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Application of autoassociative neural networks to health monitoring of the CAT 7 diesel engine

Author: Andrew J Bayba; David N Siegel; Kwok Tom; U.S. Army Research Laboratory,
Publisher: Adelphi, MD : Army Research Laboratory, [2012]
Edition/Format:   eBook : Document : National government publication : EnglishView all editions and formats
Summary:
An autoassociative neural network (AANN) algorithm was applied to fault detection and classification for seeded fault testing on a Caterpillar C7 diesel engine. Data used for this work is a subset from the seeded fault testing performed at the U.S. Army Tank and Automotive Research, Development and Engineering Center (TARDEC) test cell facilities. This report extends previous work performed on fault detection and
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Genre/Form: Text
Additional Physical Format: Print version:
Bayba, Andrew J.
Application of autoassociative neural networks to health monitoring of the CAT 7 diesel engine
(OCoLC)915952248
Material Type: Document, Government publication, National government publication, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Andrew J Bayba; David N Siegel; Kwok Tom; U.S. Army Research Laboratory,
OCLC Number: 832139089
Language Note: English.
Notes: Title from title screen (viewed on Aug. 4, 2015).
"February 2012."
"ARL-TN-0472."
Text/html.
Description: 1 online resource (iv, 16 pages) : color illustrations
Responsibility: by Andrew J. Bayba, David N. Siegel, and Kwok Tom.

Abstract:

An autoassociative neural network (AANN) algorithm was applied to fault detection and classification for seeded fault testing on a Caterpillar C7 diesel engine. Data used for this work is a subset from the seeded fault testing performed at the U.S. Army Tank and Automotive Research, Development and Engineering Center (TARDEC) test cell facilities. This report extends previous work performed on fault detection and classification performed by the U.S. Army Research Laboratory (ARL) on the C7 engine by including analysis using AANN [1]. We believed that AANN would be particularly useful in the diagnosis of faults in these tests because the correlation of several sensors appeared to be nonlinear. Although AANN performed quite well, the results were similar to the previous work using linear Principal Component Analysis (PCA) Statistics. We believe that the potential benefit in using AANN was not achieved due to the nature of the tests analyzed?in particular, data collection at discrete set-points in engine operation?and that within these set-point regimes, the sensor readings tend to be linearly correlated.

The original document contains color images.

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Primary Entity

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    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/algorithms> ; # ALGORITHMS
    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/cat_7_engines> ; # CAT 7 ENGINES
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    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/detectors> ; # DETECTORS
    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/aann_autoassociative_neural_network> ; # AANN(AUTOASSOCIATIVE NEURAL NETWORK)
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    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/data_acquisition> ; # DATA ACQUISITION
    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/reciprocating_and_rotating_engines> ; # Reciprocating and Rotating Engines
    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/diesel_engines> ; # *DIESEL ENGINES
    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/neural_nets> ; # NEURAL NETS
    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/diagnosis_general> ; # DIAGNOSIS(GENERAL)
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    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/caterpillar_c7_engine> ; # CATERPILLAR C7 ENGINE
    schema:about <http://experiment.worldcat.org/entity/work/data/1215421320#Thing/faults> ; # FAULTS
    schema:author <http://experiment.worldcat.org/entity/work/data/1215421320#Person/tom_kwok> ; # Kwok Tom
    schema:author <http://experiment.worldcat.org/entity/work/data/1215421320#Person/siegel_david_n> ; # David N. Siegel
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