| dc.contributor.advisor | Parlos, Alexander G | |
| dc.creator | Tapar, Shantur Suresh | |
| dc.date.accessioned | 2016-09-16T15:54:04Z | |
| dc.date.available | 2016-09-16T15:54:04Z | |
| dc.date.created | 2005-08 | |
| dc.date.issued | 2009-05-15 | |
| dc.date.submitted | August 2005 | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/ETD-TAMU-1644 | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/157906 | |
| dc.description.abstract | Online condition assessment, product quality assurance and improved operational
efficiency of engineering systems, such as induction motors, has increased in
significance due to the advantages it offers in terms of productivity. Early detection of
faults would not only allow for extensive trending but also provide advanced warnings
regarding the health of the machinery. The implementation of on-line fault detection
systems must not only exhibit high level of detection accuracy, but also discriminate
between actual incipient faults and false alarms caused by temporal variations in
operating conditions.
The objective of this research is to develop the elements of a fault detection
system suitable for continuous, on-line condition monitoring and assessment of 3- | en |
| dc.format.mimetype | application/pdf | |
| dc.subject | fault detection | en |
| dc.subject | induction motor | en |
| dc.subject | MCSA | en |
| dc.title | Variability of indicators used in motor fault detection based on electrical measurements | en |
| dc.type | Thesis | en |
| thesis.degree.department | Mechanical Engineering | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Texas A & M University | en |
| thesis.degree.name | Master of Science | en |
| thesis.degree.level | Masters | en |
| dc.contributor.committeeMember | Chan, Andrew | |
| dc.contributor.committeeMember | Langari, Gholamreza | |
| dc.type.material | text | en |
| dc.date.updated | 2016-09-16T15:54:09Z | |
