Abstract: A method and system for removing undesirable inferences from a machine learning model include a search component configured to receive a rejected explanation of model output provided by the machine learning model, identify data samples to unlearn by selecting training samples from training data that were used to train the machine learning model, the selected training samples being associated with explanations that are similar to the rejected explanation according to a calculated similarity measure, and pass the data samples to unlearn to a machine unlearning unit.

Abstract: A computer-implemented method for assessing trustability of operator feedback from an operator of a control system of an industrial plant, comprising obtaining current error data from an error prediction model of the control system, wherein the current error data is indicative of a current error in the industrial plant, and wherein the error prediction model is based on machine learning; obtaining different error data that is indicative of one or more different errors, which are different from the current error; providing an operator request on an operator interface of the control system, wherein the operator request relates to the current error and the one or more different errors; obtaining operator feedback from the operator on the operator interface in response to the operator request provided thereon; and assessing the trustability of the operator feedback based on the operator feedback, the current error data and the different error data.

Abstract: A method for automatically generating a textual description of time series for monitoring or forecasting a process variable, comprising training a deep learning model on a cross-modal autoencoding module having an architecture consisting of a time series encoder, a text decoder and a time series decoder, obtaining time series data by invoking a display of readings of the temporal process variable, encoding, using the time series encoder and based on the deep learning model, the time series data and generating an embedding as input for the text decoder, transferring the generated embedding from the time series encoder to the text decoder, and generating, using the text decoder and based on the deep learning model, the textual description of time series based on the embedding of the encoded time series data from the time series encoder.

Abstract: A system and method for monitoring a switchgear includes an infrared camera, a processing unit, and an output unit. The infrared camera acquires an infrared image of the switchgear, and the processing unit converts it into a binary image. Pixels in the infrared image having a temperature equal to or above a first threshold value are given the same first value. Pixels in the infrared image having a temperature below the first threshold value are given the same second value. The processing unit is configured to implement a Siamese neural network to determine if a hot spot exists in the infrared image.

Abstract: A method for applying machine learning to an application includes: a) generating a candidate policy by a learner; b) executing a program in at least one simulated application based on a set of candidate parameters provided based on the candidate policy and a state of the at least one simulated application, execution of the program providing interim results of tested sets of candidate parameters based on a measured performance information of the execution of the program; c) collecting a predetermined number of interim results and providing an end result based on a combination of the candidate parameters and/or the state with the measured performances information by a trainer; and d) generating a new candidate policy by the learner based on the end result.

Abstract: A computer implemented method for filtering user feedback and/or output of a machine learning model, comprising: providing a filter for filtering user feedback and/or output of a machine learning model; receiving user feedback and/or output of the machine learning model; filtering the user feedback and/or the output with the filter and determining a filtering result, wherein the filtering result comprises at least a detected error; providing the filtering result for further processing.

Abstract: A computer implemented method for assessing a feedback of an information model of a building, comprising: receiving raw data of a building; generating an information model of a building based on the raw data; receiving a feedback of a user for the generated information model; assessing the received feedback and determining an assessing result; providing the assessing result for further processing.

Abstract: A medium voltage switchgear or controlgear monitoring system includes: an infrared camera; and a processing unit. The infrared camera is mounted within a medium voltage switchgear or controlgear. The infrared camera is configured to acquires an infrared image including image data of two or three current carrying parts of the switchgear or control gear. The two or three current carrying parts are the same current carry part of two or three equivalent systems within the switchgear or controlgear. The infrared camera provides the infrared image to the processing unit. The processing unit determines 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. The determination includes analysis of the infrared image by an autoencoder implemented by the processing unit.

Abstract: An industrial plant operator intervention system for use in an industrial plant includes a processing unit configured to monitor and analyze industrial plant operation data to detect an anomaly in the industrial plant operation data that warrants initiating an operator intervention, and in response to detecting the anomaly, automatically determine a user interface configuration of a user interface to be presented to a designated operator who is to perform the operator intervention. The user interface configuration is determined on the basis of technical context data, including industrial plant operation data associated with the anomaly, and on the basis of operator data pertaining to the designated operator, in such a manner that an anomaly-related and operator-specific user interface configuration is obtained.

