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SPHERE is the VLT exo-planet imager and is based on XAO and coronagraphy. Malfunctioning DM actuators can have a severe impact on the instrument contrast. 18 dead and 8 sluggish actuators were identified during commissioning, but the actuator's behavior needs to be monitored during the whole instrument lifetime. Daily, the temporal responses of SPHERE's 1377 actuators are measured at 1380Hz. The method to automatically identify the status of the actuators is based on machine learning. We used the SciKit toolbox (INRIA, France) and implemented a Support Vector Machine algorithm. The model was trained on data acquired on 167 daily measurements of dead actuators, 73 daily measurements of sluggish actuators and 334 daily measurements of good actuators. The model was then validated on 73 daily measurements of dead actuators, 26 daily measurements of sluggish actuators and 147 daily measurements of good actuators. The method accurately identified malfunctioning actuators with an extremely low number of false positives (1). The method is easy to implement, fast (30ms) and easily scalable to systems with more degrees of liberty such as MOEMS DMs and the future ELT DMs. |
