Classifications

• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
  • F02K—JET-PROPULSION PLANTS
  • F02K9/00—Rocket-engine plants, i.e. plants carrying both fuel and oxidant therefor; Control thereof
  • F02K9/96—Rocket-engine plants, i.e. plants carrying both fuel and oxidant therefor; Control thereof characterised by specially adapted arrangements for testing or measuring
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
  • F02K—JET-PROPULSION PLANTS
  • F02K9/00—Rocket-engine plants, i.e. plants carrying both fuel and oxidant therefor; Control thereof
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
  • F02K—JET-PROPULSION PLANTS
  • F02K9/00—Rocket-engine plants, i.e. plants carrying both fuel and oxidant therefor; Control thereof
  • F02K9/42—Rocket-engine plants, i.e. plants carrying both fuel and oxidant therefor; Control thereof using liquid or gaseous propellants
  • F02K9/44—Feeding propellants
  • F02K9/56—Control
• • 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
  • G05B19/048—Monitoring; Safety
• • 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
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based 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
  • 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
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
  • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural 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
  • G06N3/047—Probabilistic or stochastic networks
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2220/00—Application
  • F05D2220/80—Application in supersonic vehicles excluding hypersonic vehicles or ram, scram or rocket propulsion
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2260/00—Function
  • F05D2260/80—Diagnostics
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2260/00—Function
  • F05D2260/81—Modelling or simulation
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2260/00—Function
  • F05D2260/82—Forecasts
  • F05D2260/821—Parameter estimation or prediction
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2270/00—Control
  • F05D2270/30—Control parameters, e.g. input parameters
  • F05D2270/301—Pressure
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2270/00—Control
  • F05D2270/30—Control parameters, e.g. input parameters
  • F05D2270/303—Temperature
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2270/00—Control
  • F05D2270/30—Control parameters, e.g. input parameters
  • F05D2270/304—Spool rotational speed
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2270/00—Control
  • F05D2270/30—Control parameters, e.g. input parameters
  • F05D2270/306—Mass flow
  • F05D2270/3061—Mass flow of the working fluid
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2270/00—Control
  • F05D2270/30—Control parameters, e.g. input parameters
  • F05D2270/334—Vibration measurements
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2270/00—Control
  • F05D2270/40—Type of control system
  • F05D2270/44—Type of control system active, predictive, or anticipative
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
  • F05D2270/00—Control
  • F05D2270/70—Type of control algorithm
  • F05D2270/709—Type of control algorithm with neural networks
• • 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
  • G05B2219/24—Pc safety
  • G05B2219/24015—Monitoring
• • 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
  • G05B2219/25255—Neural network
• • 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
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
  • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
  • G05B23/0235—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• • 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
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
  • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Definitions

• the invention relates to the general field of aeronautics.
• It relates more particularly to the monitoring of a rocket engine.
• the verification implemented is carried out on the one hand in real time, by ensuring that the engine operating parameters (eg engine pressure and temperature, etc.) remain within predefined acceptable limits, and on the other hand in deferred time, by comparing measurements of the operating parameters performed during tests on the engine with respect to predetermined rectilinear monitoring templates, an output of a template revealing a measuring defect or of the engine.
• the engine operating parameters eg engine pressure and temperature, etc.
• the parameterization (i.e. determination) limits and monitoring templates is done manually and presents a risk of error especially when this setting is not in line with the point of operation of the engine.
• a change in engine profile and test conditions leads to a tedious and risk-free update of the limits and monitoring templates.
• the invention also provides a device for monitoring a parameter of a rocket engine comprising:
• a transmission module of a notification in case of crossing of said at least one threshold is
• Crossing the threshold by the error means that the error is greater than the threshold if it defines an upper limit not to be exceeded, or that the error is lower than the threshold if the latter defines a low limit not to exceed not exceed.
• the step of comparing the error can be implemented in the context of the invention in various ways, obviously for the skilled person.
• the error can be defined as the absolute value of the difference between the measurement of the monitored parameter and its estimate, or simply as the difference between the measurement of the monitored parameter and its estimate.
• the error can be estimated and then compared to the threshold, or alternatively, the measure of the monitored parameter can be compared to its estimate to which the threshold has been added (or possibly subtracted), etc.
• This comparison step is equivalent regardless of how it is implemented to a comparison of the measurement of the monitored parameter to a template defined from the estimate of the monitored parameter and the threshold.
• the invention thus provides a monitoring of the parameters of a rocket engine that dynamically and automatically evolves with the operating point of this engine.
• the monitored parameters are for example:
• a flow rate of a fluid circulating in the engine and or
• the operating point of the motor is defined from one or more parameters used for motor control.
• Such regulation parameters are for example a pressure of a combustion chamber of the engine, a mixing ratio (oxygen / hydrogen) at the inlet of an engine pump, a speed of rotation of an oxygen turbopump, a speed rotation of a hydrogen turbopump, etc.
• These regulation parameters are regulated (ie controlled) in a closed loop during operation of the motor, in a manner known per se, by a control system able to act on various variable engine geometries such as, for example, the position of control valves such as only VBPO (ByPass Oxygen Valve) and VBPH (ByPass Hydrogen Valve) valves.
