WO2008104136A1 - Système et procédé pour planifier un système technique - Google Patents

Système et procédé pour planifier un système technique Download PDF

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Publication number
WO2008104136A1
WO2008104136A1 PCT/DE2007/000359 DE2007000359W WO2008104136A1 WO 2008104136 A1 WO2008104136 A1 WO 2008104136A1 DE 2007000359 W DE2007000359 W DE 2007000359W WO 2008104136 A1 WO2008104136 A1 WO 2008104136A1
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WO
WIPO (PCT)
Prior art keywords
semantic
feature
planning
technical system
subsystem
Prior art date
Application number
PCT/DE2007/000359
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German (de)
English (en)
Inventor
Ingmar Patrick Hofmann
Original Assignee
Siemens Aktiengesellschaft
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Priority to DE112007003483T priority Critical patent/DE112007003483A5/de
Priority to PCT/DE2007/000359 priority patent/WO2008104136A1/fr
Publication of WO2008104136A1 publication Critical patent/WO2008104136A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
    • G06F11/2257—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing using expert systems
    • 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
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0426—Programming the control sequence
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/23—Pc programming
    • G05B2219/23005—Expert design system, uses modeling, simulation, to control design process
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/25—Pc structure of the system
    • G05B2219/25428—Field device
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2642—Domotique, domestic, home control, automation, smart house

Definitions

  • the invention relates to a system and a method for planning a technical system.
  • a planner of such an automated system is now faced with the task of interconnecting all these components in a suitable manner, so that a desired automation task is reliably fulfilled by the resulting technical system.
  • a task entails a multitude of possibilities for error, which u.U. only when the system is later set up or even when it is being put into operation. If, for example, incompatible components are connected to one another, this can lead to a malfunction of the resulting automation system up to the destruction of one or more components.
  • the object of the invention is to provide a low-cost prognosis possibility for possible false states. which can occur in a technical system being planned.
  • This object is achieved by a method for predicting at least one state value of a technical system or a process that can be carried out with the technical system using a semantic network representing knowledge about the technical system in the form of feature variables and semantic dependencies linking the feature variables, with the following steps:
  • a planning system for planning a technical system on the basis of a semantic network, which represents knowledge about the technical system in the form of feature variables and the semantic dependencies linking the feature variables
  • a semantic network is evaluated that includes knowledge about the technical system.
  • a semantic network is a formal model of concepts and their relationships to each other.
  • the terms are represented as nodes of a graph. adjustable, which represents the semantic network. According to the invention, these terms are realized as feature variables. Relations or relations between the terms or feature variables of the semantic network correspond to the edges of the graph. In the method according to the invention, therefore, the edges of the graph are formed by the semantic dependencies between the feature variables.
  • a semantic network forms the basis for reproducing knowledge with information technology.
  • the semantic network replaces a physical or system-theoretical model, as would be used in a simulation of the technical system.
  • the semantic network can be parameterized for an operating case to be investigated , By evaluating the semantic network thus occupied, it is finally possible to make a prediction of one or more state values of the technical system.
  • planning can be understood as meaning both the design of the technical system and the engineering and project planning phase.
  • a user may use the semantic network to test an engineering data set before using it for the real plant.
  • the method according to the invention has the advantage that the outlay for creating the semantic network is considerably lower than the outlay that would be required to generate a physical and / or system-theoretical model of the technical system. Moreover, not only can errors be predicted with a semantic network, but also conclusions can be made about their causes. This can be achieved by a targeted evaluation of the semantic network taking into account the causal relationships between the feature variables represented by the semantic dependencies. At a complex simulation, on the other hand, the cause of a recognized error can often not be found directly, as would be the case with the real system.
  • the method for early detection of a faulty state of the system and / or the feasible with the system process before commissioning of the system is performed.
  • an embodiment of the invention is advantageous in which the technical system is designed as an automation system.
