Abstract
With an increasing emphasis on driving down the costs of Operations and Maintenance (O &M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain. Condition-based monitoring (CBM) has been at the forefront of recent research developing alarm-based systems and data-driven decision making. This paper provides a brief insight into the research being conducted in this area, with a specific focus on alarm sequence modelling and the associated challenges faced in its implementation. The paper proposes a novel idea to predict a set of relevant repair actions from an input sequence of alarm sequences, comparing Long Short-term Memory (LSTM) and Bidirectional LSTM (biLSTM) models. Achieving training accuracy results of up to 80.23\(\%\), and test accuracy results of up to 76.01\(\%\) with biLSTM gives a strong indication to the potential benefits of the proposed approach that can be furthered in future research. The paper introduces a framework that integrates the proposed approach into O &M procedures and discusses the potential benefits which include the reduction of a confusing plethora of alarms, as well as unnecessary vessel transfers to the turbines for fault diagnosis and correction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Almeida, F., Xexéo, G.: Word embeddings: a survey (2019). https://doi.org/10.48550/ARXIV.1901.09069. https://arxiv.org/abs/1901.09069
Aslansefat, K., Gogani, M.B., Kabir, S., Shoorehdeli, M.A., Yari, M.: Performance evaluation and design for variable threshold alarm systems through semi-Markov process. ISA Trans. 97, 282–295 (2020)
International Society of Automation: Management Of Alarm Systems for the Process Industries. Standard, International Society of Automation, North Carolina, United States (2016)
Basaldella, M., Antolli, E., Serra, G., Tasso, C.: Bidirectional LSTM recurrent neural network for keyphrase extraction. In: Serra, G., Tasso, C. (eds.) IRCDL 2018. CCIS, vol. 806, pp. 180–187. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73165-0_18
Cai, S., Palazoglu, A., Zhang, L., Hu, J.: Process alarm prediction using deep learning and word embedding methods. ISA Trans. 85, 274–283 (2019)
Camacho-Collados, J., Pilehvar, M.T.: On the role of text preprocessing in neural network architectures: an evaluation study on text categorization and sentiment analysis. arXiv preprint arXiv:1707.01780 (2017)
Cui, Z., Ke, R., Pu, Z., Wang, Y.: Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143 (2018)
De Mulder, W., Bethard, S., Moens, M.F.: A survey on the application of recurrent neural networks to statistical language modeling. Comput. Speech Lang. 30(1), 61–98 (2015)
Ding, Z., Li, H., Shang, W., Chen, T.-H.P.: Can pre-trained code embeddings improve model performance? Revisiting the use of code embeddings in software engineering tasks. Empirical Softw. Eng. 27(3), 1–38 (2022). https://doi.org/10.1007/s10664-022-10118-5
Du, M., Yi, J., Mazidi, P., Cheng, L., Guo, J.: A parameter selection method for wind turbine health management through SCADA data. Energies 10(2), 253 (2017)
Beebe, D., Ferrer, S., Logerot, D.: Alarm floods and plant incidents. https://www.digitalrefining.com/article/1000558/alarm-floods-and-plant-incidents#.YkLrZefMIuV (2012). Accessed 27 Mar 2022
Engineering Equipment and Materials Users Association: EEMUA Publication 191 Alarm systems - a guide to design, management and procurement. Standard, Engineering Equipment and Materials Users Association, London, UK (2019)
Koltsidopoulos Papatzimos, A., Thies, P.R., Dawood, T.: Offshore wind turbine fault alarm prediction. Wind Energy 22(12), 1779–1788 (2019). https://doi.org/10.1002/we.2402. https://onlinelibrary.wiley.com/doi/abs/10.1002/we.2402
Maldonado-Correa, J., Martín-Martínez, S., Artigao, E., Gómez-Lázaro, E.: Using SCADA data for wind turbine condition monitoring: a systematic literature review. Energies 13(12), 3132 (2020). https://doi.org/10.3390/en13123132. https://www.mdpi.com/1996-1073/13/12/3132
Offshore Renewable Energy (ORE) Catapult: Offshore Wind Operations and Maintenance, A £9 Billion per year opportunity by 2030 for the UK to Seize. https://ore.catapult.org.uk/wp-content/uploads/2021/05/Catapult-Offshore-Wind-OM_final-050521.pdf (2021). Accessed 29 Mar 2022
Simeu-Abazi, Z., Lefebvre, A., Derain, J.P.: A methodology of alarm filtering using dynamic fault tree. Reliab. Eng. Syst. Saf. 96(2), 257–266 (2011)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp. 3104–3112 (2014). https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
Verhelst, J., Coudron, I., Ompusunggu, A.P.: SCADA-compatible and scaleable visualization tool for corrosion monitoring of offshore wind turbine structures. App. Sci. 12(3), 1762 (2022). https://doi.org/10.3390/app12031762.www.mdpi.com/2076-3417/12/3/1762
Wei, L., Qian, Z., Pei, Y., Wang, J.: Wind turbine fault diagnosis by the approach of SCADA alarms analysis. Appl. Sci. 12(1), 69 (2022). https://doi.org/10.3390/app12010069. www.mdpi.com/2076-3417/12/1/69
Zaheer, R., Shaziya, H.: A study of the optimization algorithms in deep learning. In: 2019 Third International Conference on Inventive Systems and Control (ICISC), pp. 536–539. IEEE (2019)
Zhou, P., Yin, P.: An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics. Renew. Sustain. Energy Rev. 109, 1–9 (2019)
Acknowledgement
This work was supported by the Secure and Safe Multi-Robot Systems (SESAME) H2020 Project under Grant Agreement 101017258. We would like to thank EDF Energy R &D UK Centre, AURA Innovation Centre and University of Hull for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Walker, C., Rothon, C., Aslansefat, K., Papadopoulos, Y., Dethlefs, N. (2022). A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms. In: Seguin, C., Zeller, M., Prosvirnova, T. (eds) Model-Based Safety and Assessment. IMBSA 2022. Lecture Notes in Computer Science, vol 13525. Springer, Cham. https://doi.org/10.1007/978-3-031-15842-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-15842-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15841-4
Online ISBN: 978-3-031-15842-1
eBook Packages: Computer ScienceComputer Science (R0)