Abstract: A rule generation system includes a generator engine, an input unit and an output unit. The input unit receives a service report regarding an activity relating to a device type; the rule generator engine identifies entries from the natural language in the service report and determines whether that that are stored in a rules database apply. When the rules apply, the rule generator engine determines whether a second set rules applies. When no rules that relate to the device type are present, the rule generator engine forms a new rule, and updates this rule when technician actions are not equivalent to action steps of the rule. The output unit saves the new rule.

Abstract: A rule generation system for a low, medium or high voltage switchgear, includes a rule generator engine, an input unit; and an output unit. The input unit receives a query and provides it to the rule generator engine. The rule generator engine retrieves information from a knowledge base, the retrieval comprising utilization of the query; wherein the rule generator engine generates a prompt that comprises the query and the retrieved information; wherein the rule generator engine generates and/or extracts one or more switchgear operation rules, wherein the generation and/or extraction of the one or more switchgear operation rules comprises utilization of a large language model to process the prompt; and wherein the output unit outputs and/or saves the one or more switchgear operation rules.

Abstract: A decision support system and method for an industrial plant is configured and operates to: obtain a causal graph modeling causal assumptions relating to conditional dependence between variables in the industrial plant; obtain observational data relating to operation of the industrial plant; and perform causal inference using the causal graph and the observational data to estimate at least one causal effect relevant for making decisions when operating the industrial plant.

Abstract: A rule generation system includes an input unit, a rule generator, and a database, wherein the input unit receives a query including a name or type of a first device and provides the query to the rule generator. Service reports are stored in the database. The rule generator retrieves k service reports using the first query, uses an LLM to identify entries including issues that need to be addressed for the first device, and actions taken by service technicians to mitigate the issues; and utilizes the identified entries to form at least one rule for the first device, wherein each rule comprises a tuple of the first device name or type, one issue, and an action step or series of action steps taken to mitigate the issue.

Abstract: A rule generation system includes a rule generator engine, and input and output units. The input unit receives a query from an operator regarding an activity relating to a switchgear, the activity including operation, maintenance, and/or rectification of problems. The input unit provides the query to the rule generator engine, which provides an operation rule using the query, and the output unit outputs the operation rule. The input unit receives feedback from the operator or a second operator. The rule generator engine generates an updated operation rule using the preliminary operation rule and the operator feedback. The output unit outputs an operation rule including comprises the updated preliminary switchgear operation rule.

Abstract: A method for training and using a ML model on customer engineering data with increased customer privacy preservation in an industrial context includes, in a training phase, obtaining the customer engineering data from customer project data associated with a customer; training the ML model on the customer engineering data by using federated ML; and applying customer privacy preservation mechanisms on information inferred by the ML model. When using the ML model for inference: verifying, by using an Entailment Checking method, that the information inferred by the ML model and determined by the ML model to be associated with the customer were obtained from the customer project data.

Abstract: A method includes obtaining training data from an operation of an industrial process plant; using the training data to train a machine learning model for assisting a plant operation of the industrial process plant; determining several sub-populations in the training data, each relating to a different scenario of an operation of the industrial process plant; determining several performances of the machine learning model, each relating to a different sub-population; determining, based on the performance of each one of the sub-populations, one or more underrepresented sub-populations; and revising, based on the one or more underrepresented sub-populations, the machine learning model for increased consideration of the one or more underrepresented sub-populations when assisting the plant operation of the industrial process plant.

Abstract: A knowledge graph generation system includes an input unit and a knowledge graph generator. The input unit receives service reports, each comprising natural language; and provides the service reports to the knowledge graph generator. The knowledge graph generator implements a Large Language Model (LLM), utilizes the LLM to identify entries from the natural language in the service reports, wherein the identified entries comprise names and types of instruments, issues that need to be addressed for those instruments, and actions taken by service technicians to mitigate the issues. The identified entries form rules, each rule comprises a tuple of one instrument type, one issue, and an action step or series of action steps taken to mitigate the issue. The rules are used to generate a plurality of knowledge graph nodes. The knowledge graph generator utilizes the nodes to generate a knowledge graph.

