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Martin Hollender

Moncef Chioua

Benjamin Kloepper

Benedikt Schmidt

Andrew Cohen

Mikko Rissanen

Marco Gaertler

Sylvia Maczey

Nuo Li

Matthieu Lucke

Felix Lenders

Mario Hoernicke

Marcel Dix

Jens Doppelhamer

Kristoffer Husoy

Torgeir Enkerud

Matus Harvan

Michael Lundh

Arzam Muzaffar Kotriwala

Aldo Dagnino

Chaojun Xu

Iiro Harjunkoski

Hadil Abukwaik

Josef Bicik

Reinhard Bauer

Hans-Werner Borchers

Jan M Gjerseth

Mark Stefan Struempfler

Jan Christoph Schlake

Rebekka Litzelmann

Dominik Steickert

Emanuel Kolb

Shahram Shahinzadeh

Dennis Janka

Jens Axelsson

Jan Lagnelov

Rene Söllner

Carsten Beuthel

Tony B Atkinson

Steve Royston

Carsten Dr-Ing Beuthel

Antony B Atkinson

Sam Jacobs

K Kaushikghosh

Acharya Mithun

Marco Gärtler

Gugaliya Jinendra

Mark-Stefan STRÜMPFLER

Benjamin KLÖPPER

Lara-Marie Volkmann

Mark-Stefan Struempfler

Ruomu Tan

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

An apparatus for alarm information determination includes: an input unit; a processing unit; and an output unit. The input unit provides the processing unit with historical process control data, the process control data including a plurality of data signals, a plurality of alarm data, and data relating to an event of interest. The processing unit determines a plurality of correlation scores for the plurality of data signals paired with the plurality of alarm data, a correlation score being determined for a data signal paired with an alarm data, a high correlation score indicating a higher degree of correlation than a low correlation score. The processing unit identifies at least one first alarm data from the plurality of alarm data, the identification including utilization of the data relating to the event of interest.

Apparatus for alarm information determination

To determine a quality indicator of production batch-run of a production process, a computer compares time-series with multi-source data from a reference batch-run and time-series with multi-source data from the production batch-run. Before comparing, the computer converts multi-variate time-series to uni-variate time-series, by first multiplying data values of source-specific uni-variate time-series with source-specific factors from a conversion factor vector and second summing up the multiplied data values according to discrete time points. The source-specific factors of the conversion factor vector are obtained earlier by processing reference data, including the determination of characteristic portions of the time-series, converting, aligning by time-warping and evaluating displacement in time between characteristic portions before alignment and after alignment.

Computer-implemented determination of a quality indicator of a production batch-run of a production process

A computer-implemented method for determining an abnormal technical status of a technical system includes: receiving, from the technical system, a plurality of signals, each signal being sampled over time and reflecting the technical status of at least one system component; computing, for each signal with associated high and low alarm thresholds obtained from an alarm management system, at every sampling time point, a univariate distance to its associated alarm thresholds as a maximum of the distances between a value of the respective signal and its associated alarm thresholds to quantify a degree of abnormality for the respective at least one system component; computing, at every sampling time point, based on the univariate distances at the respective sampling time points, an aggregate abnormality indicator reflecting the technical status of the technical system; and providing, to an operator, a comparison of the aggregate abnormality indicator with a predetermined abnormality threshold.

System and methods monitoring the technical status of technical equipment

A system for action determination includes an input unit, a processing unit, and an output unit. The input unit provides the processing unit with information relating to a plurality of past actions over a period of time associated with the operation of an industrial process. The input unit provides the processing unit with information relating to a plurality of past process events over the time period associated with the operation of the industrial process. The input unit provides the processing unit with information relating to a new process event. The processing unit determines a correlation between at least some of the plurality of past actions with at least some of the past process events. The processing unit determines at least one recommended action from the information relating to the new process event, the determination including utilization of the determined correlation. The output unit outputs the at least one recommended action.

System for action determination

A control module is adapted to control technical equipment by processing batch-run data from the technical equipment. The control module operates according to parameters that are obtained by a parameter module. The module receives a reference plurality of multi-variate reference time series with data values from sources that are related to the equipment. There are time series with measurement values and time series with data that describes particular manufacturing operations during a batch-run time interval. The module splits the time interval into phases by determining transitions between the particular manufacturing operations, and divides the time series into particular phase-specific partial series. For each phase separately, and for the phase-specific partial series in combination, the module differentiates phase-specific time series into relevant partial time series or non-relevant partial time series and set the parameters accordingly.

