K. Vinoth Kumar

Soft computing based fault diagnosis

Induction machines play a pivotal role in industry and there is a strong demand for their reliable and safe operation. They are generally reliable but eventually do wear out. Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues, and this motivates the examination of on-line condition monitoring. On-line condition monitoring involves taking measurements on a machine while it is operating in order to detect faults with the aim of reducing both unexpected failures and maintenance costs. Thus the key for the success of condition based maintenance is having an accurate means of condition assessment and fault diagnosis. The stator is subjected to various stresses such as thermal, electrical, mechanical, and environmental, which severely affects the stator condition leading to faults. These stresses can be classified into phase-tophase, turn-to-turn, and turn-to-ground. The fault detection using analytical methods is not always possible because it requires a perfect knowledge of the motor model. The fuzzy logic techniques are rather easy to develop and to perform. A Simulink model are developed in Matlab/SIMULINK for Induction Motor using Fuzzy-logic Controller to analyze the performance under the turn-turn short in one phase winding, open phase faults.

Soft Computing Based Fault Diagnosis

Induction machines play a pivotal role in industry and there is a strong demand for their reliable and safe operation. They are generally reliable but eventually do wear out. Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues, and this motivates the examination of on-line condition monitoring. On-line condition monitoring involves taking measurements on a machine while it is operating in order to detect faults with the aim of reducing both unexpected failures and maintenance costs. Thus the key for the success of condition based maintenance is having an accurate means of condition assessment and fault diagnosis. The stator is subjected to various stresses such as thermal, electrical, mechanical, and environmental, which severely affects the stator condition leading to faults. These stresses can be classified into phase-tophase, turn-to-turn, and turn-to-ground. The fault detection using analytical methods is not always possible because it requires a perfect knowledge of the motor model. The fuzzy logic techniques are rather easy to develop and to perform. A Simulink model are developed in Matlab/SIMULINK for Induction Motor using Fuzzy-logic Controller to analyze the performance under the turn-turn short in one phase winding, open phase faults.

Condition Monitoring of DSP Based Online Induction Motor External Fault Detection Using TMS320LF2407 DSP

This paper presents the implementation of TMS320LF2407 DSP processor Kit to identify the single phasing fault online, based on the stator current. The Induction motors are most widely used motors in industrial, commercial and residential sectors because of enormous merits of these over the types of available electric motors, as the workhorse in industrial applications. The early detection of these deteriorating conditions in incipient phase and its removal is necessary for the prevention of external failure of the induction motors, reducing repairs costs and motor outage time. The various types of external faults in induction motor are single line-to-ground fault, double line-ground fault, line-line fault, single phasing fault, locked rotor, under voltage, overvoltage, phase sequence reversal of supply voltage and mechanical overload. This proposed method is applied to a 3.3KW three phase induction motor using TMS320LF2407 DSP processor Kit to identify the single phasing fault online, based on the stator current and then the measurement results are analyzed.

Latest diagnosis technology of asynchronous machine using model based approach

This paper shows the different analysis method with high-tech data processing then acquirement takes new areas in the monitoring of asynchronous motor. It is a non-invasive technique and it does not require any expensive sensors for taking measurements. Finding of broken bar fault also air gap eccentricity fault by using wavelet Analysis method and Motor Current Signature Analysis method are discussed here. It helps in the actual time chasing of various motor faults then regulates the cruelty. The revision on healthy motor and faulty motor under different speed condition is supported out experimentally in addition the outcomes are investigated using FFT spectrum, which is obtained by interfacing LabVIEW.

Spectrum Analysis of Sidebands in Industrial Drives

This paper deals with the diagnosis of induction motors IM with the so-called motor current signature analysis MCSA. The MCSA is one of the most efficient techniques for the detection and the localization of electrical and mechanical failures, in which faults become apparent by harmonic components around the supply frequency. This paper presents a summary of the most frequent faults and its consequences on the stator current spectrum of an IM. A three-phase IM model was used for simulation taking into account in one hand the normal healthy operation and in the other hand the broken rotor bars, the shorted turns in the stator windings, the voltage unbalance between phases of supply and the abnormal behavior of load. The MCSA is used by many authors in literature for faults detection of IM. The major contribution of this work is to prove the efficiency of this diagnosis methodology to detect different faults simultaneously, in normal and abnormal functional conditions. The results illustrate good agreement between both simulated and experimental results.

Experimental Verification of Squirrel Cage Induction Motor Using Current Signature and Virtual Instrumentation Topology

Three phase squirrel cage induction motors are workhorses of industry and are the widely used industrial drives. With the passage of time the machine may develop faults and may hamper the production line that may lead to production and financial losses. A proper planning of maintenance schedule and condition monitoring is essential to reduce such financial loss and shut down time. A condition monitoring system, which can predict and identify the prefault condition, is the need of the age to prevent such unwanted breakdown time. The MCSA (Motor Current Signature Analysis) technique is found one of the most frequently used technique to identify the prefault condition. This paper focuses on experimental results to prove that MCSA Technique can identify the good and cracked rotor bar in three phase squirrel cage induction motors under no-load and different load conditions and also simulated in Virtual Instrumentation. The diagnostics strategy is presented in this paper and variables that influence the diagnosis are discussed.

Recent Techniques to Identify the Stator Fault Diagnosis in Three Phase Induction Motor

Induction motors have gained its popularity as a suitable industrial workhorse, due to its ruggedness and reliability. With time, these workhorses are susceptible to faults, some are incipient and some are major. Such fault can be catastrophic, if unattended and may develop serious problems that may lead to shutdown to the machine causing production and financial losses. To avoid such breakdown, an early stage prognosis can help in preparing the maintenance schedule, which will lead to an improve life span. Scientist and engineers worked with different scheme to diagnose the machine faults. The authors diagnose the turn-to-turn faults condition of the stator through symmetrical component analysis. The results of the analysis are also verified through Power Decomposition Technique (PDT) in Matlab /SIMULINK. The results are compatible with the published results for known faults.