EP4363938A1 - Method for monitoring a system and associated computer program product - Google Patents
Method for monitoring a system and associated computer program productInfo
- Publication number
- EP4363938A1 EP4363938A1 EP22741493.5A EP22741493A EP4363938A1 EP 4363938 A1 EP4363938 A1 EP 4363938A1 EP 22741493 A EP22741493 A EP 22741493A EP 4363938 A1 EP4363938 A1 EP 4363938A1
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- European Patent Office
- Prior art keywords
- rules
- time series
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- abnormal
- time period
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- 238000004590 computer program Methods 0.000 title claims description 15
- 238000012544 monitoring process Methods 0.000 title abstract description 4
- 230000002159 abnormal effect Effects 0.000 claims abstract description 119
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- 230000001960 triggered effect Effects 0.000 claims abstract description 4
- 238000012512 characterization method Methods 0.000 claims description 39
- 230000008859 change Effects 0.000 claims description 23
- 238000004821 distillation Methods 0.000 claims description 23
- 238000012360 testing method Methods 0.000 claims description 21
- 238000012549 training Methods 0.000 claims description 18
- 230000008569 process Effects 0.000 claims description 8
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- 238000004364 calculation method Methods 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 2
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- 238000013461 design Methods 0.000 description 2
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- 238000012423 maintenance Methods 0.000 description 2
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- 230000000717 retained effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
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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
- 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
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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
- 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
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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
- 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
Definitions
- TITLE Process for controlling a system and associated computer program product
- the present invention relates to a method for controlling a system.
- the present invention also relates to an associated computer program product.
- petroleum refining is a process of transforming crude oil into a finished product that can be used, for example, as gasoline, diesel or other products used in the petrochemical industry.
- the distillation step is the first step in this process. It consists of purifying different substances of liquids from a mixture, for example, the different fractions of hydrocarbons contained in crude oil.
- an abnormal event called clogging, takes place, which requires the slowing down or even stopping of the transformation process for a relatively long period of time. Indeed, many hours of maintenance are required to restore the normal state of the column.
- a method for obtaining a model aimed at predicting the onset of engorgement is also known.
- Such a model is based on a random forest type algorithm. Nevertheless, such a model also generates a significant number of false positives while failing to predict certain bottlenecks. This leads to loss of time, as well as economic losses.
- the subject of the present description is a method for controlling a system, the method being implemented by computer and comprising: a. a preparatory phase comprising the following steps: i. obtaining time series, for at least one system of the same nature as the system to be controlled, each time series describing the temporal evolution of one or more predetermined parameters of the system considered between an initial instant and a final instant, the final instant being triggered by the occurrence of an abnormal event affecting the system considered, ii.
- association rules for a set of time series, called training, the determination of association rules according to the characterization metrics obtained for each parameter, each association rule predicting the occurrence or not of an abnormal event by associating a relative datum to the characterization metric of at least one parameter on the occurrence or not of the abnormal event, v. the validation of the association rules obtained on at least one time series, called test, distinct from the training time series, b. an exploitation phase comprising the following steps: i. obtaining data relating to the evolution over time of the predetermined parameter or parameters of the system to be controlled, ii. the prediction of the occurrence or not of an abnormal event likely to affect the system according to the data obtained for the system to be controlled and the validated association rules.
- the method comprises one or more of the following characteristics, taken in isolation or in all technically possible combinations:
- a first sub-period and a second sub-period are defined, the second sub- period having the same duration as the first sub-period and being spaced from the first sub-period by a predetermined duration, the characterization metric being a rate of change, during the step of determining the characterization metric, it is calculated, for each normal and abnormal time period, a characteristic datum for each parameter considered over the duration on the one hand of the first sub-period and on the other hand of the second sub-period of the time period considered, the metric characterization of each normal and abnormal time period being obtained according to the characteristic data obtained for the first sub-period and the second sub-period of the time period considered;
- the characterization metrics are classified into several classes according to the value obtained for each characterization metric, during the step of determining the association rules, the data relating to the characterization metric being the class to which the characterization metric belongs so that each association rule associates a class of a characterization metric of at least one parameter with the occurrence or not of an abnormal event;
- the determined association rules are at most a predetermined number of rules selected from a set of rules established according to the characterization metrics of the parameters over the set of training time series, the selected rules being rules whose frequency of appearance in the time series considered is greater than an appearance threshold, preferably the rules selected being the rules having the highest confidence metric among the rules whose frequency of appearance in the time series considered is greater than an appearance threshold, the confidence metric evaluating the frequency of veracity of the rule on the time series considered, preferably the rules selected being the rules having the lowest independence rate among the rules whose frequency of appearance in the considered time series is greater than an appearance threshold and whose confidence metric ance is the highest, the independence rate quantifying the independence of the associations made by a rule, preferably the selected rules being the rules with the highest conviction metric among the rules whose frequency of appearance in the series time series considered is greater than an appearance threshold, whose confidence metric is the highest and whose independence rate is the lowest, the conviction metric quantifying the frequency of non-truthfulness of the rule on the time series considered; -
- the rules of association include, on the one hand, rules predicting the occurrence of an abnormal event, and on the other hand rules predicting the absence of an abnormal event;
- the preparation phase comprises the repetition of the steps of determining association rules and validation for different sets of training time series so that each time series was once a test time series and during the other repetitions a series training time;
- the operating phase includes a step for generating an alert and/or initiating a system control action when an abnormal event is predicted;
- the system to be checked is a distillation column and the abnormal event is clogging of the distillation column.
