TY - CHAP
T1 - Enhancement of Electric Motor Reliability Through Condition Monitoring
AU - Holbert, Keith
AU - Lin, Kang
AU - Karady, George G.
PY - 2007/12/1
Y1 - 2007/12/1
N2 - This chapter describes enhancement of electric motor reliability through condition monitoring (CM). Diagnostic service offerings such as CM of electric motors, for industrial customers are potential market for electric utilities. The mechanisms of major motor component failures along with the existing techniques for detecting these defects are presented. A salient feature of the approach is that it builds upon proven fault detection and isolation (FDI) techniques. A challenge to the integrated CM system is the combining of the diverse results from the signal processing modules into a final status decision. To accomplish this, a rule-based fuzzy logic decision maker is employed. The rules are established by first noting the failure mode(s) that a specific module is capable of detecting. Weights can be assigned to quantify the level of confidence that one has for specific module in detecting a given anomaly type. The approach combines the diverse information from motor magnetic fields, vibration signals, and acoustic emissions into a more robust and comprehensive CM approach. Such multifaceted methodology, using diverse measurement signals, allows inter comparisons of diagnostic information.
AB - This chapter describes enhancement of electric motor reliability through condition monitoring (CM). Diagnostic service offerings such as CM of electric motors, for industrial customers are potential market for electric utilities. The mechanisms of major motor component failures along with the existing techniques for detecting these defects are presented. A salient feature of the approach is that it builds upon proven fault detection and isolation (FDI) techniques. A challenge to the integrated CM system is the combining of the diverse results from the signal processing modules into a final status decision. To accomplish this, a rule-based fuzzy logic decision maker is employed. The rules are established by first noting the failure mode(s) that a specific module is capable of detecting. Weights can be assigned to quantify the level of confidence that one has for specific module in detecting a given anomaly type. The approach combines the diverse information from motor magnetic fields, vibration signals, and acoustic emissions into a more robust and comprehensive CM approach. Such multifaceted methodology, using diverse measurement signals, allows inter comparisons of diagnostic information.
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U2 - 10.1016/B978-008046620-0/50043-8
DO - 10.1016/B978-008046620-0/50043-8
M3 - Chapter
AN - SCOPUS:84884839105
SN - 9780080466200
SP - 255
EP - 260
BT - Power Plants and Power Systems Control 2006
PB - Elsevier Ltd.
ER -