Abstract: A method, device for identifying causes of faults in automated systems and an automated system which forms the device for identifying causes of faults in automated systems, wherein within a digital twin of the automated system, at least one element of the digital twin is assumed to be faulty and then simulated using the digital twin until a fault time, and where at least one faulty element of the automated system is identified as the cause of a fault based on the at least one element assumed to be faulty.

Abstract: One or more ring closures of a fault tree are provided. For each one of the one or more ring closures: at least one respective edge the respective ring closure is replaced in the fault tree by a respective variable to obtain a placeholder fault tree and a normalized representation of the placeholder fault tree is determined.

Abstract: A method, device for identifying causes of faults in automated systems and an automated system which forms the device for identifying causes of faults in automated systems, wherein within a digital twin of the automated system, at least one element of the digital twin is assumed to be faulty and then simulated using the digital twin until a fault time, and where at least one faulty element of the automated system is identified as the cause of a fault based on the at least one element assumed to be faulty.

Abstract: A method and apparatus for determining differences between an automated installation and a digital twin of the automated installation and to an automated installation that includes the apparatus, wherein a sensor unit is fastened to a process material and passes through an automated installation together with the process material where, during this process, the sensor unit continuously records at least one measurement variable, a temporal profile of the recorded measurement variable is compared with a temporal profile of a corresponding simulated measurement variable and if there is a difference between the temporal profiles, then the location in the automated installation at which the difference is present is identified.

Abstract: A computer-implemented method and device for resolving closed loops in automatic fault tree analysis of a multi-component system is provided. Also provided is a method for resolving closed loops in automatic fault tree analysis of a multi-component system, the closed loops corresponding, for example, to closed-loop control circuitry of a multi-component device. The closed loops are first identified in a top-down approach within failure propagation paths. Next, the loops are resolved by setting each loop interconnection to Boolean TRUE, adjusting the fault tree in a specific way and finally setting each loop interconnection to Boolean FALSE. Embodiments of the invention are relevant for analyzing safety-critical systems. However, the present concepts are not limited to these applications and may be applied to general use cases where fault tree analysis is applicable. The proposed solution advantageously provides a method that features linear complexity.

Abstract: A method and apparatus for determining differences between an automated installation and a digital twin of the automated installation and to an automated installation that includes the apparatus, wherein a sensor unit is fastened to a process material and passes through an automated installation together with the process material where, during this process, the sensor unit continuously records at least one measurement variable, a temporal profile of the recorded measurement variable is compared with a temporal profile of a corresponding simulated measurement variable and if there is a difference between the temporal profiles, then the location in the automated installation at which the difference is present is identified.

Abstract: Provided is a method for determining at least one indication of at least one change, having the steps of receiving at least one input data record having the at least one change and associated data, and determining the at least one indication of the at least one change by applying a learning-based approach to the at least one received input data record. The invention is also directed to a determination unit and a computer program product.

Abstract: A method and a system for calculating a productivity of an industrial system consisting of system modules is provided. A Markov model is stored for each system module, the Markov model indicating the probabilities that a system module is in different operating states and indicating transition rates of state changes between the operating states of the respective system module for each operating state of the respective system module with a different productivity. A Cartesian product is calculated from a first Markov model of a first system module and from a second Markov model of a second system module linked to the first system module. The calculated Cartesian product is then reduced to a Markov model of the combined sub-system. The calculation of the Cartesian product and the subsequent reduction are carried out successively until the combined sub-system forms the entire industrial system.

Abstract: A method for analyzing functional failures of a technical system using a processor to compute a meta data model, including the following steps is provided. A first step of gathering at least one failure mode for each component of a system dataset describing the technical system. A second step of associating at least one effect and at least one related maintenance task with each failure mode to mitigate and/or to dissolve the effect in the meta data model. A third step of computing failure probabilities of said technical system based on the meta data model, while the technical system is in the specific situation.

Abstract: A method for a reusable reliability centered maintenance of a technical system is provided, by executing the following steps: In a first step the meta data model is segmented in three sections to structure datasets of at least one database, wherein the first section comprises a collaborative dataset about components and at least one dominant failure mode associated with the components, the second section comprises a reusable dataset about at least one preventive maintenance task, the third section comprises a product dataset. In a second step at least one component instance is created for the third section by selecting at least one component of the components to describe said technical system. In a third step it is checked, if the preventive maintenance task is a valid task to prevent the dominant failure mode of the dominant failure mode instance for said technical system.

Abstract: A method and a system for calculating a productivity of an industrial system consisting of system modules is provided. A Markov model is stored for each system module, the Markov model indicating the probabilities that a system module is in different operating states and indicating transition rates of state changes between the operating states of the respective system module for each operating state of the respective system module with a different productivity. A Cartesian product is calculated from a first Markov model of a first system module and from a second Markov model of a second system module linked to the first system module. The calculated Cartesian product is then reduced to a Markov model of the combined sub-system. The calculation of the Cartesian product and the subsequent reduction are carried out successively until the combined sub-system forms the entire industrial system.

