Abstract: System and methods for configuring a technical system based on generated rules and building the technical system. The technical system and the generated rules are given in graph representations including the following steps: defining rules by a user and representing the rules in a graphical interface, converting the rules from the graphical interface into a programming language and/or a natural language, validating the rules for the technical system, checking the compatibility of the rules, serializing the rules for storage in a file system or a database, using the serialized rules to configure the technical system, and building the configured technical system.

Abstract: A graph database stores a knowledge graph, with nodes of the knowledge graph corresponding to components of an engineered system and edges of the knowledge graph specifying connections between the components. A reasoning module is equipped with a first agent and a second agent. The agents have been trained with opposing goals and extract paths from the knowledge graph beginning with a node that corresponds to a first component of the engineered system. A prediction module uses a classifier to classify the extracted paths in order to produce a classification result, which indicates consistency, and in particular compatibility, of the first component in relation to the engineered system. This information is provided to an engineer, supporting him in validating the engineered system, for example an industrial automation solution. The method and system provide an automated data-driven algorithm that leverages a large collection of historical examples for consistency checking of components.

Abstract: Device, Computing Platform and Method of Analyzing Log Files of an Industrial Plant are disclosed. The method including: determining at least one block in log entries of the log files, wherein the log entries includes one or more log messages and wherein the block represents co-occurring log messages; annotating the co-occurring log messages of the block using semantic metadata, wherein the semantic metadata defines one or more message types for the co-occurring log messages, wherein the semantic metadata is indicative of at least one of a start action, an end action, a source, an anomaly, a cause and an inspect action; generating a coherent representation for the block by representing the co-occurring log messages in a graph based on the semantic metadata; and enabling detection of at least one event in the block based on a comparison the coherent representation with template representations of predefined events associated with the industrial plant.

Abstract: A computer-implemented method for providing recommendations, REC, concerning a configuration process is provided to configure an industrial system, SYS, the method including the steps of calculating by a trained graph neural network, GNN, scores, s, for components, c, of a set, C, of configurable component types, ct; generating recommendations, REC, for introducing at least one additional component, c, into the industrial system, SYS, on the basis of the calculated scores, s; and outputting the generated recommendations, REC, to a user by a user interface or executing the generated recommendations.

Abstract: A recommender system to be used in the context of an engineering tool is provided. By using the recommender, a list of items is provided in the engineering tool which are likely to be connected in a next step to an engineering project designed in the engineering tool.

Abstract: A database stores a set of items, with each item having technical attributes, and with each item representing a module that can be used in an engineering project of a first user, u1. A feature encoder embeds each item based on its technical attributes into a low-dimensional vector space. Then, in a second step, a graph neural network pools over these item embeddings to compute an updated user embedding for the first user A decoder mapping then addresses the recommendation task by outputting recommendation scores for each item. That means, heuristically speaking, that the method and system lift the recommendation task to the level of technical attributes to overcome the sparsity problem caused by item sets that are not overlapping between user groups. Thus, when matching similar users, the method does not rely on users configuring exactly the same modules but rather on configured modules that are similar from a technical point of view.

Abstract: A machine learning model processes a current partial design of a technical system and a candidate component for a next design step of designing the technical system. The model computes a probability distribution, which is a probability distribution over changes of a design KPI if the candidate component is added to the current partial design, with the design KPI describing a property of the technical system, and a predicted impact value predicting an absolute value of the design KPI or a change of the design KPI if the candidate component is added to the current partial design. These predictions (for partial designs that cannot be processed by a simulation environment due to their incompleteness) can drastically shorten the feedback loop between engineers in charge of designing a new technical system/product and a simulation environment used for estimating the performance characteristics of the product.

Abstract: An initially trained machine learning model is used by an active learning module to generate candidate triples, which are fed into an expert system for verification. As a result, the expert system outputs novel facts that are used for retraining the machine learning model. This approach consolidates expert systems with machine learning through iterations of an active learning loop, by bringing the two paradigms together, which is in general difficult because training of a neural network (machine learning) requires differentiable functions and rules (used by expert systems) tend not to be differentiable. The method and system provide a data augmentation strategy where the expert system acts as an oracle and outputs the novel facts, which provide labels for the candidate triples. The novel facts provide critical information from the oracle that is injected into the machine learning model at the retraining stage, thus allowing to increase its generalization performance.

Abstract: A computer-implemented method, computer program product, and a technical system for generating triples including providing a plurality of log entries from respective log files, wherein each log entry of the plurality of log entries includes at least one text message, generating at least one template based on the plurality of log entries using unsupervised clustering, wherein the at least one template includes at least one variable part and at least one fixed part, assigning each log entry of the plurality of log entries to one respective template based on the generated at least one template using a similarity measure, extracting the corresponding at least one variable and at least one fixed part of each text message of the plurality of text messages as key/value pairs using the respective assigned at least one template based on the plurality of log entries, and providing the text messages, keys and values as triples.

Abstract: An initial sequence representing a partially configured engineering project is processed by a recurrent neural network to generate recommendations being a sequence of complementary items that completes an engineering project. A feature predictor component computes a set of features for each recommendation. A bisection component selects a feature from the sets of features that distinguishes some of the recommendations and forms pruned recommendations by choosing all instances from the recommendations that have the selected feature. A user interface displays the selected feature, detects a user interaction indicating that the selected feature is required, outputs the pruned recommendations. The engineering project is completed by combining the initial sequence with the chosen pruned recommendation. As a result, a user is supported in choosing optimal modules, as the selected feature can distinguish the recommendations that have the desired technical properties or target system KPI.

