Abb Schweiz Ag

Benjamin Kloepper

Benedikt Schmidt

Marcel Dix

Pablo Rodriguez

Martin Hollender

Moncef Chioua

Arzam Muzaffar Kotriwala

Marco Gaertler

Sylvia Maczey

Hadil Abukwaik

Heiko Koziolek

Jens Doppelhamer

Mithun P Acharya

Subanatarajan Subbiah

Andrew Cohen

Julius Rueckert

Reuben Borrison

Boris Fiedler

Mario Hoernicke

Michael Lundh

Thomas Goldschmidt

Ido Amihai

Divyasheel Sharma

Jeffrey Harding

Ioannis Lymperopoulos

Andrea Macauda

Nuo Li

Matthias Berning

Felix Lenders

Raja Sivalingam

Simon Hallstadius Linge

Mohamed-Zied Ouertani

Andreas Berlet

Jan Christoph Schlake

Gayathri Gopalakrishnan

Karl Severin

Chandrika K R

Dennis Janka

Dawid Ziobro

Roland Braun

Ruomu Tan

Dominik Olschewski

Kai Merz

Jan-Christoph Schlake

Bernhard Wullt

Anton Ronquist

Taisuke Minagawa

Arzam Kotriwala

An industrial plant operator intervention system for use in an industrial plant includes a processing unit configured to monitor and analyze industrial plant operation data to detect an anomaly in the industrial plant operation data that warrants initiating an operator intervention, and in response to detecting the anomaly, automatically determine a user interface configuration of a user interface to be presented to a designated operator who is to perform the operator intervention. The user interface configuration is determined on the basis of technical context data, including industrial plant operation data associated with the anomaly, and on the basis of operator data pertaining to the designated operator, in such a manner that an anomaly-related and operator-specific user interface configuration is obtained.

Industrial plant operator intervention system for use in an industrial plant

A method for generating a process model modeling a manual mode procedure instance of a plant process includes providing log events of operational actions; selecting related sequences of manual mode operational actions from the log events; filtering the related sequences according to an individual plant section; identifying a sequential order from the filtered related sequences; determining statistical properties of values of related process variables and/or statistical properties of values of related set point changes to each sequential ordered manual mode operational action from the filtered related sequences; generating the process model of the manual mode procedure instance by arranging related manual mode operational actions with the sequential order of each operational action assigned with the statistical properties of the values of related process variables and/or assigned with the statistical properties of the values of the related set point changes.

Method for generating a process model

A method used for generating a zoomable diagram includes providing a topology diagram of at least a part of the industrial plant for interacting with components of the industrial plant; generating, for each zoom-level of the zoomable diagram, a zoomed diagram comprising a zoomed view of each one of the plurality of objects and connections, by applying at least following rules to each object: from a first predefined zoom-level, collapse all objects of a plant-segment to one object and keep only connections that cross the plant-segment's border, and introduce an plant-segment related alarm measure as a function of all alarm statuses of the plurality of objects of the plant-segment; and when interacting with the components of the industrial plant, determining, for each plant-segment, the plant-segment related alarm measure, and outputting the plant-segment related alarm measure for healing a cause of the alarm.

Process graphics generator for a zoomable diagram representing an industrial process plant

A method for determining an appropriate sequence of actions to take during operation of an industrial plant includes obtaining values of a plurality of state variables that characterize an operational state of the plant (or a part thereof); encoding by at least one trained state encoder network the plurality of state variables into a representation of the operating state of the plant; mapping by a trained state-to-action network the representation of the operating state to a representation of a sequence of actions to take in response to the operating state; and decoding by a trained action decoder network the representation of the sequence of actions to the sought sequence of actions to take.

Determining appropriate sequences of actions to take upon operating states of industrial plants

A method for training a machine-learning model to assess at least one condition of industrial equipment, and/or at least one condition of a process running in an industrial plant, based on measurement data gathered by a plurality of sensors, includes: obtaining a plurality of records of measurement data that correspond to a variety of operating situations and a variety of conditions; obtaining, for each record of measurement data, a label that represents a condition in the operating situation characterized by the record of measurement data; and determining a plausibility of at least one record of measurement data, and/or a plausibility of at least one label, based at least in part on a comparison with at least one other record of measurement data, with at least one other label, and/or with additional information about the industrial equipment, and/or about the industrial plant where the industrial equipment resides, and/or about the process.

Assessing conditions of industrial equipment and processes

A method for providing an attribute of an element in a processing system having a plurality of elements, the processing system being represented as a directed graph having a plurality of nodes and directed edges, each node representing an element, each node having an attribute, and each directed edge representing a relation between two elements of the plurality of elements, the method including: selecting one node of the plurality of nodes as a starting node; constructing a subgraph, the subgraph including all the nodes that are forward-connected by at least one directed edge from the starting node; and outputting all nodes and the attribute of the nodes of the subgraph.

Method for alarm handling in a processing system

A method for problem diagnosis in a robot system having one or more robots includes the steps of: a) receiving (S) a first problem message from a robot of the robot system, the first problem message including one or more data elements descriptive of a problem experienced by the robot; b) receiving (S) a subsequent problem message from a robot of the robot system; c) if a time elapsed between receipt of the subsequent problem message and receipt of an immediately preceding problem message is shorter than a predetermined threshold (S), adding the subsequent problem message to a message set which comprises the immediately preceding problem message (S); and d) if the time elapsed is longer than the predetermined threshold (S), terminating (S) the message set of the immediately preceding problem message without adding the subsequent problem message, and establishing (S, S) a new message set.