Abstract: A method for analyzing functional failures of a technical system using a processor to compute a meta data model, including the following steps is provided. A first step of gathering at least one failure mode for each component of a system dataset describing the technical system. A second step of associating at least one effect and at least one related maintenance task with each failure mode to mitigate and/or to dissolve the effect in the meta data model. A third step of computing failure probabilities of said technical system based on the meta data model, while the technical system is in the specific situation.

Abstract: A method for a reusable reliability centered maintenance of a technical system is provided, by executing the following steps: In a first step the meta data model is segmented in three sections to structure datasets of at least one database, wherein the first section comprises a collaborative dataset about components and at least one dominant failure mode associated with the components, the second section comprises a reusable dataset about at least one preventive maintenance task, the third section comprises a product dataset. In a second step at least one component instance is created for the third section by selecting at least one component of the components to describe said technical system. In a third step it is checked, if the preventive maintenance task is a valid task to prevent the dominant failure mode of the dominant failure mode instance for said technical system.

Abstract: A method for computer-assisted monitoring of the functional performance of a technical system: determining an actual functional performance for each technical component of the technical system; assigning each technical component to a component type, wherein components of identical or similar functional range are assigned to one component type; determining an additional functional performance for each component type, which describes the functional range of the hardware and/or software of a component, wherein the range, among all the available components of one component type, has the highest functional performance; determining a component-related, normalized functional performance from the actual functional performance of a given technical component and from the additional functional performance; determining a component-related, normalized functional performance from the component-related, normalized functional performances of all components of the same type; comparing the component-related, normalized functio

Abstract: A method for computer-aided simulation of operating parameters of a technical system including a plurality of modules which each contain one or more components is provided. Failure events with associated downtimes for each component are simulated in a predetermined operating period using a first probability distribution for the moment of failure of the components and a second probability distribution for the length of the failure of the components, and a third probability distribution for a degree of reliability of the modules is determined. Based upon the probability distributions for the degrees of reliability of the modules, operating parameters of the technical system are simulated for the predetermined operating period. The method is used for any technical facilities, in particular for energy generation facilities.

Abstract: A method for computer-assisted monitoring of the functional performance of a technical system: determining an actual functional performance for each technical component of the technical system; assigning each technical component to a component type, wherein components of identical or similar functional range are assigned to one component type; determining an additional functional performance for each component type, which describes the functional range of the hardware and/or software of a component, wherein said range, among all the available components of one component type, has the highest functional performance; determining a component-related, normalized functional performance from the actual functional performance of a given technical component and from the additional functional performance; determining a component-related, normalized functional performance from the component-related, normalized functional performances of all components of the same type; comparing the component-related, normalized functi

Abstract: A method for optimization of maintenance plans for a plant is provided. The method includes providing input data having at least one of a plurality of indicia regarding a configuration of the plant and a plurality of constraints regarding planned outages of the plant, optimizing the input data, and generating a maintenance plan with maximum equivalent output per a defined observation period regarding the plant.

Abstract: A nonlinear technical product or process described by stochastic system output target values dependent on stochastic system input parameter values, thereby stating discrete technical system dependencies, is optimized by using a Response Surface Methods based on discrete technical system dependencies to generate at least one continuous auxiliary function for the real dependencies of the target values on the input parameter values. Next, an auxiliary function is used to generate at least one optimizing parameter for optimization by an objective function, thereby generating an additional discrete technical system dependence. The technical system is adaptively optimized by repeating the above, using the additional discrete technical system dependence, until the difference of successive optimized optimizing parameters is below a threshold. The final additional discrete technical system dependence is an optimal technical system operating point.

Abstract: A method for computer-aided simulation of operating parameters of a technical system including a plurality of modules which each contain one or more components is provided. Failure events with associated downtimes for each component are simulated in a predetermined operating period using a first probability distribution for the moment of failure of the components and a second probability distribution for the length of the failure of the components, and a third probability distribution for a degree of reliability of the modules is determined. Based upon the probability distributions for the degrees of reliability of the modules, operating parameters of the technical system are simulated for the predetermined operating period. The method is used for any technical facilities, in particular for energy generation facilities.

Abstract: A nonlinear technical product or process described by stochastic system output target values dependent on stochastic system input parameter values, thereby stating discrete technical system dependencies, is optimized by using a Response Surface Methods based on discrete technical system dependencies to generate at least one continuous auxiliary function for the real dependencies of the target values on the input parameter values. Next, an auxiliary function is used to generate at least one optimizing parameter for optimization by an objective function, thereby generating an additional discrete technical system dependence. The technical system is adaptively optimized by repeating the above, using the additional discrete technical system dependence, until the difference of successive optimized optimizing parameters is below a threshold. The final additional discrete technical system dependence is an optimal technical system operating point.