Detection and Diagnosis of Multiple-Dependent Faults (MDFDD) of Water-Cooled Centrifugal Chillers Using Grey-Box Model-Based Method

This paper presents the development and use of benchmarking grey-box models for the detection and diagnosis of multiple-dependent faults (MDFDD) of a water-cooled centrifugal chiller. Models are developed using data recorded by a Building Automation System (BAS) from a central cooling plant of an institutional building. The forward residual-based fault detection model identifies a fault symptom, when the difference between the measured value of target variable and benchmarking value exceeds the corresponding threshold. For the fault diagnosis, most publications start from a known single fault and establish the impact on following variables in the system. This paper presents a rule-based backward approach. The proposed method identifies if (i) the fault symptom is correct (i.e., a variable has abnormal values), or (ii) the fault symptom is incorrect (i.e., the symptom of target variable is caused by impacts generated by other faulty variables due to the dependency between variables), or (iii) both target and regressor variables are abnormal. For testing the proposed MDFDD model, some artificial faults are inserted into the measurement data file, and results are discussed about the method potential for the application.

[1]  R. Zmeureanu,et al.  Evidence-based assessment of energy performance of two large centrifugal chillers over nine cooling seasons , 2021 .

[2]  Dasheng Lee,et al.  Artificial intelligence assisted false alarm detection and diagnosis system development for reducing maintenance cost of chillers at the data centre , 2021 .

[3]  James E. Braun,et al.  Development, implementation, and evaluation of a fault detection and diagnostics system based on integrated virtual sensors and fault impact models , 2020 .

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

[5]  Chunsheng Yang,et al.  Text-mining building maintenance work orders for component fault frequency , 2019 .

[6]  Kaixiang Peng,et al.  Performance-based fault detection and fault-tolerant control for automatic control systems , 2019, Autom..

[7]  Leon R. Glicksman,et al.  Case study results: fault detection in air-handling units in buildings , 2018, Advances in Building Energy Research.

[8]  Woohyun Kim,et al.  A review of fault detection and diagnostics methods for building systems , 2018 .

[9]  Youming Chen,et al.  A robust fault detection and diagnosis strategy for multiple faults of VAV air handling units , 2016 .

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

[11]  Dominic T. J. O'Sullivan,et al.  Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units , 2014 .

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

[13]  Karl Henrik Johansson,et al.  Active actuator fault detection and diagnostics in HVAC systems , 2012, BuildSys@SenSys.

[14]  Danielle Monfet,et al.  Ongoing commissioning of water-cooled electric chillers using benchmarking models , 2012 .

[15]  T. Agami Reddy,et al.  Applied Data Analysis and Modeling for Energy Engineers and Scientists , 2011 .

[16]  Michael R. Brambley,et al.  Final Project Report: Self-Correcting Controls for VAV System Faults Filter/Fan/Coil and VAV Box Sections , 2011 .

[17]  Ting Wang,et al.  Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning , 2011 .

[18]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[19]  Srinivas Katipamula,et al.  Self-Correcting HVAC Controls Project Final Report , 2010 .

[20]  Zhimin Du,et al.  Detection and diagnosis for multiple faults in VAV systems , 2007 .

[21]  James E. Braun,et al.  Decoupling features and virtual sensors for diagnosis of faults in vapor compression air conditioners , 2007 .

[22]  James E. Braun,et al.  A Methodology for Diagnosing Multiple Simultaneous Faults in Vapor-Compression Air Conditioners , 2007 .

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

[24]  Chonghun Han,et al.  Multiple-Fault Diagnosis under Uncertain Conditions by the Quantification of Qualitative Relations , 1999 .

[25]  James E. Braun,et al.  Common faults and their impacts for rooftop air conditioners , 1998 .

[26]  Venkat Venkatasubramanian,et al.  Causality‐based failure‐driven learning in diagnostic expert systems , 1989 .

[27]  Hanyuan Zhang,et al.  Incipient fault detection of chiller based on improved CVA , 2021 .

[28]  Nader Meskin,et al.  Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems , 2020 .

[29]  Atthapol Ngaopitakkul,et al.  IDENTIFYING TYPES OF SIMULTANEOUS FAULT IN TRANSMISSION LINE USING DISCRETE WAVELET TRANSFORM AND FUZZY LOGIC ALGORITHM , 2013 .

[30]  H. Lia,et al.  Decoupling features for diagnosis of reversing and check valve faults in heat pumps , 2009 .

[31]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[32]  Kurt Roth,et al.  THE ENERGY IMPACT OF FAULTS IN U.S. COMMERCIAL BUILDINGS , 2004 .

[33]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[34]  D. R. Tree,et al.  Steady State Characteristics of Failures of a Household Refrigerator , 1988 .