  • automation technology a large number of intelligent and complex components are frequently networked with one another via bus systems, whereby, for example, due to compatibility problems, there are many possibilities for error. Such errors can already be avoided in the planning of the automation system by suitable evaluations of the semantic network.
  • an embodiment of the invention is particularly advantageous in which the technical system comprises at least a first subsystem and a second subsystem functionally coupled to the first subsystem and the semantic network the following process steps are generated during the planning of the technical system:
  • the technical system should therefore be constructed by coupling two subsystems.
  • the subsystems can be individual components or even systems of already networked individual components.
  • the starting point is a semantic subnet that contains knowledge about the corresponding subsystem. Since these system descriptions are independent of each other, it is not immediately obvious how the two subsystems interact with each other. Therefore, the two semantic subnets are merged with each other via the at least one semantic dependency.
  • the at least one semantic dependence describes the functional coupling that the two subsystems have in the technical system.
  • the semantic network can also be successively expanded in the manner described.
  • the semantic network is successively extended by further semantic subnets, while a user of the
  • Planning software which is, for example, an engineering system that extends technical systems by adding new software components. In this way, the semantic network needed for the prognosis is generated "on the fly” as it were during the engineering of the system. Thus, with the corresponding modeling work of the component manufacturers, the user of the planning software has almost no extra work required to obtain the desired predictive capability for the technical system.
  • the software components are realized here in particular in the form of icons, which represent a pictorial representation of the respective subsystem within an input mask of the planning software.
  • a linking of the first and second semantic subnetwork can be achieved by an advantageous embodiment of the invention, in which the feature variables of the first and second semantic subnets are identifiable via a respective feature identifier, and wherein the linking of the first and second subnetwork by the following process steps completed becomes: Identification of a first feature variable of the first subnetwork on the basis of a searched feature identifier,
  • the feature variables of the semantic network are each assigned feature identifiers. Based on the feature identifier, the individual feature variables can be identified.
  • the feature identifier of a feature variable of the first semantic subnetwork could be, for example, "voltage”.
  • the feature variable of the said semantic subnetwork would thus be identifiable by the term "voltage”.
  • another semantic subnetwork describing the Dynamo could be a feature variable with the feature identifier "tension" aufwei- • sen.
  • the equality of the feature identifiers would first be recognized for coupling the first and second subnetworks. Appropriately, therefore, would be the first semantic subnet with the second semantic subnet over the two feature variables with the feature identifier "voltage” done.
  • the two characteristic variables with the characteristic identifier "voltage" are in semantics with further characteristic variables of their respective subnetworks.
  • a failure of the headlamp would be justified by the fact that no voltage is applied to the terminals of the headlamp.
  • Such a cause / symptom relationship is stored in the semantic subnetwork of the headlight in the form of a semantic relation.
  • at least one semantic dependency would be deposited from which a cause for a voltage drop could be derived.
  • one of the two characteristic variables with the characteristic identifier "Voltage” is deleted.
  • the semantic dependencies previously related to the deleted feature variable are now "bent over” to the non-deleted feature variable with the feature identifier "voltage”, so that a combination of the two subnetworks results from a causal dependency.
  • linking of two subnetworks can also take place via a plurality of feature variables of the respective subnetwork, if several pairs of identical feature identifications are present in the two semantic subnetworks.
  • the feature variables are executed as random variables, which in each case an absolute or a conditional probability distribution is assigned, the conditional probability distribution of a feature variable being determined by at least one further feature variable linked to said feature variable via a semantic dependency, and wherein the absolute probability distribution a priori knowledge about the probability of realization representing the connected random variables without further semantic input dependencies.
  • a RANDOM VARIABLE or random variable is a function (not a variable in the usual sense) that assigns values to the results of a random experiment. These values are called realizations of the random variable.
  • a random variable in the network with at least one incoming edge is called an effect, a random variable with at least one outgoing edge is called a CAUSE.
