On-line fault detection and diagnosis obtained by implementing neural algorithms on a digital signal processor

1996 ◽  
Vol 45 (5) ◽  
pp. 894-899 ◽  
Author(s):  
A. Bernieri ◽  
G. Betta ◽  
C. Liguori
Keyword(s):  
Fault Detection ◽  
Digital Signal Processor ◽  
Digital Signal ◽  
Fault Detection And Diagnosis ◽  
On Line ◽  
Detection And Diagnosis ◽  
Signal Processor

Induction Motor Fault Detection and Diagnosis Using Artifical Neural Networks

2005 ◽  
Author(s):  
Chris K. Mechefske ◽  
Lingxin Li
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Induction Motor ◽  
Mechanical Vibration ◽  
Fault Detection And Diagnosis ◽  
Self Organizing Maps ◽  
Artifical Neural Networks ◽  
On Line ◽  
Motor Condition ◽  
Detection And Diagnosis

This paper investigates induction motor fault detection and diagnosis using Artificial Neural Networks (ANN). The ANN techniques include feedforward backpropagation networks (FFBPN) and self organizing maps (SOM), used individually and in combination. Common induction motor faults such as bearing faults, stator winding fault, unbalanced rotor and broken rotor bars are considered. The ANNs were trained and tested using dynamic measurements of stator currents and mechanical vibration signals. The effects of different network structures and the training set sizes on the performance of the ANNs are discussed. This study shows that, while the feedforward ANNs give satisfactory results and the SOMs can classify the type of motor fault during steady state working conditions, using a combination of SOM and FFBPN techniques yields superior fault detection and diagnostic accuracy. In addition, incipient motor fault detection has been investigated. The above results show that improved induction motor maintenance strategies may be possible through the use of comprehensive on-line induction motor condition monitoring and fault diagnosis systems.

Fault detection and diagnosis in liquid chillers

2005 ◽  
Vol 219 (2) ◽  
pp. 117-125 ◽  
Author(s):  
E Rueda ◽  
S. A. Tassou ◽  
I. N. Grace
Keyword(s):  
Fault Detection ◽  
Cost Effective ◽  
Fault Detection And Diagnosis ◽  
Plant Performance ◽  
Outlet Temperature ◽  
Refrigeration Equipment ◽  
On Line ◽  
Detection And Diagnosis ◽  
Microprocessor Technology ◽  
Discharge Pressure

Automated fault detection and diagnosis of refrigeration equipment is important in maintaining efficient performance, reducing energy consumption, and increasing the reliability and availability of these systems. The reducing costs of microprocessor technology and the incorporation of more sophisticated monitoring equipment on to even fairly small refrigeration plant, now makes the introduction of on-line fault detection and diagnosis on refrigeration equipment feasible and cost effective. This paper reports on the development of a fault detection and diagnosis (FDD) system for liquid chillers based on artificial intelligence techniques. The system was designed to monitor plant performance and to detect and diagnose faults through comparison with expected behaviour and previous experience of fault characteristics. The system operates on line in real time on a Java 2 platform and was initially used to detect refrigerant charge conditions. The results indicate that the FDD system developed is able to detect and diagnose fault conditions arising from low or high refrigerant charge correctly, using two parameters as detectors: condenser refrigerant outlet temperature and discharge pressure.

Sign in / Sign up

Export Citation Format

Share Document