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This Government Contract opportunity from Department Of The Interior was posted on May 5, 2026. The submission period has ended. Browse the details below for market research, or find similar active opportunities.

Predictive Maintenance Analytics Development

Closed
Federal

Contract Overview

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This contract involves the development of advanced machine learning models and algorithms aimed at anomaly detection and predictive maintenance specifically for hydroelectric equipment. The work will utilize both operational and historical data to improve the maintenance processes and overall reliability of the hydroelectric systems. The focus is on creating data-driven solutions that prevent unexpected failures and enhance equipment performance through timely maintenance interventions. Issued by the Department of the Interior at the Denver Federal Center, this subcontract opportunity is categorized under NAICS code 541690, which pertains to specialized scientific and technical consulting services. The contract was posted on May 5, 2026, with a response deadline set for May 15, 2026. Although some organizational and location details are not specified, the contract highlights a clear emphasis on leveraging machine learning analytics to support sustainable energy infrastructure management.

General Info

Develop machine learning models for anomaly detection and predictive maintenance of hydroelectric equipment.

Agency

Department Of The Interior → Denver Federal Center

NAICS

541690 - Other Scientific and Technical Consulting ServicesView NAICS

Place of Performance

CO

Set-Aside

NONE

Documents

(0)

No documents available

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Timeline

PhaseClosed
Posted

subcontract

Response Deadline

Deadline has passed

Submission Closed

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Organization & Contact Information

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AgencyDepartment Of The Interior → Denver Federal Center
ContactsNo contacts available
OfficeN/A
Organization / Agency
Department Of The Interior → Denver Federal Center
Office AddressN/A
ContactsNo contact information available

Full Description

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Develop machine learning models and algorithms for anomaly detection and predictive maintenance in hydroelectric equipment using operational and historical data.