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: The present disclosure relates to a computer-implemented method for automated determination of a model compression technique for compression of an artificial intelligence-based model, a corresponding computer program product, and a corresponding apparatus of an industrial automation environment. The method includes automated provisioning of a set of model compression techniques using an expert rule, determining metrics for the model compression techniques of the set of model compression techniques based on weighted constraints, and selecting an optimized model compression technique based on the determined metrics.

Abstract: For improved provision of an alarm relating to an accuracy of a trained function, such as detecting an accuracy decrease of a trained function under a distribution drift of incoming data, the following computer-implemented method is suggested: receiving input data messages (140) relating to at least one variable of at least one device (142); applying a trained function (120) to the input data messages (140) to generate output data (152), the output data (152) being suitable for analyzing, monitoring, operating and/or controlling the respective device (142); determining at least one respective distance of the respective variable of a respective received input data message (140) to a reference data set, determining an accuracy value of the trained function (120) using the respective distance and a regression model (130); and if the determined accuracy value is smaller than an accuracy threshold: providing an alarm (150) relating to the determined accuracy value to a user, to the respective device (142) and/or a

Abstract: System, Device and Method of detecting at least one abnormal datapoint in operation data (U) associated with an industrial environment (610) is disclosed. The method comprising iteratively applying one or more anomaly detection models (fi) to at least one subset (S) of the operation data (U), wherein the anomaly detection models (fi) are trained based on a training dataset (L) consisting of datapoints labeled as normal; classifying subset-datapoints in the subset (S) as one of normal datapoints (N) and abnormal datapoints (A) using the anomaly detection models (fi); updating the training dataset at least with the normal datapoints; retraining the anomaly detection models (fi) with the updated training dataset after expiration of a threshold time, wherein the threshold time is based on the number of updates to the training dataset; and detecting the at least one abnormal datapoint in the operation data (U) using the anomaly detection models (f?i).

Abstract: A recommendation system and method for determining a compression rate for an AI model of an industrial task, wherein the parameters are reduced to a reduced number of parameters for the AI model, where each AI model is compressed with different compression rates in a first stage, where each compressed AI model is executed and the runtime properties are recorded as first results during the executions and an optimal compression rate is calculated by analyzing the first results and stored in a database, wherein data from the database is used to train an additional machine learning model in a second stage and, in a third stage, for a new AI model of a new task, a new set of desired runtime properties is defined and the additional model is employed for determining the optimal compression rate for that new AI model with respect to the desired runtime properties.

Abstract: A method for monitoring of industrial devices includes: receiving sensor signal data of one or more industrial devices and one or more events associated with the operation of the one or more industrial devices. The method further includes: identifying, (e.g., using a first autoencoder), an abnormal behavior of the one or more industrial devices based on the sensor signal data; creating or updating an additional autoencoder based on the sensor signal data associated with a time window relating to the abnormal behavior; and associating events located within the time window with the additional autoencoder.

Abstract: For improved provision of an alarm relating to anomaly scores assigned to input data, a method includes receiving input data relating to at least one device. The input data includes incoming data batches X relating to at least N separable classes. Respective anomaly scores are determined for the respective incoming data batch X relating to the at least N separable classes using N anomaly detection models. The anomaly detection models are applied to the input data to generate output data. A difference is determined, for the respective incoming data batch X, between the determined respective anomaly scores for the at least N separable classes and given respective anomaly scores of the N anomaly detection models. When the respective determined difference is greater than a difference threshold, an alarm relating to the determined difference is provided to a user, the respective device, and/or an IT system connected to the respective device.

Abstract: A method and an automation component for identifying a process-disrupting automation component in an industrial automation assembly, wherein a process disruption is determined in a first automation component and examined by a first local analysis device, where an automation component arranged upstream and/or an automation component arranged downstream is first determined by each automation component, an interrogation message is sent from a first automation component to a second automation component and the same interrogation message or a further interrogation message is recursively sent by the second automation component to a third automation component arranged upstream or downstream of the second automation component and processed, where in the event of a locally determined disruption, the relevant automation component sends a response message, which is back-propagated to the origin and signaled such that a decentralized error analysis becomes possible, even with a changing system topology, without the need

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: Provided is a computer-implemented method for receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; extracting at least one additional information of each log file of the at least two log files; and combining each log file of the at least two log files with the extracted additional information into at least two processed log files; wherein the at least two processed log files comply with a coherent representation. A corresponding computer program product and generating unit is also provided.

Abstract: Provided is a Computer-implemented method for Receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; Extracting at least one additional information of each log file of the at least two log files; and Combining each log file of the at least two log files with the extracted additional information into at least two processed log tiles; wherein the at least two processed log files comply with a coherent representation. Further, the invention relates to a corresponding computer program product and generating unit.