A neural network based fault detection and identification scheme for pneumatic process control valves

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

Volumn 1, Issue , 2001, Pages 93-98

  Karpenko, M ^a   Sepehri, N ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Fault detection and identification; Neural networks; Process control valves

Indexed keywords

ACTUATORS; COMPUTER AIDED SOFTWARE ENGINEERING; CONTROL SYSTEM SYNTHESIS; DYNAMICS; EQUIPMENT TESTING; FAILURE ANALYSIS; FEEDFORWARD NEURAL NETWORKS; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS; PNEUMATIC CONTROL; PROCESS CONTROL; VALVES (MECHANICAL);

ACTUATOR VENT BLOCKAGE; DIAPHRAGM LEAKAGE FAULTS; DYNAMIC ERROR BAND TEST; DYNAMIC SYSTEMS; FAULT DETECTION AND IDENTIFICATION; PNEUMATIC PROCESS CONTROL VALVE; SOFTWARE PACKAGE; SUPPLY PRESSURE;

MULTILAYER NEURAL NETWORKS;

EID: 0035726364     PISSN: 08843627     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Neural networks applied for the identification and fault diagnosis of process valves and actuators
2. Actuator and sensor fault diagnosis of non-linear dynamic systems via genetic neural networks and adaptive parameter estimation technique
3. Fault diagnosis of the IFAC benchmark problem with a model-based recurrent neural network
4. Fault diagnosis in complex systems using artificial neural networks
5. Wavelet packet feature extraction for vibration monitoring
6. Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks
7. Neural network detection and identification of actuator faults in a pneumatic process control valve
8. The development of novel control valve diagnostic software based on the visual basic programming language