Your browser does not support JavaScript!

Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder

Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Science, Measurement & Technology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Condition monitoring and fault diagnosis are important for maintaining the system performance and guaranteeing the operational safety. The traditional data-driven approaches mostly incorporate well-defined features and methodologies such as supervised artificial intelligence algorithms. Prior knowledge of possible features and a large quantity of labelled condition data are needed. Besides, many traditional approaches require rebuilding or a retraining of the original model to diagnosis new conditions. The present study proposes an intelligent fault diagnosis approach that uses a deep neural network (DNN) based on stacked denoising autoencoder. Representative features are learned by applying the denoising autoencoder to the unlabelled data in an unsupervised manner. A DNN is then constructed and fine-tuned with just a few items of labelled data. The trained DNN achieves high performance in fault classification. Furthermore, new conditions can be correctly classified by simply fine-tuning the trained DNN model using a small amount of labelled data under the new conditions. The effectiveness of the proposed approach is evaluated using a case study of fault diagnosis of a bearing unit. The results indicate that the proposed method can extract representative features from massive unlabelled data on the system condition and achieve high performance in fault diagnosis.


    1. 1)
      • 10. Yin, S., Wang, G.: ‘Data-driven design of robust fault detection system for wind turbines’, Mechatronics, 2014, 24, (4), pp. 298306.
    2. 2)
      • 31. Vincent, P., Larochelle, H., Bengio, Y., et al: ‘Extracting and composing robust features with denoising autoencoders’. Proc. 25th Int. Conf. on Machine Learning, 2008, pp. 10961103.
    3. 3)
      • 23. Di lena, P., Nagata, K., Baldi, P.: ‘Deep architectures for protein contact map prediction’, Bioinformatics, 2012, 28, (19), pp. 24492457.
    4. 4)
      • 17. Yuan, J., Liu, X.: ‘Semi-supervised learning and condition fusion for fault diagnosis’, Mech. Syst. Signal Process., 2013, 38, (2), pp. 615627.
    5. 5)
      • 11. Kang, H.-J., Van, M.: ‘Bearing-fault diagnosis using non-local means algorithm and empirical mode decomposition-based feature extraction and two-stage feature selection’, IET Sci. Meas. Technol., 2015, 9, (6), pp. 671680.
    6. 6)
      • 2. Yoon, J., He, D.: ‘Planetary gearbox fault diagnostic method using acoustic emission sensors’, IET Sci. Meas. Technol., 2015, 9, (8), pp. 936944.
    7. 7)
      • 30. Hinton, G.E., Salakhutdinov, R.R.: ‘Reducing the dimensionality of data with neural networks’, Science, 2006, 313, (5786), pp. 504507.
    8. 8)
      • 15. Yan, J., Lu, L.: ‘Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis’, Signal Process., 2014, 98, pp. 7487.
    9. 9)
      • 1. Phillips, P., Diston, D.: ‘A knowledge driven approach to aerospace condition monitoring’, Knowledge Based Syst., 2011, 24, (6), pp. 915927.
    10. 10)
      • 6. Yin, S., Ding, S.X., Xie, X., et al: ‘A review on basic data-driven approaches for industrial process monitoring’, IEEE Trans. Ind. Electron., 2014, 61, (11), pp. 64186428.
    11. 11)
      • 35. Case Western Reserve University Bearing Data Centre Website’,, accessed February 2016.
    12. 12)
      • 34. Hinton, G.E.: ‘A practical guide to training restricted Boltzmann machines’, in Montavon, G., Orr, G.B., Müller, K.-R. (EDs.): ‘Neural networks: tricks of the trade’ (Springer Berlin Heidelberg, 2012, 2nd edn.), pp. 599619.
    13. 13)
      • 12. Cococcioni, M., Lazzerini, B., Volpi, S.L.: ‘Robust diagnosis of rolling element bearings based on classification techniques’, IEEE Trans. Ind. Inform., 2013, 9, (4), pp. 22562263.
    14. 14)
      • 4. Liu, T.-I., Lee, J., Liu, G., et al: ‘Monitoring and diagnosis of the tapping process for product quality and automated manufacturing’, Int. J. Adv. Manuf. Technol., 2013, 64, (5-8), pp. 11691175.
