Three state-of-the-art methods for condition monitoring
Grimmelius, H.T.
Meiler, P.P.
Maas, H.L.M.M.
Bonnier, B.
Grevink, J.S.
van Kuilenburg, R.F.
Delft Univ. of Technol.;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Apr 1999
Volume: 46,
Issue: 2
On page(s): 407-416
ISSN: 0278-0046
References Cited: 10
CODEN: ITIED6
INSPEC Accession Number: 6221693
Digital Object Identifier: 10.1109/41.753780
Current Version Published: 2002-08-06
Abstract
This paper describes and compares three different state-of-the-art
condition monitoring techniques: first principles, feature extraction,
and neural networks. The focus of the paper is on the application of the
techniques, not on the underlying theory. Each technique is described
briefly and is accompanied by a discussion on how it can be applied
properly. The discussion is finished with an enumeration of the
advantages and disadvantages of the technique. Two condition monitoring
cases, taken from the marine engineering field, are explored: condition
monitoring of a diesel engine, using only the torsional vibration of the
crank shaft, and condition monitoring of a compression refrigeration
plant, using many different sensors. Attention is also paid to the
detection of sensor malfunction and to the user interface. The
experience from the cases shows that all techniques are showing
promising results and can be used to provide the operator with
information about the monitored machinery on a higher level. The main
problem remains the acquisition of the required knowledge, either from
measured data or from analysis
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