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EPSRC Reference: GR/J46449/01
Title: FAULT DIAGNOSIS OF FLUID POWER SYSTEMS USING A COMBINATION OF NEURAL NETWORKS AND RULE-BASED SYSTEMS
Principal Investigator: Watton, Professor J
Other Investigators:
Pham, Professor D
Researcher Co-Investigators:
Project Partners:
Oilgear Towler Ltd
Department: Sch of Engineering
Organisation: Cardiff University
Scheme: Standard Research (Pre-FEC)
Starts: 18 April 1994 Ends: 17 October 1997 Value (£): 111,497
EPSRC Research Topic Classifications:
Mech. & Fluid Power Transmiss.
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
Panel History:  
Summary on Grant Application Form
The programme will be concerned with the dynamic performance of a cylinder position control system and the way the performance changes as faults occur. A novel approach is proposed which considers the transient power distribution around the hydraulic circuit as the control system is put through a known duty cycle, i.e. demanded position change. Since faults change the shape of the transient power distribution, four parallel neural networks will be used to detect the different type of faults. This has the advantage of simplifying fault detection, increasing the reliability of fault detection, and offering a greater capability than a purely expert system approach. The complementary advantages of a rule-based system will be used to manage the parallel neural network predictions. Increasing levels of fault severity will be introduced with a view to condition monitoring as an aid to predictive maintenance in practice. Initially supervised neural networks will be exploited, and the use of unsupervised neural networks as novelty detectors will also be investigated.
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Organisation Website: http://www.cf.ac.uk