Experimental research on a Raman-based distributed temperature sensor assisted by PCA for locating the temperature abnormal event of nuclear waste drums.

Aimed at locating the temperature abnormal event of nuclear waste drums in a nuclear waste temporary storage repository by a Raman-based distributed temperature sensor, a principal component analysis (PCA)-based method for application is proposed. The effectiveness of the proposed method is verified in the physical simulation device of the nuclear waste drums. First, some samples of the temperature abnormal event with known location are taken as the reference samples, and their features are extracted by PCA. Then, the features of the test sample data to be located are also extracted by PCA. The Euclidean distance is used to measure the similarity between the features of the test sample and the feature of each reference sample. Finally, we find the reference sample that is most similar to a test sample, the location of which is considered the location of the temperature anomaly event for the test sample. The experimental results show that the proposed method can accurately locate the temperature abnormal event of the nuclear waste drums, and the accuracy rate can reach 96%. The method that is proposed in this paper can reliably locate the temperature abnormal event generated by the nuclear waste temporary storage repository induced by external factors such as landslides or earthquakes, and provide technical support for nuclear safety.