  • a random variable with at least one incoming and one outgoing edge is called CONCLUSION.
  • END EFFECT a random variable without a successor node (that is, without further effects) is called END EFFECT.
  • Each random variable is assigned a probability distribution.
  • Probabilistic distributions are usually represented in the form of probability tables (for discrete random variables) or probability functions (for continuous random variables).
  • Bayesian networks are currently being classified by many experts as a particularly promising form of knowledge representation (especially for computer-aided diagnosis systems).
  • the individual semantic subnetworks needed to describe the subsystems to be coupled together do not necessarily have to be local.
  • the subnets or knowledge bases that are assigned to the individual components or subsystems of a complex technical system need not all be stored centrally.
  • a supplier of a peripheral component for an automation system also provides a knowledge module describing the component in the form of a semantic subnetwork.
  • this semantic subnetwork could be linked to an already existing semantic subnetwork in the manner described above, as soon as the new component is integrated into the automation system during the planning.
  • An automated implementation of the method can be realized in a further advantageous embodiment of the invention with a computer program product, which contains program code means for carrying out a method according to one of the previously described embodiments, when said computer program product is executed on a data processing system.
  • FIG. 1 schematically shows the structure of a planning system for planning a technical system
  • FIG. 3 shows a knowledge library
  • FIG. 4 shows a semantic dependency between a first feature variable and a second feature variable
  • FIG. a first feature variable embodied as an error node, a second feature variable in the form of a symptom node, a third type of feature variable embodied as a conclusion node
  • a first feature variable embodied as an extensible error node an extensible symptom node defined by a second feature variable 18 an extension of a semantic network during a planning of a technical system by means of a planning system
  • three knowledge modules of a knowledge library each of which has the knowledge of a subsystem of the technical
  • FIG. 19 shows the complete resulting semantic network
  • FIG. 20 schematically shows a planning system designed as an engineering system for planning a technical system on the basis of a semantic network.
  • FIG. 1 shows schematically the structure of a planning system 9 for planning a technical system.
  • a memory 10 of the planning system 9 stores the so-called active knowledge.
  • the planning system 9 can immediately access this active knowledge in order to determine a state value of a technical system being planned or a process to be carried out by means of the technical system, without having to load knowledge from other memory components.
  • the memory 10 includes a first semantic subnet 11 and further subnets 12, which, however, are not associated with the first semantic subnet 11.
  • the subnetworks contain knowledge about subsystems or components that can be functionally linked to the technical system or that form it through their interaction.
  • FIG. 2 shows a knowledge module 13, which serves as a knowledge network template for sub-knowledge networks 11, 12 stored in the memory 10 of the planning system 9 (see FIG. 1).
  • the sub-knowledge networks 11, 12 are created by instantiating such knowledge network templates or knowledge modules 13.
  • a knowledge module 13 comprises a knowledge network 14 and an interface 15, which is provided for coupling to further knowledge networks.
  • FIG. 3 shows a knowledge library 16.
  • the knowledge library 16 contains knowledge modules 13 whose instances can be linked in the active knowledge of the planning system 9 to form a semantic network.
  • FIGS. 4 to 10 show modes of representation of feature variables which are used in the following figures to illustrate an expansion of a semantic network during a planning of a technical system performed, for example, with an engineering system.
  • FIG. 4 shows by way of example a semantic dependency 19 between a first feature variable 17 and a second feature variable 18.
  • the feature variables 17, 18 are subdivided below into three groups: error nodes, symptom nodes and inference nodes.
  • the first feature variable 17 shown in FIG. 4 represents an error node
  • the second feature variable 18 a symptom node.
  • Error nodes generally have a characteristic characterizable feature.
  • symptom nodes carry a characteristic that can be characterized as an effect.
  • the arrows representing the semantic dependence 19 point from an error node to a symptom node and represent a causal dependence on cause and effect. Since the error node in the figure has no further in-depth causal dependencies, this represents an "initial cause". Since the symptom node in the figure has no further outgoing causal dependencies, this represents an "end effect”.