- the present description also relates to a computer program product comprising program instructions recorded on a computer-readable medium, for the execution of a method as described above when the computer program is executed on a computer.
- This description also relates to a readable information medium on which a computer program product as previously described is stored.
- Figure 1 a schematic view of an example of a computer allowing the implementation of a system control method
- FIG. 2 a flowchart of an example implementation of a system control method
- Figure 3 is a schematic representation of an example of a time series including a normal time period and an abnormal time period, each of the normal time period and the abnormal time period including a first and a second sub-period.
- Computer 10 is preferably a computer.
- the computer 10 is an electronic computer capable of manipulating and/or transforming data represented as electronic or physical quantities in computer registers 10 and/or memories into other similar data corresponding to physical data in memories, registers or other types of display, transmission or storage devices.
- the computer 10 interacts with the computer program product 12.
- the computer 10 comprises a processor 14 comprising a data processing unit 16, memories 18 and an information carrier reader 20.
- the computer 10 comprises a keyboard 22 and a display unit 24.
- the computer program product 12 has an information carrier 26.
- the information medium 26 is a medium readable by the computer 10, usually by the data processing unit 16.
- the readable information medium 26 is a medium suitable for storing electronic instructions and capable of being coupled to a computer system bus.
- the information medium 26 is a floppy disk or floppy disk (from the English name "floppy say"), an optical disk, a CD-ROM, a magneto-optical disk, a ROM memory, a memory RAM, EPROM memory, EEPROM memory, magnetic card or optical card.
- the computer program 12 comprising program instructions.
- the computer program 12 is loadable on the data processing unit 16 and is adapted to cause the implementation of a system control method, when the computer program 12 is implemented on the processing unit 16 of computer 10.
- FIG. 2 schematically illustrates an example of implementation of a system control method
- FIG. 3 is an example illustrating certain steps of the method. .
- the control process aims to control a system, i.e. to initiate actions relating to the control of the system.
- the system is a distillation column and the abnormal events that can interfere with the normal operation of the distillation column are column jams.
- the control method is, for example, implemented by the computer 10 in interaction with the computer program 12, that is to say is implemented by computer.
- the control method comprises a preparatory phase 90 and an exploitation phase 190.
- the preparatory phase 90 makes it possible to obtain association rules predicting the occurrence or not of an abnormal event as a function of the temporal evolution of a or several parameters Xi , ... , X n of the system to be controlled.
- the phase 190 allows the prediction of the occurrence or not of an abnormal event likely to affect the system to be controlled according to the rules of association and data relating to the temporal evolution of the predetermined parameter(s) Xi, X n of the system to be controlled.
- Phase 190 is, for example, implemented in real time during operation of the system to be controlled.
- the preparatory phase 90 includes a step 100 of obtaining time series for at least one system of the same nature as the system to be controlled.
- time series are, for example, obtained by measurements made by sensors.
- Each time series describes the time evolution of one or more predetermined Xi, ... , X n of the system considered between an initial instant t, and a final instant t f .
- the final instant t f is triggered by the occurrence of an abnormal event affecting the system considered.
- Each time series has a duration greater than a predetermined duration.
- each time series is the result of a breakdown of a parent time series obtained for a system over a long time period (several days typically). The slicing is done so that the end of each time series coincides with the occurrence of an abnormal event.
- the predetermined duration is, for example, greater than or equal to 20 hours.
- the predetermined parameters X1 , ... , Xn are, for example, chosen from: different temperatures upstream or in the distillation column at different levels, different pressures of liquids upstream or in the distillation column at different levels, flow rates, the type of raw liquid entering the distillation column, the openings of various valves upstream or in the column, and chemical or quality parameters. All these parameters are measured at a given frequency (sometimes close to real time for some of them) at different points in the process not far from the distillation column.
