Failures prediction based on performance monitoring of a gas turbine: a binary classification approach

Bartłomiej Mulewicz,

Mateusz Marzec,

Paweł Morkisz,

Piotr Oprocha

Abstrakt

This paper is dedicated to employ novel technique of deep learning for machines failures prediction. General idea of how to transform sensor data into suitable data set for prediction is presented. Then, neural network architecture that is very successful in solving such problems is derived. Finally, we present a case study for real industrial data of a gas turbine, including results of the experiments.

Słowa kluczowe: Predictive Maintenance, Deep Learning, binary classification, convolutional neural network
References

[1] Rosmaini A., Shahrul K., An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering, 2012, 63 (1), pp. 135149.

[2] Jardine A.K.S., Lin D., Banjevic D., A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 2006, 20 (7), pp. 14831510.

[3] Compare M., Zio E., Predictive maintenance by risk sensitive particle ltering. IEEE Transactions on Reliability, 2014, 63 (1), pp. 134143.

[4] Uysal H., A genetic programming approach to classication problems. University College Dublin Dublin, Ireland, 2013.

[5] Bishop C.M., Pattern recognition and machine learning. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.

[6] Mather P., Tso B., Classication methods for remotely sensed data. CRC Press, Boca Raton, 2016.

[7] Aggarwal C.C., Data classication: algorithms and applications. Chap-man |& Hall/CRC, 1st edition, 2014.

[8] Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classication with deep con- volutional neural networks. In Advances in neural information processing systems, 2012, pp. 10971105.

[9] Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115 (3), pp. 211252.

[10] He K., Zhang X., Ren S., Sun J.. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770778.

[11] Susto G.A., Schirru A., Pampuri S., McLoone S., Beghi A., Machine learning for predictive maintenance: A multiple classier approach. IEEE Transactions on Industrial Informatics, 2015, 11 (3), pp. 812820, 2015.

[12] Breiman L., Random forests. Machine learning, 2001, 45 (1), pp. 532.

[13] Che T., Guestrin C., Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785794.

[14] Hubel D.H., Wiesel T.N., Receptive elds and functional architecture of monkey striate cortex. The Journal of physiology, 1968, 195 (1), pp. 215243.

[15] LeCun Y., Bengio Y., et al. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 1995, 3361 (10), pp.  1-14.

[16] Yang J., Nguyen M.N., San P.P., Li X., Krishnaswamy S., Deep convolutional neural networks on multichannel time series for human activity recognition. IJ-CAI, 2015, pp. 39954001.

[17] Fukushima, K., Miyake S., Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. Competition and cooperation in neural nets, 1982, pp. 267285.

[18] Ioe S., Szegedy C., Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, 2015, pp. 448456.

[19] Courville A., Goodfellow I., Bengio Y., Deep Learning. MIT Press, 2016.

[20] Zeiler M.D., Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701, 2012.

[21] He K., Zhang X., Ren S., Sun J., Delving deep into rectiers: Surpassing human-level performance on imagenet classication. In Proceedings of the IEEE international conference on computer vision, 2015, pp. 10261034.

[22] Caruana R., Niculescu-Mizil A., An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning, 2006, pp. 161168.

Czasopismo ukazuje się w sposób ciągły on-line.
Pierwotną formą czasopisma jest wersja elektroniczna.

Wersja papierowa czasopisma dostępna na www.wuj.pl