Developed a distributed shipboard system to perform diagnostics and prognostics on mechanical equipment (e.g. engines, generators, and chilled water systems) for the Office of Naval Research (ONR). This Condition Based Maintenance (CBM) system (called MPROS for Machinery Prognostics/Diagnostics System) consists of MEMS and conventional sensors on the machinery, local intelligent signal processing devices (called "Data Concentrators"), and a centrally located subsystem (called the PDME for Prognostics, Diagnostics, Monitoring Engine) which is designed so that it can run under shipboard monitoring systems such as ICAS (Integrated Condition Assessment System). MPROS includes and augments periodic vibration analysis by collecting data continuously from vibration and other sensors, including temperature, pressure, current, voltage, and so on. These data streams are integrated as necessary in the Data Concentrators (data fusion). A second level of integration (knowledge fusion) occurs in the PDME. At this level, the conclusions of different diagnostic and prognostic reasoning mechanisms are fused to yield the best possible analysis. In this paper, we discuss recent progress in design and implementation of the software and hardware required to support our system.
[1]
George Vachtsevanos,et al.
An application of fuzzy logic and Dempster-Shafer theory to failure detection and identification
,
1991,
[1991] Proceedings of the 30th IEEE Conference on Decision and Control.
[2]
G. Vachtsevanos,et al.
DETECTION AND IDENTIFICATION OF AXIAL FLOW COMPRESSOR INSTABILITIES
,
1992
.
[3]
M. Mufti,et al.
An intelligent approach to fault detection and identification
,
1995,
Proceedings of 1995 American Control Conference - ACC'95.
[4]
George D. Hadden,et al.
Condition-Based Maintenance: Algorithms and Applications for Embedded High Performance Computing
,
1999,
IPPS/SPDP Workshops.
[5]
George D. Hadden,et al.
Real-Time Feature Recognition in Medical Data
,
2002
.