RECENT DEVELOPMENTS OF INDUCTION MOTOR DRIVES FAULT DIAGNOSIS USING AI TECHNIQUES. Oly Paz. 1. ARTIFICIAL INTELLIGENCE. It is the science and engineering of making intelligent machines, specially intelligent computer programs.
Human involvement in the actual fault detection decision making is slowly being replaced by automated tools such as expert systems, neural networks and fuzzy logic based systems.
Input current variation for a 5.5 kW machine with a load torque of 30 N starting at 0.5 sec.
AI-BASED TECHNIQUES: torque of 30 N starting at 0.5 sec.
ANN based fault diagnosis torque of 30 N starting at 0.5 sec.
NN-Based Diagnosis Examples torque of 30 N starting at 0.5 sec.
ANN architecture for stator short circuit diagnosis.In=negative sequence stator currentIp=positive sequence stator currentIp=positive sequence component of the healthy machineIr=rated currentfp= output fault percentages= slipsr=rated slip
Fuzzy-Logic-Based Diagnosis Examples torque of 30 N starting at 0.5 sec.
Input variables fuzzy sets for I1
Fuzzy rules for the detection of broken bars fault severity, using as input variables the fault components I1 and I2:
3-D map of the input-output relationships between the sideband components I1 and I2
Adaptative ANFIs architecture for rotor fault diagnosis based on the sideband components I1 and I2
Experimental spectra and instantaneous supply current and output converter current in (a), (b) healthy condition and (c), (d) fault condition.
GENETIC ALGORITHMS using as input variables the fault components I