EP3928173A1 - Verfahren und system zum management von meldungen eines automatisierungssystems, insbesondere einer industriellen anlage - Google Patents
Verfahren und system zum management von meldungen eines automatisierungssystems, insbesondere einer industriellen anlageInfo
- Publication number
- EP3928173A1 EP3928173A1 EP20714887.5A EP20714887A EP3928173A1 EP 3928173 A1 EP3928173 A1 EP 3928173A1 EP 20714887 A EP20714887 A EP 20714887A EP 3928173 A1 EP3928173 A1 EP 3928173A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- messages
- data
- training
- function
- automation system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims description 31
- 230000006870 function Effects 0.000 claims abstract description 63
- 238000010801 machine learning Methods 0.000 claims abstract description 15
- 238000012549 training Methods 0.000 claims description 99
- 238000011156 evaluation Methods 0.000 claims description 24
- 238000004891 communication Methods 0.000 claims description 22
- 238000004590 computer program Methods 0.000 claims description 3
- 241001465754 Metazoa Species 0.000 claims 1
- 230000005540 biological transmission Effects 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 description 13
- 230000008569 process Effects 0.000 description 11
- 230000015654 memory Effects 0.000 description 7
- 238000001914 filtration Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
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- 238000012544 monitoring process Methods 0.000 description 2
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- 238000010248 power generation Methods 0.000 description 2
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Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
- G05B23/0272—Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4185—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the network communication
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4188—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by CIM planning or realisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
Definitions
- the invention relates to a method and a system for managing messages from an automation system, in particular an industrial plant, according to claims 1 and 8.
- the invention also relates to an evaluation method and an evaluation system according to claims 6 and 13 and a program and a computer program product according to patent claim 15 or 16.
- Automation systems are used to monitor, control and / or regulate mobile and stationary devices and systems and enable control devices, sensors, machines and industrial systems to be operated as independently as possible and independently of human intervention.
- Automation systems are used, for example, in industrial production, energy generation and distribution, building management, logistics, drive technology, etc. They often generate a large number of messages for e.g. System operations and maintenance personnel. These messages are usually sent to the staff on a human-machine interface (HMI), e.g. an operator station, a PC or a portable communication-capable device such as displayed on a tablet or smartphone. They are intended to draw the staff's attention to changes in the condition of the facility or plant that may require their intervention.
- HMI human-machine interface
- a message can be, for example, an alarm message or a notice message.
- An alarm message is a message that an error or an abnormal event has occurred and the personnel must initiate measures promptly.
- a notification message can, for example, indicate the need for maintenance.
- the event report can include text and other information such as the time and place of generation of the report.
- the messages are usually generated by a message system that communicates with the human-machine interface (HMI), e.g. via a communication network.
- HMI human-machine interface
- Industrial systems are used in a wide variety of industrial sectors, for example in power generation, the process industry (e.g. paper, chemicals, pharmaceuticals, metal, oil and gas) and the discrete manufacturing industry.
- the actual industry-specific process e.g. Energy generation or production process is controlled and / or regulated by the automation system.
- An automation system without the field devices i.e. without actuators and sensors
- reporting system is then often implemented as a function in a so-called “operator system server” or “application server”, in which one or more system-specific application programs are stored and executed when the system is in operation.
- the message system may generate hundreds of messages per minute. Many of the messages are important in indicating a possible anomaly, but not all of them have the same effect on the operation of the process. It can happen that the operator of the system is flooded with alarm messages and is not able to react appropriately to the really important alarms. Sometimes up to 80% of the messages can actually be ignored by the operating / monitoring personnel, but filtering out the important, possibly critical for the system see, reporting requires many years of experience. Even if the operator filters out an important alarm very quickly, there is often too little time for suitable countermeasures. This can lead to system downtimes and production failures.
- EP 3 187 950 A1 discloses a method for managing alarms in a control system for an industrial process.
