Abstract
Abstract At the in – service inspection of the steam generator (SG) tubings in PWR plants, eddy current testing (ECT) has been widely used at each outage. At present, ECT data evaluation is mainly performed by ECT data analyst, therefore it has the following problems. Only ECT signal configuration on the impedance trajectory is used in the evaluation. ) It is an enormous time consuming process. The evaluation result is influenced by the ability and experience of the analyst. The evaluation result depends on the analyst's health condition. Especially, ECT data evaluation by only ECT signal configuration on the impedance trajectory makes it difficult to identify the true defect signal hidden in background signals such as lift – off noise and deposit signals. In this work, we performed the preliminary study on the possibility of the application of neural network to ECT data evaluation.
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