  • FIG. 5 shows a first feature variable 17 embodied as an error node.
  • this error node 17 can also have a plurality of semantic dependencies 19.
  • the cause represented by the error node 17 is correspondingly associated with three effects represented by each symptom or inference node.
  • FIG. 6 correspondingly shows a second feature variable 18 in the form of a symptom node. This too can be linked to several semantic dependencies 19. In the illustrated example, three causes lead to the effect represented by the symptom node 18. 7 shows a third type of feature variable, which is designed as a conclusion node 20. This is connected to both incoming and outgoing arrows, each representing a semantic dependency 19.
  • FIG. 8 shows a first feature variable 17, which is designed as an expandable error node.
  • An expandable node is represented here and below by the double outline.
  • An extensible node (in addition to its significance, for example, as a carrier of a probability distribution, feature representative, or cause or effect) represents an interface via which the associated semantic subnetwork can be merged with a complementary extensible node of another semantic subnetwork.
  • An expandable error node such as that shown in FIG.
  • Error node (represented by the first feature variable 17) can be coupled to an expandable symptom node having the same feature identifier (or semantics).
  • FIG. 9 shows an expandable symptom node that is represented by a second feature variable 18. Again, the double outline indicates the expandability of the symptom node 18 again.
  • the extensible symptom node 18 can be coupled to the expandable error node 17 of FIG. 8, provided that these two feature variables 17, 18 have the same feature identifier (or semantics).
  • the planning system is designed as an engineering system for planning or extending an industrial automation system. It provides a user interface with a graphical user interface in which he can design the automation system by software-based graphical interconnection of individual software components 1, 2 (which respectively represent system subsystems or individual system components).
  • the left half of the figure outlines the steps that the user performs for the functional interconnection of two software components 1, 2, a first software component 1 representing a first subsystem and a second software component 2 representing a second subsystem of the automation system.
  • the active knowledge is tracked during the design work of the user in particular such that it corresponds to the current planning state of the automation system.
  • the user draws, for example with a computer mouse, the first software component from a component or system library into an input area of the engineering system, which is provided for defining the structural and functional structure of the automation system. He plans to use the associated automation component in the engineering system under construction.
  • the memory 10 of the planning system is stored according to a semantic network, which includes only a first semantic subnet 11 that includes knowledge about the automation component.
  • the internal structure of the knowledge in the subnetwork 11 is not relevant for this figure and is therefore simplified as a cloud.
  • the active knowledge of the engineering system is represented only by the knowledge represented by the first subnetwork 11.
  • the first semantic subnet 11 includes a first feature variable 17, which is executed as an expandable error node and thus serves as an interface for merging the first semantic subnet 11 with a further semantic subnet.
  • the user then moves the second software component 2 from the component library into the input area in order to expand the planned automation system by a second component. Finally, it couples the two software components 1, 2 via a functional link 3 within the input mask. This represents, for example, a data connection of an output of the first software components 1 with an input of the second software component 2 or vice versa.
  • a second semantic subnet 21 is selected from a knowledge library with knowledge modules representing knowledge about the second component. This should, if the coupling of the two components is allowed, have an expandable symptom node that has the same feature identifier (resp.
  • the second subnet 21 is finally loaded into the active knowledge. Since the feature identifiers of the extensible symptom node 18 and the expandable error node 17 are identical and thus both feature variables 17, 18 have the same meaning, these two nodes can be merged into a single node. Practically, this is implemented, as shown in the next method step, by deleting one of the feature variables 17, 18. The semantic dependencies previously targeted at the deleted node are referred to the undeleted node. The result is an inference node 20, from which both semantic dependencies 19 start and end.
  • FIGS. 11 to 19 explain an extension of knowledge, which is carried out, for example, during or after a planning of a technical system for determining at least one state value of the technical system, in a somewhat larger example.
