Fault-analysis for condition monitoring of induction motors /

Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down...

Full description

Bibliographic Details
Main Author: Nandi, Subhasis
Format: Thesis Book
Language:English
Published: [Place of publication not identified] : [publisher not identified] ; 2000.
Subjects:
Online Access:http://proxy.library.tamu.edu/login?url=http://proquest.umi.com/pqdweb?did=731990301&sid=1&Fmt=2&clientId=2945&RQT=309&VName=PQD
Description
Summary:Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this possible. Primarily, these techniques depend upon locating specify harmonic components in the line current, also known as motor current signature analysis (MCSA). These harmonic components are usually different for different types of faults. However, with multiple faults or different varieties of drive schemes, MCSA can become an onerous task as different types of faults and time harmonics may end up generating similar signatures. Thus, other signals such as speed, torque, noise, vibration, etc., are also explored for their frequency contents. Sometimes, altogether different techniques such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. It is indeed evident that this area is vast in scope. Going by the present trend, human involvement in the actual fault detection decision making is slowly being replaced by automated tools such as expert systems, neural networks, fuzzy logic based systems; to name a few. However, this cannot be achieved without detailed fault analysis and subsequent recognition of the fault pattern. Keeping this in mind, simulation studies of the broken bar and eccentricity related faults using MCSA have been taken up. Also, a common theoretical basis for the different types (static, dynamic and mixed) of eccentricity related faults which give different signatures for different pole and rotor bar combinations has been developed. This will be of great importance 60th from fault diagnosis as well as sensorless drive applications' viewpoint. Finally, the insight gained from the analysis of eccentricity related faults leads to a novel detection technique of stator inter-turn faults by analyzing the frequency content of the transient line to line voltage, after the motor is switched off.
Item Description:Vita.
"Major Subject: Electrical Engineering".
Physical Description:xvi, 118 leaves : illustrations ; 28 cm.
Issued also on microfiche from University Microfilm Inc.
Bibliography:Includes bibliographical references (leaves 93-98).