- a time series is illustrated by way of example in figure 3.
- the preparatory phase 90 includes a step 110 of defining an abnormal time period T A and a normal time period T N for each time series.
- the abnormal time period T A takes place immediately before the occurrence of the abnormal event.
- the end of the abnormal time period T A coincides with the triggering of the abnormal event, and therefore the final instant t f of the time series considered.
- the normal time period T N is an earlier period and of the same duration as the abnormal time period T A .
- the normal time period T N is such that the time difference E between the normal time period T N and the abnormal time period T A is greater than or equal to a predetermined difference.
- the time difference E is chosen so that the normal time period T N is very far from the abnormal time period T A .
- the time difference E is greater than or equal to 5 hours, preferably greater than or equal to 10 hours.
- the preparatory phase 90 includes a step 120 of determining, for each time series, a characterization metric for each parameter Xi, ..., X n of the series considered, on the one hand over the abnormal time period T A , and on the other hand over the normal time period T N .
- Step 120 thus makes it possible to obtain data according to a case cross-over design, that is to say that the characterization metrics obtained for each time series are directly comparable because obtained on the same system during the same process. Indeed, the comparison of the characteristics of parameters between the normal time periods T N and the abnormal time periods T A makes it possible to estimate the regular changes which induce an abnormal event.
- the characterization metric of a parameter Xi,..., X n is a quantity characterizing the parameter Xi,..., X n over the period considered.
- the characterization metric is an average, a standard deviation, a residual or even a rate of change.
- the average is the average value of the parameter Xi,..., X n considered over the duration of the normal or abnormal time period considered.
- the standard deviation is the value of the standard deviation of the parameter Xi,..., X n considered over the duration of the normal or abnormal time period considered.
- the residual is the error between, on the one hand, the prediction made by a prediction model of a value of the parameter Xi, ..., X n considered over the duration of the normal or abnormal time period considered, and d on the other hand, the effective value of the parameter over the period considered.
- the prediction model is, for example, an autoregressive model, that is to say a model previously trained on part of the data of the time series considered (but not the data relating to the normal or abnormal time period considered).
- the rate of change of a parameter Xi,..., X n is a quantity quantifying the changes (variations) of the parameter Xi,..., X n over the period considered.
- An example of rate of change is described more specifically in the following.
- the determining step 120 includes defining a first sub-period Ti and a second sub-period T 2 for each of the normal time period T N and the abnormal time period T A of each time series.
- the second sub-period T 2 has the same duration d as the first sub-period T and is spaced from the first sub-period T by a predetermined duration D.
- the duration d is, for example, greater than or equal to one hour and the predetermined duration D is, for example, greater than or equal to two hours.
- the determination step 120 comprises the calculation, for each normal T N and abnormal T A time period, of a datum characteristic of each parameter X , ... , X n considered on the duration d, on the one hand, of the first sub-period Ti and, on the other hand, of the second sub-period T 2 of the time period considered.
- four characteristic data are obtained for each parameter X , ...
- X n of each time series namely: a characteristic data on the first sub-period T of the normal time period T N , a characteristic datum over the second sub-period T 2 of the normal time period T N , a characteristic datum over the first sub-period T of the abnormal time period T A and a characteristic datum over the second sub-period T 2 of the abnormal time period T A .
- the characteristic datum considered is, for example, the mean, the standard deviation or else the residual of the parameter X , ... , X n considered over the duration d of the normal or abnormal time period considered.
- the characteristic datum is the mean.
- the rate of change of each normal TN and abnormal T A time period is then determined based on the data characteristics obtained for the first sub-period Ti and the second sub-period T 2 of the time period considered.
- the rate of change is, for example, given by the following formula:
- the preparatory phase 90 includes a step 130 of determining association rules according to the characterization metrics obtained for each parameter Xi, ..., X n of a set of time series, called training.
- the set of training time series is such that at least one time series does not belong to the set so as to be usable as a test time series.
- the association rules are, for example, determined from an algorithm of the “Association Rules Mining” type.
- An example of an algorithm used is the Apriori algorithm.
- Each association rule predicts the occurrence or not of an abnormal event by associating a datum relating to the characterization metric of at least one parameter Xi, ..., X n with the occurrence or not of the abnormal event.
- the association rules preferably have the form of an implication, i.e. of the type a ® b with a a set of elements and b another element which does not belong to the set of elements.
- a denotes a set of data relating to the characterization metric of one or more parameters Xi, ..., X n and b denotes an abnormal event or the absence of abnormal event.
