EP3959571A1 - Procédé servant à définir des cycles d'utilisation restante, circuit de définition de cycle d'utilisation restante, dispositif de définition de cycle d'utilisation restante - Google Patents

Procédé servant à définir des cycles d'utilisation restante, circuit de définition de cycle d'utilisation restante, dispositif de définition de cycle d'utilisation restante

Info

Publication number
EP3959571A1
EP3959571A1 EP20712294.6A EP20712294A EP3959571A1 EP 3959571 A1 EP3959571 A1 EP 3959571A1 EP 20712294 A EP20712294 A EP 20712294A EP 3959571 A1 EP3959571 A1 EP 3959571A1
Authority
EP
European Patent Office
Prior art keywords
determining
remaining
value
cycle
remaining usage
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20712294.6A
Other languages
German (de)
English (en)
Inventor
Jonathan MANRIQUE GARAY
Carsten Trimborn
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Volkswagen AG
Original Assignee
Volkswagen AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from DE102020200051.4A external-priority patent/DE102020200051A1/de
Application filed by Volkswagen AG filed Critical Volkswagen AG
Priority claimed from PCT/EP2020/057149 external-priority patent/WO2020216530A1/fr
Publication of EP3959571A1 publication Critical patent/EP3959571A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • 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
    • G05B2219/37—Measurements
    • G05B2219/37209—Estimate life of gear, drive
    • 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
    • G05B2219/37—Measurements
    • G05B2219/37252—Life of tool, service life, decay, wear estimation

Definitions

  • the invention relates to a method for determining remaining usage cycles, a
  • RUL Remaining Useful Lifetime
  • Such methods are typically based on stochastic methods or models and can be used to predict a threshold value at which a
  • methods for determining a RUL for a production device are often based on online adaptation, a value that is used for the determination being continuously updated in such methods, so that the determination is continuously updated.
  • US Pat. No. 8,725,456 B1 discloses a prognostic tool for determining a RUL of a component or a sub-system on the basis of two different regression models.
  • the RUL is determined here based on an artificial intelligence training.
  • Degradation modeling determines a remaining service life.
  • the laid-open specification CN 107194478 A describes a method for predicting a remaining service life in which a drift parameter and a diffusion parameter are used to describe a degradation process. With such methods, however, no future usage cycle is determined, but a duration.
  • the object of the present invention is to provide a method for determining
  • a method according to the invention for determining remaining usage cycles of a production device due to wear includes:
  • Determination of a future usage cycle of the part of the production device for which the maintenance variable has a predetermined second status value the future usage cycle being determined based on the first status value and based on a discrete stochastic degradation model.
  • Remaining usage cycle determination circuit set up to carry out a method according to the invention according to the first aspect.
  • Remaining usage cycle determining device comprises a remaining usage determining circuit according to the second aspect.
  • RUL remaining useful life
  • a prediction can be imprecise because simplifications have to be made, which can result in the complexity of the device being lost, and interactions of the device with the environment not
  • labeled data may have to be used to create a
  • Conditions can lead to an inaccurate forecast.
  • a Monte Carlo simulation based on a linear Wiener process model can be carried out, which can lead to large variations or inaccuracies if there are small variations in a drift of the Wiener process, so that initial values are falsified by condition monitoring For example, a positive value can become negative or close to zero.
  • a determined confidence interval in a forecast can be too large, so that a Monte Carlo simulation does not converge to a defined failure threshold.
  • a drift control can be implemented in order to exclude a drift around a zero value or a negative drift, so that a Monte Carlo simulation converges. In this way, computing power can advantageously also be reduced.
  • a negative drift can also indicate a statement about an improvement in a state of a production device.
  • some embodiments relate to a method for determining
  • Residual usage cycles of a production device due to wear and tear comprising:
  • Determination of a future usage cycle of the part of the production device for which the maintenance variable has a predetermined second status value the future usage cycle being determined based on the first status value and based on a discrete stochastic degradation model.
  • the device can comprise any device that must or can be serviced due to wear, such as a production device in FIG.
