Cross-level fault detection and diagnosis of building HVAC systems

Abstract This paper presents a cross-level fault detection method. Two key features of the proposed method are 1) an energy description of all the units in an HVAC system and 2) a spatial–temporal partition strategy, which allows us to apply the FDD strategy to the entire building in a uniform manner. Energy flow models for HVAC units at all levels are presented. The concept of absolute and relative references for monitoring the energy performance is introduced. We have discussed the inherent complexity of HVAC systems, and proposed a grouping strategy of VAVs via correlation analysis. Examples of the temporal and spatial partitions are presented. Numerical examples are given to demonstrate the cross-level detection of two faults on AHU level and one fault on VAV level.

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

[2]  Natascha S. Milesi-Ferretti.  Performance Evaluation of a Reciprocating Chiller Using Experimental Data and Model Predictions for Fault Detection and Diagnosis | NIST , 2002 .

[3]  Yasunori Akashi,et al.  A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems , 2008 .

[4]  Zhimin Du,et al.  Tolerant control for multiple faults of sensors in VAV systems , 2007 .

[5]  L. Trefethen,et al.  Numerical linear algebra , 1997 .

[6]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[7]  Zhimin Du,et al.  A novel model-based fault detection method for temperature sensor using fractal correlation dimensio , 2011 .

[8]  Fu Xiao,et al.  Detection and diagnosis of AHU sensor faults using principal component analysis method , 2004 .

[9]  Steven T. Bushby,et al.  A rule-based fault detection method for air handling units , 2006 .

[10]  Vojislav Novakovic,et al.  Review of possibilities and necessities for building lifetime commissioning , 2009 .

[11]  Fu Xiao,et al.  Progress and methodologies of lifecycle commissioning of HVAC systems to enhance building sustainability , 2009 .

[12]  Krishna R. Pattipati,et al.  Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers , 2007, IEEE Transactions on Automation Science and Engineering.

[13]  Joaquín Navarro-Esbrí,et al.  A vapour compression chiller fault detection technique based on adaptative algorithms. Application to on-line refrigerant leakage detection , 2006 .

[14]  Phil Haves,et al.  Model-based performance monitoring: Review of diagnostic methods and chiller case study , 2000 .

[15]  Syed M. Zubair,et al.  A comprehensive design and performance evaluation study of counter flow wet cooling towers , 2004 .

[16]  Vojislav Novakovic,et al.  Heating system performance estimation using optimization tool and BEMS data , 2008 .

[17]  Cristian Ghiaus.  Fault diagnosis of air conditioning systems based on qualitative bond graph , 1999 .

[18]  Joseph Andrew Clarke,et al.  Simulation-assisted control in building energy management systems , 2002 .

[19]  Margaret B Bailey,et al.  Creating an automated chiller fault detection and diagnostics tool using a data fault library. , 2003, ISA transactions.

[20]  G. Strang.  Introduction to Linear Algebra , 1993 .

[21]  Gábor Szederkényi,et al.  Grey box fault detection of heat exchangers , 2000 .

[22]  Philip Haves,et al.  STUDY OF ON-LINE SIMULATION FOR WHOLE BUILDING LEVEL ENERGY CONSUMPTION FAULT DETECTION AND OPTIMIZATION , 2003 .

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

[24]  Byung-Cheon Ahn,et al.  Transient pattern analysis for fault detection and diagnosis of HVAC systems , 2005 .

[25]  Jonathan A. Wright,et al.  Demonstration of Fault Detection and Diagnosis Methods for Air-Handling Units , 2002 .

[26]  Xinqiao Jin,et al.  A robot fault diagnostic tool for flow rate sensors in air dampers and VAV terminals , 2009 .

[27]  Michael Wetter,et al.  Modelica-based modelling and simulation to support research and development in building energy and control systems , 2009 .

[28]  Fu Xiao,et al.  AHU sensor fault diagnosis using principal component analysis method , 2004 .

[29]  M. Kendall,et al.  An Introduction to the Theory of Statistics. , 1911 .

[30]  Aldomar Pedrini,et al.  A methodology for building energy modelling and calibration in warm climates , 2002 .

[31]  Shengwei Wang,et al.  A fault detection and diagnosis strategy of VAV air-conditioning systems for improved energy and control performances , 2005 .

[32]  Steven T. Bushby,et al.  A Hierarchical Rule-Based Fault Detection and Diagnostic Method for HVAC Systems , 2006 .