WO2019057489A1 - Procédé et générateur de données d'entraînement destinés à configurer un système technique et équipement de commande destiné à commander le système technique (ts) - Google Patents

Procédé et générateur de données d'entraînement destinés à configurer un système technique et équipement de commande destiné à commander le système technique (ts) Download PDF

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Publication number
WO2019057489A1
WO2019057489A1 PCT/EP2018/073806 EP2018073806W WO2019057489A1 WO 2019057489 A1 WO2019057489 A1 WO 2019057489A1 EP 2018073806 W EP2018073806 W EP 2018073806W WO 2019057489 A1 WO2019057489 A1 WO 2019057489A1
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operating
distribution function
technical system
dimensional
record
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PCT/EP2018/073806
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German (de)
English (en)
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Stefanie VOGL
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Siemens Aktiengesellschaft
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Publication of WO2019057489A1 publication Critical patent/WO2019057489A1/fr

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    • 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
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32193—Ann, neural base quality management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • trainable computational modules that implement methods of machine learning to optimize the configuration of the technical system through training.
  • Such trainable computational modules are often trained with known standard methods of machine learning on the basis of input operating data sets that describe specific operating conditions or other operating conditions of the technical system, future Radiozu ⁇ states, optimal control actions or other target operating ⁇ parameters of the technical system as closely as possible to ermit ⁇ stuffs.
  • a respective target operating parameter derived from the arithmetic module from the input operating data sets can be compared with an actual, optimal or desired target operating parameter, and the arithmetic module can be trained to minimize a deviation.
  • Such learning is often referred to as supervised learning.
  • the more training data ie operating data that can be used for training, and / or comparison data for the target parameters, the more successful training is.
  • the training data should representatively cover the largest possible range of possible operating conditions of the technical system. Of particular importance in many cases is a representative coverage of extreme operating conditions, as such conditions can increasingly lead to critical or undesirable system conditions. However, extreme operating conditions usually occur only rarely or even actively avoided in running systems so that stand for such operating ranges frequently ent ⁇ speaking less training data.
  • a known measure for improving training also in operating areas which are sparsely covered by training data is to allocate a higher weight during training to seldom occurring operating conditions or to select such operating conditions with an increased probability.
  • Control means having the features of claim 17, by a computer program product having the features of Pa ⁇ tent lengths 18 as well as by a computer readable Speicherme ⁇ dium with the features of patent claim 19.
  • a technical system for computer-aided configuration of a technical system, in particular a gas turbine, a wind turbine, a solar power plant, an internal combustion engine, a Fer ⁇ tion system or a power grid based training data te a variety of first and second operating data sets of the technical system are detected, a respec ⁇ ger first operating data set assigned to a respective second operating data record and with this dynamically correct is lier.
  • a respective empirical n-dimensional correlation vector with n> 2 is derived from a respective first operating data record and the respectively assigned second operating data record. According to the invention, an empirical n-dimensional distribution function of the empirical correlation vectors is determined.
  • a first operating data record is read in as the generator operating data record, for which a multiplicity of potential n-dimensional correlation vectors are generated.
  • a potential second operating data set is generated in each case for the potential correlation vectors.
  • one or more training ⁇ records are generated and output for configuring the technical system.
  • a training data generator for carrying out the method according to the invention, a training data generator, a control device, a computer program product and a computer-readable storage medium are provided.
  • the method according to the invention and the training data generator according to the invention and the control device according to the invention can be implemented or implemented, for example, by means of one or more processors, application-specific integrated circuits (ASIC), digital signal processors (DSP) and / or so-called field programmable gate arrays (FPGA).
  • ASIC application-specific integrated circuits
  • DSP digital signal processors
  • FPGA field programmable gate arrays
  • One advantage of the invention is that the generated training data sets can significantly increase a set of available training data for training control devices, especially in otherwise sparsely covered operating areas of a technical system correla ⁇ functions and dependencies between first and second operating data sets realistic, representative and consistent be mapped to the generated training records.