  • a first knowledge module 29 comprises error, symptom and inference nodes, wherein two error nodes are implemented as expandable error nodes 27.
  • a second knowledge module 30 which comprises two expandable error nodes 27 and two expandable symptom nodes 28.
  • the knowledge library contains a third knowledge module 31, which also includes an expandable symptom node 28 in addition to internal error, symptom and inference nodes. From the represented knowledge modules 29, 30, 31 individual semantic subnetworks can be instantiated.
  • FIG. 12 shows a first semantic subnet 11, which has emerged by instantiation from the first knowledge module 29 from FIG.
  • the first semantic subnetwork 11 has been loaded into the active knowledge of the paging system due to a user input. For example, when engineering the technical system, the user plans to use a subsystem represented by the first semantic subnet 11.
  • a first feature variable 17 of the first subnet 11 has emerged from the instantiation of the first knowledge module 29 and the associated instantiation of one of the expandable error nodes 27.
  • the first feature variable 17 represents an extension possibility for the first semantic subnet 11
  • a second semantic subnet 21 is now loaded into the active knowledge, which represents knowledge about the second subsystem.
  • the instance of the second knowledge module 30 is a second semantic subnet 21, in which a second feature variable 18 has been found, which has the same feature identifier (or semantics) Like the first feature variable 17 of the first semantic subnet 11.
  • the second feature variable 18 is accordingly an instance of one of the expandable symptom nodes 28 of the second knowledge module 30.
  • FIG. 14 shows a merger of the first and second semantic subnetwork 11, 21 into a common semantic network 22.
  • the first feature variable 17 has been deleted and the second feature variable 18 has been converted into an inference node. Accordingly, the semantic dependence 19 originally emanating from the first feature variable 17 now starts from the second feature variable 18.
  • a third feature variable 32 which has also been instantiated from an expandable error node, represents an extension option for a third semantic subnet representing knowledge about the third subsystem.
  • FIG. 15 shows an extension of the semantic network with the third semantic subnetwork 25, which likewise emerges from an instantiation of the second knowledge module 30.
  • the third semantic subnetwork 25 contains a fourth feature variable 33, which is the instance of an expandable symptom node (here of the second knowledge module 24 of FIG. 11) and the same feature identifier as the third feature. malsvariable 32 has. Accordingly, the diagnostic system will merge the subnets between the third and fourth feature variables 32, 33.
  • FIG. 16 shows the integration of the third semantic subnetwork 25 into the semantic network 22 stored in active knowledge.
  • the third feature variable 32 from FIG. 15 has now been deleted, the original semantic dependency 19 now starting from the fourth feature variable 33 resulting from the merging was converted into an inference node.
  • a fourth subnet is accordingly loaded into the active knowledge, which represents knowledge about the fourth subsystem.
  • the current semantic network in active knowledge contains a fifth node variable 34, which is the instance of an extensible error node.
  • FIG. 17 shows the expansion of the active knowledge by the fourth semantic subnet 26, which represents an instance of the third knowledge module 31 from the knowledge library.
  • the third knowledge module 31 a corresponding expandable symptom node has been found.
  • FIG. 18 shows the integration of the fourth semantic subnetwork 26 into the semantic network stored in the active knowledge by deleting the fifth feature variable 34 and adopting its semantic dependency 19 through the sixth feature variable 35.
  • FIG. 19 finally shows the complete resulting semantic network 22, which was created by the expansions of the active knowledge during the planning of the technical system and for the prognosis of possible state values of the planned th system can be used.
  • some feature values of the semantic network 22 can be pre-assigned to define an initial situation. Unoccupied values can then be determined by evaluating the semantic network in order, for example, to identify possible error states at an early stage, which are caused, for example, by interconnecting incompatible components.
  • FIG. 20 schematically shows a planning system 4 designed as an engineering system for planning a technical system on the basis of a semantic network.
