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.
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References
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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.
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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
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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
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DOI: https://doi.org/10.1007/978-3-031-46002-9_18
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