Abstract
Semiconductor manufacturing involves many critical steps,
wherein maintaining an ultra-high vacuum is mandatory. To
this end, cryogenic pumps are used to create a controlled
ultra-low-pressure environment through the use of cryogenic
cooling. However, a sudden pump malfunction leads to
contamination in the processing chamber, disrupting
production. The primary focus of this study is preventing
unplanned shutdowns of cryogenic pumps. The data was
collected from various pump sensors also known as status
variable identification (SVID) that reveals current behavior
of the pump. A comprehensive framework is presented here
to develop a condition monitoring and fault detection. In the
proposed framework, a drift detection method is used for
condition monitoring of the pump to locate gradual and
abrupt drifts. Additionally, during regeneration (or
maintenance) phase, intrinsic features are extracted to
distinguish between normal and abnormal regeneration,
achieving an accuracy of 90.91% and a precision of 66.67%.
Utilizing the proposed system, cryo-pump operators can be
given maintenance guidelines and warnings about potential
health degradation of the pumps.
wherein maintaining an ultra-high vacuum is mandatory. To
this end, cryogenic pumps are used to create a controlled
ultra-low-pressure environment through the use of cryogenic
cooling. However, a sudden pump malfunction leads to
contamination in the processing chamber, disrupting
production. The primary focus of this study is preventing
unplanned shutdowns of cryogenic pumps. The data was
collected from various pump sensors also known as status
variable identification (SVID) that reveals current behavior
of the pump. A comprehensive framework is presented here
to develop a condition monitoring and fault detection. In the
proposed framework, a drift detection method is used for
condition monitoring of the pump to locate gradual and
abrupt drifts. Additionally, during regeneration (or
maintenance) phase, intrinsic features are extracted to
distinguish between normal and abnormal regeneration,
achieving an accuracy of 90.91% and a precision of 66.67%.
Utilizing the proposed system, cryo-pump operators can be
given maintenance guidelines and warnings about potential
health degradation of the pumps.
Original language | English |
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Publication status | Accepted/In press - 22 Jul 2024 |
Event | 16th Annual Conference of the Prognostics and Health Management Society - Loews Vanderbilt Hotel, Nashville, United States Duration: 9 Nov 2024 → 14 Nov 2024 |
Conference
Conference | 16th Annual Conference of the Prognostics and Health Management Society |
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Country/Territory | United States |
City | Nashville |
Period | 9/11/24 → 14/11/24 |
Keywords
- Predictive Maintenance
- Cryogenic Pumps
- Drift identification
- Direct ratio estimation
- novelty detection