  • software components 6 which represent subsystems of the overall system 5, are interconnected and parameterized. These software components 6 can represent components of the overall system 5 realized both in hardware and in software. Thanks to a formal interface description, a "virtual" circuit and parameterization is possible even if the real components do not yet exist. Therefore, such an engineering system can be used not only for commissioning but also for designing equipment.
  • Each subsystem represented by a software component 6 fulfills at least one sensible setpoint function 7 in the overall system 5.
  • a knowledge module 13 describes the behavior of a subsystem relevant for fulfilling a desired function 7.
  • a knowledge module 13 is thus assigned in each case to a pair from a software component 6 representing the subsystem and a satisfied function 7.
  • the knowledge module 13 is a semantic network 22 of features 8 executed as a Bayesian network, which are interconnected by conditional probability distributions 36 with precisely quantified causal dependencies 19.
  • Features 8 are perceptible properties of a subsystem, each by position in the semantic network 22 represent cause or effect.
  • the knowledge modules 13 are interconnected from a knowledge library 38 via special features that can be imported or exported, which are referred to as expandable nodes or interface nodes 39. Instantiated and interconnected knowledge modules 13 form the active knowledge of the planning system 4.
  • the engineering system sets feature values 40 of features 41 in knowledge modules 13 analogously to a user-assigned assignment of parameters 42 to the software component components 6.
  • An inference engine 43 calculates from the a priori probabilities stored by knowledge editors in the knowledge modules 13 and the feature value selection given evidence the probabilities of all known symptom features. Error symptoms of high probability are effects of incorrect interconnection or parameterization and are displayed to the user.

Abstract

L'invention concerne un système et un procédé pour planifier un système technique. L'invention vise à fournir une possibilité de prévision la moins coûteuse possible pour d'éventuels états d'erreur qui peuvent apparaître pour un système technique en cours de planification. A cet effet, l'invention fournit un procédé pour prévoir au moins une valeur d'état d'un système technique ou d'un processus pouvant être exécuté avec le système technique, à l'aide d'un réseau sémantique (22) qui représente des connaissances sur le système technique sous la forme de variables caractéristiques (17, 18, 32, 33, 34) et de relations sémantiques (19) combinant les variables caractéristiques (17, 18, 32, 33, 34). Ce procédé comprend les étapes suivantes : préaffectation d'un paramètre de système ou d'état (42) du système technique à au moins une variable caractéristique (17, 18, 32, 33, 34); et prévision de la valeur d'état au moins unique par interprétation du réseau sémantique (22) préalablement occupé. Le procédé est exécuté pendant la planification du système technique.
PCT/DE2007/000359 2007-02-26 2007-02-26 Système et procédé pour planifier un système technique WO2008104136A1 (fr)

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DE112007003483T DE112007003483A5 (de) 2007-02-26 2007-02-26 System und Verfahren zur Planung eines technischen Systems
PCT/DE2007/000359 WO2008104136A1 (fr) 2007-02-26 2007-02-26 Système et procédé pour planifier un système technique

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EP3151076A1 (fr) * 2015-09-29 2017-04-05 Siemens Aktiengesellschaft Procédé de modélisation d'un système technique
CN107037770A (zh) * 2015-09-29 2017-08-11 西门子公司 用于对技术系统进行建模的方法
DE102016222640A1 (de) * 2016-11-17 2018-05-17 Bayerische Motoren Werke Aktiengesellschaft Überwachungssystem, Verfahren, insbesondere zur Detektion von Fertigungsfehlern, sowie Verwendung eines Überwachungssystems
EP4141595A1 (fr) * 2021-08-26 2023-03-01 Siemens Aktiengesellschaft Procédé de détection des causes d'anomalie d'un produit physique
WO2023025431A1 (fr) * 2021-08-26 2023-03-02 Siemens Aktiengesellschaft Procédé de reconnaissance de causes d'anomalies dans un produit physique

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