Abstract: Provided is a recommendation engine to provide automatically recommendations for the completion of an engineering project, the recommendation engine including: a first artificial intelligence, AI, module adapted to provide latent representations of a sequence of selected items; and a second artificial intelligence, AI, module adapted to process the latent representations of the sequence of selected items provided by the first artificial intelligence, AI, module to generate at least one sequence of complementary items required to complement the sequence of selected items to provide a complete sequence of items output via an interface as a recommendation to complete the engineering project.

Abstract: The invention is directed to a computer-implemented method for determining at least one completed item of at least one product solution, comprising the steps of: a. Providing at least one input data set with at least one partial item of the at least one product solution; wherein b. the at least one partial item comprises at least one initial feature; c. Complementing the at least one partial item of the at least one product solution with at least one additional alternative feature using a trained machine learning model on the basis of at least one partial item of the at least one product solution to determine a plurality of alternative complete items of the at least one product solution; and d. Determining at least one evaluated complete item of the plurality of alternative items of the at least one product solution as output data set using a market impact evaluation. Further, the invention relates to a corresponding computer program product and system.

Abstract: The invention specifies a computerized method for configuring a technical system comprising a sequence (Pi) of system components (Si), whereby each sequence (Pi) of system components (Si) can be assigned to a sequence pattern and whereby the sequence comprises at least one system component (Si), whereby the method comprises the following steps: specifying a support threshold for sequence patterns (M1) by a user, starting the configuration of the technical system (M2) by choosing a sequence of system components (Si) by the user (U), analysing (M3) the chosen sequence (Pi) of system components (Si) and extracting sequence patterns by an automation unit (A), and introducing (M4) at least one further system component (Si) based on the extracted sequence pattern and according to the support threshold by the automation unit (A). The invention further specifies a computer program product and a computer-readable storage medium.

Abstract: A computer-implemented method for efficient processing of pooled data shared by users of a cloud platform, the method includes the steps of uploading at least one dataset by a client device of a user to said cloud platform; calculating similarity scores indicating a degree of similarity between the current uploaded dataset and datasets previously uploaded by client devices of other users; and performing a procedure selected by a user on the cloud platform based on pooled data including the current dataset of the respective user and the datasets previously uploaded from client devices of other users stored in a database of the cloud platform having calculated similarity scores in relation to the current uploaded dataset of the respective user exceeding a configurable similarity score threshold, is provided.

Abstract: Provided is a computer-implemented method and platform for context aware sorting of items available for configuration of a system during a selection session, the method including the steps of providing a numerical input vector, V, representing items selected in a current selection session as context; calculating a compressed vector, Vcomp, from the numerical input vector, V, using an artificial neural network, ANN, adapted to capture non-linear dependencies between items; multiplying the compressed vector, Vcomp, with a weight matrix, EI, derived from a factor matrix, E, obtained as a result of a tensor factorization of a stored relationship tensor, Tr, representing relations, r, between selections of items performed in historical selection sessions, available items and their attributes to compute an output score vector, S; and sorting automatically the available items for selection in the current selection session according to relevance scores of the computed output score vector, S.

Abstract: A plurality of basic simulations independent of one another are carried out, which determine respective remaining service life predictions for the machine. The remaining service life predictions and characteristic data are fed to a neural network, which outputs weights for the remaining service life predictions. A final prediction is calculated from the remaining service life predictions by weighting the remaining service life predictions relative to one another. A hybrid model is produced, which results from the combination of the basic simulations with the neural network. The remaining service life can be predicted not only for a small number of machines for which a specific simulation model has been manually created. The hybrid model enables condition monitoring for any further types and configurations of machines that merely belong to the same machine class. The basic simulations can therefore also be applied to previously unknown machines.

Abstract: A plurality of basic simulations independent of one another are carried out, which determine respective remaining service life predictions for the machine. The remaining service life predictions and characteristic data are fed to a neural network, which outputs weights for the remaining service life predictions. A final prediction is calculated from the remaining service life predictions by weighting the remaining service life predictions relative to one another. A hybrid model is produced, which results from the combination of the basic simulations with the neural network. The remaining service life can be predicted not only for a small number of machines for which a specific simulation model has been manually created. The hybrid model enables condition monitoring for any further types and configurations of machines that merely belong to the same machine class. The basic simulations can therefore also be applied to previously unknown machines.

Abstract: A method for generating graph-structured representations of a brownfield system including collecting training data of training systems. Training data includes training pairs, with each training pair including training sensor observations and a training digital twin model. The method includes transforming the training digital twin models into training graph-structured representations. The training graph-structured representations include nodes and links. The nodes represent components of the training system and the links represent relations between the components of the training system. A graph generative model is trained to generate graph-structured representations of the brownfield system using the training sensor observations and the training graph-structured representations of the training digital twin models. Graph-structured representations of the brownfield system are generated using the trained graph generative model and sensor observations of the brownfield system.

Abstract: System and methods for configuring a technical system based on generated rules and building the technical system. The technical system and the generated rules are given in graph representations including the following steps: defining rules by a user and representing the rules in a graphical interface, converting the rules from the graphical interface into a programming language and/or a natural language, validating the rules for the technical system, checking the compatibility of the rules, serializing the rules for storage in a file system or a database, using the serialized rules to configure the technical system, and building the configured technical system.