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Proceedings Paper

Application of sensor fusion and signal classification techniques in a distributed machinery condition monitoring system
Author(s): Karl M. Reichard; Mike Van Dyke; Ken Maynard
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Paper Abstract

A new paradigm for machinery maintenance is emerging as preventive maintenance strategies are being replaced by condition-based maintenance. In condition-based maintenance, machinery is repaired or serviced only when an intelligent monitoring system indicates that the system cannot fulfill mission requirements. The implementation of such systems requires a combination of sensor data fusion, feature extraction, classification, and prediction algorithms. In addition, new system architectures are being developed to facilitate the reduction of wide bandwidth sensor data to concise predictions of ability of the system to complete its current mission or future missions. This paper describes the system architecture, data fusion, and classification algorithms employed in a distributed, wireless bearing and gear health monitoring system. The role and integration of prognostic algorithms--required to predict future system health--are also discussed. Examples are provided which illustrate the application of the system architecture and algorithms to data collected on a machinery diagnostics test bed at the Applied Research Laboratory at The Pennsylvania State University.

Paper Details

Date Published: 3 April 2000
PDF: 8 pages
Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); doi: 10.1117/12.381646
Show Author Affiliations
Karl M. Reichard, The Pennsylvania State Univ. (United States)
Mike Van Dyke, The Pennsylvania State Univ. (United States)
Ken Maynard, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 4051:
Sensor Fusion: Architectures, Algorithms, and Applications IV
Belur V. Dasarathy, Editor(s)

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