Valve fault detection and diagnosis based on CMAC neural networks

This paper presents a method for monitoring and diagnosing the degradation in the performance of heating/cooling coil valves, which might result in serious energy waste, without requiring the valves to be demounted or adding additional sensors. A recurrent cerebellar model articulation controller (RCMAC) is developed to learn the normal characteristics of the valve. When degradation in the performance of the valve occurs, the response of the RCMAC deviates from the normal. Two characteristic variables are defined as the degradation index and the waveform index for analysing the residual errors. A strategy is developed to identify the type of degradation and estimate the severity of the degradation. Tests on a typical valve with five faulty cases in an AHU demonstrate the effectiveness and robustness of the strategy.

[1]  James S. Albus,et al.  Data Storage in the Cerebellar Model Articulation Controller (CMAC) , 1975 .

[2]  Vaughn Bradshaw.  Building Control Systems , 1985 .

[3]  M A Sharif,et al.  Sensor-based performance monitoring of a control valve unit , 1999 .

[4]  Shengwei Wang,et al.  Dynamic simulation of building VAV air-conditioning system and evaluation of EMCS on-line control strategies , 1999 .

[5]  Joseph McGhee,et al.  Neural networks applied for the identification and fault diagnosis of process valves and actuators , 1997 .

[6]  J A Twiddle,et al.  Fuzzy model-based condition monitoring and fault diagnosis of a diesel engine cooling system , 2002 .

[7]  Dave Renfrey.  Total valve management—The key to better process plant operations , 1995 .

[8]  G. Horvath,et al.  Recurrent CMAC: a powerful neural network for system identification , 1996, Quality Measurement: The Indispensable Bridge between Theory and Reality (No Measurements? No Science! Joint Conference - 1996: IEEE Instrumentation and Measurement Technology Conference and IMEKO Tec.

[9]  Chiman Kwan,et al.  Machine performance degradation monitoring using fuzzy CMAC , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[10]  Jay Lee,et al.  Measurement of machine performance degradation using a neural network model , 1996 .

[11]  Jay Lee,et al.  Analysis of machine degradation using a neural network based pattern discrimination model , 1993 .

[12]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[13]  Rick Diamond,et al.  Fault detection in HVAC systems using model-based feedforward control , 2001 .

[14]  James S. Albus,et al.  I A New Approach to Manipulator Control: The I Cerebellar Model Articulation Controller , 1975 .

[15]  Hsu-Pin Wang,et al.  Performance analysis of rotating machinery using enhanced cerebellar model articulation controller (E-CMAC) neural networks , 1996 .

[16]  Shengwei Wang,et al.  Law-based sensor fault diagnosis and validation for building air-conditioning systems , 1999 .