- the characterization metrics obtained are classified into several classes according to the value of the metric. This is particularly the case when the characterization metrics are quantities taking continuous values, as is the case for the mean, the standard deviation, the residual and the rate of change.
- the classes quantify the magnitude of the change. For example, for rates of change whose value is between 0 and 1, the values obtained for each parameter Xi, ..., X n of each time period, are assigned to a class among at least two classes.
- each parameter Xi, ..., X n considered is assigned three pieces of information for the abnormal time period T A , namely: a first piece of information relating to the veracity or otherwise of the fact that the rate of change of the parameter Xi, ...
- X n over the abnormal time period TA is included in the low class
- a second piece of information relating to the veracity or otherwise of the fact that the rate of change of the parameter Xi, ... , X n over the abnormal time period T A is included in the medium class
- third information relating to the veracity or not of the fact that the rate of change of the parameter Xi, ... , X n over the abnormal time period T A is included in the upper class.
- three items of information are also obtained for the parameter Xi, ..., X n over the normal time period TN.
- the association rules then associate a class of a characterization metric (rate of change for example) of at least one parameter Xi, ... , X n with the occurrence or not of an abnormal event.
- a rule is of the following form:
- association rules predict the occurrence of an abnormal event.
- other (non-zero) part of the association rules predicts the absence of an abnormal event.
- the determined association rules include at most a predetermined number of rules, selected from a set of rules established according to the characterization metrics of the parameters Xi, ..., X n over the set of time series of coaching.
- the predetermined number is, for example, equal to ten.
- a set of rules is first established, for example by means of an Apriori algorithm.
- the rules are, for example, of different types, and only the rules predicting the occurrence or absence of an abnormal event are retained. Then, at most a number of rules corresponding to the predetermined number are selected.
- the rules selected are rules verifying first a first criterion, preferably also a second criterion, preferably also a third criterion and more preferably a fourth criterion.
- the first criterion stipulates that the rules selected are rules whose frequency of appearance (“support”) in the time series considered is greater than an appearance threshold.
- the frequency of appearance is, for example, calculated by a metric called support.
- the support is, for example, defined as follows: )
- supp(a ® b ) designates the support, that is to say the frequency of appearance of a in b for all the normal time periods when b designates the absence of abnormal event, and for the set of abnormal time periods when b designates the occurrence of an abnormal event, supp(a ® b ) is included in the broad sense between 0 and 1,
- #(aub ) designates the number of elements of D which contain the set aub.
- the second criterion stipulates that the rules selected are the rules having a metric, called confidence (in English “confidence range”), the highest among the rules whose frequency of appearance in the time series considered is greater than a threshold of appearance.
- the confidence metric evaluates the frequency of veracity of the rule on the considered time series.
- suppia denotes the frequency of occurrence of a in normal time periods when b denotes the absence of an abnormal event, and in abnormal time periods when b denotes the occurrence of an abnormal event, suppia ) is understood to mean wide between 0 and 1 .
- the third criterion stipulates that the rules selected are the rules having the lowest rate of independence (in English “lift”) among the rules whose frequency of appearance in the time series considered is greater than an appearance threshold and whose confidence metric is the highest.
- the independence rate quantifies the independence of the associations made by a rule.
- liftia ® b designates the rate of independence, ie the support that would have been obtained if a and b were independent, liftia ® b ) is between 0 and infinity so that if the rate of independence is close to one, it means that a and b are independent, and if the independence rate is higher or close to zero, it means that a and b are associated, and
- suppib designates the frequency of occurrence of b in normal time periods when b designates the absence of an abnormal event, and in abnormal time periods when b designates the occurrence of an abnormal event, suppib ) is understood as meaning wide between 0 and 1 .
- the fourth criterion stipulates that the rules selected are the rules having a metric, called conviction (in English “conviction range”), the highest among the rules whose frequency of appearance in the time series considered is greater than a threshold of appearance, whose confidence metric is the highest and whose independence rate is the lowest.
- the conviction metric quantifies the frequency of non-truthfulness of the rule on the considered time series.
- the conviction metric is, for example, defined as follows:
- the preparatory phase 90 includes a step 140 of validating the association rules obtained on at least one time series, called test, distinct from the training time series.
- the method is, for example, reiterated from the step of determining the association rules.
- a different predetermined threshold is for example fixed for the medium (metric).
- a prediction is obtained for each of the association rules on the test time series.
- Such a prediction is, for example, obtained as a function of the characterization metrics obtained for the parameters Xi, ..., X n of the test time series over each of the normal and abnormal time periods.
