Three state-of-the-art methods for condition monitoring

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.