  • a cycle can comprise a predetermined operating duration, a predetermined operating time, a predetermined period, and the like, in which the device executes a predetermined or device-typical process or method.
  • Production device are subject to wear, for example by mechanical
  • a first status value of a maintenance variable of a part of the production device is determined.
  • the maintenance variable can be a physical, mechanical and / or electronic variable, and the like, which can indicate wear.
  • the maintenance quantity can include an elongation of the part, a temperature, a thickness, a pressure, a humidity, a filling amount, and the like.
  • the first status value can comprise a measured value of the maintenance variable, for example ten bar if the maintenance variable comprises a pressure.
  • the sensor can accordingly be set up to generate sensor data which are indicative of the state value.
  • the senor can, for example, be a pressure sensor, a distance sensor, a
  • Temperature sensor a humidity sensor, a color sensor, and the like.
  • the sensor data can also be indicative for several (e.g.
  • CM data may include or be indicative of statistical parameters of each work cycle of the production device and stored in the production device and retrieved after (or during) each production cycle or after each part produced by the production device.
  • a buffer can thereby advantageously be defined, which should be present so that if the production device fails, enough parts are present so that a production line does not have to be shut down.
  • such a buffer can advantageously be dispensed with in some exemplary embodiments, since as a result no space or storage capacity (which is typically limited) has to be released and no financial means have to be expended for the buffer.
  • a cycle can be for a produced component (or product) (or for a specific number of produced components or
  • the future usage cycle can comprise a usage cycle which is determined on the basis of a current and / or past usage cycle or from the first status value in the current and / or past usage cycle, for which the
  • Maintenance variable assumes a predetermined second state value.
  • the second status value (for example five bar) can be a threshold value at which maintenance must be carried out or at which maintenance must be scheduled.
  • the second status value can also be a value from which another method can be used or from which subsequent values can be known.
  • the second state value can be determined based on the first state value and on a discrete stochastic degradation model. For example, the first
  • State value can be used as the initial value for the discrete stochastic degradation model.
  • the discrete stochastic degradation model can generally include a model, an algorithm, and the like, which, based on a stochastic and / or statistical analysis and based on the first state value, allows a prediction of the maintenance variable in future usage cycles.
  • the stochastic degradation model is discrete in that a variable des
  • the degradation model is discrete.
  • the usage cycle is a discrete variable as opposed to a continuous variable such as time.
  • a problem with the continuous variable time can, for example, be that in such a model it cannot typically be taken into account that the
  • Production device or the part of the production device can be switched off.
  • each usage cycle is evenly distributed in the time dimension.
  • the method further comprises: determining a number of remaining usage cycles of the production device based on the future one
  • the remaining usage cycles can result from or correspond to the future usage cycle, for example the remaining usage cycles can be the number of
  • Use cycles include until the future use cycle is reached, or another use cycle, which is determined on the basis of the future use cycle, is reached, and the like.
  • the method further comprises: generating a
  • the probability density function can be indicative of a probability.
  • a probability can be specified as a function of a variable, for example as a function of time.
  • using the continuous variable of time can be disadvantageous, so that, in some
  • the variable is a usage cycle, so that the future
  • Use cycle can advantageously be determined.
  • the probability density function is indicative of a probability for which future usage cycle the maintenance variable assumes the second state value.
  • the degradation model includes a Wiener process model.
  • degradation for example in an electrochemical device, can occur in a random manner.
  • a stochastic model such as a Wiener process, can therefore be used to describe the degradation.
  • the degradation can be described partly deterministically and partly stochastically, with the deterministic part being able to be the same for all tested devices (that is to say for one
  • the stochastic part can represent an uncertainty which is generated by a difference in the devices within the total population.
  • a Wiener process which does not describe a pure random walk, but a random walk with a drift.
  • a Wiener process can be described with the following formula (1):
  • X (t) corresponds to the degradation
  • x i to the first state value
  • l (t) to a drift coefficient
  • B (t) to a Brownian movement
  • the continuous variable becomes time
  • T describes the remaining service life
  • inf stands for infimum (as is generally known)
  • w stands for a failure threshold (FT) to which the
  • Equation (3) stands for the probability density function, p for the circle number, and exp for an exponential function to the base e (Euler number), without restricting the present invention thereto, since every possible exponential function can be used.
