Fully automatic AI-based leak detection system

Energy

Volumn 35, Issue 9, 2010, Pages 3838-3848

  Tylman, Wojciech ^a   Kolczyński, Jakub ^a   Anders, George J ^b  

^a LODZ UNIVERSITY OF TECHNOLOGY   (Poland)

^b KINECTRICS INC   (Canada)

Author keywords

Bayesian networks; HPFF; Neural networks; Pipe type cables

Indexed keywords

CABLES; DATA PROCESSING; DECISION MAKING; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEAK DETECTION; NEURAL NETWORKS; PIPE; SPEECH RECOGNITION; UNDERGROUND CABLES;

AUTOMATIC SYSTEMS; AUTOTUNING; CABLE SYSTEMS; DATA PROCESSING TECHNIQUES; DETECTION CAPABILITY; DIELECTRIC FLUID; FLUID-FILLED; LEAK DETECTION SYSTEMS; NEW YORK CITY; PIPE-TYPE CABLES;

BAYESIAN NETWORKS;

ALUMINUM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; BURIED STRUCTURE; CABLE; DECISION MAKING; DETECTION METHOD; DIELECTRIC PROPERTY; LEAKAGE; PRECISION;

EID: 77955267490     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2010.05.038     Document Type: Article

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