Abstract: A method and an apparatus for optimizing diagnostics of rotating equipment is provided. The apparatus includes a device for providing status information about status of the rotating equipment over a series of time windows whereby status can be derived from sensor features of at least one available sensor taking measurements during a predefinable time period, a device for using deep learning which combines provided historic sensor information with sequence of events data indicating warnings and/or alerts of the rotating equipment, whereby status information is supplemented with via deep learning predicted probabilities whether a warning and/or an alert has occurred within a time window, device for providing an amount of textual diagnostic knowledge cases, device for extracting semantic information on text features from the textual diagnostic knowledge cases, and device for combining status information and semantic information into a unified representation enabling optimization of the diagnostics.

Abstract: A method for identifying automatically an inner node within a hierarchical network causing an outage of a group of leaf nodes at the lowest hierarchical level, the method including providing an outage state matrix representing an outage state of leaf nodes at the lowest hierarchical level; decomposing the state matrix into a first probability matrix indicating for each inner node the probability that the inner node forms the origin of an outage at the lowest hierarchical level of the hierarchical network and into a second probability matrix indicating for each leaf node at the lowest hierarchical level of the hierarchical network the probability that an inner node forms a hierarchical superordinate node of the respective leaf node at the lowest hierarchical level of the hierarchical network and evaluating the first probability matrix to identify the inner node having caused the outage of the group of leaf nodes.

Abstract: A method and apparatus for determining a network topology of a hierarchical network, the apparatus including: a memory unit to store an outage state matrix representing an outage state of leaf nodes at the lowest hierarchical level of the hierarchical network; and a processing unit to decompose the stored state matrix into a first probability matrix indicating for each inner node of the hierarchical network the probability that the respective inner node forms the origin of an outage at the lowest hierarchical level of the hierarchical network and into a second probability matrix indicating for each leaf node at the lowest hierarchical level of the hierarchical network the probability that an inner node forms a hierarchical superordinate node of the respective leaf node at the lowest hierarchical level of the hierarchical network, wherein the decomposed second probability matrix is evaluated by the processing unit to determine the network topology.

Abstract: A method and an apparatus for optimizing diagnostics of rotating equipment, in particular a gas turbine is provided.

Abstract: A method and apparatus for determining a network topology of a hierarchical network, the apparatus including: a memory unit to store an outage state matrix representing an outage state of leaf nodes at the lowest hierarchical level of the hierarchical network; and a processing unit to decompose the stored state matrix into a first probability matrix indicating for each inner node of the hierarchical network the probability that the respective inner node forms the origin of an outage at the lowest hierarchical level of the hierarchical network and into a second probability matrix indicating for each leaf node at the lowest hierarchical level of the hierarchical network the probability that an inner node forms a hierarchical superordinate node of the respective leaf node at the lowest hierarchical level of the hierarchical network, wherein the decomposed second probability matrix is evaluated by the processing unit to determine the network topology.

Abstract: A method for identifying automatically an inner node within a hierarchical network causing an outage of a group of leaf nodes at the lowest hierarchical level, the method including providing an outage state matrix representing an outage state of leaf nodes at the lowest hierarchical level; decomposing the state matrix into a first probability matrix indicating for each inner node the probability that the inner node forms the origin of an outage at the lowest hierarchical level of the hierarchical network and into a second probability matrix indicating for each leaf node at the lowest hierarchical level of the hierarchical network the probability that an inner node forms a hierarchical superordinate node of the respective leaf node at the lowest hierarchical level of the hierarchical network and evaluating the first probability matrix to identify the inner node having caused the outage of the group of leaf nodes.

Abstract: For controlling a target system, e.g. a gas or wind turbine or another system, operational data of a plurality of source systems are used. The operational data of the source systems are received and are distinguished by source system specific identifiers. By a neural network a neural model is trained on the basis of the received operational data of the source systems taking into account the source system specific identifiers, where a first neural model component is trained on properties shared by the source systems and a second neural model component is trained on properties varying between the source systems. After receiving operational data of the target system, the trained neural model is further trained on the basis of the operational data of the target system, where a further training of the second neural model component is given preference over a further training of the first neural model component.

Abstract: For controlling a target system, operational data of a plurality of source systems are used. The data of the source systems are received and are distinguished by source system specific identifiers. By a neural network, a neural model is trained on the basis of the received operational data of the source systems taking into account the source system specific identifiers, where a first neural model component is trained on properties shared by the source systems and a second neural model component is trained on properties varying between the source systems. After receiving operational data of the target system, the trained neural model is further trained on the basis of the operational data of the target system, where a further training of the second neural model component is given preference over a further training of the first neural model component. The target system is controlled by the further trained neural network.