• Nuclear Engineering and Technology
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• v.35 no.5
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• pp.454-470
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• 2003

In this paper, a preliminary study for development of a fault diagnosis is presented for monitoring and diagnosing faults in the agitator driving system of a high temperature reduction reactor. In order to identify a fault occurrence and classify the fault cause, vibration signals measured by accelerometers on the outer shroud of the agitator driving system are firstly decomposed by wavelet transform (WT) and the features corresponding to each fault type are extracted. For the diagnosis, the fuzzy ARTMAP is employed and thereby, based on the features extracted from the WT, the robust fault classifier can be implemented with a very short training time - a single training epoch and a single learning iteration is sufficient for training the fault classifier. The test results demonstrate satisfactory classification for the faults pre-categorized from considerations of possible occurrence during experiments on a small-scale reduction reactor.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.32.1-32
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• 2002

The fault monitoring technique is presented for identifying the status of the agitator driving system in thermal reduction reactor. For identifying a fault such as bearing defect or clearance blocking, Wavelet transform (WT) is applied to vibration signals and features are extracted. For classification, the fuzzy ARTMAP is employed. With the features from WT, a single training epoch and a single learning iteration are sufficient for the fuzzy ARTMAP to classify the faults. The test results show the perfect classification though some features extracted from the test data are distorted against those in the training data