{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:10:50Z","timestamp":1774023050641,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:00:00Z","timestamp":1615507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swiss Data Science Center, EPFL and ETH Z\u00fcrich","award":["C18-07 PACMAN"],"award-info":[{"award-number":["C18-07 PACMAN"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71\u00b10.01 compared to 0.65\u00b10.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5\u00b10.2 s per interlock.<\/jats:p>","DOI":"10.3390\/info12030121","type":"journal-article","created":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T11:56:55Z","timestamp":1615550215000},"page":"121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4881-2166","authenticated-orcid":false,"given":"Sichen","family":"Li","sequence":"first","affiliation":[{"name":"Paul Scherrer Institut, 5232 Villigen, Switzerland"}]},{"given":"M\u00e9lissa","family":"Zacharias","sequence":"additional","affiliation":[{"name":"Paul Scherrer Institut, 5232 Villigen, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1455-3226","authenticated-orcid":false,"given":"Jochem","family":"Snuverink","sequence":"additional","affiliation":[{"name":"Paul Scherrer Institut, 5232 Villigen, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6899-3809","authenticated-orcid":false,"given":"Jaime","family":"Coello de Portugal","sequence":"additional","affiliation":[{"name":"Paul Scherrer Institut, 5232 Villigen, Switzerland"}]},{"given":"Fernando","family":"Perez-Cruz","sequence":"additional","affiliation":[{"name":"Swiss Data Science Center, ETH Z\u00fcrich and EPFL, Universit\u00e4tstrasse 25, 8092 Z\u00fcrich, Switzerland"}]},{"given":"Davide","family":"Reggiani","sequence":"additional","affiliation":[{"name":"Paul Scherrer Institut, 5232 Villigen, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7230-7007","authenticated-orcid":false,"given":"Andreas","family":"Adelmann","sequence":"additional","affiliation":[{"name":"Paul Scherrer Institut, 5232 Villigen, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"ref_1","unstructured":"Edelen, A., Mayes, C., Bowring, D., Ratner, D., Adelmann, A., Ischebeck, R., Snuverink, J., Agapov, I., Kammering, R., and Edelen, J. (2018). Opportunities in machine learning for particle accelerators. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1109\/TNS.2016.2543203","article-title":"Neural networks for modeling and control of particle accelerators","volume":"63","author":"Edelen","year":"2016","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"164652","DOI":"10.1016\/j.nima.2020.164652","article-title":"Machine learning for beam dynamics studies at the CERN Large Hadron Collider","volume":"985","author":"Arpaia","year":"2020","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.radonc.2020.09.057","article-title":"Beam data modeling of linear accelerators (linacs) through machine learning and its potential applications in fast and robust linac commissioning and quality assurance","volume":"153","author":"Zhao","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1137\/16M1061928","article-title":"On nonintrusive uncertainty quantification and surrogate model construction in particle accelerator modeling","volume":"7","author":"Adelmann","year":"2019","journal-title":"SIAM\/ASA J. Uncertain. Quantif."},{"key":"ref_6","unstructured":"Kirschner, J., Nonnenmacher, M., Mutn\u1ef3, M., Krause, A., Hiller, N., Ischebeck, R., and Adelmann, A. (2019, January 26\u201330). Bayesian Optimisation for Fast and Safe Parameter Tuning of SwissFEL. Proceedings of the 39th International Free-Electron Laser Conference, FEL2019, Hamburg, Germany."},{"key":"ref_7","unstructured":"Fol, E., Coello de Portugal, J., Franchetti, G., and Tom\u00e1s, R. (2019, January 19\u201324). Optics corrections using Machine Learning in the LHC. Proceedings of the 2019 International Particle Accelerator Conference, Melbourne, VIC, Australia."},{"key":"ref_8","unstructured":"Azzopardi, G., Muscat, A., Redaelli, S., Salvachua, B., and Valentino, G. (2019, January 19\u201324). Operational Results of LHC Collimator Alignment Using Machine Learning. Proceedings of the 10th International Particle Accelerator Conference (IPAC\u201919), Melbourne, VIC, Australia."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"194801","DOI":"10.1103\/PhysRevLett.123.194801","article-title":"Demonstration of Machine Learning-Based Model-Independent Stabilization of Source Properties in Synchrotron Light Sources","volume":"123","author":"Leemann","year":"2019","journal-title":"Phys. Rev. Lett."},{"key":"ref_10","unstructured":"Fol, E., Coello de Portugal, J., Franchetti, G., and Tom\u00e1s, R. (2019, January 26\u201330). Application of Machine Learning to Beam Diagnostics. Proceedings of the FEL\u201919, Hamburg, Germany."},{"key":"ref_11","unstructured":"Tilaro, F., Bradu, B., Gonzalez-Berges, M., Roshchin, M., and Varela, F. (2017, January 8\u201313). Model Learning Algorithms for Anomaly Detection in CERN Control Systems. Proceedings of the 16th International Conference on Accelerator and Large Experimental Control Systems (ICALEPCS\u201917), Barcelona, Spain."