Abstract
We introduce an LSTM-based method for predicting compressor failures using aggregated sensory data, and evaluate it using historical information from over 1000 heavy duty vehicles during 2015 and 2016. The goal is to proactively identify trucks that will require maintenance in the near future, so that component replacement can be scheduled before the failure happens, translating into improved uptime. The problem is formulated as a classification task of whether a compressor failure will happen within the specified prediction horizon. A recurrent neural network using Long Short-Term Memory (LSTM) architecture is employed as the prediction model, and compared against Random Forest (RF), the solution used in industrial deployment at the moment. Experimental results show that while Random Forest slightly outperforms LSTM in terms of AUC score, the predictions of LSTM stay significantly more stable over time, showing a consistent trend from healthy to faulty class. Additionally, LSTM is also better at detecting the switch from faulty class to the healthy one after a repair. We demonstrate that this stability is important for making repair decisions, especially in questionable cases, and therefore LSTM model is likely to lead to better results in practice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Prytz, R.: Machine learning methods for vehicle predictive maintenance using off-board and on-board data. Licentiate Thesis (2014)
Prytz, R., Nowaczyk, S., Byttner, S.: Towards relation discovery for diagnostics. In: Proceedings of the First International Workshop on Data Mining for Service and Maintenance, KDD4Service 2011, San Diego, California (2011)
Rognvaldsson, T., Byttner, S., Prytz, R., Nowaczyk, S.: Wisdom of crowds for self-organized intelligent monitoring of vehicle fleets. IEEE Trans. Knowl. Data Eng. (TKDE) (2014)
Prytz, R., Nowaczyk, S., Rognvaldsson, T., Byttner, S.: Analysis of truck compressor failures based on logged vehicle data. In: Proceedings of the 9th International Conference on Data Mining (DMIN 2013), Las Vegas, NV, USA, July 2013
Prytz, R., Nowaczyk, S., Rognvaldsson, T., Byttner, S.: Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. In: Engineering Applications of Artificial Intelligence (2014)
Gugulothu, N., Vishnu, T.V., Malhotra, P., Vig, L., Agarwal, P., Shroff, G.: Predicting remaining useful life using time series embeddings based on recurrent neural networks. arXiv:1709.01073 (2017)
Liu, J., Saxena, A., Goebel, K., Saha, B., Wang, W.: An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. National Aeronautics and Space Administration, Moffett Field, CA, AMES Research Center, Technical report (2010)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Sak, H., Senior, A., Beaufays, F..: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)
Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 545–552 (2009)
Zheng, S., Ristovski, K., Farahat, A., Gupta, C.: Long short-term memory network for remaining useful life estimation. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 88–95 (2017)
Zhao, G., Zhang, G., Liu, Y., Zhang, B., Hu, C.: Lithiumion battery remaining useful life prediction with deep belief network and relevance vector machine. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 7–13. IEEE (2017)
Chen, K., Fan, Y., Pashami, S., Nowaczyk, S.: Recurrent neural networks for fault detection. Master Thesis, Halmstad (2018)
Ho, T.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995
Schwabacher, M., Goebel, K.: A survey of artificial intelligence for prognostics. In: AAAI Fall Symposium, pp. 107–114 (2007)
Ahmadimanesh, A., Shahrtash, S.: Transient-based fault-location method for multiterminal lines employing s-transform. IEEE Trans. Power Delivery 28(3), 1373–1380 (2013)
Liu, J.: Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection. Measur. Sci. Technol. 23(5) (2012). https://doi.org/10.1088/0957-0233/23/5/055604
Chibani, A., Chadli, M., Shi, P., Braiek, N.: Fuzzy fault detection filter design for t-s fuzzy systems in the finite-frequency domain. IEEE Trans. Fuzzy Syst. 25(5), 1051–1061 (2017)
Rai, V.K., Mohanty, A.R.: Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform. Mech. Syst. Signal Process. 21(6), 2607–2615 (2007)
Zhong, G., Yang, G.: Fault detection for discrete-time switched systems in finite-frequency domain. Circuits Syst. Signal Process. 34(4), 1305–1324 (2015)
Heimes, F.: Recurrent neural networks for remaining useful life estimation. In: International Conference on Prognostics and Health Management, pp. 1–6. IEEE (2008)
Malhi, A., Yan, R., Gao, R.: Prognosis of defect propagation based on recurrent neural networks. IEEE Trans. Instrum. Meas. 60(3), 703–711 (2011)
Rigamonti, M., Baraldi, P., Zio, E.: Echo state network for the remaining useful life prediction of a turbofan engine. In: Third European Conference of the Prognostics and Health Management Society (2016)
Sateesh Babu, G., Zhao, P., Li, X.-L.: Deep convolutional neural network based regression approach for estimation of remaining useful life. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 214–228. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32025-0_14
Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: International Conference on the Prognostics and Health Management, pp. 1–9. IEEE (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, K., Pashami, S., Fan, Y., Nowaczyk, S. (2019). Predicting Air Compressor Failures Using Long Short Term Memory Networks. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-30241-2_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30240-5
Online ISBN: 978-3-030-30241-2
eBook Packages: Computer ScienceComputer Science (R0)