Recent developments of induction motor drives fault diagnosis using ai techniques
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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.

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Recent developments of induction motor drives fault diagnosis using ai techniques

RECENT DEVELOPMENTS OF INDUCTION MOTOR DRIVES FAULT DIAGNOSIS USING AI TECHNIQUES

Oly Paz

1


Artificial intelligence
ARTIFICIAL INTELLIGENCE DIAGNOSIS USING AI TECHNIQUES

  • It is the science and engineering of making intelligent machines, specially intelligent computer programs.

  • It is important for AI is to have algorithms as capable as people at solving problems, and the identification of subdomains for which good algorithms exit .


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.


Data adquisition system
DATA ADQUISITION SYSTEM making is slowly being replaced by automated tools such as expert systems, neural networks and fuzzy logic based systems.


The main step of a procedure can be classified as
The main step of a procedure can be classified as : making is slowly being replaced by automated tools such as expert systems, neural networks and fuzzy logic based systems.

  • Signature extraction;

  • Fault identification;

  • Fault severity evaluation.


Basic stator current monitoring system configuration
Basic stator current monitoring system configuration making is slowly being replaced by automated tools such as expert systems, neural networks and fuzzy logic based systems.


Single phase stator current monitoring scheme
Single-phase stator current monitoring scheme making is slowly being replaced by automated tools such as expert systems, neural networks and fuzzy logic based systems.



Data retrieving strategies
DATA RETRIEVING STRATEGIES: torque of 30 N starting at 0.5 sec.


Spectrum line search and fault classification
SPECTRUM LINE SEARCH AND FAULT CLASSIFICATION torque of 30 N starting at 0.5 sec.


AI-BASED TECHNIQUES: torque of 30 N starting at 0.5 sec.

  • Artificial Neural Networks (ANN),

  • Fuzzy Logic,

  • Fuzzy-NNs,

  • Genetic Algorithms (GAs).


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 diagnostic system layout with feature extraction
Fuzzy diagnostic system layout with feature extraction torque of 30 N starting at 0.5 sec.


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


Fuzzy nn based diagnosis examples
FUZZY NN-BASED DIAGNOSIS EXAMPLES using as input variables the fault components I

Adaptative ANFIs architecture for rotor fault diagnosis based on the sideband components I1 and I2


Fault diagnosis of drives
FAULT DIAGNOSIS OF DRIVES using as input variables the fault components I

Experimental spectra and instantaneous supply current and output converter current in (a), (b) healthy condition and (c), (d) fault condition.


Stator current park s vector pattern
Stator current Park’s vector pattern using as input variables the fault components I


GENETIC ALGORITHMS using as input variables the fault components I

  • GAs are stochastic optimization techniques inspired by laws of natural selection and genetics. They use the concept of Darwin’s theory of evolution, which is based on the ruled of the survival of the fittest.

  • These algorithms do not need functional derivative information to search for a set of parameters that minimize a given objective function.


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