  • X (t) can be used, where c stands for a (future) cycle of use and in formula (1), each t can be replaced with a c without loss of generality, so that the formula is not repeated here .
  • a remaining usage cycle C can be defined as in formula (4):
  • the remaining usage cycle as described herein, can be indicative of the future
  • the future usage cycle can be or correspond to a predetermined number of usage cycles before the remaining usage cycle.
  • cQ which can also be briefly defined as n, describes the number of past cycles since a previous determination of residual usage cycles or since the determination of the first status value.
  • the Wiener process model is based on a drift, as described herein.
  • a Wiener process model represents a Markovian property whereby the Wiener process model is memoryless, i.e. if the second state value were determined exclusively based on the Wiener process model, a prediction would always be based on the first state value x 1, but no historical first state values (from previous measurements) would be taken into account.
  • the degradation model comprises a Bayesian forecasting model.
  • the method further comprises: determining a drift in the Wener process model based on the Bayesian prediction model. It is generally known that the drift coefficient l (t) is subject to a process that changes over time. Therefore, the first state value can be the only known value, which is why the drift must be determined.
  • the drift is generally considered to be deterministic. However, in some exemplary embodiments it can be modeled as a random variable (or stochastic variable).
  • the drift is determined based on a Bayesian filter and / or a Kalman filter.
  • the drift can in a
  • State space model can be constructed as shown in formulas (6) and (7):
  • h can be proportional to a normally distributed noise
  • e can be proportional to a further normally distributed noise be as a variance to represent a Brownian motion.
  • formula (6) represents a system equation
  • formula (7) represents an observation equation. H and e may be uncorrelated.
  • the present invention is not limited to this.
  • n can originate from the natural numbers and comprises the past cycles since a method according to the invention was previously carried out.
  • c i corresponds to a current cycle or a cycle of a last measurement by a sensor and C in corresponds to a cycle of the last execution of a
  • a strand tracking filter algorithm is used to detect sudden signal changes (or sudden (short-term) changes in the first
  • the degradation model includes a Monte Carlo simulation.
  • c i is a usage cycle on which a past (or the last) sensor measurement was carried out and c f is a future usage cycle on which the FT is achieved.
  • the method further comprises: processing the first status value.
  • the first state value can, for example, be noisy, an outlier from a statistic, and the like.
  • the first status value can therefore be processed, i.e. processed to filter and / or normalize the first state value.
  • the processing includes filtering.
  • a Kalman filter can be used to filter a noise.
  • noise can make a prediction or the determination of the second status value and / or the future usage cycle incorrect. This can be caused, for example, by an indirect measurement.
  • a Kalman filter can be implemented in the form of the following third algorithm:
  • Noise can thus advantageously be removed and the determination of the second state value can be carried out based on a more stable first state value.
  • the processing includes a rolling window regression.
  • the rolling window regression can be applied after the Kalman filter in order to filter a fluctuation of the first state value on a short time scale.
  • a mean value is formed for several (consecutive) first status values. For example, a first mean value is formed for the first through the nth first state value (in a first window). The window is then shifted by one position so that a second mean value is formed for the second to n + 1-th first state values. Then the window can be shifted one more place so that for the third to n + 2th
  • the stability of the first state value and thus the determination of the future state value can advantageously be increased.
  • the processing is therefore also based on one
  • the method further comprises: determining at least one threshold value based on the first status value, which is indicative of the second status value.
  • the first threshold value can include, for example, a healthy threshold, a failure threshold, and the like, as described herein.
  • a (predetermined) standard can advantageously be met and a
  • a pressure can be an indirect measurement of an amount of current flowing through a motor.
  • an FT can be based on a standardized The (maximum) value of such a current flow rate can be given, which is expressed by pressure values in a chamber of the motor.
