Difference between revisions of "GasDay Alarm Prediction"
From REU@MU
(Created page with "As an infrastructure, pipelines are vulnerable to damaging conditions that warrant action, which results in a loss of profit and extra labor. Sensors are placed along a pipe w...") |
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| + | '''Researcher: [[user:jaired|Jaired Collins]]''' | ||
| + | <br>'''Mentors: '''[http://povinelli.eece.mu.edu/ Dr. Richard Povinelli], Dr. Corliss | ||
| + | == Abstract == | ||
As an infrastructure, pipelines are vulnerable to damaging conditions that warrant action, which results in a loss of profit and extra labor. Sensors are placed along a pipe which can set off an alarm if a measured value exceeds safe levels. At this point, it is reactive instead of preventative maintenance. Using data from the sensors, we anticipate the ability to forecast when an alarm will go off within an hour. With the ability to accurately predict alarms, action can be taken to avoid a loss of profit. Without notification of anomalies, preventable maintenance, cleaning, or catastrophic failure may take place. | As an infrastructure, pipelines are vulnerable to damaging conditions that warrant action, which results in a loss of profit and extra labor. Sensors are placed along a pipe which can set off an alarm if a measured value exceeds safe levels. At this point, it is reactive instead of preventative maintenance. Using data from the sensors, we anticipate the ability to forecast when an alarm will go off within an hour. With the ability to accurately predict alarms, action can be taken to avoid a loss of profit. Without notification of anomalies, preventable maintenance, cleaning, or catastrophic failure may take place. | ||
| + | |||
| + | == Milestones and Goals == | ||
| + | {| class="wikitable" | ||
| + | |- | ||
| + | ! Week 1 | ||
| + | | Literature search | ||
| + | |- | ||
| + | ! Week 2 | ||
| + | | Literature search and data cleaning | ||
| + | |- | ||
| + | ! Week 3 | ||
| + | | Literature search and code state-of-the-art algorithms | ||
| + | |- | ||
| + | ! Week 4 | ||
| + | | Run first set of experiments | ||
| + | |- | ||
| + | ! Week 5 | ||
| + | | First pass of research paper | ||
| + | |- | ||
| + | ! Week 6 | ||
| + | | Generate new ideas for algorithms | ||
| + | |- | ||
| + | ! Week 7 | ||
| + | | Code and test new ideas | ||
| + | |- | ||
| + | ! Week 8 | ||
| + | | Experiment with new algorithms | ||
| + | |- | ||
| + | ! Week 9 | ||
| + | | Finish final draft of research paper | ||
| + | |- | ||
| + | ! Week 10 | ||
| + | | Prepare poster and final presentation | ||
| + | |} | ||
Revision as of 13:25, 1 June 2018
Researcher: Jaired Collins
Mentors: Dr. Richard Povinelli, Dr. Corliss
Abstract
As an infrastructure, pipelines are vulnerable to damaging conditions that warrant action, which results in a loss of profit and extra labor. Sensors are placed along a pipe which can set off an alarm if a measured value exceeds safe levels. At this point, it is reactive instead of preventative maintenance. Using data from the sensors, we anticipate the ability to forecast when an alarm will go off within an hour. With the ability to accurately predict alarms, action can be taken to avoid a loss of profit. Without notification of anomalies, preventable maintenance, cleaning, or catastrophic failure may take place.
Milestones and Goals
| Week 1 | Literature search |
|---|---|
| Week 2 | Literature search and data cleaning |
| Week 3 | Literature search and code state-of-the-art algorithms |
| Week 4 | Run first set of experiments |
| Week 5 | First pass of research paper |
| Week 6 | Generate new ideas for algorithms |
| Week 7 | Code and test new ideas |
| Week 8 | Experiment with new algorithms |
| Week 9 | Finish final draft of research paper |
| Week 10 | Prepare poster and final presentation |
