RECENT DEVELOPMENTS OF INDUCTION MOTOR

DRIVES FAULT DIAGNOSIS USING AI TECHNIQUES

1Oly Paz

ARTIFICIAL INTELLIGENCE

• 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

The main step of a procedure can be classified as :

• Signature extraction;

• Fault identification;

• Fault severity evaluation.

Basic stator current monitoring system configuration

Single-phase stator current monitoring scheme

Input current variation for a 5.5 kW machine with a load torque of 30 N starting at 0.5 sec.

DATA RETRIEVING STRATEGIES:

SPECTRUM LINE SEARCH AND FAULT CLASSIFICATION

AI-BASED TECHNIQUES:

• Artificial Neural Networks (ANN),

• Fuzzy Logic,

• Fuzzy-NNs,

• Genetic Algorithms (GAs).

ANN based fault diagnosis

NN-Based Diagnosis Examples

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

Input variables fuzzy sets for I1

Fuzzy-Logic-Based Diagnosis Examples

3-D map of the input-output relationships between the sideband components I1 and I2

Fuzzy rules for the detection of broken bars fault severity, using as input variables the fault components I1 and I2:

FUZZY NN-BASED DIAGNOSIS EXAMPLES

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

FAULT DIAGNOSIS OF DRIVES

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

GENETIC ALGORITHMS

• 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.