  • the FT can represent a value at which the device can no longer be operated.
  • a threshold value can be established up to which the device can still be operated without damage having typically already occurred.
  • a threshold value can include security until the FT is reached and can be referred to as HT (Healthy Threshold).
  • a BU (Business as Usual) threshold value can be defined, which can designate a state in which the device is operated.
  • the HT can be defined based on the BU and using a standard deviation s from first state values (or raw data) during a BU operating mode. Without limiting the present invention to this, the HT can be defined as follows:
  • the BU can be determined on the basis of filtered first state values (which have been filtered with a Kalman filter, as described herein), wherein advantageously a probability that the BU assumes a value above the HT is low as a result of the filtering as long as the BU is below of the HT lies.
  • a random (or stochastic) degradation or wear takes place.
  • the HT can be recognized before damage to a device occurs.
  • Some exemplary embodiments relate to a remaining usage cycle determining circuit which is set up to carry out a method according to the invention.
  • the remaining usage cycle determination circuit can comprise, for example, a CPU (Central Processing Unit), GPU (Graphic Processing Unit) or any other type of at least one processor, an FPGA (Field Programmable Gate Array), and the like.
  • the circuit can receive sensor data in that it is connected to at least one sensor or comprises at least one sensor. Furthermore, it can be connected to at least one sensor and at the same time comprise at least one (other) sensor.
  • the at least one sensor can be set up to measure at least one status value of at least one maintenance variable or to generate measurement data that are indicative of the at least one status value, for example by means of a direct or indirect measurement.
  • the senor can also be set up to measure several maintenance variables, such as a pressure and a humidity, a distance, a volume, and the like.
  • the remaining usage cycle determination circuit can also have one (or more)
  • inventive method can be carried out.
  • Some exemplary embodiments relate to a remaining usage cycle determining device which comprises a remaining usage cycle determining circuit according to the invention.
  • the device for determining the remaining usage cycle can, for example, comprise a computer, server, and the like, and at least one sensor or be connected to it.
  • the device for determining the remaining usage cycle can also comprise a device for which a number of remaining usage cycles is to be determined.
  • FIG. 1 schematically shows an embodiment of a method according to the invention for
  • FIG. 2 shows a further exemplary embodiment of a method according to the invention for determining remaining usage cycles of a production device due to wear in a block diagram
  • 3 shows a production device for which a number of remaining usage cycles can be determined
  • FIG. 4 shows a further exemplary embodiment of a method according to the invention for determining remaining usage cycles of a production device due to wear in a flow chart
  • Fig. 6 is a graph showing degradation
  • FIG. 7 shows a device for determining the remaining usage cycle according to the invention with a circuit for determining the remaining usage cycle according to the invention.
  • FIG. 1 The remaining usage cycles of a production device due to wear and tear is shown in a block diagram in FIG. 1.
  • a first state value of a maintenance variable of a part of a production device is determined based on sensor data of a sensor for the production device, as described herein.
  • FIG. 3 shows a further exemplary embodiment of a method 10 according to the invention for determining remaining usage cycles of a production device due to wear.
  • the first status value is processed as described herein.
  • At least one threshold value is determined based on the first state value, which is indicative of the second state value, as described herein.
  • a drift in a Wiener process model is determined based on a Bayesian prediction model as described herein.
  • a probability density function for determining the remaining usage cycles is generated as described herein.
  • a future usage cycle of the part of the production device for which the maintenance variable has a predetermined second status value is determined, the future usage cycle being determined based on the first status value and based on a discrete stochastic degradation model, as described herein.
  • a number of remaining usage cycles of the production device is determined based on the future usage cycle, as described herein.
  • Fig. 3 shows a production device for which a number of remaining usage cycles can be determined.
  • the production device is a gluing machine 20 which has a motor 21 with a motor axis 22, a spindle 23, a nut 24 with rolling elements, guide rods 25, a piston 26, an adhesive chamber 27, an inlet valve 28, an outlet valve 29, and a Has nozzle 30.
  • the gluing machine 20 can distribute glue.
