Computer Implemented Method for Controlling a Winding Machine and for Training a Machine Learning Algorithm, Computer Program and Winding Machine

A computer implemented method for training a machine learning algorithm which provides an anomaly indicator as an output for controlling a winding machine and which initiates an amendment of at least one control parameter of the winding machine in an event of an indicated anomaly, and the winding machine comprising at least a winder and a rewinder, the method comprising:

• determining an actual velocity of the winder during operation of the winding machine;
• performing signal processing of the determined actual velocity to extract a winder-related feature, said signal processing comprising subtracting a command velocity from the determined actual velocity, determining an envelope signal of a subtracted signal, and filtering the envelope signal, the filtering preserving an amplitude-related information;
• utilizing the filtered envelope signal as winder-related feature;
• and
• training the machine learning algorithm based on the winder-related feature.

A winding machine comprising:

• a data-processing system including a processor and memory;
• wherein the processor is configured to:
• determine an actual velocity of the winder during operation of the winding machine;
• perform signal processing of the determined actual velocity to extract a winder-related feature, said signal processing comprising subtracting a command velocity of the winder from the determined actual velocity, determining an envelope signal of a subtracted signal, and filtering the envelope signal, the filtering preserving an amplitude-related information;
• utilize the filtered envelope signal as a winder-related feature and as an input for a trained machine learning algorithm;
• and
• execute the machine learning algorithm based on the winder-related feature and issue an anomaly indicator as an output.