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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Deep Auto-Encoder and Deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling.

Hui Yu¹, Chuang Chen², Ningyun Lu²

¹Integrated System Integration Department, No. 38 Research Institute of CETC, Hefei 230088, China.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

This study introduces a new dynamic predictive maintenance scheduling (DPMS) strategy. It integrates failure prognosis and maintenance decisions, outperforming existing methods for improved system reliability and reduced costs.

Keywords:

deep auto-encoder deep forest failure prognosis maintenance cost maintenance decision-making

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Area of Science:

Engineering
Computer Science
Data Science

Background:

Prognostics and Health Management (PHM) is crucial for system safety and economy.
Separate studies on failure prognosis and maintenance decision-making present integration challenges.
Existing methods lack a unified approach for dynamic predictive maintenance scheduling.

Purpose of the Study:

To develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS).
To combine failure prognosis and maintenance decision-making into a single framework.
To enhance system reliability and reduce operational costs through advanced PHM.

Main Methods:

Utilized a deep auto-encoder for feature extraction from raw sensor data, reflecting system degradation.
Employed a deep forest model to compute failure probabilities over moving time horizons.
Integrated failure probabilities into a decision-making process for optimal maintenance scheduling.

Main Results:

The proposed DPMS method demonstrated superior performance compared to state-of-the-art techniques.
Experimental validation using NASA's aircraft engine datasets confirmed the method's effectiveness.
The approach successfully integrates failure prognosis with maintenance decision-making.

Conclusions:

The developed DPMS strategy offers a comprehensive solution for predictive maintenance.
This integrated approach leads to more precise maintenance decisions.
Significant reductions in maintenance costs and improvements in operational efficiency are achievable.