Monitoring a multi-axis machine using interpretable time series classification

A method for evaluating and/or monitoring a process and/or a multi-axis machine, wherein the method comprises:

• recording at least one data time series, wherein the at least one data time series comprises at least one channel describing at least one parameter of the process and/or of the multi-axis machine, and wherein the at least one data time series is caused by the process;
• determining an interpretable result by means of a machine learning algorithm based on the at least one data time series, wherein the result describes a classification value of a state in the process and/or of a state of the multi-axis machine;
• wherein a warning is output when determining the result if the classification value of the state in the process and/or of the state of the multi-axis machine is assigned to a value of an error class that is in a warning range or corresponds to a warning range and an all-clear signal is output if the classification value of the state in the process and/or of the state of the multi-axis machine is assigned to a value of an error class that is in an all-clear range or corresponds to an all-clear range;
• and
• ascertaining an average distance, in particular a Wasserstein distance, alternatively with an intersection-over-unit metric, for different error classes.