Abb Schweiz Ag

Hitachi Energy Switzerland Ag

Abb Technology Ag

Moncef Chioua

Martin Hollender

Benjamin Kloepper

Andrew Cohen

Nuo Li

Matthieu Lucke

Yao Liang Chen

Mario Hoernicke

RongRong Yu

Benedikt Schmidt

Jinendra K Gugaliya

Michael Lundh

Subanatarajan Subbiah

Pablo Rodriguez

Nandkishor Kubal

Ido Amihai

Naveen Bhutani

Kaushik Ghosh

Alexander Fay

Ni Ya Chen

Yingya Zhou

Dennis Janka

Emanuel Kolb

Benjamin KLÖPPER

Arzam Muzaffa Kotriwala

Esteban Arroyo

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

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 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 method for monitoring turbines of a windmill farm includes: providing a global nominal dataset containing frame data of the turbines of the windmill farm and continuous reference monitoring data of the turbines for a first period in a fault free state, the reference monitoring data including at least two same monitoring variables for each turbine; building a nominal global model based on the global nominal dataset which describes the relationship in between the windmill turbines and clustering the turbines according thereto; assigning the data of the global nominal dataset to respective nominal local datasets according to the clustering; and building a nominal local model for the turbines of each cluster based on the respective assigned nominal local datasets, the nominal local model being built such that a nonconformity index is providable which indicates a degree of nonconformity between data projected on the local model and the model itself.

Method for windmill farm monitoring

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

The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

Method for controlling a process plant using transition data

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

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Heidelberg, Germany

Moncef Chioua