## 1 Introduction  
The most promising and studied reactor configuration aimed at producing electricity from nuclear fusion is the Tokamak, in which specific mixtures of gases (hydrogen and its isotopes) are confined in a toroidal shaped chamber by a magnetic field having two main components: a toroidal and a poloidal one [(1)]. The magnetic topology associated to this configuration has been studied for decades [(1; 2; 3)]. The magnetically confined plasma is a challenging system, both under a physical and an engineering point of view, since it requires achieving high performances and stability at the same time. The latter, being the primary matter of concern for industrial applications, is often compromised by the so called plasma instabilities, whose occurrence can affect the performances and even cause the premature termination of the pulses; consequently, the study of their zoology in a plasma for fusion applications represents a wide branch of the existing literature [1; 2; 3; 4]. Such instabilities change locally the magnetic topology and their dynamics can lead to dangerous events, having the potential to threat the safety and the operational continuity of a reactor: the so called disruptions. Endemic ones in Tokamaks, whose uncontrolled evolution has proven experimentally to cause the occurrence of the aforementioned catastrophic events, are the so called tearing modes [5; 6; 7]. Historically, the real time mitigation of disruptions has always been based on thresholds set on specific signals, whose \"anomalous\" behaviour has been considered as a marker of their imminent occurrence. Quite recently, another approach has been followed using machine learning techniques that have demonstrated to reach excellent performances [8; 9], like APODIS on data of the Joint European Torus (JET), that has also been the first support vector machine based indicator to be implemented in real time. The latest generation of predictors have also demonstrated to be able of learning from a very few numbers of training data and to adapt their models when required [10; 11], a key feature for their application on the next generation of devices like the International Thermonuclear Experimental Reactor (ITER). However, the safe termination of a pulse, by the application of triggered mitigation responses, cannot be the definitive answer for the future: the next generation of devices can tolerate only a small number of even fully mitigated disruptions. Therefore, scientists moved from studying mitigation schemes to avoidance. Avoiding a disruption is a challenging and still open issue due to the complexity, high nonlinearity and the huge number of mutual interactions between different phenomena in the plasma. While many paths can lead to a disruption, it has been observed experimentally that a few quantities are really informative shortly before it is going to occur. Among these, the locking of macroscopic magnetic modes, mentioned above, occurs in a wide percentage of disruptive pulses [4; 5]. This article describes a new estimator based on the measurements of the perturbed radial magnetic field of the axially symmetric divertor experiment--upgrade (AUG from now on) to detect the slowing down of the aforementioned family of instabilities. This in order to assess an analytically simple and easy to implement tool to start tracking back the chain of events leading to the slowing down of a mode and consequently to a disruption. At the present stage, the suggested estimator is directly suitable for implementation in the control room (CR), while for offline purposes it can detect automatically the time instance at which the mode can be considered locked in the reference frame of the laboratory. Section 2 defines in more details the problem, while Section 3 describes the new statistical estimator based on physical assumptions and aimed at the detection of the dynamic of the instabilities.