Abstract: A method of monitoring a milling machine includes deploying an untrained machine learning model for determining one or more anomalies in time series data. During operation of the milling machine, first time series data representing a rotational speed of a milling head of the milling machine and at least one further operating parameter of the milling machine are obtained by the untrained machine learning model. The untrained machine learning model is trained, during operation of the milling machine, based on the obtained first time series data. Second time series data representing the rotational speed of the milling head of the milling machine and the further operating parameter are obtained by the trained machine learning model during operation of the milling machine. One or more anomalies in the second time series data are determined by the trained machine learning model during operation of the milling machine.

Abstract: The present disclosure relates to a computer-implemented method of, a data processing system for, and a computer program product for indicating machine failures as well as to a corresponding machine and a computer-implemented method of training a neural network for indicating machine failures. At least one input signal based on at least one physical quantity of at least one machine part is transformed into at least one feature. A neural network predicts a class and/or a severity of at least one machine failure based on the at least one feature.

Abstract: The present disclosure relates to a computer-implemented method of, a data processing system for, and a computer program product for indicating machine failures as well as to a corresponding machine and a computer-implemented method of training a neural network for indicating machine failures. At least one input signal based on at least one physical quantity of at least one machine part is transformed into at least one feature. A neural network predicts a class and/or a severity of at least one machine failure based on the at least one feature.

Abstract: The invention relates to a system and to a method for determining a state of a device by means of a trained support-vector machine. According to the invention, an operating parameter space is divided into classification volumes, at least one of which indicates a normal state and at least one other of which indicates a fault state of the device. A current state of the device can therefore be determined by determining where a current operating parameter point is to be arranged in the operating parameter space. The invention further relates to methods and to variants of the system in order to facilitate a cause evaluation and to determine particularly relevant operating parameters for the fault determination.

Abstract: The invention relates to a system and to a method for determining a state of a device by means of a trained support-vector machine. According to the invention, an operating parameter space is divided into classification volumes, at least one of which indicates a normal state and at least one other of which indicates a fault state of the device. A current state of the device can therefore be determined by determining where a current operating parameter point is to be arranged in the operating parameter space. The invention further relates to methods and to variants of the system in order to facilitate a cause evaluation and to determine particularly relevant operating parameters for the fault determination.