AN APPROACH TO CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MACHINES USING KEY PERFORMANCE INDICATORS

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Suter, F. (2017). AN APPROACH TO CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MACHINES USING KEY PERFORMANCE INDICATORS. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

One of the great difficulties associated with monitoring the condition of an electric motor is that the user must typically possess some degree of expertise to distinguish between a normal operating condition and a potential failure. The typical approach is to monitor various electrical and mechanical parameters such as current and vibration. Key Performance Indicators (KPI) are extracted from these parameters and are monitored for changes. The changes could result from any number of sources, including those related to normal operating conditions. For instance, cyclically varying loads and mechanical unbalances can have the same effect on KPIs extracted from the motor current spectrum as a broken rotor bar or bent shaft. These types of issues, in combination with the vast amount of available information, make it difficult to determine a set of rules for fault detection. When using hard-and-fast rules, for instance, it is possible to omit certain situations out of ignorance or fail to implement rules that deal with the dynamic nature of certain conditions. Without knowing all possible fault conditions and symptoms, the developer finds himself in a difficult position. In this thesis, a new approach is proposed that does not require a rule based approach but instead relies on an unsupervised fault detection algorithm that learns the normal operating condition of a particular motor and then monitors for changes that indicate a potential fault condition has occurred.

Details
Author

Suter, Forrest

Title

AN APPROACH TO CONDITION MONITORING AND FAULT DIAGNOSIS OF INDUCTION MACHINES USING KEY PERFORMANCE INDICATORS

Physical Description

1 online resource (125 pages) : PDF

Date

2017

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

One of the great difficulties associated with monitoring the condition of an electric motor is that the user must typically possess some degree of expertise to distinguish between a normal operating condition and a potential failure. The typical approach is to monitor various electrical and mechanical parameters such as current and vibration. Key Performance Indicators (KPI) are extracted from these parameters and are monitored for changes. The changes could result from any number of sources, including those related to normal operating conditions. For instance, cyclically varying loads and mechanical unbalances can have the same effect on KPIs extracted from the motor current spectrum as a broken rotor bar or bent shaft. These types of issues, in combination with the vast amount of available information, make it difficult to determine a set of rules for fault detection. When using hard-and-fast rules, for instance, it is possible to omit certain situations out of ignorance or fail to implement rules that deal with the dynamic nature of certain conditions. Without knowing all possible fault conditions and symptoms, the developer finds himself in a difficult position. In this thesis, a new approach is proposed that does not require a rule based approach but instead relies on an unsupervised fault detection algorithm that learns the normal operating condition of a particular motor and then monitors for changes that indicate a potential fault condition has occurred.

Genre

masters theses

Subjects--Topics

Electrical engineering

Degree

M.S.

Keywords

Condition Monitoring
Current Monitoring
Fault Detection
Induction Motors
Key Performance Indicators
Vibration Analysis

Subject Area

Electrical Engineering

Advisor(s)

Cox, Robert

Committee Members

Parkhideh, Babak
Manjrekar, Madhav

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2017.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Suter_uncc_0694N_11602

Permalink

http://hdl.handle.net/20.500.13093/etd:1440