Siamese Inception Time Network for Remaining Useful Life Estimation

Yıl 2021, Cilt: 1 Sayı: 2, 165 - 175, 30.12.2021

Uğur Ceylan Yakup Genç

Öz

Predictive maintenance tries to reduce cost in engine maintenance in power plants, aircraft, and factories by predicting when maintenance is needed (or by estimating the remaining useful life). Recently, with significant advances in deep learning and the availability of high volumes of data extracted from manufacturing processes, data-driven methods for predicting remaining useful life (RUL) have received a lot of attention. A major problem with data-based prognosis is that it is costly and requires large amounts of training data that could be difficult to obtain in large numbers of failure cases (often impossible and expensive to obtain in the real world). This paper proposes a learning-based method for fault diagnosis requiring fewer failure data. To this end, we use a Siamese network architecture based on a specific deep Convolutional Neural Network (CNN) called InceptionTime. The Siamese part of the network allows repeated use of the existing data to establish a similarity metric for two separate time windows. The turbofan engines C-MAPSS dataset supplied by NASA is used to verify the proposed model. Experiments are conducted using various sizes of the turbofan engines data to compare the performance on small datasets between the proposed model and a base-line model. The results demonstrate that our model can be used in fault diagnosis and provide satisfying prediction results with fewer data with comparable performances against state-of-art methods for RUL prediction.

Anahtar Kelimeler

C-MAPSS, deep learning, prognostics and health management, remaining useful life, siamese networks

Kaynakça

• [1] D. Wang, K. Tsui, and Q. Miao, “Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators”. IEEE Access, 2018, pp. 665–676.
• [2] R. Zhao, “Deep Learning and its Applications to Machine Health Monitoring”. Mechanical Systems and Signal Processing 115, 2019, pp. 213–237.
• [3] X. Si, “Remaining Useful Life Estimation - A Review on the Statistical Data Driven Approaches”. European Journal Operation Research, 213, 2011, pp. 1–14.
• [4] Ö. Eker, F. Camci, and I. Jennions, “Major Challenges in Prognostics: Study on Benchmarking Prognostic Datasets”, Proc. European Conference of the Prognostics and Health Management (PHM) Society, 2012.
• [5] N. Gebraeel, A. Elwany, and Jing Pan. “Residual Life Predictions in the Absence of Prior Degradation Knowledge”. IEEE Transactions on Reliability 58. 2009, pp. 106–117.
• [6] O. Eker et al. “A Simple State-Based Prognostic Model for Railway Turnout Systems”. IEEE Transactions on Industrial Electronics 58, 2011, pp. 1718–1726.
• [7] Y. Bengio, Y. LeCun, and G. E. Hinton, “Deep learning for AI”, Communications of the ACM. 64, 2021, pp. 58–65.
• [8] G. Koch, R. Zemel, and R. Salakhutdinov. “Siamese Neural Networks for One-shot Image Recognition”. Proc. International Conference on Machine Learning, 2015.
• [9] C. Gal ́an et al. “A hybrid ARIMA-SVM Model for the Study of the Remaining Useful Life of Aircraft Engines”. Journal of Computational and Applied Mathematics, 346, 2019, pp. 184–191.
• [10] G. A. Susto and A. Beghi. “Dealing with Timeseries Data in Predictive Maintenance Problems”. Proc. IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), 2016, pp. 1–4.
• [11] R. Khelif et al. “Direct Remaining Useful Life Estimation Based on Support Vector Regression”. IEEE Transactions on Industrial Electronics 64, 2017, pp. 2276–2285.
• [12] S. Behera et al. “Ensemble Trees Learning based Improved Predictive Maintenance using IoT for Turbofan Engines”. Proc. ACM/SIGAPP Symposium on Applied Computing, 2019.
• [13] C. Zhang, J. H. Sun, and K. Tan. “Deep Belief Networks Ensemble with Multi-objective Optimization for Failure Diagnosis”. Proc. IEEE International Conference on Systems, Man, and Cybernetics, 2015, pp. 32–37.
• [14] G. K. Durbhaka and B. Selvaraj. “Predictive Maintenance for Wind Turbine Diagnostics using Vibration Signal Analysis based on Collaborative Recommendation Approach”. Proc. International Conference on Advances in Computing, Communications, and Informatics (ICACCI), 2016, pp. 1839–1842.
• [15] J. Xu, Y. Wang, and L. Xu. “PHM Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data”. IEEE Sensors Journal 14, 2014, pp. 1124–1132.
• [16] X. Li, Q. Ding, and J. Sun. “Remaining Useful Life Estimation in Prognostics using Deep Convolution Neural Networks”. Reliability Engineering Systems, 172 (2018), pp. 1–11.
• [17] J. Zhang et al. “Long Short-term Memory for Machine Remaining Life Prediction”. Journal of Manufacturing Systems 48 (2018), pp. 78–86.
• [18] A. Z. Hinchi and M. Tkiouat. “Rolling Element Bearing Remaining Useful Life Estimation Based on a Convolutional Long-short- term Memory Network”. Procedia Computer Science 127 (2018), pp. 123–132.
• [19] A. Zhang et al. “Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation”. Applied Sciences 8 (2018).
• [20] J. Li, X. Li, and D. He, “A Directed Acyclic Graph Network Combined with CNN and LSTM for Remaining Useful Life Prediction”, IEEE Access, June 2019, pp. 75464–75475.
• [21] S. C. Hsiao, “Malware Image Classification Using One-Shot Learning with Siamese Networks”. Proc. Knowledge-Based and Intelligent Information & Engineering Systems (KES2019). 2019.
• [22] A. Saxena, “Damage Propagation Modeling for Aircraft Engine Run-to-failure Simulation”. Proc. International Conference on Prognostics and Health Management, 2008, pp. 1–9.
• [23] I. Fawaz, H. Lucas, B. Forestier, “InceptionTime: Finding AlexNet for Time Series Classification”. Data Mining and Knowledge Discovery, 34, pp. 1936–1962, 2020.
• [24] C. Szegedy et al. “Going Deeper with Convolutions”. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9.
• [25] G. S. Babu, P. Zhao, X-L. Li, “Deep Convolutional Neural Network-based Regression Approach for Estimation of Remaining Useful Life”. Proc. International Conference on Database Systems for Advanced Applications, 2016. pp. 214–28.
• [26] C. Zhang, P. Lim, AK. Qin, KC. Tan, “Multi-objective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics”. IEEE Transactions on Neural Network and Learning System 2016, pp. 1–13.