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@InProceedings{jiang:ibic2020-wepp28,
author = {R. Jiang and J. Chen and Y.B. Leng},
title = {{High-Accuracy Diagnostic Tool for Beam Position Monitor Troubleshooting in SSRF Based on Clustering Analysis}},
booktitle = {Proc. IBIC'20},
pages = {166--169},
paper = {WEPP28},
language = {english},
keywords = {SRF, storage-ring, operation, experiment, electron},
venue = {Santos, Brazil},
series = {International Beam Instrumentation Conference},
number = {9},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2020},
issn = {2673-5350},
isbn = {978-3-95450-222-6},
doi = {10.18429/JACoW-IBIC2020-WEPP28},
url = {https://www.jacow.org/ibic2020/papers/wepp28.pdf},
note = {https://doi.org/10.18429/JACoW-IBIC2020-WEPP28},
abstract = {Beam position monitors (BPMs) are important to monitor the beam moving steadily. In spite of some data is viewed and analysed, a large fraction of data has never been effectively analysed in accelerator operation. It lead to some useful information not coming to the surface during the beam position monitor troubleshooting processing. We will describe in this paper our efforts to use clustering analysis techniques to pull out new information from existing beam data. Our focus has been to look at malfunction of BPM, associating basic running data that is ß oscillation of X and Y directions, energy oscillation and doing predictive analysis. Clustering analysis results showed that 140 BPMs could be classify into normal group and fault group and abnormal BPM could be separated. Based on the results, the algorithm could locate fault BPM and it could be an effective supplement for data analysis in accelerator physics.},
}