Abstract: A method for generating a process model modeling a manual mode procedure instance of a plant process includes providing log events of operational actions; selecting related sequences of manual mode operational actions from the log events; filtering the related sequences according to an individual plant section; identifying a sequential order from the filtered related sequences; determining statistical properties of values of related process variables and/or statistical properties of values of related set point changes to each sequential ordered manual mode operational action from the filtered related sequences; generating the process model of the manual mode procedure instance by arranging related manual mode operational actions with the sequential order of each operational action assigned with the statistical properties of the values of related process variables and/or assigned with the statistical properties of the values of the related set point changes.

Abstract: A system and method for mitigating data drift in an industrial plant includes monitoring, by a processor, one or more process parameters associated with an industrial plant; detecting, by the processor, a drift in one or more process parameters based on a deviation from one or more predefined process parameters; determining, by the processor, one or more drift context and process context based on drift and one or more process parameters; determining, by the processor, sampling strategy from plurality of sampling strategies based on one or more drift and process context for sampling one or more process parameters using first Artificial Intelligence (AI) model; and training, by the processor, a second AI model based on sampling strategy for mitigating data drift.

Abstract: A computer-implemented method for monitoring machine learning models in a distributed setup includes obtaining model activity data relating to activity of the machine learning models in the distributed setup; analyzing the obtained model activity data; and, based on the analysis of the model activity data, outputting model management data for managing the activity of the machine learning models in the distributed setup.

Abstract: A method for interactive explanations in industrial artificial intelligence systems includes providing a machine learning model and a set of test data, a set of training data and a set of historical data simulating a piping and process equipment; predicting a result for the piping and process equipment based on the machine learning model using the set of test data and the set of training data, wherein the set of historical data is used by the machine learning model to predict at least one parameter of the piping and process equipment; and presenting the predicted at least one parameter on a piping and instrumentation diagram of the piping and process equipment.

Abstract: A method for managing alarms in a distributed control system, wherein the method comprises the following steps: monitoring at least one process parameter, activating an alarm when the process parameter exceeds an alarm threshold, deactivating an alarm when the process parameter no longer exceeds the alarm threshold, and determining by an AI based alarm control system before the alarm is activated at least one predicted alarm information, which comprises information about whether the alarm threshold will be exceeded.

Abstract: A method for recommending an operational command includes receiving an alarm from a sensor and/or an operator; obtaining the current state of the plant that includes a current process value and/or operational command; comparing the current state to a list of historic states, each comprising a plurality of historic process values and/or historic operational commands; when the current state matches a subset of at least one of the historic states, starting a simulation and running a plurality of simulations, each based on a variation of at least one of the historic operational commands; determining, for each simulation of the plurality of simulations, a quality value, based on at least one quality criterion; and recommending the variation of the operational command that resulted in the simulation with the highest quality value.

Abstract: A method for deciding on a machine learning model result quality based on the identification of distractive samples in the training data includes providing a first result of the model based on initial training data; determining a first performance of the first result of the model; logging input data; providing a second result of the model based on initial training data and the input data, determining a second performance of the second result of the model and thereon based identifying erroneous data within the input data and/or the training data.

Abstract: A computer-implemented resource management method for modular plants may include: receiving data identifying a required module type to be assembled into the modular plant as part of a module pipeline including one or more modules; and executing an optimization algorithm to select, from a plurality of modules having the required module type, a module for inclusion in the module pipeline on the basis of one or more predetermined optimization criteria.

Abstract: A method for explanation of machine learning results based on using a model collection includes training at least two machine learning models with at least two competing strategies for the at least one dataset; and using the least two machine learning models to yield at least two different predictions and/or at least two explanations for the at least one dataset.

Abstract: A method for enabling user feedback and summarizing return of investment for machine learning systems includes providing a training data set and an initial machine learning model; providing a result of the initial machine learning model; receiving feedback on the result of the initial machine learning model from a user enriching the training dataset based on the feedback to an enriched data set; and retraining the initial machine learning model to a retrained machine learning model based on an enriched data set.

Abstract: There is provided an explainer system for explaining an alarm raised by a machine learned model of an industrial automation system. The explainer system is configured to: receive model output from the machine learned model trained to predict anomalous behaviour in the industrial automation system and to raise the alarm; process the model output using at least one prediction explanation technique to identify at least one influential feature which contributed to the model output; use the identified at least one influential feature to extract contextual information from at least one machine-readable information source pertaining to the industrial automation system; and prepare the extracted contextual information for display to an operator of the industrial automation system, to enable the operator to select an appropriate action to take in response to the alarm for ensuring proper functioning of the industrial automation system.