Abstract: A method predicts a derivable event in a logistic network. The method includes generating a Bayesian network describing a structure of at least a part of a logistic network. A query is received for the derivable event that depends on a combination of base events in the logistic network. The Bayesian network is instantiated for a plurality of points in time. A prediction of the derivable event is deduced from the instantiated Bayesian network by use of complex event processing.

Abstract: A method and an apparatus for operating an automation system is provided. The method for operating an automation system includes the method steps of: providing a learning-based prediction model for the automation system trained by process data including context of an automation process, receiving information about current context of the automation process, verifying context change by comparing the current context to the context of said process data, in the case of any context change verifying a concept drift by comparing pre-drift process data and post-drift process data, in the case of any concept drift re-training said model with post-drift process data, in the case of no context change testing for random concept drift not detected by verifying context change, in the case of any random concept drift extend the current context by using data comprising previous context changes, otherwise no further method steps are required.

Abstract: A method of operating an intelligent programmable logic controller over a plurality of scan cycles includes the intelligent programmable logic controller executing a control program and one or more reasoning algorithms for analyzing data received and transmitted by the intelligent programmable logic controller. The intelligent programmable logic controller receives one or more user-specified declarative knowledge models from an external source via a deployment interface included in the intelligent programmable logic controller. The intelligent programmable logic controller dynamically modifies the reasoning algorithms during runtime of the control program based on the user-specified declarative knowledge models.

Abstract: In a monitoring phase, live instance vectors including data from all devices of a manufacturing system are acquired. A constraint-based clustering algorithm assigns each live instance vector to a cluster, thereby forming a live sequence of clusters. The live sequence is classified based on at least one behavior model. An anomaly is detected depending on the classification result. Each cluster represents a state of the manufacturing system. The sequences of clusters can be generated by consecutive operations that are performed in the manufacturing system. The constraint-based clustering algorithm facilitates an unsupervised (automated) or semi-supervised learning of system behavior that may be supplemented with supervised or unsupervised learning of the behavior models. The method provides a way of automated learning of discrete event dynamic systems from data generated by sensors and actuators without requiring manual input.

Abstract: A method and an apparatus for operating an automation system is provided. The method for operating an automation system includes the method steps of: providing a learning-based prediction model for the automation system trained by process data including context of an automation process, receiving information about current context of the automation process, verifying context change by comparing the current context to the context of said process data, in the case of any context change verifying a concept drift by comparing pre-drift process data and post-drift process data, in the case of any concept drift re-training said model with post-drift process data, in the case of no context change testing for random concept drift not detected by verifying context change, in the case of any random concept drift extend the current context by using data comprising previous context changes, otherwise no further method steps are required.

Abstract: A method and a system for re-sequencing client production orders where client-individualized JIS components have not been delivered in due time according to the planned production schedule due to disturbances or have been delivered, but are defective. Three major steps are performed. First, by the introduction of the concept of differently permutable sub-sequences, the frozen time zone is divided into a plurality of sub-sequences of different length in time wherein the length is adapted to the time variants in the re-planning process. Second, using a knowledge-based model, the consequences of a disturbance are classified to be caused either by delayed supply or by defective parts. Third, after determining the type of distortion combining the type of sub-sequence with the status of the supply leads to the generation of three possible algorithms for the re-sequencing of the client production order.

Abstract: A method of operating an intelligent programmable logic controller over a plurality of scan cycles includes the intelligent programmable logic controller executing a control program and one or more reasoning algorithms for analyzing data received and transmitted by the intelligent programmable logic controller. The intelligent programmable logic controller receives one or more user-specified declarative knowledge models from an external source via a deployment interface included in the intelligent programmable logic controller. The intelligent programmable logic controller dynamically modifies the reasoning algorithms during runtime of the control program based on the user-specified declarative knowledge models.

