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dc.creatorMcFatter, Justin Roberten_US
dc.descriptionDue to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to, referencing the URI of the item.en_US
dc.descriptionIncludes bibliographical references (leaves 75-78).en_US
dc.descriptionIssued also on microfiche from Lange Micrographics.en_US
dc.description.abstractRotating machine failures are a major cause of downtime in a wide variety of industrial processes and are a burden on maintenance personnel and facilities. Some of these failures occur suddenly and are seemingly unpredictable. However, the overwhelming majority develop slowly over time and produce characteristic warning signs. A system capable of detecting and diagnosing these incipient faults before they become critical would significantly reduce downtime and serve to facilitate maintenance and repair of these machines. The ability to accurately distinguish between different types of incipient faults would be a critical aspect of such a system. In this research, a model-based method for diagnosing motor faults is examined and tested using two squirrel-cage AC induction motors with staged fault conditions. The proposed method involves the multi-resolution signal analysis of the current residuals. These residuals are generated by comparing the measured motor current with the current predicted by a recurrent neural network. The frequency content of the distortion of the residuals is used to identify the type of fault present. Although "steady-state" conditions are examined exclusively in this research, the nonstationarities of the current signals are sufficient to warrant the use of multi-resolution analysis. The fault diagnosis system is tested using data taken from an 800 hp motor and a 3 hp motor. The method is successful in identifying residual distortion in the frequency range expected for broken-bar faults. Because the magnitude of the distortion grows with increasing fault severity, the method is also useful for evaluating fault severity for broken-bar faults. However, the current distortions caused by rotor eccentricities and damaged bearings are too small to be identified in a statistically significant manner using this approach. Nevertheless, this research demonstrates the feasibility of a general method by which the characteristic frequencies produced by a particular type of fault can be identified in the output of a system.en_US
dc.publisherTexas A&M Universityen_US
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en_US
dc.subjectmechanical engineering.en_US
dc.subjectMajor mechanical engineering.en_US
dc.titleDiscrimination among mechanical fault types in induction motors using electrical measurementsen_US
dc.typeThesisen_US engineeringen_US
dc.format.digitalOriginreformatted digitalen_US

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