Abstract
We propose a predictive maintenance application as a benchmark problem for verification of neural networks (VNN). It is a deep learning based estimator of remaining useful life (RUL) of aircraft mechanical components, such as bearings. We implement the estimator as a convolutional neural network. We then provide mathematical formalizations of its non-functional requirements, such as stability and monotonicity, as properties. These properties can be used to assess the applicability and the scalability of existing VNN tools.
URL. Benchmark materials, such as trained models (.onnx), examples of properties (.vnnlib), test datasets, and property generation procedures, are available at https://github.com/loonwerks/vnncomp2022.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Remaining Useful Life Estimation using Convolutional Neural Network. https://www.mathworks.com/help/releases/R2021a/predmaint/ug/remaining-useful-life-estimation-using-convolutional-neural-network.html
EASA and Collins Aerospace: Formal Methods use for Learning Assurance (ForMuLA). Technical report (2023)
European Union Aviation Safety Agency (EASA): Concept Paper: Guidance for Level 1 &2 Machine Learning Applications. Concept paper for consultation (2023)
Li, X., Ding, Q., Sun, J.Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. In: Reliability Engineering & System Safety, pp. 1–11 (2018)
Ren, L., Cui, J., Sun, Y., Cheng, X.: Multi-bearing remaining useful life collaborative prediction: a deep learning approach. J. Manuf. Syst. 43, 248–256 (2017)
Yuan, M., Wu, Y., Lin, L.: Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. In: 2016 IEEE International Conference on Aircraft Utility Systems (AUS), pp. 135–140. IEEE (2016)
Acknowledgement
The authors wish to thank Eric DeWind and David F. Larsen for fruitful discussions and feedback about the RUL estimator and its properties, as well as for providing mechanical bearing degradation datasets to prepare this benchmark problem.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kirov, D., Rollini, S.F. (2024). Benchmark: Remaining Useful Life Predictor for Aircraft Equipment. In: Steffen, B. (eds) Bridging the Gap Between AI and Reality. AISoLA 2023. Lecture Notes in Computer Science, vol 14380. Springer, Cham. https://doi.org/10.1007/978-3-031-46002-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-46002-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46001-2
Online ISBN: 978-3-031-46002-9
eBook Packages: Computer ScienceComputer Science (R0)