- the control system comprises a notification system with a notification server for generating alarm messages based on predetermined notification generation criteria, an operator station that is in communication with the server, and a database with historical alarm data.
- Historical alarm data are received in the notification server, a first group of alarms is defined as "important alarms" and a second group of alarms as "less important alarms" on the basis of predetermined rules.
- a plurality of historical data segments are identified in the historical alarm data, each of these data segments containing at least one alarm message of an alarm of the first group.
- a large number of association scores are determined with regard to the large number of alarms of the second group, and finally a predetermined notification generation criterion for the alarms is modified on the basis of the large number of association scores.
- a predetermined notification generation criterion for the alarms is modified on the basis of the large number of association scores.
- less important alarms of the second group are prioritized or suppressed.
- important alarms of the first group can be predicted together with a presumed period of their occurrence.
- the inventive method is based on an automation system which
- the reporting system communicatively, in particular via a communication network of the automation system with the
- a function is applied at least to input data that represent messages that are assigned to the second category (optionally also those of the first category) and which are displayed on the first human-machine interface, the function being controlled by a Algorithm was trained from the field of machine learning, and this generates output data that represent a message from the automation system that is assigned to the first category,
- the message which is represented by the output data, is displayed on a second man-machine interface, the second man-machine interface being communicatively separated from the reporting system.
- the invention is based on the knowledge that machine learning is useful for filtering messages in the first category from the large number of messages (ie at least the amount of messages in the second category, and optionally also messages in the first Category) can be used.
- the output data represent one of the messages that is already represented by the input data within a defined period of time.
- machine learning can be used for a prediction of messages of the first category on the basis of at least the amount of messages of the second category, optionally also the messages of the first category.
- the output data usually represent a message that is not yet represented by the input data within a defined period of time. But you can also represent a message that is already represented by the input data within a defined period of time.
- the attention of staff can be drawn to the defined or definable Important messages can be steered or their occurrence can even be predicted.
- the output data preferably include a point in time or a time period for the occurrence of the message and a probability for the occurrence of the message at this point in time or in this time period.
- the different categories can preferably express how critical the respective type of message is for the reliability of the system.
- the different categories can also refer to other aspects such as Relate production costs, production volume, production quality, raw material consumption, electricity consumption, emissions, etc.
- a continuous machine training of the function is possible, taking into account the current status of the system.
- This allows the function and thus the filtering and / or prediction of messages to be continuously adapted to changes in the system (e.g. due to wear and tear or changes in the performance of system components, changes in the quality / quantities of raw materials, changes in the operating mode of the system) thus a high quality of filtering and / or prediction can be achieved.
- the message that is represented by the output data is only displayed in addition to the messages of the reporting system.
- the communicative separation can be implemented particularly simply by receiving the input data of the function from a transmitter and providing the output data from the function to a receiver that is different from the transmitter.
- the function can adapt to new circumstances and identify and extrapolate patterns in the messages.
- Parameters of the function can be adjusted through training. There can be e.g. Supervised training, semi-supervised training, unsupervised training, reinforcement learning and / or active training can be used. Representation learning (an alternative term is "feature learning”) can also be used.
- the parameters of the function can be adapted iteratively through several training steps.
- the function is preferably based on cross correlation of messages, a neural network (preferably long / short term memory - LSTM) or on association rules.
- a neural network preferably long / short term memory - LSTM
- association rules preferably used, whereby the result can be obtained, for example, by the sum of the results of all methods, by majority results, by individual results, etc.
- information about a quality of the message, which is represented by the output data is recorded by the second human-machine interface and is used for training the function. This enables the function to be optimized particularly quickly.
- personnel of the facility or plant can also be offered a selection from several predefined categorizations via the second human-machine interface, and information about the selection can then be recorded via the second human-machine interface.
- Possible categorizations can e.g. Reliability, emissions, power consumption, production costs, etc.
