Use of autoassociative neural networks for sensor diagnostics

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Title: Use of autoassociative neural networks for sensor diagnostics
Author: Najafi, Massieh
Abstract: The new approach for sensor diagnostics is presented . The approach , Enhanced Autoassociative Neural Networks (E -AANN ) , adds enhancement to Autoassociative Neural Networks (AANN ) developed by Kramer in 1992 . This enhancement allows AANN to identify faulty sensors . E -AANN uses a secondary optimization process to identify and reconstruct sensor faults . Two common types of sensor faults are investigated , drift error and shift or offset error . In the case of drift error , the sensor error occurs gradually while in the case of shift error , the sensor error occurs abruptly . EAANN catches these error types . A chiller model provided synthetic data to test the diagnostic approach under various noise level conditions . The results show that sensor faults can be detected and corrected in noisy situations with the E -AANN method described . In high noisy situations (10 % to 20 % noise level ) , E -AANN performance degrades . E -AANN performance in simple dynamic systems was also investigated . The results show that in simple dynamic situations , E -AANN identifies faulty sensors .
URI: http : / /hdl .handle .net /1969 .1 /1392
Date: 2005-02-17

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Use of autoassociative neural networks for sensor diagnostics. Available electronically from http : / /hdl .handle .net /1969 .1 /1392 .

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