  • By training with realistic and representative training data records a configuration of the technical system, in particular for rarely occurring operating ranges, can generally be considerably improved.
  • the invention typically requires only relatively ge ⁇ rings computing resources.
  • a trainable calculation module can be trained to control the technical ⁇ rule system based on the generated training data sets.
  • the trainable calculation module can in this case an artificial neural network, a recurrent neural network, a fold ⁇ the neural network, a AutoEncoder, a Deep-learning architecture, a support vector machine, a data transmission ⁇ nes trainable regression model, a k-nearest - Neighbor classifier, a physical model and / or a decision tree include.
  • a respective first operating data set may be an input operating data record for the trainable computing module and a respective second operating data set may be a target operating parameter for the trainable computing module.
  • the input operational data set can this operating states in particular specific treatment or describe other operating conditions or operation ⁇ parameters of the technical system.
  • the target operating parameter can, for example, a predicted operating state, a return that a resource requirements, material emitting a pollutant, a wear and / or optimal control ⁇ actions of the technical system concern.
  • the trainable computation module can be trained accordingly, based on the input operation records the target operating parameters as accurately as possible to determine or predict. Training data for predetermined or suitably determined input operating data sets can then be generated by the invention in a targeted manner.
  • a respective first operating data set may be a target operating parameters for the trainable processing module and a respective second operation ⁇ record an input operation data set for the trainable computing module.
  • Trainingsda ⁇ th operating parameters can be generated selectively at predetermined or suitable determined target.
  • a multiplicity of randomly distributed data sets can be generated for the generator operating data record.
  • a respective potential correlation vector can be formed on the basis of the generator operating data record and of a respective data record distributed to ⁇ .
  • a range of operating states of the technical system can be tektiert de-, which is covered only sparingly by the first operational records, or contains the observed extreme values of the second operational records and / or adjacent to this on ⁇ .
  • a multiplicity of generator operating data sets preferably as a function of a random process, can then be generated and used according to the invention. In this way, training data can be selectively generated in extreme and / or under-represented operating areas to improve training in these areas.
  • an approximate numerical n-dimensional distribution function can be provided based on the empirical n-dimensional distribution function the potential correlation vectors are respectively supplied.
  • a respective potential second operating data record can then be derived from a respective return value of the numerical n-dimensional distribution function.
  • Such a numerical n-dimensional distribution function can often be evaluated faster and applied more flexibly.
  • an empirical n-dimensional copula distribution function he ⁇ averages can be used as empirical n-dimensional distribution function.
  • An n-dimensional copula distribution function ie a copula distribution function with n arguments, can in principle completely map a stochastic dependency structure between its n arguments, thus allowing a substantially precise modeling of the dependency structure between first and second operational data records.
  • an empirical n-dimensional Copula distribution function can be determined with relatively low Rechenres ⁇ resources.
  • the determination and application of an n-dimensional copula distribution function advantageously scales with increasing dimension n.
  • an n-dimensional copula model function can be adapted by parameter fitting to the empirical n-dimensional distribution function and provided as a numerical n-dimensional distribution function.
  • a respective first component-specific univariate distribution function of values of this component and for a respective component of the second operation data sets a respective second component-specific univariate distribution function of values of this component can be determined for a respective component of the first operational data sets.
  • the empirical n-dimensional correlation vectors can then each consist of a first operating data set transformed component-by-component on the basis of the at least one first univariate distribution function and a component be formed tenweise based on the at least one second univariate distribution function transformed second operating record.
  • the respective return value of the numerical n-dimensional distribution function can be transformed by a function which is inverse to a second univariate distribution function.
  • the respga can ⁇ valued are mapped to a range of values of the second operating parameters.
  • an average value and / or a median of values of the potential second operational data records can be formed. In this way, a statistical distribution of the potential second operating data records can be mapped to a representative value.
  • FIG. 1 shows a gas turbine with a control device according to the invention
  • FIG. 2 shows a control device according to the invention in a configuration phase
  • FIG. 3 shows a flow chart of the method according to the invention
  • FIG. 4 shows a training data generator according to the invention in a setting phase
  • FIG. 5 shows the training data generator according to the invention in a generation phase.