- the function is advantageous remotely from the reporting system, in particular on a cloud-based computer system. Training data representing messages from other automation systems can then also be recorded there and used for training the function. This can increase the quality of the function and thus the output data.
- reporting system is communicatively connected to the human-machine interface, in particular via a communication network of the automation system
- An evaluation unit which is designed to apply a function at least to input data that is based on the first Human-machine interfaces are displayed and represent the messages that are assigned to the second category (optionally also those of the first category), the function being trained by an algorithm from the area of machine learning, and thereby Output data are generated that represent a message from the automation system that is assigned to the first category,
- a first interface which is designed to receive the input data from a transmitter
- a second interface which is designed to provide the output data to a receiver that is different from the transmitter.
- the system according to the invention comprises a training unit for providing the trained function
- a first training interface which is designed to receive input training data that represent messages from the automation system
- a second training interface which is designed to receive output training data representing messages from the automation system, the output training data being related to the input training data
- a training computing unit that is designed to train the function based on the input training data and the output training data
- the second human-machine interface is designed to detect information about a quality of the message, which is represented by the output data, and to transmit this information to the training unit.
- the evaluation unit preferably also the training unit, is particularly advantageous, remotely from the reporting system, in particular on a cloud-based computer system.
- FIG 2 the cloud-based evaluation system of FIG 1 in a more detailed representation
- the automation system 2 here includes one or more industrial controls (here the controls 4) and at least one automation server 5.
- process 3 comprises actuators 6 that can be controlled by the controls. These can be individual actuators (e.g. a motor, a pump, a valve, a switch) or groups of such actuators or entire sections of a system.
- the process also includes sensors 7, which make actual values of process variables (e.g. temperatures, pressures, speeds) available to the controls 4.
- a communication network of the system 1 comprises at a higher level a system network 11, via which the automation server 5 is in communication with human-machine interfaces (HMI) 10, and a control network 12 (often also called a “system Network "), via which the controls 4 are in communication with each other and with the automation server 5.
- the controls 4 can be connected to the actuators 6 and sensors 7 via discrete signal lines 13 or via a field bus.
- the human-machine interfaces (HMI) 10 are usually designed as operating and monitoring stations and are arranged in a control room 13 of the system 1.
- the automation server 5 can be, for example, a so-called “operator system server” or “application server”, in which one or more system-specific application programs 8 are stored and during operation of the system 1 can be executed. These serve, for example, to configure the controls 4 in the system 1, to record and execute operator activities on the human-machine interface (HMI) 10 (e.g. set or change setpoints of process variables) or to generate messages for system personnel - gen and on the human-machine interface (HMI) 10 to display.
- HMI human-machine interface
- HMI human-machine interface
- the automation system 2 without the field devices is often referred to as a "process control system".
- the automation server 5 includes a message system 9, which is also implemented here by the application programs 8.
- a message can be an alarm message or a notice message, for example.
- An alarm message is a message that an error or an abnormal event has occurred and that the personnel must take immediate action.
- a notification message can, for example, indicate the need for maintenance.
- the event message can contain text and other information such as Include time and place of generation of the report.
- the automation system additionally includes a connection server 14, an evaluation system 20 and an additional man-machine interface 30, which preferably acts as an operator Station is trained.
- a mobile human-machine interface 31 for example a smartphone, notebook or tablet, can also be present.
- the man-machine interfaces 30, 31 are separated from the communication network 11, ie no communication is possible from the communication network 11 to the man-machine interfaces 30, 31 or vice versa.
- the human-machine interfaces 30 can be in communication with the evaluation system 20 via an office network 15. Firewalls 16, 17 can also be arranged between the connection server 14 and the evaluation system 20 and between the evaluation system 20 and the man-machine interfaces 30, 31.