    15. 15)
      • 29. Bengio, Y., Lamblin, P., Popovici, D., et al: ‘Greedy layer-wise training of deep networks’, Adv. Neural Inf. Process. Syst., 2007, 19, (1), p. 153.
    16. 16)
      • 24. Tao, S., Zhang, T., Yang, J., et al: ‘Bearing fault diagnosis method based on stacked autoencoder and softmax regression’. Proc. 34th Chinese Control Conf., 2015, pp. 63316335.
    17. 17)
      • 5. Gao, Z., Cecati, C., Ding, S.X.: ‘A survey of fault diagnosis and fault-tolerant techniques – Part I: fault diagnosis with model-based and signal-based approaches’, IEEE Trans. Ind. Electron., 2015, 62, (6), pp. 37573767.
    18. 18)
      • 26. Vincent, P., Larochelle, H., Lajoie, I., et al: ‘Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion’, J. Mach. Learn. Res., 2010, 11, (3), pp. 33713408.
    19. 19)
      • 25. Jia, F., Lei, Y., Lin, J., et al: ‘Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data’, Mech. Syst. Signal Process., 2015, 73, pp. 303315.
    20. 20)
      • 22. Graves, A., Mohamed, A., Hinton, G.: ‘Speech recognition with deep recurrent neural networks’. Proc. 2013 IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2013, pp. 66456649.
    21. 21)
      • 3. Lu, S., He, Q., Hu, F., et al: ‘Sequential multiscale noise tuning stochastic resonance for train bearing fault diagnosis in an embedded system’, IEEE Trans. Instrum. Meas., 2014, 63, (1), pp. 106116.
    22. 22)
      • 8. Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N., et al: ‘A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models’, IEEE Trans. Reliab., 2012, 61, (2), pp. 491503.
    23. 23)
      • 9. Gao, R.X., Chen, X.: ‘Wavelets for fault diagnosis of rotary machines: a review with applications’, Signal Process., 2014, 96, pp. 115.
    24. 24)
      • 7. Dai, X., Gao, Z.: ‘From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis’, IEEE Trans. Ind. Inform., 2013, 9, (4), pp. 22262238.
    25. 25)
      • 14. Wang, J., He, Q., Kong, F.: ‘Multiscale envelope manifold for enhanced fault diagnosis of rotating machines’, Mech. Syst. Signal Process., 2015, 52, pp. 376392.
    26. 26)
      • 28. Betechuoh, B.L., Marwala, T., Tettey, T.: ‘Autoencoder networks for HIV classification’, Curr. Sci., 2006, 91, (11), pp. 14671473.
    27. 27)
      • 20. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘ImageNet classification with deep convolutional neural networks’, Adv. Neural Inf. Process. Syst., 2012, pp. 10971105.
    28. 28)
      • 36. Van Der Maaten, L.J.P., Hinton, G.E.: ‘Visualizing high-dimensional data using t-SNE’, J. Mach. Learn. Res., 2008, 9, pp. 25792605.
    29. 29)
      • 33. D'Ambrosio, R., Iannello, G., Soda, P.: ‘Softmax regression for ECOC reconstruction’. Int. Conf. Image Analysis and Processing, 2013, pp. 682691.
    30. 30)
      • 18. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, (7553), pp. 436444.
    31. 31)
      • 32. Feng, X., Zhang, Y., Glass, J.: ‘Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition’. Proc. 2014 IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2014, pp. 17591763.
    32. 32)
      • 16. Amarnath, M., Praveen Krishna, I.R.: ‘Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings’, IET Sci. Meas. Technol., 2012, 6, (4), p. 279.
    33. 33)
      • 19. Schmidhuber, J.: ‘Deep Learning in neural networks: an overview’, Neural Netw., 2015, 61, pp. 85117.
    34. 34)
      • 13. Meeker, W.Q., Hong, Y.L.: ‘Reliability meets big data: opportunities and challenges’, Qual. Eng., 2014, 26, (1), pp. 102116.
    35. 35)
      • 21. Collobert, R., Weston, J.: ‘A unified architecture for natural language processing: deep neural networks with multitask learning’. Proc. 25th Int. Conf. Machine Learning, 2008, pp. 160167.
    36. 36)
      • 27. Le, Q.V.: ‘Building high-level features using large scale unsupervised learning’. Proc. 2013 IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2013, pp. 85958598.

Related content

This is a required field
Please enter a valid email address