Abstract
Remaining useful life predictions depend on the quality of health indicators (HIs) generated from condition monitoring sensors, evaluated by predefined prognostic metrics such as monotonicity, prognosability, and trendability. Constructing these HIs requires effective models capable of automatically selecting and fusing features from pertinent measurements, given the inherent noise in sensory data. While deep learning approaches have the potential to automatically extract features without the need for significant specialist knowledge, these features lack a clear (physical) interpretation. Furthermore, the evaluation metrics for HIs are nondifferentiable, limiting the application of supervised networks. This research aims to develop an intrinsically interpretable ANN, targeting qualified HIs with significantly lower complexity. A semi-supervised paradigm is employed, simulating labels inspired by the physics of progressive damage. This approach implicitly incorporates nondifferentiable criteria into the learning process. The architecture comprises additive and newly modified multiplicative layers that combine features to better represent the system’s characteristics. The developed multiplicative neurons are not restricted to pairwise actions, and they can also handle both division and multiplication. To extract a compact HI equation, making the model mathematically interpretable, the number of parameters is further reduced by discretizing the weights via a ternary set. This weight discretization simplifies the extracted equation while gently controlling the number of weights that should be overlooked. The developed methodology is specifically tailored to construct interpretable HIs for commercial turbofan engines, showcasing that the generated HIs are of high quality and interpretable.
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Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 859957 “ENHAnCE, European training Network in intelligent prognostics and Health mAnagement in Composite structurEs ”.
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Morteza Moradi: Conceptualization, Methodology, Software, Validation, Data curation, Formal analysis, Investigation, Visualization, Writing - Original Draft, Writing - Review & Editing. Panagiotis Komninos: Methodology, Software, Formal analysis, Investigation, Data curation, Writing - Review & Editing. Dimitrios Zarouchas: Writing - Review & Editing, Supervision, Funding acquisition.
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Moradi, M., Komninos, P. & Zarouchas, D. Constructing explainable health indicators for aircraft engines by developing an interpretable neural network with discretized weights. Appl Intell 55, 143 (2025). https://doi.org/10.1007/s10489-024-05981-2
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DOI: https://doi.org/10.1007/s10489-024-05981-2