Vega, J.Hernández, F.Isayama, A.Joffrin, E.Matsunaga, G.Suzuki, T.Dormido Canto, Sebastián2024-05-202024-05-202019-091873-7196http://doi.org/10.1016/j.fusengdes.2019.02.061https://hdl.handle.net/20.500.14468/12240Disruptions are dangerous events in tokamaks that require mitigation methods to alleviate its detrimental effects. A prerequisite to trigger any mitigation action is the existence of a reliable disruption predictor. This article assesses a predictor that relates in a linear way consecutive samples of a single quantity (in particular, the magnetic perturbation time derivative signal has been used). With this kind of predictor, the recognition of disruptions does not depend on how large the signal amplitude is but on how large the signal increments are: small increments mean smooth plasma evolution whereas abrupt increments reflect a non-smooth evolution and potential risk of disruption. Results are presented with data from the JT-60U tokamak and high-beta discharges. Two training methods have been tested: a classical approach in which the more data for training the better and an adaptive method that starts from scratch. In both cases the success rate is about 95%. It should be noted that predictors based on signal increments and their adaptive versions can be of big interest for next devices such as JT-60SA or ITER.eninfo:eu-repo/semantics/openAccessjournal articleDisruption predictionSignal incrementsJT-60SANearest centroidITERNuclear fusion