- the cloud-based evaluation system 20 comprises a first communication interface in the form of a cloud-internet gateway 21, a data stream processing unit 22, a memory 23 for actual data
- (Real-time data) a memory 24 for historical data, a training unit 25, an evaluation unit 26, a web app 27, a second communication interface 28 for wired communication and additionally or alternatively a further second communication interface 29 for wireless communication.
- At least a first category A and a second category B of messages are defined in the message system 9, and the messages of the automation system are each assigned to at least one of the two categories A, B.
- the reporting system 9 sends a large number of type B messages (the data of which are designated here by B1, B2, B3, etc.) and a somewhat smaller number of type A (the data of which here is A1, A2 , etc. are designated) and via the communication network 11 on the man-machine interface parts 10 for display ge brought (see step 51 in FIG 3).
- data representing these messages are transmitted via the connection server 14 to the evaluation system 20 (see step 52 in FIG. 3).
- the cloud internet gateway 21 is designed to receive this data from the connection server 14 as a transmitter.
- the gateway 21 forwards this data to the data stream processing unit 22. On the one hand, this saves them for subsequent (almost) real-time processing in the memory. rather 23 and for archiving at the memory 24 (see step 53 in FIG. 3).
- the data in the memory 23 are forwarded as input data to the evaluation unit 26 without any great delay.
- the evaluation unit 26 is designed to apply a function to the input data, the function being trained by an algorithm from the field of machine learning, and output data being generated as a result that represent a message from the automation system that belongs to the first category A. is assigned (see step 54 in FIG 3).
- the function trained by machine learning can be used to good effect on the one hand for filtering messages of the first category from the total amount of all messages (i.e. messages of the first and second category).
- the output data represent one of the messages that is already represented by the input data within a defined period of time.
- machine learning can be used to predict messages of the first category on the basis of at least the messages of the second category, and optionally also the messages of the first category.
- the output data usually represent a message that is not yet represented by the input data within a defined period of time. But they can also represent a message that is already represented by the input data within a defined period. This is the example message A2.
- the output data A1 and A2 are transmitted via the web app 27 either via the communication interface 28 to the man-machine interface 30 and / or via the communication interface 29 to the man-machine interface 31 and displayed there (see step 55 in FIG. 3).
- the interfaces 28, 29 are thus designed to provide the output data to a receiver that is different from the sender of the input data.
- the message which is represented by the output data A1 or A2, is thus displayed in the control room 13 in addition to all other messages from the message system 9.
- the attention of the staff is particularly drawn to these messages.
- the communicative separation of the second human-machine interface 30, 31 from the reporting system 9 or the automation system 2 enables undesired, safety-relevant repercussions on the automation system 2 to be avoided. There is also no intervention in the reporting system 9 intended and necessary.
- the evaluation system includes the training unit 25, which includes:
- a first training interface 41 which is designed to receive input training data ET, which represent messages from the automation system 2,
- a second training interface 42 which is designed to receive output training data AT, which represent messages from the automation system 2, the output training data being related to the input training data ET,
- a training arithmetic unit 43 which is designed to train the function 45 based on the input training data ET and the output training data AT
- a third training interface 44 which is designed to provide the trained function 45 for the evaluation unit 26.
- the input training data ET are received by the data memory 24.
- the output training data AT are received by experts.
- the training of function 45 is based on the input training data ET and the output training data AT.
- Training data which represent messages from other automation systems can also be recorded in memory 24 and used for training function 45. This can increase the quality of function 45 and thus of the output data.
- the function 45 By training the function 45 based on training data, the function can adapt to new circumstances and identify and extrapolate patterns in the message sequences.
- Function parameters can be adjusted through training. It can e.g. Supervised training, semi-supervised training, unsupervised training, reinforcement learning and / or active training can be used. Representation learning (an alternative term is "feature learning") can also be used.
- the parameters of the function can be adapted iteratively through several training steps.
- the function 45 is preferably based on cross correlation of messages, a neural network (preferably long / short term memory - LSTM) or on association rules. Preferably a combination of these methods is used, the result being e.g. can be obtained by the sum of the results of all procedures, by majority results, by individual results, etc.
