Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving

Abstract The fault diagnosis of air-conditioning systems is of great significance to the energy saving of buildings. This study proposes a novel fault diagnosis approach for building energy saving based on the deep learning method which is deep belief network, and its application potential in the air conditioning fault diagnosis field is investigated. Then, a parameter optimization selection strategy is developed for model optimization. Four kinds of faults of the variable flow refrigerant system under heating mode are used to evaluate the performance of the models. The fault diagnosis results show that the deep belief network model with initial parameters can be used to diagnose the faults of the variable flow refrigerant system. Through the parameter optimization selection strategy, the fault diagnosis correct rate of the optimized model is 97.7%, which is improved by 5.05% compared with the model with initial parameters. The number of hidden layers of the deep belief network model is selected to be 2 layers. This result indicates that the fault diagnosis for variable flow refrigerant systems may not require a very deep model. Additionally, the performance of the optimized deep belief network model is compared with that of the traditional back propagation neural network, and the former is better. This finding also shows that the unsupervised restricted Boltzmann machine layer for data feature reconstruction can improve the fault diagnosis performance.

[1]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[2]  Shengwei Wang,et al.  Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method , 2005 .

[3]  Xinhua Xu,et al.  Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods , 2008 .

[4]  Wen Shen,et al.  ARX model based fault detection and diagnosis for chillers using support vector machines , 2014 .

[5]  Sri Harish Reddy Mallidi,et al.  On the relevance of auditory-based Gabor features for deep learning in robust speech recognition , 2017, Comput. Speech Lang..

[6]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[7]  Frederico G. Guimarães,et al.  A GPU deep learning metaheuristic based model for time series forecasting , 2017 .

[8]  Qinglong Meng,et al.  Fault diagnosis and energy consumption analysis for variable air volume air conditioning system: a case study , 2017 .

[9]  Huanxin Chen,et al.  An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis , 2017 .

[10]  Jiong Li,et al.  Liquid floodback detection for scroll compressor in a VRF system under heating mode , 2017 .

[11]  Shengwei Wang,et al.  Valve fault detection and diagnosis based on CMAC neural networks , 2004 .

[12]  Yonghua Zhu,et al.  Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal , 2012 .

[13]  Ezio Bassi,et al.  Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors , 2010, IEEE Transactions on Industrial Electronics.

[14]  Bo Fan,et al.  Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis , 2014 .

[15]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[16]  Panagiotis D. Christofides,et al.  Isolation and handling of actuator faults in nonlinear systems , 2008, at - Automatisierungstechnik.

[17]  Liming Wang,et al.  An artificial intelligence platform for the multihospital collaborative management of congenital cataracts , 2017, Nature Biomedical Engineering.

[18]  Osman Bilgin,et al.  Efficiency Analysis of Submersible Induction Motor with Broken Rotor Bar , 2014 .

[19]  Zhimin Du,et al.  Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network , 2009 .

[20]  Antonio Piacentino,et al.  Innovative thermoeconomic diagnosis of multiple faults in air conditioning units: Methodological improvements and increased reliability of results , 2013 .

[21]  Prashant Mhaskar,et al.  Heating, ventilation and air conditioning systems: Fault detection and isolation and safe parking , 2018, Comput. Chem. Eng..

[22]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[23]  Taehoon Hong,et al.  A review on sustainable construction management strategies for monitoring, diagnosing, and retrofitting the building's dynamic energy performance: Focused on the operation and maintenance phase , 2015 .

[24]  Heaton T. Jeff,et al.  Introduction to Neural Networks with Java , 2005 .

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

[26]  Xinhua Xu,et al.  An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems , 2009 .

[27]  Francis W.H. Yik,et al.  A study on the energy penalty of various air-side system faults in buildings , 2010 .

[28]  Yuebin Yu,et al.  A review of fault detection and diagnosis methodologies on air-handling units , 2014 .

[29]  Gurmeet Singh,et al.  Efficiency monitoring as a strategy for cost effective maintenance of induction motors for minimizing carbon emission and energy consumption , 2018, Reliab. Eng. Syst. Saf..

[30]  Miriam Bellver,et al.  Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster , 2017, ICCS.

[31]  Woohyun Kim,et al.  Evaluation of the impacts of refrigerant charge on air conditioner and heat pump performance , 2010 .

[32]  Fu Xiao,et al.  A diagnostic tool for online sensor health monitoring in air-conditioning systems , 2006 .

[33]  Shengwei Wang,et al.  A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression , 2013 .

[34]  Jin Wen,et al.  Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors , 2017 .

[35]  Shengwei Wang,et al.  An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network , 2013 .

[36]  Phuc Do,et al.  Energy efficiency performance-based prognostics for aided maintenance decision-making: Application to a manufacturing platform , 2017 .

[37]  Yitao Liu,et al.  Deep belief network based deterministic and probabilistic wind speed forecasting approach , 2016 .

[38]  Zhanpeng Zhang,et al.  A deep belief network based fault diagnosis model for complex chemical processes , 2017, Comput. Chem. Eng..

[39]  Fu Xiao,et al.  A short-term building cooling load prediction method using deep learning algorithms , 2017 .

[40]  Fu Xiao,et al.  Bayesian network based FDD strategy for variable air volume terminals , 2014 .

[41]  Reinhard Radermacher,et al.  Field performance measurements of a VRF system with sub-cooler in educational offices for the cooling season , 2012 .

[42]  Zhenjun Ma,et al.  A decision tree based data-driven diagnostic strategy for air handling units , 2016 .

[43]  Yang Zhao,et al.  Diagnostic Bayesian networks for diagnosing air handling units faults, Part II::Faults in coils and sensors , 2015 .

[44]  Jiong Li,et al.  Identification and isolation of outdoor fouling faults using only built-in sensors in variable refrigerant flow system: A data mining approach , 2017 .

[45]  Min Hu,et al.  Modularized PCA method combined with expert-based multivariate decoupling for FDD in VRF systems including indoor unit faults , 2017 .

[46]  Da Yan,et al.  Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings , 2016 .

[47]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[48]  Bo Fan,et al.  Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks , 2014 .

[49]  Huanxin Chen,et al.  Optimized neural network-based fault diagnosis strategy for VRF system in heating mode using data mining , 2017 .

[50]  Bo Fan,et al.  A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis , 2010 .

[51]  Jose A. Antonino-Daviu,et al.  Use of the infrared data for heating curve computation in induction motors: Application to fault diagnosis , 2013 .

[52]  Pal Toth,et al.  Image-based deep neural network prediction of the heat output of a step-grate biomass boiler , 2017 .

[53]  Shengwei Wang,et al.  Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD) , 2013 .

[54]  Liu Yang,et al.  Thermal comfort and building energy consumption implications - A review , 2014 .

[55]  Hua Han,et al.  Study on a hybrid SVM model for chiller FDD applications , 2011 .