- the final prediction is obtained by aggregating the predictions obtained for each of the association rules according to an aggregation criterion.
- the aggregation criterion stipulates for example that the final prediction is the majority prediction obtained. In another example, the aggregation criterion states that the final prediction predicts the occurrence of an abnormal event when at least one rule predicts the occurrence of such an abnormal event.
- the association rules are validated when the final prediction corresponds to the proven prediction of an abnormal event on the test time series.
- the rules are, for example, validated when the rules are validated for each test series, or at least for a predetermined percentage of the test series (for example 80%).
- the association rules comprise, on the one hand, rules predicting the occurrence of an abnormal event (first type of rules), and on the other hand rules predicting the absence of an abnormal event (second type of rules, or contrapositive)
- first type of rules rules predicting the occurrence of an abnormal event
- second type of rules or contrapositive
- a final prediction is obtained on the one hand for the rules of the first type, and on the other hand for the rules of the second type.
- the association rules are then for example validated when the final predictions obtained for each type of rule are confirmed.
- the preparation phase includes repeating the steps of determining association rules and validating for different sets of training time series such that each time series was once a test time series. and during the other repetitions a training time series.
- This approach makes it possible to carry out cross-validation, and thus to exploit all the time series for both training and testing.
- the rules are, for example, validated when, after these repetitions, the rules are validated for each test series, or at least for a predetermined percentage of the test series (for example 80%).
- the exploitation phase 190 is implemented once the association rules have been validated.
- the operating phase is, for example, implemented in real time on a system so as to predict in advance the occurrence of an abnormal event likely to affect the system (bottlenecking in the case of a distillation column) .
- the exploitation phase 190 comprises a step 200 of obtaining data relating to the temporal evolution of the predetermined parameter(s) Xi,...,X n of the system to be controlled.
- the data is, for example, obtained by measurements carried out by sensors (for example temperature, pressure and flow sensor in the case of a distillation column).
- the exploitation phase 190 includes a step 210 of predicting the occurrence or not of an abnormal event likely to affect the system according to the data obtained for the system to be controlled and the validated association rules.
- the prediction is, for example, performed by analyzing the characterization metrics (chosen as a function of the characterization metrics considered for the association rules) of the considered parameters Xi, ..., X n of the system over the last time period for which measurements have been taken (advantageously the duration of the last time period is equal to the duration of the normal time period TN and abnormal time period TA of the training phase).
- the previously validated association rules predict or not the occurrence of an abnormal event (the final prediction is for example obtained by aggregating the predictions of each rule according to the aggregation criterion).
- the exploitation phase includes a step 220 for generating an alert and/or for initiating a system control action when an abnormal event is predicted.
- the present method implements an original exploitation (in the form of a case cross-over design) of data relating to a system so as to extract association rules predicting the occurrence or not of an abnormal event.
- the association rules thus make it possible to better understand the causes (combinations of values of parameters Xi, ..., X n ) leading to an abnormal event.
- Such a method thus allows a better prediction of the occurrence of an abnormal event affecting a system.
- a method makes it possible to reduce the false positives (erroneous prediction) and the false negatives (absence of prediction), and to better identify the parameters Xi, .. , X n or operating characteristics capable of triggering an abnormal event.
- the present method is applicable to multiple applications, not only to the monitoring of the fouling of distillation columns but to other applications, such as the monitoring of the damage or deterioration of systems, or to perform prescriptive and predictive maintenance.
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Abstract
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FR2107171A FR3124868B1 (en) | 2021-07-01 | 2021-07-01 | Method for controlling a system and associated computer program product |
PCT/EP2022/068307 WO2023275371A1 (en) | 2021-07-01 | 2022-07-01 | Method for monitoring a system and associated computer program product |
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FR3108415B1 (en) * | 2020-03-18 | 2022-04-01 | Total Sa | Method for predicting clogging of distillation column(s) of a refinery, computer program and associated prediction system |
CN116150233B (en) * | 2023-04-17 | 2023-08-15 | 国网江西省电力有限公司电力科学研究院 | Minimum mapping processing method and system for active power distribution station operation state data |
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JP2010506269A (en) * | 2006-09-28 | 2010-02-25 | フィッシャー−ローズマウント システムズ, インコーポレイテッド | Prevention of abnormal situations in the coker heater |
FR3066844B1 (en) * | 2017-05-23 | 2021-12-24 | Monixo | METHOD FOR PREDICTING THE OCCURRING OF A FAILURE ON A DEVICE OVER TIME |
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WO2023275371A1 (en) | 2023-01-05 |
FR3124868A1 (en) | 2023-01-06 |
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