Diagnosis method and apparatus

A method for determining an interdependency between a plurality of elements in an industrial processing system includes: providing a process flow diagram (PFD) of a topology of the processing system; transforming the PFD into a directed graph, each element of the plurality of elements being transformed into a node and each relation between the plurality of elements being transformed into a directed edge; selecting one node of the plurality of nodes as a starting node; and constructing a subgraph, the subgraph including all the nodes that are forward-connected from the starting node so as to show at least one interdependency between the plurality of elements in the subgraph.

Method for analyzing effects of operator actions in industrial plants

A system and method provides an impact list of affecting equipment elements that affect an industrial sub-process. The method comprises the steps of selecting, in a topology model, the sub-process, wherein the sub-process is an equipment element that is a part of an industrial plant or process, and wherein the topology model is a graph, whose nodes represent equipment elements and whose edges represent interconnections between the equipment elements; traversing the nodes of the topology model, wherein the traversing starts from the selected sub-process and uses a traversing strategy; and for each of the at least one equipment elements, if the equipment element affects the industrial sub-process by an affecting degree greater than a first predefined affecting degree, adding the equipment element to the impact list of affecting equipment elements.

Method for providing a list of equipment elements in industrial plants

A data processing device capable of performing problem diagnosis in a production system with a plurality of robots includes: a first time series obtaining part for obtaining historical event data used for determining some historical alarm indicator in time series and storing the historical event data as first time series data; a historic alarm indicator calculation part for calculating a series of historic alarm indicators using statistic characteristics of the first time series data; a threshold definition part for defining at least one threshold value based on a statistical distribution of the historical alarm indicators; a second time series obtaining part for obtaining operational event data during operation of the robots used for determining some operational alarm indicator in time series and storing the operational event data as second time series data; and an operational alarm indicator calculation part for calculating a series of operational alarm indicators.

Data processing device capable of performing problem diagnosis in a production system with plurality of robots and method

A method for controlling an industrial process includes: determining, by a process controller, based at least in part on a set of current values and/or past values of state variables of the industrial process, a set of control outputs to be applied to at least one actor and/or lower-level controller configured to cause a performing of at least one physical action on the process; querying, based on at least a subset of the set of current values and/or past values of state variables and on at least a subset of the set of control outputs, a trained machine-learning model configured to output a classification value, and/or a regression value, that is indicative of a propensity of a watching human operator to at least partially override the control outputs delivered by the process controller; and determining that the classification value, the regression value, and/or the propensity, meets a predetermined criterion.

Human-plausible automated control of an industrial process

A computer system can be configured to: receive, in a low-precision mode, first status data generated by one or more sensors, the first status data reflecting technical parameters of a technical system, the first status data exhibiting a first precision level; apply a low-precision machine learning model to analyze the first status data for one or more indicators of an abnormal technical status, the machine learning model having been trained with data exhibiting the first precision level; send, based on an abnormal technical status being indicated, instructions for the one or more sensors to generate second status data exhibiting a second precision level, the second precision level being associated with greater accuracy than the first precision level; receive the second status data exhibiting the second precision level based on the sent instructions; providing the second status data to a data analyzer.

Computer system and method for monitoring the status of a technical system

A computer system for controlling a dialogue between a user and the computer system, the computer system being communicatively coupled with an industrial control system controlling an industrial system, the computer system including: an interface for receiving intent inputs representing a respective desired interaction with the computer system; an intent determination module for determining the desired interactions of received intent inputs; a data storage for storing one or more directed graphs, each graph specifying an industrial control domain specific dialogue, a particular graph defining a dialogue state machine with a plurality of nodes representing states of the dialogue, and with edges representing transitions between the states, each state transition from a first state to a second state depending on at least one precondition, the desired interaction of a received intent input corresponding to a target node to be reached from a current node of the particular graph.

Computer system and method for controlling user-machine dialogues

An anomaly detection module is configured to apply a plurality of machine learning models to received technical status data to detect one or more indicators of an abnormal technical status prevailing in the industrial process system. The plurality of machine learning models are trained on historic raw or pre-processed sensor data and the anomaly detection module configured to generate the anomaly alert based on the one or more indicators. The received technical status data is assigned to signal groups and the generated anomaly alert is a vector with each vector element representing a group anomaly indicator for the respective signal group. Each vector element is determined by applying a respective group specific machine learning model.

Computer system and method for monitoring the technical state of industrial process systems

An alarm handling system in plant process automation with a data processing device includes: at least one interface for accessing and/or processing one or more process signals and for determining corresponding process variables; an alarm configuration device for accessing and/or providing alarm configuration information including at least one setpoint for one or more determined process variables; and a prediction device for determining and processing a current rate of change of at least one process variable to predict how long it will take and/or a period until and/or predict at which date and/or time a provided setpoint and/or threshold is reached and/or crossed, and/or for determining whether and/or when at least one of the monitored and/or determined process variable values will cross the respective setpoint.

Alarm handling system and method in plant process automation

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Mannheim, Germany

Benjamin Kloepper