Fault diagnosis of pneumatic systems with artificial neural network algorithms

dc.contributor.authorDemetgul M.
dc.contributor.authorTansel I.N.
dc.contributor.authorTaskin S.
dc.date.accessioned2024-07-22T08:21:39Z
dc.date.available2024-07-22T08:21:39Z
dc.date.issued2009
dc.description.abstractPneumatic systems repeat the identical programmed sequence during their operation. The data was collected when the pneumatic system worked perfectly and had some faults including empty magazine, zero vacuum, inappropriate material, no pressure, closed manual pressure valve, missing drilling stroke, poorly located material, not vacuuming the material and low air pressure. The signals of eight sensors were collected during the entire sequence and the 24 most descriptive features of the data were encoded to present to the ANNs. A synthetic data generation process was proposed to train and test the ANNs better when signals are extremely repetitive from one sequence to other. Two artificial neural networks (ANN) were used for interpretation of the encoded signals. The tested ANNs were Adaptive Resonance Theory 2 (ART2), and Back propagation (Bp). ART2 correctly distinguished the perfect and faulty operations at all the tested vigilance values. It classified 11 faulty and 1 normal modes in seven or eight categories at the best vigilance values. Bp also distinguished perfect and faulty operations without even the slightest uncertainty. In less than 10 cases, it had difficulty identifying the 11 types of possible faults. The average estimation error of the Bp was better than 2.1% of the output range on the test data which was created by deviating the encoded values. The ART2 and Bp performance was found excellent with the proposed encoding and synthetic data generation procedures for extremely repetitive sequential data. © 2009 Elsevier Ltd. All rights reserved.
dc.identifier.DOI-ID10.1016/j.eswa.2009.01.028
dc.identifier.issn09574174
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18712
dc.language.isoEnglish
dc.subjectAtmospheric pressure
dc.subjectBackpropagation
dc.subjectElectric fault currents
dc.subjectPneumatic control
dc.subjectPneumatic equipment
dc.subjectProduction engineering
dc.subjectResonance
dc.subjectSCADA systems
dc.subjectAdaptive resonance theories
dc.subjectAdaptive resonance theory2 (ART2)
dc.subjectArtificial neural network algorithms
dc.subjectArtificial neural networks
dc.subjectBack propagation (Bp)
dc.subjectEncoded signals
dc.subjectEstimation errors
dc.subjectFault diagnosis
dc.subjectFaulty operations
dc.subjectLow air pressures
dc.subjectModular production system
dc.subjectNormal modes
dc.subjectOutput ranges
dc.subjectPneumatic systems
dc.subjectPressure valves
dc.subjectSequential datum
dc.subjectSynthetic datum
dc.subjectTest datum
dc.subjectNeural networks
dc.titleFault diagnosis of pneumatic systems with artificial neural network algorithms
dc.typeArticle

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