A Multi-Output Deep Learning Model for Fault Diagnosis Based on Time-Series Data
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Published
Apr 18, 2024
Ahmed Al-Ajeli
Eman S. Alshamery
Abstract
In this work, a method for fault diagnosis and localization is proposed. This method adopts the long short-term memory (LSTM) neural network to detect, isolate and determine the component of the system in which a fault has occurred. Unlike the traditional methods used for fault diagnosis, which first extract features from the raw data and then use a classifier in order to diagnose the fault; the LSTM-based method works directly on raw data and builds the classifier. This can be accomplished by training the neural network using the raw data resulting in a trained model (classifier) capturing generalized patterns from this data. This model is used online to diagnose faults and determine the faulty component. The performance of the resulting model is evaluated on testing data. The proposed method has been applied to real time-series data representing sensor readings in spacecraft electrical power distribution systems. The experimental results show promising performance in separating fault modes and identifying the faulty components.
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Keywords
Fault diagnosis, time-series data, sensor measurements, deep learning, LSTM, multioutput model
References
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Al-Ajeli, A., & Parker, D. (2021). Fault diagnosis in labelled petri nets: A fourier–motzkin based approach. Automatica, 132, 109831.
Angeli, C. (2008). Online expert systems for fault diagnosis in technical processes. Expert Systems, 25(2), 115–132.
Dai, X., & Gao, Z. (2013). From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis. IEEE Transactions on Industrial Informatics, 9(4), 2226–2238.
Daigle, M. J., Roychoudhury, I., & Bregon, A. (2015). Qualitative event-based diagnosis applied to a spacecraft electrical power distribution system. Control Engineering Practice, 38, 75–91.
Elsisi, M., Tran, M.-Q., Mahmoud, K., Mansour, D.-E. A., Lehtonen, M., & Darwish, M. M. (2022). Effective iot-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties. Measurement, 190, 110686.
Feldman, A., Kurtoglu, T., Narasimhan, S., Poll, S., & Garcia, D. (2013). Empirical evaluation of diagnostic algorithm performance using a generic framework. International Journal of Prognostics and Health Management Volume 1 (color), 24.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kurtoglu, T., Narasimhan, S., Poll, S., Garcia, D., Kuhn, L., de Kleer, J., & Feldman, A. (2010). Second international diagnostic competition (dxc’10).
Li, W., Li, G., & Kamarthi, S. (2023). The study of trends in ai applications for vehicle maintenance through keyword co-occurrence network analysis. International Journal of Prognostics and Health Management, 14(2).
Lunde, K., Lunde, R., & Münker, B. (2006). Model-based failure analysis with rodon. In Proceedings of the 2006 conference on ecai 2006: 17th european conference on artificial intelligence august 29–september 1, 2006, riva del garda, italy (pp. 647–651).
Ma, J., Ni, S., Xie, W., & Dong, W. (2017). Deep autoencoder observer multiple-model fast aircraft actuator fault diagnosis algorithm. International Journal of Control, Automation and Systems, 15, 1641–1650.
Mange, J., Daniszewski, D., & Dunn, A. (2011). Artificial immune systems for diagnostic classification problems. In Proceedings of 21st international workshop of principles of diagnosis.
Mustafa, Z., Awad, A. S., Azzouz, M., & Azab, A. (2023). Fault identification for photovoltaic systems using a multi-output deep learning approach. Expert Systems with Applications, 211, 118551.
Poll, S., de Kleer, J., Abreau, R., Daigle, M., Feldman, A., Garcia, D., & Sweet, A. (2011). Third international diagnostics competition–dxc’11. In Proc. of the 22nd international workshop on principles of diagnosis (pp. 267–278).
Poll, S., Patterson-Hine, A., Camisa, J., Garcia, D., Hall, D., Lee, C., . . . others (2007). Advanced diagnostics and prognostics testbed. In Proceedings of the 18th international workshop on principles of diagnosis (dx-07) (pp. 178–185).
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1996). Failure diagnosis using discrete-event models. IEEE Transactions on Control Systems Technology, 4(2), 105-124. doi: 10.1109/87.486338
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(56), 1929–1958.
Sweet, A., Feldman, A., Narasimhan, S., Daigle, M., & Poll, S. (2013). Fourth international diagnostic competition–dxc’13. In Proc. of the 24th international workshop on principles of diagnosis (pp. 224–229).
Wu, Z., Guo, Y., Lin, W., Yu, S., & Ji, Y. (2018). A weighted deep representation learning model for imbalanced fault diagnosis in cyber-physical systems. Sensors, 18(4), 1096.
Xu, X., Zheng, H., Guo, Z.,Wu, X.,&Zheng, Z. (2019). Sddcnn: Small data-driven convolution neural networks for subtle roller defect inspection. Applied Sciences, 9(7), 1364.
Yang, J., & Kim, J. (2018). An accident diagnosis algorithm using long short-term memory. Nuclear Engineering and Technology, 50(4), 582–588.
Zaytoon, J., & Lafortune, S. (2013). Overview of fault diagnosis methods for discrete event systems. Annual Reviews in Control, 37(2), 308–320.
Zhang, L., Lin, J., & Karim, R. (2015). An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection. Reliability Engineering & System Safety, 142, 482–497.
Zhang, L., Lin, J., & Karim, R. (2016). Sliding window-based fault detection from high-dimensional data streams. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(2), 289–303.
Zhang, W., Jha, D. K., Laftchiev, E., & Nikovski, D. (2020). Multi-label prediction in time series data using deep neural networks. arXiv preprint arXiv:2001.10098.
Zhao, H., Sun, S., & Jin, B. (2018). Sequential fault diagnosis based on lstm neural network. Ieee Access, 6, 12929–12939.
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Technical Papers