Controlling technical equipment through quality indicators using parameterized batch-run monitoring

A computer-implemented method to control technical equipment that performs a production batch-run of a production process, the technical equipment providing data in a form of time-series from a set of data sources, the data sources being related to the technical equipment, includes: accessing a reference time-series with data from a previously performed batch-run of the production process, the reference time-series being related to a parameter for the technical equipment; and while the technical equipment performs the production batch-run: receiving a production time-series with data, identifying a sub-series of the reference time-series, and comparing the received time-series and the sub-series of the reference time-series, to provide an indication of similarity or non-similarity, in case of similarity, controlling the technical equipment during a continuation of the production batch-run, by using the parameter as control parameter.

Computer-implemented determination of a quality indicator of a production batch-run that is ongoing

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

A computer system is configured to process alarm activations received from technical systems, where an alarm activation represents a deviation of the technical status of a technical system from normal. The system includes: a data storage interface for receiving alarm activations in data storage, where the recorded alarm activations correspond to alarms; a data processor for: determining, from the recorded alarm activations, time intervals for alarm analysis; and computing similarity measures between the time intervals that depend on the occurrence of the recorded alarm activations in the time intervals, and where the contribution of an alarm activation to the similarity of two time intervals is reduced with an increasing occurrence of the alarm in the time intervals; and a user interface configured to provide pairs of time intervals to an operator of the one or more technical systems that include time intervals with similarity measures indicating similar alarm.

Computer system and method to process alarm signals

A method for sorting alarm-messages of a plant having at least one alarm-area each having at least two plant-devices connected by at least one connection, wherein the at least one alarm-area is defined so that the plant-devices and the respective connection provide a directed graph, involves: providing at least one database containing alarm-data and topology-data; wherein the topology-data describe a directed graph having at least one connection with a related flow direction between the respective plant-devices of the respective assigned alarm-area; generating at least one partly complete first alarm-order of the alarm-messages of the at least one alarm-area by sorting the alarm-messages according to the flow direction of the directed graph.

Optimized method for sorting alarms

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

An operation and observation system of a technical plant and/or a technical process and associated method are provided for operating components and for displaying measurement readings, process variables and/or status messages of the components of the plant and/or process. The system includes at least one large screen for displaying the components of the technical process and/or technical plant, and a plurality of operator workstations. A movement detection and movement control component is installed on the screen and on the operator workstations, which allows an operation and/or polling of the components, the measurement readings, the process variables and/or the status messages of the components of the technical plant or of the technical process by means of a body movement of an operator.

Device and method for the gesture control of a screen in a control room

The disclosure relates to a method and system for intelligent visualization in the monitoring of process and/or system data used in technical processes and in the operation of technical systems. The behavior of signals in the past is analyzed and used for a future optimized visualization. Continuously running online algorithms support the system operator by detecting and correspondingly highlighting deviations from the historically observed signal patterns.

Intelligent visualization in the monitoring of process and/or system variables

The disclosure relates to a safety device and a method for detecting and evaluating the degree of attention of an operator of a technical installation or a technical process, which includes a first module which records values of predefined physiological parameters of the operator and determines a first characteristic number from the recorded values, compares the determined first characteristic number with a predefined first characteristic value, which activates a second module which records further values provided by a checking or triggering mechanism. The second module determines a second characteristic number for the degree of attention of the operator from the recorded further values, compares the second characteristic number with at least one second predefined characteristic value, which triggers an activation module in order to take countermeasures which increase the attention of the operator of the installation.

Safety device for a technical installation or a technical process

A display system includes a processing device for graphic representation of alarm reports from measured values of a technical plant or process. Measurement points of the measured values each respectively have a hierarchically structured identifier held in the processing device. The processing device decomposes the hierarchically structured identifiers into hierarchical levels of different significance, selects the identifiers of the hierarchical levels with the highest significance, and transmits them to a preconfigured mass alarm display unit. The identifiers of the hierarchical levels with the highest significance can be assigned to a correspondingly identified area within an overview image of the plant or process of the preconfigured mass alarm display unit. The alarm reports which are generated from the measured values can be represented graphically, taking into account the generated assignment of the identifier of the measurement points to the correspondingly identified area within the overview image of the plant or process.

Display system for graphic representation of alarm reports of a technical plant or technical process

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Dossenheim, Germany

Martin Hollender