DOI · DOI-0041 · record R2707

Improved Processing and Analysis of Test and Operating Data from Rotating Machines [2024 INV#DOI-62]

Department of the Interior · BOR

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Energy and the Environment
Operational functions: Forecasting and prediction Derived by keyword rules; see methodology

Problem the AI is intended to solve

This research strives to aid in the development of condition-based maintenance (CBM) and predictive maintenance (PdM) tools for Hydroelectric Facilities (Generators and Pumps) by exploring, testing, and developing software tools to process data collected from rotating machines. These software tools use various forms of AI/ML.

Expected benefits

CBM and PdM aim to increase hydropower generation by reducing outages for maintenance and lower O&M costs by only performing maintenance when needed.

System outputs

Data-driven tools using AI/ML techniques to inform power plant maintenance.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported