Feature-level interpretability in transfer learning-based chiller fault diagnosis

This publication has 42 references indexed in Scilit:

• Effects of various information scenarios on layer-wise relevance propagation-based interpretable convolutional neural networks for air handling unit fault diagnosis
  Building Simulation, 2024
• How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method
  Applied Energy, 2023
• A review of data-driven fault detection and diagnostics for building HVAC systems
  Applied Energy, 2023
• Interpretable machine learning for building energy management: A state-of-the-art review
  Advances in Applied Energy, 2023
• Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
  Advances in Applied Energy, 2022
• A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning
  Applied Energy, 2019
• XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
  2016
• A fault detection and diagnosis strategy with enhanced sensitivity for centrifugal chillers
  Applied Thermal Engineering, 2011
• Sensitivity of refrigeration system performance to charge levels and parameters for on-line leak detection
  Applied Thermal Engineering, 2005
• Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
  Biological Cybernetics, 1980