Transfer learning-based strategies for fault diagnosis in building energy systems

Abstract Data-driven fault detection and diagnosis (FDD) in building energy systems is typically limited by the quantity and quality of training data. These methods can be only used for individual systems due to the insufficient extrapolation capabilities of most machine learning algorithms. A desirable solution is to utilize transfer learning, which can transfer the knowledge learned from data-rich building energy systems to FDD tasks in data-sparse systems. However, the potential of applying transfer learning to such FDD has not been systematically investigated. Accordingly, this paper proposes a transfer-learning-based methodology for fault diagnosis in building chillers. Experiments were conducted on two water-cooled screw chillers to collect both fault and fault-free data. Transfer-learning-based fault diagnosis experiments were implemented with consideration of different transfer learning tasks, training cases, learning scenarios, and transfer learning implementation strategies. The experimental results validate the value of transfer learning for FDD in building energy systems, especially when the experimental data available for model development are limited. The maximum accuracy improvements were 12.63% and 8.18% in the two learning tasks. The research outcomes provide practical guidelines for developing transfer-learning-based solutions for FDD in building energy systems.

[1]  Ahmet Teke,et al.  Assessing the energy efficiency improvement potentials of HVAC systems considering economic and environmental aspects at the hospitals , 2014 .

[2]  Fariborz Haghighat,et al.  Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review , 2020 .

[3]  Franck Davoine,et al.  Transfer learning in computer vision tasks: Remember where you come from , 2020, Image Vis. Comput..

[4]  Bryan P. Rasmussen,et al.  A review of fault detection and diagnosis methods for residential air conditioning systems , 2019, Building and Environment.

[5]  Yan Cui,et al.  Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[6]  Yingning Qiu,et al.  Diagnosis of wind turbine faults with transfer learning algorithms , 2021 .

[7]  Dongqing Xie,et al.  Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis. , 2018 .

[8]  Yong Hwan Eom,et al.  Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving , 2019, Energy.

[9]  Guannan Li,et al.  Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems , 2021, IEEE Access.

[10]  Daniel Studer,et al.  Assessing barriers and research challenges for automated fault detection and diagnosis technology for small commercial buildings in the United States , 2018 .

[11]  Pooja Samudre,et al.  Optimizing Performance of Convolutional Neural Network Using Computing Technique , 2019, 2019 IEEE 5th International Conference for Convergence in Technology (I2CT).

[12]  Jun Zhu,et al.  A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions , 2020, IEEE Sensors Journal.

[13]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[14]  Konstantinos Gryllias,et al.  Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network , 2020, IEEE Transactions on Industrial Informatics.

[15]  Miao He,et al.  Deep Learning Based Approach for Bearing Fault Diagnosis , 2017, IEEE Transactions on Industry Applications.

[16]  Xiao Chen,et al.  Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning , 2020 .

[17]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[18]  Chuan Li,et al.  A systematic review of deep transfer learning for machinery fault diagnosis , 2020, Neurocomputing.

[19]  Hong Qu,et al.  A deep reinforcement learning based long-term recommender system , 2021, Knowl. Based Syst..

[20]  Shunming Li,et al.  A New Transfer Learning Method and its Application on Rotating Machine Fault Diagnosis Under Variant Working Conditions , 2018, IEEE Access.

[21]  Jiangyan Liu,et al.  Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers , 2020, Energy and Buildings.

[22]  Yang Zhao,et al.  Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future , 2019, Renewable and Sustainable Energy Reviews.

[23]  Eduardo L. Pasiliao,et al.  Multitask deep learning for native language identification , 2020, Knowl. Based Syst..

[24]  Ravi Prakash,et al.  Life cycle energy analysis of buildings: An overview , 2010 .

[25]  Youchao Sun,et al.  Hierarchical deep convolution neural networks based on transfer learning for transformer rectifier unit fault diagnosis , 2021 .

[26]  William O'Brien,et al.  Development and implementation of automated fault detection and diagnostics for building systems: A review , 2019, Automation in Construction.

[27]  Xiang Li,et al.  Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks , 2019, IEEE Transactions on Industrial Electronics.

[28]  François Chollet,et al.  Deep Learning with Python , 2017 .

[29]  Jiayuan Wang,et al.  Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units , 2021 .

[30]  Xiaohui Liu,et al.  A review on transfer learning in EEG signal analysis , 2021, Neurocomputing.

[31]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[32]  Zhiwei Wang,et al.  Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications , 2018 .

[33]  Bi-Liang Lu,et al.  Fault Diagnosis for Electromechanical Drivetrains Using a Joint Distribution Optimal Deep Domain Adaptation Approach , 2019, IEEE Sensors Journal.

[34]  Dongpu Cao,et al.  Virtual-to-Real Knowledge Transfer for Driving Behavior Recognition: Framework and a Case Study , 2019, IEEE Transactions on Vehicular Technology.

[35]  Yang Hu,et al.  Fault diagnostics between different type of components: A transfer learning approach , 2020, Appl. Soft Comput..

[36]  Ahmed Braham,et al.  Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors , 2020, IEEE Transactions on Instrumentation and Measurement.

[37]  Nalini M.K,et al.  Comparative analysis of deep network models through transfer learning , 2020, 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).

[38]  Bin Liu,et al.  Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system , 2019, Energy.

[39]  Yongjun Sun,et al.  Statistical investigations of transfer learning-based methodology for short-term building energy predictions , 2020 .

[40]  Jessica Granderson,et al.  Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance , 2020, Building and Environment.

[41]  Qu Hong,et al.  Research on ELM Soft Fault Diagnosis of Analog Circuit Based on KSLPP Feature Extraction , 2019, IEEE Access.