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On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse.

Irene Niyonambaza Mihigo1, Marco Zennaro2, Alfred Uwitonze3

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This summary is machine-generated.

This study introduces two real-time predictive maintenance models, TinyLSTM and an Edge Impulse TinyModel, for industrial equipment. The Edge Impulse model demonstrated superior ease of development and deployment for predicting remaining useful life.

Keywords:
TinyModeledgeequipmentmaintenance actionspredictive maintenancereal-time dataremaining useful life

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Area of Science:

  • Industrial IoT and Predictive Maintenance
  • Machine Learning for Equipment Prognostics
  • Edge Computing for Real-time Analytics

Background:

  • Accurate prediction of industrial equipment health is crucial for reliability and lifespan management.
  • Data-driven prognostic models for Remaining Useful Life (RUL) estimation require continuous updates.
  • Real-time monitoring and prediction can prevent failures and optimize maintenance costs.

Purpose of the Study:

  • To evaluate the efficacy of two real-time tiny predictive analytics models for equipment RUL prediction.
  • To compare TinyLSTM and an Edge Impulse TinyModel in terms of performance, development, and deployment.
  • To assess the potential of on-device deployment for predictive maintenance.

Main Methods:

  • Developed and evaluated two real-time predictive models: TinyLSTM and an Edge Impulse TinyModel.
  • Utilized real-time operational equipment data for assessing degradation insights.
  • Employed fuzzy logic based on expert knowledge to label datasets and compute actual RUL.
  • Converted models into TinyModels for on-device deployment and simulated performance on unseen data.

Main Results:

  • Both TinyLSTM and the Edge Impulse TinyModel showed strong performance in real-time predictive maintenance.
  • Evaluation loss was 0.01 for TinyLSTM and 0.11 for the Edge Impulse TinyModel.
  • The Edge Impulse TinyModel proved significantly easier for development, conversion, and deployment.

Conclusions:

  • TinyLSTM and the Edge Impulse TinyModel are effective for real-time predictive maintenance.
  • The Edge Impulse TinyModel offers a more streamlined development and deployment process.
  • On-device deployment of these tiny models enhances predictive maintenance capabilities.