Loren Data's SAM Daily™

Home Today's SAM Search Archives Numbered Notes CBD Archives Subscribe

A -- Condition Based Maintenance

Notice Date
5/25/2023 8:57:18 AM
Notice Type
541715 — Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
Contracting Office
ZIP Code
Solicitation Number
Response Due
6/15/2023 2:00:00 PM
Archive Date
Point of Contact
General Email
E-Mail Address
Condition Based Maintenance The maintenance of Navy vessels is a crucial and costly process that requires proper planning and execution to ensure the vessels' operational readiness. The traditional approach of performing routine maintenance tasks at fixed intervals can result in unnecessary downtime and increased costs, especially if the maintenance is performed when it is not required. Moreover, if a crucial system failure is missed during routine maintenance, it can lead to catastrophic consequences. Condition-based maintenance (CBM) can provide a cost-effective and reliable solution to the Navy's maintenance needs. CBM is a maintenance strategy that relies on the monitoring of critical systems and components to detect and address potential issues before they lead to failure. �However, the implementation of CBM for Navy vessels presents several challenges, such as selecting the appropriate sensors, determining the optimal frequency and timing of inspections, collecting and analyzing data, and integrating the CBM system with the vessel's existing infrastructure. Additionally, there is a need for the development of accurate prediction models that can anticipate future failures based on past data and other variables. Several key CBM technologies being pursued include digital twins, advanced sensors, and machine learning algorithms: ��� �Digital twins can enable a more accurate and proactive approach to CBM, allowing for early detection of potential failures, and the ability to simulate different maintenance scenarios before they are implemented in the real world. However, the effectiveness of digital twin-based CBM depends on the accuracy and completeness of the digital twin model, the quality of data used to generate the model, and the ability to maintain and update the model as the physical asset changes over time. ��� �Advanced sensor technologies, including vibration sensors, infrared thermography, ultrasonic and acoustic emission sensors could be applied to CBM systems to reduce maintenance costs, minimize equipment downtime, and increase equipment reliability. ��� �AI and machine learning algorithms can analyze data from sensors, historical maintenance records, and other sources to identify patterns and anomalies that can indicate potential equipment failures. These algorithms can then generate alerts or recommendations for maintenance activities based on the likelihood of a failure occurring. Solutions are being sought for equipment and components in a naval nuclear propulsion plant including turbine generators, pumps and motors, condensers / heat exchangers, and valves. �The proposed solution should enable more effective and cost-efficient maintenance operations, improving asset availability, reducing downtime, and enhancing safety and reliability. Solicitation Closing Date: 6/15/2023 5pm EST.� The Naval Nuclear Lab will answer questions during the Q&A session to be held on 5/15/2023 at 1PM-2PM EST. �The call-in information for the Q&A session is below. �Results of the Q&A Session will be shared with any interested parties. Please contact ATIP@unnpp.gov for a copy of the results of the Q&A session or other inquiries.� Call in (audio only)� +1 332-206-0599,,606392755# � United States, New York City (New York)� Phone Conference ID: 606 392 755#� �
Web Link
SAM.gov Permalink
Place of Performance
Address: USA
Country: USA
SN06694237-F 20230527/230525230110 (samdaily.us)
SAM.gov Link to This Notice
(may not be valid after Archive Date)

FSG Index  |  This Issue's Index  |  Today's SAM Daily Index Page |
ECGrid: EDI VAN Interconnect ECGridOS: EDI Web Services Interconnect API Government Data Publications CBDDisk Subscribers
 Privacy Policy  © 1994-2020, Loren Data Corp.