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Condition monitoring and fault detection of inverter-fed rotating machinery
Condition monitoring of rotating machinery is crucial in industry. It can prevent long term outages that can prove costly, prevent injury to machine operators, and lower product quality. Induction motors, often described as the workhorse of industry, are popular in industry because of their robustne...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2017
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| _version_ | 1867613322577182720 |
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| access_status_str | Open Access |
| author | Ipurale, Andrew |
| author2 | Barendse, Paul |
| author_browse | Barendse, Paul Ipurale, Andrew |
| author_facet | Barendse, Paul Ipurale, Andrew |
| author_sort | Ipurale, Andrew |
| collection | Thesis |
| description | Condition monitoring of rotating machinery is crucial in industry. It can prevent long term outages that can prove costly, prevent injury to machine operators, and lower product quality. Induction motors, often described as the workhorse of industry, are popular in industry because of their robustness, efficiency and the need for low maintenance. They are, however, prone to faults when used improperly or under strenuous conditions. Gearboxes are also an important component in industry, used to transmit motion and force by means of successively engaging teeth. They too are prone to damage and can disrupt industrial processes if failure is unplanned for. Reciprocating compressors are widely used in the petroleum and the petrochemical industry. Their complex structure, and operation under poor conditions makes them prone to faults, making condition monitoring necessary to prevent accidents, and for maintenance decision-making and cost minimization. Various techniques have been extensively investigated and found to be reliable tools for the identification of faults in these machines. This thesis, however, sets out to establish a single non-invasive tool that can be used to identify the faults on all these machines. Literature on condition monitoring of induction motors, gearboxes, and reciprocating compressors is extensively reviewed. The time, frequency, and time-frequency domain techniques that are used in this thesis are also discussed. Statistical indicators were used in the time domain, the Fourier Transform in the frequency domain, and Wavelet Transforms in the time-frequency domain. Vibration and current, which are two of the most popular parameters for fault detection, were considered. The test rig equipment that is used to carry to the experiments, which comprised a modified Machine Fault Simulator -Magnum (MFS-MG), is presented and discussed. The fault detection strategies rely on the presence of a fault signature. The test rig that was used allows for the simulation of individual or multiple concurrent faults to the test machinery. The experiments were carried out under steady-state and transient conditions with the faults in the machines isolated, and then with multiple faults implemented concurrently. The results of the fault detection strategies are analysed, and conclusions are drawn based on the performances of these tools in the detection of the faults in the machinery. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/25278 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:17.944Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/25278 Condition monitoring and fault detection of inverter-fed rotating machinery Ipurale, Andrew Barendse, Paul Electrical Engineering Condition monitoring of rotating machinery is crucial in industry. It can prevent long term outages that can prove costly, prevent injury to machine operators, and lower product quality. Induction motors, often described as the workhorse of industry, are popular in industry because of their robustness, efficiency and the need for low maintenance. They are, however, prone to faults when used improperly or under strenuous conditions. Gearboxes are also an important component in industry, used to transmit motion and force by means of successively engaging teeth. They too are prone to damage and can disrupt industrial processes if failure is unplanned for. Reciprocating compressors are widely used in the petroleum and the petrochemical industry. Their complex structure, and operation under poor conditions makes them prone to faults, making condition monitoring necessary to prevent accidents, and for maintenance decision-making and cost minimization. Various techniques have been extensively investigated and found to be reliable tools for the identification of faults in these machines. This thesis, however, sets out to establish a single non-invasive tool that can be used to identify the faults on all these machines. Literature on condition monitoring of induction motors, gearboxes, and reciprocating compressors is extensively reviewed. The time, frequency, and time-frequency domain techniques that are used in this thesis are also discussed. Statistical indicators were used in the time domain, the Fourier Transform in the frequency domain, and Wavelet Transforms in the time-frequency domain. Vibration and current, which are two of the most popular parameters for fault detection, were considered. The test rig equipment that is used to carry to the experiments, which comprised a modified Machine Fault Simulator -Magnum (MFS-MG), is presented and discussed. The fault detection strategies rely on the presence of a fault signature. The test rig that was used allows for the simulation of individual or multiple concurrent faults to the test machinery. The experiments were carried out under steady-state and transient conditions with the faults in the machines isolated, and then with multiple faults implemented concurrently. The results of the fault detection strategies are analysed, and conclusions are drawn based on the performances of these tools in the detection of the faults in the machinery. 2017-09-22T11:57:00Z 2017-09-22T11:57:00Z 2017 Master Thesis Masters MSc (Eng) http://hdl.handle.net/11427/25278 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Ipurale, Andrew Condition monitoring and fault detection of inverter-fed rotating machinery |
| thesis_degree_str | Master's |
| title | Condition monitoring and fault detection of inverter-fed rotating machinery |
| title_full | Condition monitoring and fault detection of inverter-fed rotating machinery |
| title_fullStr | Condition monitoring and fault detection of inverter-fed rotating machinery |
| title_full_unstemmed | Condition monitoring and fault detection of inverter-fed rotating machinery |
| title_short | Condition monitoring and fault detection of inverter-fed rotating machinery |
| title_sort | condition monitoring and fault detection of inverter fed rotating machinery |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/25278 |
| work_keys_str_mv | AT ipuraleandrew conditionmonitoringandfaultdetectionofinverterfedrotatingmachinery |