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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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A Fault Detection Method Based on an Oil Temperature Forecasting Model Using an Improved Deep Deterministic Policy

Lei Wei1,2,3, Zhe Cheng1,2, Junsheng Cheng3

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

Entropy (Basel, Switzerland)
|July 8, 2023
PubMed
Summary

This study introduces an advanced AI model for helicopter gearbox oil temperature forecasting. The new method enhances fault detection accuracy and reduces detection time, improving operational safety.

Keywords:
data drivendeep deterministic policy gradientfault detectionhelicopter main gearboxoil temperature

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Area of Science:

  • Aerospace Engineering
  • Artificial Intelligence
  • Mechanical Engineering

Background:

  • Helicopter gearbox safety is critical for operation.
  • Gearbox oil temperature is a key indicator of gearbox health.
  • Accurate oil temperature forecasting is essential for reliable fault detection.

Purpose of the Study:

  • To develop an accurate helicopter gearbox oil temperature forecasting model.
  • To improve the reliability and efficiency of fault detection systems.
  • To enhance the overall safety of helicopter operations.

Main Methods:

  • An improved deep deterministic policy gradient algorithm with a CNN-LSTM learner.
  • A reward incentive function for accelerated training and model stabilization.
  • A variable variance exploration strategy for effective state-space exploration.
  • A multi-critics network structure for accurate Q-value estimation.
  • Kernel Density Estimation (KDE) for fault threshold determination after EWMA processing.

Main Results:

  • The proposed model demonstrates higher prediction accuracy for gearbox oil temperature.
  • The model significantly reduces fault detection time costs.
  • The integration of CNN-LSTM effectively captures complex temperature-working condition relationships.
  • The multi-critics network improves Q-value estimation accuracy.

Conclusions:

  • The developed AI model offers a significant advancement in helicopter gearbox health monitoring.
  • The proposed forecasting method enhances the speed and accuracy of fault detection.
  • This approach contributes to improved helicopter operational safety through predictive maintenance.