• the monitoring proposed by the invention advantageously adapts to the dynamic and non-linear character of the rocket engine systems, this character being linked in particular to the variation as a function of time of the engine regulation setpoints, these instructions being limited in values and in gradient by the regulation system.
• it relies on an estimation of the monitored parameter (s) (eg by simulation or using models) from a regulated and therefore validated value of the regulation parameters, so as to limit the uncertainties related in particular to the valves and to detect anomalies affecting the engine only, or a filtered setpoint of the control parameters so as to detect anomalies affecting the entire chain downstream of the control loop, that is to say ie not only affecting the motor but also the actuators for controlling the variable geometry of the engine.
• the estimate thus obtained is then compared to a measurement of the monitored parameter taking into account a threshold determined automatically according to the operating point of the engine.
• the threshold is determined from the particular uncertainties of measurement and estimation of the parameter to be monitored encountered at the point of operation.
• the invention does not rely, contrary to the state of the art, on a preset monitoring template or preset limits at which one (I) we compare a measurement of the parameter to monitor. On the contrary, it automatically and dynamically defines the limits and / or the rocket engine monitoring gauge based on an estimation of the parameters to be monitored and a measurement and estimation precision (standard deviation) evaluated. both taking into account the operating point of the engine.
• the operating point used to evaluate the error uncertainty is determined from the regulated value of said at least one control parameter.
• This embodiment is based on an accurate estimate of the operating point. It makes it possible to monitor the rocket engine both in steady state and transient conditions. Indeed, it makes it possible to overcome, in transient conditions, sometimes significant delays that may be encountered between the measurements of the control parameters and the setpoint values of these parameters during the control.
• the operating point used to evaluate the error uncertainty can be determined from the filtered setpoint value of the at least one control parameter or the setpoint value of said at least one control parameter (this last being ahead of the actual operating point of the engine).
• the step of estimating the value of the monitored parameter is performed using an artificial neural network having as input (s) the regulated value or the filtered set value of said at least one a motor control parameter.
• an artificial neural network makes it possible to reach a compromise between estimation accuracy and computational load that allows a real-time application of the invention.
• the number of calculations necessary to estimate the value of the parameter to be monitored is indeed limited via the use of such a model.
• the uncertainty on the error can take into account in particular an estimation uncertainty of the monitored parameter determined according to:
• estimators than an artificial neural network may be used, such as an offline nonlinear estimator based on nonlinear regression, etc.
• the inventors have found that, in the presence of abrupt variations of the setpoint values of a regulation parameter, the error between the measurement of the monitored parameter and its estimation can be very close to the threshold determined in accordance with the invention, depending on the from the operating point.
• This template can be defined for example:
• the various steps of the monitoring method are determined by computer program instructions.
• the invention also relates to a computer program on an information carrier, this program being capable of being implemented in a monitoring device or more generally in a computer or in a computer, this program comprising instructions adapted to the implementation of the steps of a monitoring method as described above.
• This program can use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other form desirable shape.
• the invention also relates to a computer-readable information medium, comprising instructions of a computer program as mentioned above.
• the information carrier may be any entity or device capable of storing the program.
• the medium may comprise storage means, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or a magnetic recording medium, for example a floppy disk or a disk. hard.
• the information medium may be a transmissible medium such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means.
• the program according to the invention can be downloaded in particular on an Internet type network.
• the invention also relates to a rocket engine comprising a monitoring device according to the invention. It may also be envisaged, in other embodiments, that the monitoring method, the monitoring device and the rocket motor according to the invention present in combination all or part of the aforementioned characteristics.
• FIG. 1 shows schematically a rocket engine comprising a monitoring device according to the invention, in a particular embodiment
• FIG. 4 represents an artificial neural network used by the monitoring device of FIG. 1;
• the rocket motor 1 is furthermore equipped with a sensor 9 making it possible to measure the "regulated" value of the regulation parameter or to estimate it from the measured position u 'of the valve by the sensor 8.
• This sensor 9 is for example a pressure sensor, a speed sensor, a position sensor, a flow meter, etc., depending on the control parameter considered.
• the regulated value after having been validated by a module 10 for validating the computer (able in particular to eliminate the outliers), is supplied to the error calculation module 4 to adapt the command u to reach the setpoint yC, etc. Since the operation of the regulation system 2 is known to those skilled in the art, it is not described further here.
• W1 denotes the matrix of synaptic weights between the Lin input layer and the Lhid hidden layer, the last column of this matrix representing the biases of the hidden layer neurons
• W2 denotes the synaptic weight matrix between the Lhid hidden layer and the output layer Lout, the last column of this matrix representing the bias of the neurons of the output layer.
• the synaptic weights and biases contained in the matrices W1 and W2 are obtained for the rocket engine 1 using an automatic learning algorithm based on the conjugate gradient method and applied to the training database. known from the person skilled in the art and not described here. Alternatively, other known learning algorithms may be implemented to construct the RNA neural network from a training database.
