On-line diagnosis algorithm based on noise analysis

Abstract A minicomputer-based on-line diagnosis algorithm was developed and its verification test was performed. This algorithm identifies the plant status by applying a statistical pattern recognition technique to various multidimensional noise pattern vectors. In case the reference noise patterns are prepared for such abnormal plant states that the noise response characteristics are linear to different abnormality severeness values, this algorithm can discriminate the category and relative severeness of abnormality, irrespective of its severeness. The test was performed on-line with a simple analog simulator which was modeled on the experimental fast reactor JOYO. Seven pattern vectors were used to diagnose three categories of abnormality including abnormal control rod vibration. Reference patterns were learned under only one degree of severeness for each abnormality category. Test results showed the effectiveness of the proposed algorithm. This indicates that the number of reference patterns to be prepared can be significantly decreased.