Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284493
Title: The application of artificial intelligence to fault detection in hydraulic cylinder drive systems.
Author: Stewart, James.
Awarding Body: University of Wales.Cardiff
Current Institution: Cardiff University
Date of Award: 1995
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Abstract:
An expert system approach to fault diagnosis of fluid power circuits is considered with emphasis on leakage flow detection, and for valvecontrolled cylinders. Two test rigs were used, one being a solenoid-valve controlled cylinder operated directly and in an open-loop mode, the other being a servo-valve controlled actuator operated by microcomputer and in a closed-loop mode. Both systems incorporated the use of on-line dynamic data, and for the closed-loop case operation and fault diagnosis was integrated into an automated procedure. Flow leakage detection was considered a priority, and an alternative approach using displaced volumes was successfully implemented. The research work concentrated initially on the use of an expert system and the establishment of an appropriate knowledge base using a hybrid reasoning approach. This approach was found to be excellent for single-fault conditions but could not differentiate components of multiple-fault conditions, other than that they existed, due to the use of a minimum number of flow sensors. Additional techniques were then considered for the closed-loop control system utilising steady-state position error, time series analysis, and Artificial Neural Networks. It was found that the consideration of steady-state error gave information complementary to the existing knowledge base but could not give any additional information. The use of an artificial neural network was found to give more information with regards to multiple-fault conditions, resulting in a percentage probability for each fault combination.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.284493  DOI: Not available
Keywords: Computer Aided Manufacturing Computer integrated manufacturing systems Bionics Automatic control Control theory
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