Abstract: A method for initializing an optimization function for parameters of a controller controlling a controlled system is provided. The controlled system includes at least one sensory component and at least one actuator component. The method includes receiving process data over time of the controller, determining a value of at least one prior specified key performance indicator (KPI) of the controller, and identifying a model of the controlled system. The method also includes comparing the at least one determined KPI value with at least one provided former KPI value, and comparing the identified model with at least one provided former model in the case of any significant difference between said values. The method includes initializing an optimization function of the controller parameters by using the determined KPI values as a cost function in the case of any significant difference between the models.

Abstract: First inter-organizational tracking & tracing systems facilitate timely identification and handling of disruptions along the supply chain. However, these systems typically operate at supply chain management (SCM) and enterprise resource planning system (ERP) level and therefore lack knowledge and control over production processes. The idea is to bridge the gap between logistics and production IT by proposing a novel architecture for coupling the manufacturing operations as defined in IEC 62264 with an EPCIS-compliant real-time tracking & tracing system. The system leverages logic-based complex event processing for detecting critical disruptions in the supply chain and initiates rescheduling of production. It is shown that in the presence of unexpected events the rescheduling algorithm minimizes delays and inventory costs while avoiding <<nervous schedules>> caused by frequent changes.

Abstract: A method and a server for generating a display and operating view for an operating and monitoring device of an industrial automation arrangement, wherein after an event has been logged, information relating to the event and details of the identity or role of a user of the operating and monitoring device are transmitted to an entity, after which planned information relating to at least one component of the industrial automation arrangement affected by the event is retrieved from an engineering database using the event and specifically for the identity or role of the user, current state information is also retrieved from the at least one affected component, and finally the planned information and the state information is used to generate the display and operating view such that, the display and operating images are advantageously set up and activated in an event-controlled manner.

Abstract: A method and a system for re-sequencing client production orders where client-individualized JIS components have not been delivered in due time according to the planned production schedule due to disturbances or have been delivered, but are defective. Three major steps are performed. First, by the introduction of the concept of differently permutable sub-sequences, the frozen time zone is divided into a plurality of sub-sequences of different length in time wherein the length is adapted to the time variants in the re-planning process. Second, using a knowledge-based model, the consequences of a disturbance are classified to be caused either by delayed supply or by defective parts. Third, after determining the type of distortion combining the type of sub-sequence with the status of the supply leads to the generation of three possible algorithms for the re-sequencing of the client production order.

Abstract: A method predicts a derivable event in a logistic network. The method includes generating a Bayesian network describing a structure of at least a part of a logistic network. A query is received for the derivable event that depends on a combination of base events in the logistic network. The Bayesian network is instantiated for a plurality of points in time. A prediction of the derivable event is deduced from the instantiated Bayesian network by use of complex event processing.

Abstract: A method and a server for generating a display and operating view for an operating and monitoring device of an industrial automation arrangement, wherein after an event has been logged, information relating to the event and details of the identity or role of a user of the operating and monitoring device are transmitted to an entity, after which planned information relating to at least one component of the industrial automation arrangement affected by the event is retrieved from an engineering database using the event and specifically for the identity or role of the user, current state information is also retrieved from the at least one affected component, and finally the planned information and the state information is used to generate the display and operating view such that, the display and operating images are advantageously set up and activated in an event-controlled manner.

Abstract: First inter-organizational tracking & tracing systems facilitate timely identification and handling of disruptions along the supply chain. However, these systems typically operate at supply chain management (SCM) and enterprise resource planning system (ERP) level and therefore lack knowledge and control over production processes. The idea is to bridge the gap between logistics and production IT by proposing a novel architecture for coupling the manufacturing operations as defined in IEC 62264 with an EPCIS-compliant real-time tracking & tracing system. The system leverages logic-based complex event processing for detecting critical disruptions in the supply chain and initiates rescheduling of production. It is shown that in the presence of unexpected events the rescheduling algorithm minimizes delays and inventory costs while avoiding <<nervous schedules>> caused by frequent changes.