- information Q can also be provided by personnel in the system via a Quality of the message, which is represented by the output data, recorded and transmitted to the evaluation system 20 and used there for training the function 45. This enables function 45 to be optimized particularly quickly.
- the information Q can relate, for example, to whether a predicted message has occurred at all, and in the case of occurrence, whether the specified time or the period for its occurrence was correctly predicted.
- the output data preferably include a point in time or a period for the message to occur and a probability for the message to occur at this point in time or in this time period.
- the different categories can preferably express how critical the respective type of message is for the reliability of the system.
- the different categories can also refer to other aspects such as Production costs, production volume, production quality, raw material consumption, electricity consumption, emissions, etc.
- Human-machine interface parts 30 are offered a selection from several predefined categorizations and information about the selection can then be recorded via the second human-machine interface parts 30.
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Manufacturing & Machinery (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Human Computer Interaction (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Alarm Systems (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP19000158.6A EP3715987A1 (de) | 2019-03-29 | 2019-03-29 | Verfahren und system zum management von meldungen eines automatisierungssystems |
PCT/EP2020/056649 WO2020200687A1 (de) | 2019-03-29 | 2020-03-12 | Verfahren und system zum management von meldungen eines automatisierungssystems, insbesondere einer industriellen anlage |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3928173A1 true EP3928173A1 (de) | 2021-12-29 |
Family
ID=66290163
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP19000158.6A Ceased EP3715987A1 (de) | 2019-03-29 | 2019-03-29 | Verfahren und system zum management von meldungen eines automatisierungssystems |
EP20714887.5A Pending EP3928173A1 (de) | 2019-03-29 | 2020-03-12 | Verfahren und system zum management von meldungen eines automatisierungssystems, insbesondere einer industriellen anlage |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP19000158.6A Ceased EP3715987A1 (de) | 2019-03-29 | 2019-03-29 | Verfahren und system zum management von meldungen eines automatisierungssystems |
Country Status (5)
Country | Link |
---|---|
US (1) | US20220187800A1 (de) |
EP (2) | EP3715987A1 (de) |
CN (1) | CN113646720A (de) |
BR (1) | BR112021018983A2 (de) |
WO (1) | WO2020200687A1 (de) |
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GB0318339D0 (en) * | 2003-08-05 | 2003-09-10 | Oxford Biosignals Ltd | Installation condition monitoring system |
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CN101242549A (zh) * | 2007-11-22 | 2008-08-13 | ***通信集团山东有限公司 | 通信网络告警关联的神经网络构建方法 |
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CN107427268B (zh) * | 2014-11-14 | 2023-07-28 | 西门子保健有限责任公司 | 用于基于纯几何机器学习的血流储备分数的方法和*** |
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EP3187950B1 (de) | 2015-12-31 | 2019-11-27 | ABB Schweiz AG | Verfahren zur verwaltung von alarmen in einem steuerungssystem |
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2019
- 2019-03-29 EP EP19000158.6A patent/EP3715987A1/de not_active Ceased
-
2020
- 2020-03-12 EP EP20714887.5A patent/EP3928173A1/de active Pending
- 2020-03-12 US US17/442,805 patent/US20220187800A1/en active Pending
- 2020-03-12 BR BR112021018983A patent/BR112021018983A2/pt unknown
- 2020-03-12 WO PCT/EP2020/056649 patent/WO2020200687A1/de unknown
- 2020-03-12 CN CN202080025915.1A patent/CN113646720A/zh active Pending
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Publication number | Publication date |
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CN113646720A (zh) | 2021-11-12 |
WO2020200687A1 (de) | 2020-10-08 |
BR112021018983A2 (pt) | 2021-11-30 |
EP3715987A1 (de) | 2020-09-30 |
US20220187800A1 (en) | 2022-06-16 |
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