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Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model.

Andressa Borré1, Laio Oriel Seman2,3, Eduardo Camponogara1

  • 1Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil.

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Summary

Predictive maintenance for electrical machines uses time series analysis and a hybrid CNN-LSTM model to forecast failures. This approach improves efficiency and reduces downtime by managing data uncertainties.

Keywords:
Savitzky–Golay filterelectrical machinesempirical wavelet transformfault detectiontemporal fusion transformer

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Area of Science:

  • Electrical Engineering
  • Data Science
  • Machine Learning

Background:

  • Predictive maintenance is crucial for reducing costs and downtime in electrical machine operations.
  • Identifying anomalies in sensor data is key to predicting machine failures.

Purpose of the Study:

  • To address the challenge of predicting electrical machine failures using time series analysis.
  • To develop a robust model for anomaly detection in machine vibration data.

Main Methods:

  • Utilized time series data from electrical machine vibration sensors (X, Y, Z axes).
  • Trained a hybrid Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) architecture.
  • Incorporated quantile regression to manage data uncertainties.

Main Results:

  • The hybrid CNN-LSTM attention-based model demonstrated superior performance.
  • Quantile regression effectively captured and managed uncertainties in the vibration data.
  • Achieved better results compared to traditional reference models.

Conclusions:

  • The proposed approach optimizes maintenance schedules for electrical machines.
  • Enhanced predictive capabilities lead to improved overall machine performance and reliability.