Law-based sensor fault diagnosis and validation for building air-conditioning systems

This paper presents an automatic strategy that can be used in a building energy management and control system to detect, diagnose, and evaluate soft sensor faults in building air-conditioning systems. The strategy is based on fundamental conservation (mass and energy conservation) relations and accommodates changes of plant performance and working conditions. The existence and magnitude of non-abrupt biases in chilled water flow meters and temperature sensors due to miscalibration and drift are detected. Sensor biases are estimated by minimizing the sum of the squares of the mass and energy balance residuals of selected control volumes. Validation of the strategy on a central chilling system is presented.

[1]  Manus P. Henry,et al.  The Integration of Fault Detection within Plant-Wide Data Quality Management , 1994 .

[2]  Michèle Basseville,et al.  Detecting changes in signals and systems - A survey , 1988, Autom..

[3]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[4]  G. Vachkov,et al.  Identification of Fuzzy Rule Based System for Fault Diagnosis in Chemical Plants , 1992 .

[5]  Masami Suzuki,et al.  Typical faults of air conditioning systems and fault detection by ARX model and extended Kalman filter , 1996 .

[6]  N. D. Walker,et al.  Sensor signal validation using analytical redundancy for an aluminum cold rolling mill , 1995 .

[7]  Hongwei Tong,et al.  Detection of gross erros in data reconciliation by principal component analysis , 1995 .

[8]  C. Park,et al.  Fault detection in an air-handling unit using residual and recursive parameter identification methods , 1996 .

[9]  Jie Chen,et al.  Robust fault detection of jet engine sensor systems using eigenstructure assignment , 1991 .

[10]  E. T. Pierce,et al.  Sensor errors: their effects on building energy consumption , 1983 .

[11]  Frank L. Lewis,et al.  Optimal Control , 1986 .

[12]  Manus P. Henry,et al.  The self-validating sensor: rationale, definitions and examples , 1993 .

[13]  Shengwei Wang,et al.  Dynamic simulation of a building central chilling system and evaluation of EMCS on-line control strategies , 1998 .

[14]  Spyros G. Tzafestas Second Generation Diagnostic Expert Systems: Requirements, Architectures and Prospects , 1991 .

[15]  David M. Himmelblau Use of Artificial Neural Networks to Monitor Faults and for Troubleshooting in the Process Industries , 1992 .

[16]  T. W. Anderson,et al.  The New Statistical Analysis of Data , 1986 .

[17]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[18]  Rahmat Shoureshi,et al.  A robust failure diagnostic system for thermofluid processes , 1992, Autom..

[19]  Joseph H. Noggle,et al.  Practical curve fitting and data analysis: software and self-instruction for scientists and engineers , 1993 .

[20]  G. E. Kelly,et al.  Fault diagnosis of an air-handling unit using artificial neural networks , 1996 .

[21]  Kazuyuki Kamimura,et al.  Chiller condition monitoring using topological case-based modeling , 1996 .

[22]  Jan F. Kreider,et al.  A quasi-real-time expert system for commercial building HVAC diagnostics , 1989 .

[23]  Norman R. Draper,et al.  Applied regression analysis (2. ed.) , 1981, Wiley series in probability and mathematical statistics.

[24]  Philip Haves,et al.  Condition monitoring in HVAC subsystems using first principles models , 1996 .

[25]  Alice M. Agogino,et al.  Multiple sensor expert system for diagnostic reasoning, monitoring and control of mechanical systems , 1988 .

[26]  Ron J. Patton,et al.  Robust Model-Based Fault Diagnosis: The State of the ART , 1994 .

[27]  W. Y. Lee,et al.  Fault diagnosis and temperature sensor recovery for an air-handling unit , 1997 .

[28]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[29]  Shahriar Negahdaripour,et al.  An Innovation-Based Methodology for HVAC System Fault Detection , 1985 .

[30]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[31]  H. C. Peitsman,et al.  Application of black-box models to HVAC systems for fault detection , 1996 .

[32]  Frédéric Kratz,et al.  Detection, isolation, and identification of sensor faults in nuclear power plants , 1996, IEEE Trans. Control. Syst. Technol..

[33]  F. Kirwan Complex Algebraic Curves , 1992 .