Estimation of current and future machine states

A computer-implemented method for predicting behavior of a first industrial machine of a plurality of industrial machines, the plurality of industrial machines sharing common properties, the method comprising:

• generating a prediction model by receiving historical event data comprising event codes from a second industrial machine of the plurality of industrial machines;
• extracting event features from the event codes and structuring the event features into feature vectors, wherein a first dimension of a first feature vector of the feature vectors corresponds to a first event feature of the extracted event features, and a second dimension of the first feature vector corresponds to a second event feature of the extracted event features;
• generating the prediction model by clustering the feature vectors into a plurality of vector clusters, the vector clusters being assigned to respective machine states;
• receiving, with the prediction model, one or more of the event input codes and outputting, with the prediction model, one or more probabilities of the first industrial machine transitioning between a first of the machine states to a second of the machine states;
• and
• adjusting a state of the first industrial machine based on the one or more probabilities.

A computer-implemented method for generating a prediction model, the model being for use in processing machine event data generated by one or more of a plurality of industrial machines sharing common properties, the method comprising:

• receiving an event log comprising a plurality of codes representing events that occurred during operation of at least one industrial machine of the plurality of industrial machines during an observation time interval, the event representations comprising respective time stamps and event codes;
• extracting event features from the event codes and structuring the event features into feature vectors, wherein a first dimension of a first feature vector of the feature vectors corresponds to a first event feature of the extracted event features, and a second dimension of the first feature vector corresponds to a second event feature of the extracted event features;
• generating the prediction model by clustering the feature vectors into a plurality of vector clusters, the vector clusters being assigned to respective machine states;
• and
• after the prediction model has been generated, assigning a semantic meaning to at least one of the respective machine states, wherein the semantic meanings describe a meaning of the machine state,
• wherein the prediction model is configured to receive, as an input, one or more of the event codes and to provide, as an output, one or more probabilities of the at least one industrial machine transitioning between a first of the machine states to a second of the machine states.