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108116","DOI":"10.1016\/j.measurement.2020.108116","article-title":"Convolutional neural network architecture for beam instabilities identification in Synchrotron Radiation Systems as an anomaly detection problem","volume":"165","author":"Piekarski","year":"2020","journal-title":"Measurement"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Donon, Y., Kupriyanov, A., Kirsh, D., Di Meglio, A., Paringer, R., Serafimovich, P., and Syomic, S. (October, January 30). Anomaly Detection and Breakdown Prediction in RF Power Source Output: A Review of Approaches. Proceedings of the NEC 2019 27th Symposium on Nuclear Electronics and Computing, Budva, Montenegro.","DOI":"10.1109\/ITNT49337.2020.9253296"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Donon, Y., Kupriyanov, A., Kirsh, D., Di Meglio, A., Paringer, R., Rytsarev, I., Serafimovich, P., and Syomic, S. (2020, January 26\u201329). Extended anomaly detection and breakdown prediction in LINAC 4\u2019s RF power source output. Proceedings of the 2020 International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russia.","DOI":"10.1109\/ITNT49337.2020.9253296"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"163240","DOI":"10.1016\/j.nima.2019.163240","article-title":"Predicting particle accelerator failures using binary classifiers","volume":"955","author":"Rescic","year":"2020","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/TIM.2010.2047662","article-title":"State-of-the-art predictive maintenance techniques","volume":"60","author":"Hashemian","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1177\/0954405415601640","article-title":"Predictive maintenance, its implementation and latest trends","volume":"231","author":"Selcuk","year":"2017","journal-title":"Proc. Inst. Mech. Eng. Part B J. Eng. Manuf."},{"key":"ref_18","unstructured":"Scheffer, C., and Girdhar, P. (2004). Practical Machinery Vibration Analysis and Predictive Maintenance, Elsevier."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kovalev, D., Shanin, I., Stupnikov, S., and Zakharov, V. (2018, January 20\u201321). Data mining methods and techniques for fault detection and predictive maintenance in housing and utility infrastructure. Proceedings of the 2018 International Conference on Engineering Technologies and Computer Science (EnT), Moscow, Russia.","DOI":"10.1109\/EnT.2018.00016"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3233\/JNR-200162","article-title":"Improving beam simulations as well as machine and target protection in the SINQ beam line at PSI-HIPA","volume":"22","author":"Reggiani","year":"2020","journal-title":"J. Neutron Res."},{"key":"ref_21","unstructured":"Dalesio, L.R., Kozubal, A., and Kraimer, M. (1991). EPICS Architecture, Los Alamos National Lab.. Technical Report."},{"key":"ref_22","unstructured":"Lutz, H., and Anicic, D. (2011). Database driven control system configuration for the PSI proton accelerator facilities. Proceedings of the ICALEPCS\u201911, Grenoble, France, 10\u201315 October 2011, JACoW Publishing."},{"key":"ref_23","unstructured":"Ebner, S. (2021, March 10). Data API for PSI SwissFEL DataBuffer and EPICS Archiver. Available online: https:\/\/github.com\/paulscherrerinstitute\/data_api_python."},{"key":"ref_24","unstructured":"Chollet, F. (2021, March 10). Keras. Available online: https:\/\/keras.io."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1209\/0295-5075\/4\/9\/004","article-title":"Recurrence Plots of Dynamical Systems","volume":"4","author":"Eckmann","year":"1987","journal-title":"EPL Europhys. Lett."},{"key":"ref_26","unstructured":"Zbilut, J.P., Koebbe, M., Loeb, H., and Mayer-Kress, G. (1990, January 23\u201326). Use of recurrence plots in the analysis of heart beat intervals. Proceedings of the Computers in Cardiology, Chicago, IL, USA."},{"key":"ref_27","first-page":"26","article-title":"Recurrence quantification analysis of nonlinear dynamical systems","volume":"94","author":"Webber","year":"2005","journal-title":"Tutor. Contemp. Nonlinear Methods Behav. Sci."},{"key":"ref_28","unstructured":"(2021, January 15). Recurrence Plots and Cross Recurrence Plots. Available online: www.recurrence-plot.tk."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.physrep.2006.11.001","article-title":"Recurrence plots for the analysis of complex systems","volume":"438","author":"Marwan","year":"2007","journal-title":"Phys. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Webber, C.L., and Marwan, N. (2015). Recurrence Quantification Analysis: Theory and Best Practices, Springer.","DOI":"10.1007\/978-3-319-07155-8"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/ICDAR.1995.598994","article-title":"Random decision forests","volume":"Volume 1","author":"Ho","year":"1995","journal-title":"Proceedings of the 3rd International Conference on Document Analysis and Recognition"},{"key":"ref_33","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_35","unstructured":"Grillenberger, J., Humbel, J.M., Mezger, A., Seidel, M., and Tron, W. (2013, January 16\u201320). Status and Further Development of the PSI High Intensity Proton Facility. Proceedings of the 20th International Conferenceon Cyclotrons and Their Applications (Cyclotrons\u201913), Vancouver, BC, Canada."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/3\/121\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:34:40Z","timestamp":1760160880000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/3\/121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,12]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["info12030121"],"URL":"https:\/\/doi.org\/10.3390\/info12030121","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,12]]}}}