Title
A system for turbogenerator predictive maintenance based on Electrical Signature Analysis
Abstract
This paper proposes a prototype to detect incipient faults in non-salient pole synchronous generators based on the Electrical Signature Analysis (ESA) technique. The methodology consists in taking measurements of the machine stator voltages and currents and processing the acquired signals. By analyzing the voltages and currents spectra, it is possible to distinguish a faulty condition from a healthy one and to infer the failure type of the generator. This is summarized in a set of ESA failure patterns, which were proved through tests conducted in a custom made scale model laboratory. The custom laboratory setup consists of a small fault injection capable two-pole synchronous generator driven by an inverter-fed induction motor. The proposed prototype is operating in four turbogenerators of a Brazilian power plant. The main advantages of the presented system are the low intrusiveness, the ease of installation and it is economically viable. Moreover, the ESA failure patterns are based on defined frequencies and the structural features of the machine.
Year
DOI
Venue
2015
10.1109/I2MTC.2015.7151244
Instrumentation and Measurement Technology Conference
Keywords
Field
DocType
failure analysis,fault diagnosis,induction motors,invertors,maintenance engineering,signal processing,stators,synchronous generators,turbogenerators,Brazilian power plant,ESA technique,electrical signature analysis,fault injection,incipient fault detection,inverter-fed induction motor,machine stator voltage,nonsalient pole synchronous generator,signal processing,turbogenerator predictive maintenance,condition monitoring,electrical signature analysis,fault detection,scale model laboratory,synchronous generator
Scale model,Induction motor,Voltage,Control engineering,Electronic engineering,Electromagnetic coil,Predictive maintenance,Permanent magnet synchronous generator,Fault injection,Mathematics,Power station
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
11