Abstract: An apparatus for equipment monitoring includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with batches of temporal sensor data for an item of equipment. Each batch of temporal sensor data includes temporal sensor values as a function of time. The processing unit is configured to process the batches of temporal sensor data to determine batches of spectral sensor data. Each batch of spectral sensor data includes spectral sensor values as a function of frequency. The processing unit is configured to implement at least one statistical process algorithm to process the spectral sensor values for the batches of spectral sensor data to determine index values. For each batch of spectral sensor data there is an index value determined by each of the statistical process algorithms.

Abstract: A method for controlling an industrial process includes: determining, by a process controller, based at least in part on a set of current values and/or past values of state variables of the industrial process, a set of control outputs to be applied to at least one actor and/or lower-level controller configured to cause a performing of at least one physical action on the process; querying, based on at least a subset of the set of current values and/or past values of state variables and on at least a subset of the set of control outputs, a trained machine-learning model configured to output a classification value, and/or a regression value, that is indicative of a propensity of a watching human operator to at least partially override the control outputs delivered by the process controller; and determining that the classification value, the regression value, and/or the propensity, meets a predetermined criterion.

Abstract: An apparatus for equipment monitoring includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with batches of temporal sensor data for an item of equipment. Each batch of temporal sensor data includes temporal sensor values as a function of time. The processing unit is configured to process the batches of temporal sensor data to determine batches of spectral sensor data. Each batch of spectral sensor data includes spectral sensor values as a function of frequency. The processing unit is configured to implement at least one statistical process algorithm to process the spectral sensor values for the batches of spectral sensor data to determine index values. For each batch of spectral sensor data there is an index value determined by each of the statistical process algorithms.

Abstract: An apparatus for monitoring a switchgear includes: an input unit; a processing unit; and an output unit. The input unit is provides the processing unit with a monitor infra-red image of the switchgear. The processing unit implements a machine learning classifier algorithm to analyse the monitor infra-red image and determine if there is one or more anomalous hot spots in the switchgear. The machine learning classifier algorithm has been trained based on a plurality of different training infra-red images. The plurality of training infra-red images include a plurality of modified infra-red images generated from a corresponding plurality of infra-red images, each of the modified infra-red images having been modified to remove an effect of obscuration in the image. The output unit outputs information relating to the one or more anomalous hot spots.

Abstract: An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with sensor data for an item of equipment. The processing unit is configured to implement at least one machine learning algorithm, which has been trained on the basis of a plurality of calibration sensor data for the item of equipment. Training of the at least one machine learning algorithm includes processing the plurality of calibration sensor data to determine at least two clusters representative of different equipment states. The processing unit is configured to implement the at least one machine learning algorithm to process the sensor data to assign the sensor data to a cluster of the at least two clusters to determine an equipment state for the item of equipment. The output unit is configured to output the equipment state for the item of equipment.

Abstract: An apparatus for equipment monitoring includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with batches of temporal sensor data for an item of equipment. Each batch of temporal sensor data includes temporal sensor values as a function of time. The processing unit is configured to process the batches of temporal sensor data to determine batches of spectral sensor data. Each batch of spectral sensor data includes spectral sensor values as a function of frequency. The processing unit is configured to implement at least one statistical process algorithm to process the spectral sensor values for the batches of spectral sensor data to determine index values. For each batch of spectral sensor data there is an index value determined by each of the statistical process algorithms.

Abstract: An apparatus for monitoring a switchgear includes: an input unit; a processing unit; and an output unit. The input unit provides the processing unit with a monitor infra-red image of a switchgear. The processing unit implements a machine learning classifier algorithm to analyse the monitor infra-red image and determine if there is one or more anomalous hot spots in the switchgear. The machine learning classifier algorithm has been trained based on a plurality of different training infra-red images, the plurality of training infra-red images including a plurality of synthetic infra-red images generated from a corresponding plurality of visible images. The output unit outputs information relating to the one or more anomalous hot spots.

Abstract: An apparatus for monitoring a switchgear includes: an input unit; a processing unit; and an output unit. The input unit is provides the processing unit with a monitor infra-red image of a switchgear. The processing unit implements a machine learning classifier algorithm to analyse the monitor infra-red image and determine if there is one or more anomalous hot spots in the switchgear. The machine learning classifier algorithm has been trained based on a plurality of different training images, the plurality of training images including a plurality of synthetic infra-red images generated by an image processing algorithm. The output unit outputs information relating to the one or more anomalous hot spots.

Abstract: A method for training a machine-learning model to assess at least one condition of industrial equipment, and/or at least one condition of a process running in an industrial plant, based on measurement data gathered by a plurality of sensors, includes: obtaining a plurality of records of measurement data that correspond to a variety of operating situations and a variety of conditions; obtaining, for each record of measurement data, a label that represents a condition in the operating situation characterized by the record of measurement data; and determining a plausibility of at least one record of measurement data, and/or a plausibility of at least one label, based at least in part on a comparison with at least one other record of measurement data, with at least one other label, and/or with additional information about the industrial equipment, and/or about the industrial plant where the industrial equipment resides, and/or about the process.

Abstract: A method for controlling an industrial process includes: determining, by a process controller, based at least in part on a set of current values and/or past values of state variables of the industrial process, a set of control outputs to be applied to at least one actor and/or lower-level controller configured to cause a performing of at least one physical action on the process; querying, based on at least a subset of the set of current values and/or past values of state variables and on at least a subset of the set of control outputs, a trained machine-learning model configured to output a classification value, and/or a regression value, that is indicative of a propensity of a watching human operator to at least partially override the control outputs delivered by the process controller; and determining that the classification value, the regression value, and/or the propensity, meets a predetermined criterion.

Abstract: An exemplary control apparatus for an energy distribution system includes a connection to an alternating current supply system and a plurality of predetermined loads each having an energy storage capacity and being electrically connected as by a respective switching device to the alternating current supply system for a duration of a half-wave or a whole number times said half-wave. The respective switching devices are supplied electrical energy from the alternating current supply system for a respective control time period within a predetermined common cycle time period. The start and end of the respective control time periods within the common cycle time period are established prior to the start of said common cycle time period to create a substantially constant loading graph. The optimization procedure is based at least one load in lieu of a continuous control time period having at least two non-continuous block time periods of equal total duration.

Abstract: An exemplary control apparatus for an energy distribution system includes a connection to an alternating current supply system and a plurality of predetermined loads each having an energy storage capacity and being electrically connected as by a respective switching device to the alternating current supply system for a duration of a half-wave or a whole number times said half-wave. The respective switching devices are supplied electrical energy from the alternating current supply system for a respective control time period within a predetermined common cycle time period. The start and end of the respective control time periods within the common cycle time period are established prior to the start of said common cycle time period to create a substantially constant loading graph. The optimization procedure is based at least one load in lieu of a continuous control time period having at least two non-continuous block time periods of equal total duration.