020) 180967–180976.

29. Y. H. Chen, G. L. Peng, C. H. Xie, W. Zhang, C. H. Li, and S. H. Liu, ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis, Neurocomputing. 294 (2018) 61–71.

30. X. Wang and F. Liu, Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis, Sensors, 20 (2020) 1-19.

31. J. Jiao, M. Zhao, J. Lin, and K. Liang, A comprehensive review on convo-lutional neural network in machine fault diagnosis. Neurocomputing, 417 (2020) 36–63.

32. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-Based Learning Applied to Document Recognition, Proc. of the IEEE. 86 (1998) 2278–2324.

33. J. Brownlee, Deep Learning for Computer Vision: Image Classification, Ob-ject Detection and Face Recognition in Python. Independently Published, 2019.

34. S. Wu, G. Wang, P. Tang, F. Chen, and L. Shi, Convolution with even-sized kernels and symmetric padding. Adv. Neural Inf. Process. Syst. 32 (2019) 1-12.

35. C. R. Atmaja Perdana, H. Adi Nugroho, and I. Ardiyanto, Comparison of text-image fusion models for high school diploma certificate classification, Commun. Sci. Technol. 5 (2020) 5–9.