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Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep

Joaquín Figueroa Barraza1, Enrique López Droguett2, Marcelo Ramos Martins1

  • 1LabRisco-Analysis, Evaluation and Risk Management Laboratory, Department of Naval Architecture and Ocean Engineering, University of São Paulo, São Paulo 05508-030, Brazil.

Sensors (Basel, Switzerland)
|September 10, 2021
PubMed
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This summary is machine-generated.

This study introduces a novel feature selection technique for deep learning models in prognostics and health management (PHM). The method enhances model interpretability without sacrificing performance, improving deployment readiness.

Area of Science:

  • Engineering
  • Computer Science

Background:

  • Deep Learning (DL) models have advanced prognostics and health management (PHM) for diagnostics, prognostics, and anomaly detection.
  • A key challenge is the interpretability tradeoff, where complex DL models offer high accuracy but lack transparency, hindering deployment.

Purpose of the Study:

  • To address the accuracy/interpretability tradeoff in DL-based PHM.
  • To propose an embedded feature selection (FS) technique within deep neural networks for PHM.

Main Methods:

  • Developed a novel FS layer integrated into deep neural networks.
  • Trained the FS layer concurrently with the network to assess input feature importance.
  • Introduced a new metric, the ranking quality score (RQS), to evaluate performance evolution based on feature ranking.
Keywords:
deep learningdeep neural networksfeature selectioninterpretable AIprognostics and health management

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Main Results:

  • The proposed embedded FS technique achieved higher RQS compared to other methods.
  • The technique maintained performance levels consistent with DL models lacking the FS layer.
  • Demonstrated effectiveness across three case studies in health state diagnostics, prognostics, and remaining useful life prediction.

Conclusions:

  • The proposed embedded FS technique effectively balances accuracy and interpretability in DL-based PHM.
  • This approach facilitates the deployment of more transparent and reliable PHM models.
  • The RQS metric provides a valuable tool for evaluating feature selection strategies in PHM.