  • FIG. 1 illustrates a schematic representation of a gas turbine as a technical system TS by way of example.
  • the technical system TS is also a wind turbine, a solar power plant, an internal combustion ⁇ machine, a power grid, a manufacturing plant or at ⁇ particular facility or a combination thereof.
  • the gas turbine TS is coupled to a control device CTL according to the invention, which can be implemented as part of the gas turbine TS or wholly or partially external to the gas turbine TS.
  • the control device CTL is used to control the technical system TS.
  • Under a control of the technical system TS is here also a provision of the technical system TS and an output and use of control ⁇ relevant, that is to control the technical system TS understood contributing data and control signals.
  • control-relevant data may in particular include prognosis data, monitoring data and / or classification data, which may be used in particular for operational optimization, monitoring or maintenance of the technical system TS and / or for wear or damage detection.
  • the gas turbine TS further has sensors S coupled to the control device CTL, which continuously measure one or more operating parameters BP of the gas turbine TS and transmit them to the control device CTL.
  • the control device CTL can also record further operating parameters from other data sources of the technical system TS or from external data sources.
  • operating parameters BP here and below, in particular, physical, control engineering, operational and / or design-related operating variables, properties, performance data, impact data, yield data, demand data, condition data, system data, default values, control data, sensor data data, readings, environmental data, monitoring data,
  • This can be, for example, data on temperature, pressure, emissions, vibrations, oscillation states, resource consumption, etc.
  • a turbine Leis ⁇ tung, a rotational speed, vibration frequencies and / or amplitudes of vibration can affect.
  • One or more values of one or more operating parameters may be represented or summarized by an operational record.
  • Such an operating data set can in particular include a one-dimensional or multidimensional, in particular high-dimensional operating parameter vector.
  • the term operating parameter is to include in the following also such operating parameter vectors.
  • a respective operating parameter can therefore also represent a vector of operating parameters.
  • FIG. 2 shows a schematic illustration of a control device CTL according to the invention in a configuration phase.
  • the control device CTL is coupled to a technical system TS, which can be designed as described above.
  • the control device CTL has one or more processors PROC for carrying out all method steps of the control device CTL as well as one or more memories MEM coupled to the processor PROC for storing the data to be processed by the control device CTL.
  • the control device CTL acquires from the technical system TS a chronological sequence of operating parameter values BP in the form of a chronological sequence of operating data sets.
  • an operating parameter value may also include an operating parameter vector.
  • the control device CTL has an artificial neural network NN as a trainable computing module and a training data generator TG coupled thereto, to which the sequence of operating parameter values BP is transmitted in each case.
  • the neural network NN is data driven trainable and has a training structure during a training ausbil ⁇ det.
  • a training is generally understood as an optimization of a mapping of input parameters of a parameterized system model, for example of a neural network, to one or more target parameters.
  • a mapping is optimized according to predetermined, learned and / or learning criteria during a training phase.
  • prediction models can use a prediction error or, in the case of control models, a success of a control action, which may relate, for example, to yield, resource requirements, pollutant emissions and / or wear of the technical system.
  • a training structure may for example comprise a cross-linking structure of neurons of a neural network and / or weights of connections between neurons that are formed by training in such a way that the criteria are as good as possible it ⁇ filled.
  • the neural network NN is trained to predict future operating parameter values based on previous and current operating parameter values.
  • the neural network NN can be trained based on past and current operating Para ⁇ meter values to determine a control action that optimizes a pre-admit ⁇ nes or a learned criterion.
  • the control device is CTL and the technical system TS confi ⁇ riert.
  • the technical system TS can be controlled in a predictive manner.
  • ⁇ ⁇ is sought by training the neural network NN that this from the sequence of captured Radiopa- rameterhong BP continuously a future Radiome ⁇ terwert predicts accurately as possible as a prediction value PP.