• the measurement m 'and the estimate p_est of the parameter p are respectively provided by the obtaining module 14 and by the estimation module 15 to the comparison module 16 of the monitoring device 11.
• the uncertainty ⁇ on the measurement m of the parameter p is known for a given sensor. It can for example be extracted or determined from the specifications provided by the manufacturer of the sensor 13 (and possibly validated by means of test recipes performed on the sensor). It should be noted that this uncertainty (or equivalently the measurement inaccuracy of the sensor) is not necessarily constant over the entire operating range of the rocket motor 1 but may be varied according to the operating point of the sensor. engine.
• the monitoring device 11 therefore uses here to evaluate the uncertainty ⁇ , a table TAB1 of predetermined values from the specifications of the manufacturer of the sensor 13 in particular, and giving, for different torque values (PGC, RMEP) defining the operating point PF, the value of the resulting uncertainty ⁇ .
• the uncertainty ⁇ 2 on the estimate p_est of the parameter p depends here on several sources of uncertainty, including:
• the accuracy of the estimator characterizes its ability to faithfully reconstruct the learning database from which it derives;
• the uncertainty (or, conversely, the accuracy, on the contrary) on the basis of training data used to construct the estimator, that is to say the artificial neural network RNA.
• the estimator may have excellent accuracy and therefore the value of the parameter estimated using this estimator may be far from the measurement of this parameter if the learning base is not reliable, for example when this database is not reliable.
• learning is itself derived from a mathematical model.
• a bias can then be applied during the construction of the estimator to take into account the difference between the real engine and its modeling by the learning base in order to limit this uncertainty.
• the monitoring device 11 uses, to evaluate the uncertainty ⁇ 2 resulting from the combination of the aforementioned uncertainties, a table TAB2 of determined values, for example, by simulation using a Monte Carlo method. classic known to those skilled in the art.
• the table TAB2 gives for different values of the regulation parameter pair (PGC, RMEP), the value of the resulting uncertainty ⁇ 2 (in the unit of parameter p monitored, that is to say in the example envisaged here in bar, the monitored parameter being a pressure).
• the domain of the values of the regulation parameters PGC and RMEP has been arbitrarily divided into 195 subdomains (these domains are not all represented for the sake of simplification) in each of which prints were performed according to the Monte Carlo method. The number of domains considered depends on a compromise between complexity and homogeneity of the value of the uncertainty within the same domain.
• the comparison module 16 compares the error ⁇ with the thresholds thus determined (steps E40).
• the comparison module 16 determines that the error ⁇ is greater than the threshold S2 (yes answer to the test step E50 and crossing the threshold S2), a notification of the crossing of the threshold S2 is emitted by the module of notification 17 of the monitoring device 11 to an entity 18 for managing the alerts and the firing sequence (step E60).
• the entity 18 for managing the alerts and the firing sequence implements a majority logic to determine the appropriate action to take if necessary on the rocket engine 1 in response to the notifications of threshold exceeded received.
• a modular and gradual response depending on the exceeded speed can be implemented (eg maintenance to be planned, motor stop, adaptation of the set value yC, etc.).
• the thresholds considered by the monitoring device are set only according to the uncertainty ⁇ (eg equal to multiples of this uncertainty), which itself depends on the operating point PF considered rocket engine 1.
• the monitoring device 11 determines the thresholds used during the comparison step from the uncertainty ⁇ and takes account of the in addition to the dynamics of change of the operating point of the motor and more specifically, setpoints (raw or filtered) of the control parameters.
• the monitoring device 11 uses thresholds determined from the uncertainty ⁇ and the operating point PF of the engine, but it also takes account of the dynamics of change of the operating point of the motor, and more precisely of the set values of the control parameter or parameters. This consideration is reflected in the definition of a larger monitoring template around the measurement of the monitored parameter in case of abrupt transitions (ie fast) of the operating point of the motor, and on the contrary narrower during smooth transitions of the operating point (ie no or little change of the latter).
• this template is defined by:
• the choice of the cut-off frequencies and the delays of the filters of order 1 and 2 is realized offline for a given monitored parameter and a given rocket engine, for example in an experimental way, by calculations or by tests, starting from the knowledge of the dynamics of the monitored parameter as a function of time and variations of the setpoint values of the control parameters.
• filters of different orders can be envisaged.
• the error between the measurement m 'of the monitored parameter and its estimate p is weighted (ie multiplied) by the response of the filter d order 2 at a step representative of the set value yC (or the filtered set value yCf);
• the error between the measurement m 'of the monitored parameter and its estimate p_ is weighted by the response of the predefined order 1 filter. step audit.
• FIG. 7 represents, by way of illustration, the monitoring template elaborated in this second embodiment around the measurement m 'of the monitored parameter p.
• the dynamic of the measurement of the monitored parameter p in response to a step f0 reflecting the raw reference value applied to the regulation parameters is represented by the curve f1.
• dynamics here we mean the variations of its amplitude as a function of time.

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• 2014
  • 2014-04-03 FR FR1452954A patent/FR3019592B1/fr active Active
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