| Nov. 27, 2023, 2:11 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 20, 2023, 2:02 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 13, 2023, 1:33 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 6, 2023, 1:30 p.m. |
Added
35
|
{"external_links": []}
|
|
| Aug. 14, 2023, 1:30 p.m. |
Added
35
|
{"external_links": []}
|
|
| Aug. 7, 2023, 1:31 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 31, 2023, 1:33 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 24, 2023, 1:35 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 17, 2023, 1:33 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 10, 2023, 1:25 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 3, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 26, 2023, 1:25 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 19, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 12, 2023, 1:29 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 5, 2023, 1:32 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 29, 2023, 1:27 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 22, 2023, 1:28 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 15, 2023, 1:30 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 8, 2023, 1:36 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 1, 2023, 1:27 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 24, 2023, 1:34 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 17, 2023, 1:29 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 10, 2023, 1:24 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 3, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| Jan. 28, 2023, 11:08 a.m. |
Created
43
|
[{"model": "core.projectfund", "pk": 24547, "fields": {"project": 1733, "organisation": 2, "amount": 0, "start_date": "2020-04-30", "end_date": "2024-04-29", "raw_data": 38675}}]
|
|
| Jan. 28, 2023, 10:51 a.m. |
Added
35
|
{"external_links": []}
|
|
| April 11, 2022, 3:45 a.m. |
Created
43
|
[{"model": "core.projectfund", "pk": 16649, "fields": {"project": 1733, "organisation": 2, "amount": 0, "start_date": "2020-04-30", "end_date": "2024-04-29", "raw_data": 7382}}]
|
|
| April 11, 2022, 3:45 a.m. |
Created
41
|
[{"model": "core.projectorganisation", "pk": 62756, "fields": {"project": 1733, "organisation": 2073, "role": "STUDENT_PP_ORG"}}]
|
|
| April 11, 2022, 3:45 a.m. |
Created
41
|
[{"model": "core.projectorganisation", "pk": 62755, "fields": {"project": 1733, "organisation": 1047, "role": "LEAD_ORG"}}]
|
|
| April 11, 2022, 3:45 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 38796, "fields": {"project": 1733, "person": 2463, "role": "STUDENT_PER"}}]
|
|
| April 11, 2022, 3:45 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 38795, "fields": {"project": 1733, "person": 1838, "role": "SUPER_PER"}}]
|
|
| April 11, 2022, 1:47 a.m. |
Updated
35
|
{"title": ["", "Remaining useful life and lifetime extension of wind turbine drivetrains"], "description": ["", "\nTo optimally make decisions for wind turbine maintenance, predictions on the future health states of the wind turbine drivetrain must be carried out. Prognostics is the process whereby past and present condition monitoring data of a system or component is used to estimate its health state into the future. The wind turbine drivetrain is a critical subassembly in terms of downtime and replacement costs; therefore, it is very important to monitor it and perform accurate prognostics. Monitoring is usually done using vibration, SCADA, and oil data. An integrated decision support system fusing the aforementioned multiple sources of data can increase the confidence of a maintenance action under a condition-based monitoring scheme.\n\nThis EngD will focus on the wind turbine drivetrain fault detection, isolation and remaining useful life esti-mation using advanced signal processing and machine learning methods and considering component de-pendencies. The work will involve the following:\n*Research of various signal processing methods applicable to wind turbine vibration signals.\n*Extraction of health indicators from multiple data sources/streams.\n*Research of various applicable machine learning methods for prediction using the extracted indica-tors as features.\n*Model dependencies between drivetrain components.\n*Develop a multi-component degradation model.\n*Model lifetime extension scenarios.\n\nThe work will be validated using data from operating wind farms.\n\nAs a collaborative research project, the research student will work together with Natural Power and the University of Strathclyde research teams, spending time in both organisations.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Active"]}
|
|
| April 11, 2022, 1:47 a.m. |
Added
35
|
{"external_links": [6259]}
|
|
| April 11, 2022, 1:47 a.m. |
Created
35
|
[{"model": "core.project", "pk": 1733, "fields": {"owner": null, "is_locked": false, "coped_id": "54e86b27-0dec-4bdf-a380-ee8da67b44fc", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 7367, "created": "2022-04-11T01:32:24.319Z", "modified": "2022-04-11T01:32:24.319Z", "external_links": []}}]
|
|