  • the prediction values PP (t) predicted by the neural network NN which are predicted for a respective time t, are compared with actually acquired, respectively correspondingly timely, ie related to the same time t operating parameter values BP (t).
  • a prediction value PP (t) related to a point in time t is to be temporarily stored until the operating parameter value BP (t) related to the same point in time t is actually detected and available.
  • a distance D is formed between the prediction values PP (t) and the temporally corresponding, actually acquired operating parameter values BP (t).
  • the distance D represents a recuperdikti ⁇ onsfree the neural network NN.
  • the distance D is fed back to the neural network NN, which - as indicated in FIG. 2 by a dashed arrow - is trained to minimize the distance D, ie to predict the operating parameter as precisely as possible.
  • the distance D to be minimized can be represented by a suitable cost function.
  • a gradient descent method can be used in particular to ⁇ .
  • the neural network NN not only receives the acquired operating parameter values BP for training, but also training data sets TD generated by the training data generator TG.
  • the training data generator TG generates the training data sets TD based on the supplied Operating parameter values BP and transmits the generated training data sets TD to the neural network NN.
  • the training data sets TD are generated in such a way that they represent in particular statistical dependencies between the Be ⁇ operating parameter values statistically correct BP, which are characteristic of the dynamics of the technical system TS.
  • these are correla ⁇ tions between predicted and actual prediction values detected operating parameters lent.
  • the generated training data sets TD are used in addition to the operating parameter values BP of the neural network training technical system TS, by minimizing, as described above, a distance between prediction values determined by the neural network NN and corresponding values in the training data sets TD.
  • FIG. 3 shows a flowchart of a method according to the invention with method sections S1,..., S6 in a schematic representation.
  • a temporal sequence of loading ⁇ operating parameter values in a time series of operating records BP of the technical system TS is read.
  • This sequence BP a temporal sequence of ers ⁇ th operating records IBP and a temporal sequence of the second operating records TBP are selected.
  • the first operating data sets IBP are hereby input operating data records, ie input parameters for the neural Network NN.
  • a jeweili ⁇ ger input operation data set IBP a n-1 dimensional vector (n> 2) of the technical system of detected state parameters TS.
  • the second operating data sets TBP are target operating parameters, ie target parameters for the neural network NN.
  • Such a target operating parameter TBP can be, for example, an operating parameter of the technical system TS, which is to be determined as accurately as possible by the neural network NN on the basis of the input operating data sets IBP.
  • a target operating parameter TBP may be a current state parameter of the technical system TS, which is to be predicted from earlier state parameters, here IBP.
  • the target operating parameter TBP is one-component for reasons of clarity.
  • a respective target operating parameter TBP is correlated with a respective input operating data record IBP with regard to an impact dynamics of the technical system TS and is allocated correspondingly to this input operating data record IBP. This assignment is subsequently expressed by referring to a same time t in the sequences IBP (t) and TBP (t).
  • target operating parameters for the neural network NN may be provided as first operating data sets, and input operating data records for the neural network NN as second operating data sets.
  • process step S2 that is, distribution functions of a single variable determined empirically based on the sequence of A ⁇ handover operational records IBP and the sequence of the target operating parameters TBP stochastic distribution ⁇ univariate features.
  • Such distribution functions advertising as the cumulative univariate distribution functions be ⁇ records.
  • a second univariate Ver ⁇ distribution function F2 of the detected target operating parameters TBP is determined empirically.
  • the univariate distribution functions Fli and F2 can be determined, for example, from frequency distributions of the components of the input operating data sets IBPi and the target operating parameters TBP.
  • a respective ith component of the input operation data sets IBPi is transformed by the associated first univariate distribution function Fli, and the target operating parameters TBP are transformed by the second univariate distribution function F2.
  • Empirical correlation vectors EKV are formed from transformed operating data records relating to the same time t. This results in a time sequence of empirical correlation vectors according to FIG.
  • EKV (t) (F2 (TBP (t)), Fli (IBPI (t)) Fl n _i (IBP _i n (t))).
