4.7 Article

Knowledge-infused deep learning diagnosis model with self-assessment for smart management in HVAC systems

Related references

Note: Only part of the references are listed.
Article Construction & Building Technology

An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems

Guannan Li et al.

Summary: A novel explainable deep learning based fault diagnosis method is proposed for HVAC systems, with distinctive features including the exclusion of the pooling layer, a convolution filter kernel size of 1, and the use of softsign as an activation function, achieving over 98.5% fault diagnosis accuracy for seven chiller faults and providing fault-discriminative information through the Grad-Absolute-CAM method.

BUILDING AND ENVIRONMENT (2021)

Article Thermodynamics

Fault detection and diagnosis for the screw chillers using multi-region XGBoost model

Shuai Zhang et al.

Summary: This study introduces a novel multi-region XGBoost model, integrating parameter-optimized XGBoost model with mean shift clustering method, for fault detection and diagnosis in chillers. Experimental results show that the multi-region XGBoost model outperforms other models in fault detection and diagnosis, demonstrating higher accuracy and generalization ability.

SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT (2021)

Article Construction & Building Technology

Chiller fault detection and diagnosis with anomaly detective generative adversarial network

Ke Yan

Summary: Data augmentation is essential for automated fault detection and diagnosis in chillers, and generative adversarial networks (GAN) can be used to generate synthetic faulty data samples. It has been shown that selecting high-quality synthetic faulty samples with GAN can improve FDD accuracy.

BUILDING AND ENVIRONMENT (2021)

Article Thermodynamics

Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings

Majid Emami Javanmard et al.

Summary: This study optimized the unconcentrated water and energy consumption in buildings using mixed-integer linear programming and investigated it economically and environmentally with 12 machine learning algorithms, with results showing higher prediction accuracy in Light Gradient Boosting Machine and Extra Tree algorithms.

ENERGY CONVERSION AND MANAGEMENT (2021)

Review Computer Science, Information Systems

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Guannan Li et al.

Summary: Faults in building energy systems require efficient fault detection and diagnosis to ensure smooth operation and performance. Feature engineering plays a key role in generating optimal model inputs for fault detection models. Challenges include data volume, diversity, quality, and performance evaluation of feature engineering algorithms and fault detection models. Future work should focus on designing a robust and automated feature engineering strategy considering the relationships between fault-related features and real-time impacts.

IEEE ACCESS (2021)

Article Automation & Control Systems

A semi-physical static model for optimizing power consumption of HVAC systems

Wanqi Xiong et al.

CONTROL ENGINEERING PRACTICE (2020)

Article Construction & Building Technology

Unsupervised learning for fault detection and diagnosis of air handling units

Ke Yan et al.

ENERGY AND BUILDINGS (2020)

Article Construction & Building Technology

Generative adversarial network for fault detection diagnosis of chillers

Ke Yan et al.

BUILDING AND ENVIRONMENT (2020)

Article Computer Science, Artificial Intelligence

Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions

Xiaoan Yan et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Article Engineering, Electrical & Electronic

Deep Clustering With Variational Autoencoder

Kart-Leong Lim et al.

IEEE SIGNAL PROCESSING LETTERS (2020)

Review Construction & Building Technology

Development and implementation of automated fault detection and diagnostics for building systems: A review

Zixiao Shi et al.

AUTOMATION IN CONSTRUCTION (2019)

Review Construction & Building Technology

A review of fault detection and diagnosis methods for residential air conditioning systems

A. P. Rogers et al.

BUILDING AND ENVIRONMENT (2019)

Article Construction & Building Technology

Optimal chiller loading for saving energy by exchange market algorithm

Farnaz Sohrabi et al.

ENERGY AND BUILDINGS (2018)

Article Thermodynamics

Review of modeling methods for HVAC systems

Abdul Afram et al.

APPLIED THERMAL ENGINEERING (2014)