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 system and method for outlier detection based on process fingerprints from robot cycle data includes a data collection component, which is configured to collect cyclic data, wherein the cyclic data comprises multiple vectors each of which comprises data from one individual cycle of the robot cycle data; a data storage component, wherein which is configured to store the collected cyclic data; and a data processing component, which is configured to perform cloud processing of the stored cyclic data triggered by a cycle-start signal, wherein the data processing component is configured to parse the stored cyclic data and to process the stored cyclic data based on a configuration file defining metadata of the stored cyclic data, wherein the data processing component is configured extract process fingerprints from the stored cyclic data using the metadata.

Abstract: A system for monitoring a circuit breaker includes: at least one sensor and a processor. The at least one sensor is configured to be located and utilized to obtain at least one sensor data of a main shaft of an operational circuit breaker, and the at least one sensor is configured to provide the at least one sensor data of the main shaft of the operational circuit breaker to the processor. The processor is configured to determine position and/or velocity information for a moveable contact of the operational circuit breaker, where the determination comprises analysis of the at least one sensor data of the main shaft of the operational circuit breaker by a trained neural network implemented by the processor.

Abstract: A computer-implemented method of generating a training data set for training an artificial intelligence module includes providing first and second data sets, the first data set including first data elements indicative of a first operational condition, the second data set including second data elements indicative of a second operational condition that matches the first operational condition. The method further comprises determining a data transformation for transforming the first data elements into the second data elements; applying the data transformation to the first data elements and/or to further data elements of further data sets, thereby generating a transformed data set; and generating a training data set for training the AI module based on at least a part of the transformed data set.

Abstract: A circuit breaker monitoring system and method includes a vibration sensor, a processing unit, and an output unit. The vibration sensor is configured to be mounted to a circuit breaker and to acquire a temporal vibration signal over at least a temporal portion of a closing operation of the circuit breaker. The closing operation comprises: initiation of the closing operation; latch release; movement of a moveable contact towards a fixed contact; contact touch of the moveable contact with the fixed contact; stop of movement of the moveable contact. The sensor provides the temporal vibration signal to the processing unit, which determines a plurality of physical switch events and corresponding plurality of time points of the plurality of physical switch events over the closing operation of the circuit breaker.

Abstract: A system and method for monitoring a circuit breaker monitoring system includes a vibration sensor, a processing unit, and an output unit. The vibration sensor is mounted to a circuit breaker acquires a temporal vibration signal over at least a temporal portion of a closing operation of the circuit breaker, wherein the closing operation comprises: initiation of the closing operation, latch release, movement of a moveable contact towards a fixed contact, contact touch of the moveable contact with the fixed contact, and stop of movement of the moveable contact. The vibration sensor provides the temporal vibration signal to the processing unit, which determines a plurality of features of the closing operation and an indication of operational functionality of the circuit breaker.

Abstract: A method of hierarchical machine learning includes receiving a topology model having information on hierarchical relations between components of the industrial plant, determining a representation hierarchy comprising a plurality of levels, wherein each representation on a higher level represents a group of representations on a lower level, wherein the representations comprise a machine learning model, and training an output machine learning model using the determined hierarchical representations.

Abstract: A method of transfer learning for a specific production process of an industrial plant includes providing data templates defining expected data for a production process, and providing plant data, wherein the data templates define groupings for the expected data according to their relation in the industrial plant; determining a process instance and defining a mapping with the plant data; determining historic process data; determining training data using the determined process instance and the determined historic process data, wherein the training data comprises a structured data matrix, wherein columns of the data matrix represent the sensor data that are grouped in accordance with the data template and wherein rows of the data matrix represent timestamps of obtaining the sensor data; providing a pre-trained machine learning model using the determined process instance; and training a new machine learning model using the provided pre-trained model and the determined training data.

Abstract: An industrial plant machine learning system includes a machine learning model, providing machine learning data, an industrial plant providing plant data and an abstraction layer, connecting the machine learning model and the industrial plant, wherein the abstraction layer is configured to provide standardized communication between the machine learning model and the industrial plant, using a machine learning markup language.

Abstract: A method includes training a first control model by utilizing a first set of input data as first input, resulting in a trained first control model; copying the trained first control model to a second control model, wherein, after copying, the second input layer and the plurality of second hidden layers is identical to the plurality of first hidden layers, and the first output layer is replaced by the second output layer; freezing the plurality of second hidden layers; training the second control model by utilizing the first set of input data as second input, resulting in a trained second control model; and running the trained second control model by utilizing a second set of input data as second input, wherein the second output outputs the quality measure of the first control model.

Abstract: A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.

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: The invention relates to a medium voltage switchgear or control gear monitoring system (10), comprising: an infrared camera (20); and a processing unit (30); wherein the infrared camera is configured to be mounted within a medium voltage switchgear or control gear (40); wherein the infrared camera is configured to acquire an infrared image, wherein the infrared image comprises image data of two or three current carrying parts of the switchgear or control gear (50), and wherein the two or three current carrying parts are the same current carry part of two or three equivalent systems within the switchgear or control gear; wherein the infrared camera is configured to provide the infrared image to the processing unit; wherein the processing unit is configured to determine that the two or three current carrying parts are operating correctly or that one of the two or three current carrying parts has a fault, wherein the determination comprises analysis of the infrared image by an autoencoder implemented by the p

Abstract: A system for monitoring a circuit breaker includes: at least one sensor and a processor. The at least one sensor is configured to be located and utilized to obtain at least one sensor data of a main shaft of an operational circuit breaker, and the at least one sensor is configured to provide the at least one sensor data of the main shaft of the operational circuit breaker to the processor. The processor is configured to determine position and/or velocity information for a moveable contact of the operational circuit breaker, where the determination comprises analysis of the at least one sensor data of the main shaft of the operational circuit breaker by a trained neural network implemented by the processor.

Abstract: A system for monitoring a circuit breaker includes at least one sensor and a processor. The at least one sensor is configured to be located and utilized to obtain at least one time series sensor data of at least one first part an operational circuit breaker. The at least one sensor is configured to provide the at least one time series sensor data of the at least one first part of the operational circuit breaker to the processor. The processor is configured to determine that at least one second part of the operational circuit breaker is operating correctly or has a fault, where the determination includes analysis of the at least one time series sensor data of the at least one first part of the operational circuit breaker by a trained neural network implemented by the processor.

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.