  • the empirical correlation vectors EKV are obviously n-dimensional.
  • a n-dimensional stochastic ⁇ specific distribution function of the empirical correlation vectors EKV is determined.
  • a n-dimensional stochastic distribution ⁇ function is preferably an n-dimensional Copula distribution function, that is, a copula distribution function with n-variables, in this case the n components F2 (TBP (t)),
  • the copula distribution function completely maps out a stochastic dependence structure between its n variables, while the univariate distribution functions Fli and F2 map stochastic edge distributions of the operating data sets IBPi and TBP.
  • the univariate distribution functions Fli and F2 are also referred to as edge distribution functions.
  • edge distribution functions Fli and F2 on the one hand and an n-dimensional copula distribution function on the other hand, a modeling of the edge distributions and a modeling of the dependency structure between input operational data sets and target operating parameters can be separated from one another. In this way, the stochastic properties and dependencies of the operating parameters can be mapped with little effort and in a consistent manner to the generated training data sets TD.
  • n-dimensional empirical distribution function copula EC empirical correlation vectors EKV is determined using the rule empiri ⁇ correlation vectors EKV. For such Determined ⁇ lung Standard procedures are defined and well known.
  • the n-dimensional copula distribution empirical function EC determined is an n-dimensional copula model function MC, e.g. adjusted by parameter fitting.
  • n-dimensional copula model function MC e.g. a so-called Gumbel-Hougaard copula can be used.
  • the adapted n-dimensional copula model function MC is provided for performing the subsequent process sections as a numerical n-dimensional copula distribution function.
  • areas of operating states of the technical system TS are based on the first operation ⁇ data sets IBP and / or the second operational records TBP tektiert de-, covered sparsely by entering operating records IBP, or the observed extreme values of the target operating parameters TBP contain or border on these.
  • the generator operational records GBP (k) are preferably distributed by means of a random process in the detected preparation ⁇ chen that these be covered representative in terms of their potential impact dynamics.
  • the generator operating records GBP are transmitted to a second generator GEN2 which generates a plurality of n-dimensional potential correlation vectors PKV for a respective producer operation record GBP (k).
  • the potential correlation vectors PKV are then formed analogously to the empirical correlation vectors EKV according to
  • the generated potential correlation vectors PKV are supplied to the adapted copula model function MC, ie the n-dimensional copula model function MC is called with a respective potential correlation vector PKV (k, l) as an n-dimensional argument.
  • the respective return values MC (PKV (k, l)) of these calls are transformed by a function F2 _1 , which is inverse to the second univariate distribution function F2, in order in each case to obtain a potential target operating parameter PTBP as a potential second operating data record.
  • the resulting potential target operating parameters PTBP are for each He ⁇ generator operation data set GBP (k) (k, l) statistically distributed for varying 1 substantially so as to actual target operating parameter to this generator operating data set GBP (k) for the technical system TS would be expected.
  • training data formed therefrom realistically reflects stochastic dependencies between the first and second operational data records and can be successfully used to consistently fill gaps or sparsely covered areas in the operating states of the technical system TS.
  • a respective training data set TD may be generated as a pair of a first and a second operational data set according to FIG.
  • TD (k) (GBP (k), AVG (k)).
  • the neuro ⁇ dimensional network NN may additionally and stochastic consistent training and so the controller CTL and the technical system TS are configured in an advantageous manner.
  • FIG. 4 shows a schematic representation of a training data generator TG according to the invention in a setting phase when determining the univariate distribution functions Fli and F2 as well as the copula distribution functions EC and MC.
  • the training data generator TG can have one or more dedicated processors for executing the method steps of the training data generator TG and one or more with the Processor coupled memory for storing the data to be processed by the training data generator TG.
  • the training data generator TG receives from the technical system TS a time sequence of operating parameters in the form of operating data sets BP, which are continuously recorded and processed.
  • the sequence of operating data records BP is fed to a selection module SEL of the training data generator TG.
  • a target operating parameter TBP (t) and an input operating data vector IBPi (t) are, as described above, related to a respective respective time t and correlated with respect to an impact dynamics of the technical system TS.
