Accession Number:

ADP013506

Title:

Assessment of Data and Knowledge Fusion Strategies for Diagnostics and Prognostics

Descriptive Note:

Conference paper

Corporate Author:

IMPACT TECHNOLOGIES LLC ROCHESTER NY

Report Date:

2001-04-05

Pagination or Media Count:

10.0

Abstract:

Various data, feature and knowledge fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of anomaly, diagnostic and prognostic technologies. Fusion of relevant sensor data, maintenance database information, and outputs from various diagnostic and prognostic technologies has proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. The data fusion strategies discussed in this paper are principally probabilistic in nature and are used to aid in directly identifying confidence bounds associated with specific component fault identifications and predictions. Dempster-Shafer fusion, Bayesian inference, fuzzy-logic inference, neural network fusion and simple weightingvoting are the algorithmic approaches that are discussed in this paper. Data fusion architectures such as centralized fusion, autonomous fusion, and hybrid fusion are described in terms of their applicability to fault diagnosis and prognosis. The final goal is to find the optimal combination of measured system data, data fusion algorithms, and associated architectures for obtaining the highest overall predictiondetection confidence levels associated with a specific application Evaluation of the fusion and diagnostic strategies was performed using gearbox seeded- fault and accelerated failure data taken with the MDTB Mechanical Diagnostic Test Bed at the ARL Lab at Penn State University.

Subject Categories:

  • Computer Programming and Software
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE