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Prediction of follower jumps in cam-follower mechanisms : the benefit of using physics-inspired features in recurrent neural networks

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Abstract
The high functional performance exhibited by modern applications is very often established by an aggregation of various intricate mechanical mechanisms, providing the required motion dynamics to the overall system. Above all, the mechanism's behavior should be reliable for a wide range of operating conditions to assure at all times appropriate functioning of the entire application. In particular, cam-follower mechanisms, which translate a rotational movement into a linear displacement, are plagued by the high dynamics induced by the reciprocating motions. For specific operating conditions, the follower tends to detach from the cam perimeter, resulting in harmful bouncing behavior. This paper presents the use of recurrent neural networks to estimate the follower jump trajectory, based on cam rotation measurements, for a wide range of operating conditions and system modifications. Although these data-driven models are typically known to learn intricate patterns directly from raw data, enhanced prediction performances are observed when providing physics-inspired features to the model. The effect is especially more pronounced when learning from a small amount of data or from datasets for which the data are not uniformly distributed along the parameter space. In addition, this paper presents the use of an additive feature attribution method to quantify the contribution of features in multivariate timeseries on the prediction output of recurrent neural network models. Hence, we show that, by means of the Shapley additive explanation (SHAP) values, the model prioritizes the incorporation of physics-inspired features, explaining the improved generalization capabilities of the prediction model. In general, these presented results indicate the potential to incorporate physics-inspired expert knowledge into various other prediction models, enabling advanced methodologies to monitor inconvenient phenomena in mechanical systems.
Keywords
Computer Science Applications, Mechanical Engineering, Aerospace Engineering, Civil and Structural Engineering, Signal Processing, Control and Systems Engineering, Cam-follower mechanism, LSTM neural networks, Interpretable machine learning, Additive feature attribution methods, SHAP, SYSTEM, BIFURCATIONS, FORCE

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MLA
De Groote, Wannes, et al. “Prediction of Follower Jumps in Cam-Follower Mechanisms : The Benefit of Using Physics-Inspired Features in Recurrent Neural Networks.” MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 166, 2022, doi:10.1016/j.ymssp.2021.108453.
APA
De Groote, W., Van Hoecke, S., & Crevecoeur, G. (2022). Prediction of follower jumps in cam-follower mechanisms : the benefit of using physics-inspired features in recurrent neural networks. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 166. https://doi.org/10.1016/j.ymssp.2021.108453
Chicago author-date
De Groote, Wannes, Sofie Van Hoecke, and Guillaume Crevecoeur. 2022. “Prediction of Follower Jumps in Cam-Follower Mechanisms : The Benefit of Using Physics-Inspired Features in Recurrent Neural Networks.” MECHANICAL SYSTEMS AND SIGNAL PROCESSING 166. https://doi.org/10.1016/j.ymssp.2021.108453.
Chicago author-date (all authors)
De Groote, Wannes, Sofie Van Hoecke, and Guillaume Crevecoeur. 2022. “Prediction of Follower Jumps in Cam-Follower Mechanisms : The Benefit of Using Physics-Inspired Features in Recurrent Neural Networks.” MECHANICAL SYSTEMS AND SIGNAL PROCESSING 166. doi:10.1016/j.ymssp.2021.108453.
Vancouver
1.
De Groote W, Van Hoecke S, Crevecoeur G. Prediction of follower jumps in cam-follower mechanisms : the benefit of using physics-inspired features in recurrent neural networks. MECHANICAL SYSTEMS AND SIGNAL PROCESSING. 2022;166.
IEEE
[1]
W. De Groote, S. Van Hoecke, and G. Crevecoeur, “Prediction of follower jumps in cam-follower mechanisms : the benefit of using physics-inspired features in recurrent neural networks,” MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 166, 2022.
@article{8721548,
  abstract     = {{The high functional performance exhibited by modern applications is very often established by an aggregation of various intricate mechanical mechanisms, providing the required motion dynamics to the overall system. Above all, the mechanism's behavior should be reliable for a wide range of operating conditions to assure at all times appropriate functioning of the entire application. In particular, cam-follower mechanisms, which translate a rotational movement into a linear displacement, are plagued by the high dynamics induced by the reciprocating motions. For specific operating conditions, the follower tends to detach from the cam perimeter, resulting in harmful bouncing behavior. This paper presents the use of recurrent neural networks to estimate the follower jump trajectory, based on cam rotation measurements, for a wide range of operating conditions and system modifications. Although these data-driven models are typically known to learn intricate patterns directly from raw data, enhanced prediction performances are observed when providing physics-inspired features to the model. The effect is especially more pronounced when learning from a small amount of data or from datasets for which the data are not uniformly distributed along the parameter space. In addition, this paper presents the use of an additive feature attribution method to quantify the contribution of features in multivariate timeseries on the prediction output of recurrent neural network models. Hence, we show that, by means of the Shapley additive explanation (SHAP) values, the model prioritizes the incorporation of physics-inspired features, explaining the improved generalization capabilities of the prediction model. In general, these presented results indicate the potential to incorporate physics-inspired expert knowledge into various other prediction models, enabling advanced methodologies to monitor inconvenient phenomena in mechanical systems.}},
  articleno    = {{108453}},
  author       = {{De Groote, Wannes and Van Hoecke, Sofie and Crevecoeur, Guillaume}},
  issn         = {{0888-3270}},
  journal      = {{MECHANICAL SYSTEMS AND SIGNAL PROCESSING}},
  keywords     = {{Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering,Cam-follower mechanism,LSTM neural networks,Interpretable machine learning,Additive feature attribution methods,SHAP,SYSTEM,BIFURCATIONS,FORCE}},
  language     = {{eng}},
  pages        = {{20}},
  title        = {{Prediction of follower jumps in cam-follower mechanisms : the benefit of using physics-inspired features in recurrent neural networks}},
  url          = {{http://dx.doi.org/10.1016/j.ymssp.2021.108453}},
  volume       = {{166}},
  year         = {{2022}},
}

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