  • the target operating parameters TBP (t) by the determined second univariate distribution function F2 are Entspre ⁇ accordingly transformer mized. From the transformed values, a time interval of n-dimensional empirical correlation vectors is derived according to
  • EKV (t) (F2 (TBP (t)), Fli (IBPI (t)) Fl n _i (IBP _i n (t))).
  • the empirical n-dimensional copula distribution function EC of the empirical correlation vectors EKV (t) is determined.
  • the n-dimensional copula model function as MC and nu ⁇ meric n-dimensional copula distribution function penetratege ⁇ represents.
  • FIG. 5 shows a schematic representation of the training data generator TG according to the invention in a generation phase when generating training data sets TD for training a neural network NN.
  • the univariate distribution functions Fli and F2 determined in the adjustment phase as well as the n-dimensional copula distribution function MC are already available.
  • these reference numerals designate the same entities that may be implemented or implemented as described above.
  • the training data generator TG receives from the technical system TS a time sequence of operating parameters of the technical system TS in the form of operating data records BP. As described above, areas of operating states of the technical system TS for which coverage with training data should be improved are detected on the basis of the operating data sets BP.
  • the multiplicity of potential correlation vectors PKV (k, l) are supplied by the second generator GEN2 to the numerical n-dimensional copula distribution function MC determined in the adjustment phase, i. the copula distribution function MC is called with the PKV (k, l) as an argument.
  • the potential target operating parameters PTBP become
  • the generator operation data sets GBPi (k) are transmitted from the first generator GEN1 to the evaluation module AW.
  • the generated, synthetic training data sets TD (k) are output by the evaluation module AW for training the neural network NN and thus for configuring the control device CTL and the technical system TS.
  • the adjustment phase and the training phase, generation phase or configuration phase can also at least partially run pa rallel ⁇ .
  • the neural network NN can continue to be trained on the basis of newly acquired operating data sets, and / or the distribution functions Fli, F2, EC and / or MC can be continuously adapted to the recorded operating data sets.
  • the technical system TS can be controlled by means of a partially trained neural network NN.

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Abstract

Afin de configurer informatiquement un système technique (TS), par ex. une turbine à gaz au moyen de données d'entraînement (TD), une pluralité de premiers et de deuxièmes ensembles de données de fonctionnement (IBP, TBP) corrélés dynamiquement entre eux du système technique (TS) est détectée. Un vecteur empirique de corrélation (EKV) respectif à n dimensions est déduit d'un premier ensemble de données de fonctionnement (IBP) respectif et d'un deuxième ensemble de données de fonctionnement (TBP) respectif. Selon l'invention, une fonction empirique de répartition (EC) à n dimensions des vecteurs empiriques de corrélation (EKV) est calculée. Par ailleurs, un premier ensemble de données de fonctionnement est lu en tant qu'ensemble-source de données de fonctionnement (GBP) pour lequel une pluralité de vecteurs potentiels de corrélation (PKV) à n dimensions est générée. Un deuxième ensemble potentiel de données de fonctionnement (PTBP) est généré respectivement pour les vecteurs potentiels de corrélation (PKV) au moyen de la fonction de répartition. Un ou plusieurs ensembles de données d'entraînement (TD) sont générés au moyen de l'ensemble-source de données de fonctionnement (GBP) et des deuxièmes ensembles potentiels de données de fonctionnement (PTBP) et transmis afin de configurer le système technique (TS).
PCT/EP2018/073806 2017-09-20 2018-09-05 Procédé et générateur de données d'entraînement destinés à configurer un système technique et équipement de commande destiné à commander le système technique (ts) WO2019057489A1 (fr)

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DE102017216634.7 2017-09-20
DE102017216634.7A DE102017216634A1 (de) 2017-09-20 2017-09-20 Verfahren und Trainingsdatengenerator zum Konfigurieren eines technischen Systems sowie Steuereinrichtung zum Steuern des technischen Systems

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