Abstract: A method for obtaining foundation model functionalities for time series data of operational processes with industrial context information includes comprising transforming the time series data into a sequence of time units associated with respective pieces of the operational processes; attaching time units to industrial context information; obtaining a sequence of context tokens from the attaching, wherein one context token represents one time unit to which concurrent pieces of the industrial context information are attached; training a generative AI model on the sequence of context tokens using a loss function; and using the trained generative AI model having the foundation model functionalities for at least one of: i) finetuning the trained generative AI model for a second industrial plant different from the first industrial plant and ii) forecasting a further progression of an operational process for the second industrial plant.

Abstract: A system and method for enabling an efficient data processing in a distributed network of devices includes collecting first data from at least one client device sent by at least one plant device via a first communication interface; storing the first data in a data storage; profiling the first data to obtain characteristic information of the collected first data; determining a data processing strategy upon a result of the characteristic information of the first data; processing the first data according to the data processing strategy and deciding which data of the first data need to be sent to the master device via a second communication interface.

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 rule utilization system for switchgear includes a deterministic rule engine; an input unit; and an output unit; wherein the input unit is configured to receive a request based on input from an operator relating to an operation to be performed on a switchgear; wherein the deterministic rule engine executes rules relating to the operation to be performed on the switchgear based on a request; utilization of the output unit calls a sensor system to measure and to receive a result of the measurement to call a software system to carry out an assessment of a part of the switchgear and utilization of the input unit to receive a result of the assessment of the part of the switchgear; wherein the rule engine generates an action for the operator; and wherein the output unit is configured to output the at least one action for the operator.

Abstract: A system and method for providing a configuration for data extraction from an automation system includes a signal selection agent configured to: receive a user selection of at least one module of the automation system; generate, for display to the user, a user interface identifying one or more selectable signals associated with the selected module and displaying one or more guidance elements comprising data mined from data sources pertaining to the automation system for guiding the user in the selection of relevant signals; receive a user selection of one or more of the selectable signals; and automatically generate the configuration for data extraction on the basis of the selected signals.

Abstract: A method for providing an efficient communication in a hierarchical network of distributed devices includes collecting first data from a first client device sent by a sensor via a second communication interface; storing the first data in a data storage; determining metadata of the first data; receiving a requirement information of the first data; generating classification information of the first data according to the metadata and the requirement information; providing the classification information to a master device and/or to a second client device according to a rule; and updating the classification information when the first data is changed.

Abstract: A method for obtaining a search result for a search query within a database system of a plant, the database system configured for storing multimodal operating data of the plant, the method comprising: obtaining the search query indicative of an operating characteristic in the operating of the plant to be searched within the database system; executing the search query by an artificial intelligence/machine learning, AI/ML, search component within the database system for finding the searched operating characteristic and time intervals of the operating characteristic among different data modalities of the multimodal data therein; and obtaining a search result indicative of the operating characteristic and its time intervals found by the execution of the search query by the AI/ML search component.

Abstract: A method for assisting a user in computer-aided generation of at least a part of a software application for an industrial process includes obtaining assistance data for assisting the user in the computer-aided generation of the at least part of the software application, wherein the assistance data is based on process mining of computer-aided generation data, the computer-aided generation data relating to previous computer-aided generations of at least parts of software applications for other industrial processes by one or more users, the process mining being at least supported by an artificial intelligence (AI) model; and providing the assistance data for assisting the user in computer-aided generation of the at least part of the software application by the user.

Abstract: A method for saving and utilizing expert process knowledge with regard to an industrial plant includes obtaining first data indicative of historical processes comprising sequences of historical process steps occurred in an industrial plant; obtaining second data based on analysing the first data, wherein the second data is indicative of occurrence statistics and thereon-based probabilities for several historical processes and several historical process steps; based on the second data, suggesting, for at least one of a process step, a sequence of process steps and a process, at least one of a next process step, a next sequence of process steps and a next process; analysing the first data using process mining and/or processing the first data using generative AI; and suggesting the at least one of the next process step, the next sequence and the next process by using process mining and/or by using generative AI.

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: 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.