Vibration Analysis of Rotating Plant Machinery

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9101-08-M-0021
Agency Tracking Number: F073-135-1477
Amount: $99,765.00
Phase: Phase I
Program: SBIR
Awards Year: 2008
Solicitation Year: 2007
Solicitation Topic Code: AF073-135
Solicitation Number: 2007.3
Small Business Information
850 Energy Drive, Suite 307, Idaho Falls, ID, 83401
DUNS: 089822014
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Jerome Palazzolo
 Senior Research Engineer
 (802) 876-3100
Business Contact
 Paula Lee
Title: Contracts Administrator
Phone: (802) 876-3100
Research Institution
Health monitoring and management of critical machinery is becoming state-of-the-art for military aircraft. Application of advanced health monitoring technology to ground-based plant equipment is an obvious, needed extension. Advanced signal processing algorithms that can analyze vibration signals to identify fault source and severity and track trends of data features is key to efficient maintenance practices for rotating plant machinery. This is the basis of Condition-Based Maintenance (CBM), in which maintenance is performed based on the measured condition of the mechanical components. Maintenance is performed only after fault detection but before failure. Condition-based maintenance minimizes downtime, eliminates unnecessary maintenance, and prevents secondary damage from in-service failures. Rudimentary technologies to monitor rotating plant machinery systems exist, taking the form of stand-alone monitoring devices or manual machine monitoring. Application of advanced diagnostic and prognostic algorithms recently developed for aerospace applications will allow the development of intelligent health monitoring system that maximizes diagnostic coverage while minimizing the number of sensors added. Sentient will develop a low-cost, robust health monitoring system applicable to a wide range of commercial rotating machinery. Phase I will demonstrate the underlying sensing and diagnostic algorithms. Phase II will develop and demonstrate a complete prototype installed on operating plant machinery.

* Information listed above is at the time of submission. *

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