  • the manifold, as it operates under high pressure, can be prone to wear.
  • the spindle 23 can be difficult to maintain, which can be expensive.
  • a method according to the invention is therefore used for the spindle 23.
  • a sensor system that monitors the gluing machine 20 can measure a pressure in the gluing chamber 27 in addition to eight further parameters. In this exemplary embodiment, the pressure is the relevant maintenance variable.
  • the (maximum) pressure (during a production cycle) is the main maintenance variable in this exemplary embodiment, according to which a possible failure of the spindle 23 can be predicted or estimated.
  • the nozzle may have a momentary blockage while the adhesive is being applied. As a result, it may be necessary to increase the pressure in the glue chamber 27 in order to free the nozzle.
  • a current of the motor 21 can be increased.
  • the current, and thus the pressure can, however, be subject to fluctuations, as a result of which a data point which reflects the maximum pressure which after a production cycle (measured indirectly) can be an outlier and / or noisy.
  • the pressure is filtered with a Kalman filter and a rolling window regression with a moving average.
  • FIG. 4 shows a further exemplary embodiment of a method 40 according to the invention in a flowchart.
  • Condition monitoring is carried out in 41, whereby a first status value of a maintenance variable is obtained, i.e. a pressure data set is determined by a pressure sensor.
  • the obtained print dataset is filtered as described herein.
  • a drift estimate for a Wiener process based on a Bayesian network is carried out in 45, as described herein.
  • the drift estimation is applied in such a way that a negative value or a zero value for the drift is avoided in order to advantageously avoid a divergence of a Monte Carlo simulation, which advantageously makes it possible to save computing power.
  • a future usage cycle is determined. This means that it is determined how often the pressure is likely to exceed the maximum pressure value in future usage cycles.
  • the future usage cycle then indicates a failure threshold.
  • a number of remaining usage cycles is determined based on this. If in 47 the number of remaining usage cycles is less than the usage cycles per day (i.e. the gluing machine can then still be used on the production day), a new print data record is determined in 48. If the number of remaining usage cycles is greater, a report is generated in 49 and maintenance is initiated.
  • maintenance can be carried out when a production break occurs or when one is planned.
  • the production break can be planned based on the remaining usage cycles determined so that
  • FIG. 5 shows a graph 50 for determining remaining usage cycles, which shows on an ordinate 51 a measured pressure in an adhesive chamber (as described above), which is plotted as data points 52 against a time 53. Between the data points 52 there are empty spaces 54 which are created by production breaks (for example weekends). A healthy threshold 55 is also shown. The data points which are above the HT 55 correspond to second state values, have thus been determined using a method according to the invention. The data points above the HT 55 provide information about a number of remaining usage cycles up to a failure threshold 56.
  • FIG. 6 shows a graph 60 which shows a degradation, with a multiplicity of probability density functions 61. The graph 60 has an ordinate 62
  • Probability density functions 61 normally distributed.
  • Probability density functions 61 indicate an expected number of remaining usage cycles, while crosses 65 represent an actual number of remaining usage cycles before a failure.
  • an accurate prediction can be made. For example, here eighty percent of the determined remaining usage cycles remain below a predefined confidence interval, so that an accuracy is advantageously high, so that a
  • FIG. 7 shows a remaining usage cycle determining device 70 according to the invention for determining a number of remaining usage cycles of a production device 73
  • Remaining usage cycle determining device 70 comprises a sensor interface 71 which receives sensor data from a sensor 72.
  • the sensor 72 is set up to generate sensor data which are indicative of a first state value (or a plurality of first state values) of a maintenance variable, as described herein.
  • the sensor interface 71 is set up to determine the first status value from the sensor data.
  • the remaining-use cycle determining device 70 further includes a
  • Embodiment is designed as a CPU, and which is set up to a
  • the present invention apart from gluing machines, can generally be used in
  • Such production devices can contain, for example, a spindle drive, a gear drive, a roller bearing, and / or other components.
  • the present invention can be used for all components affected by wear, such as vehicle components that are monitored with appropriate sensors (for example vibration sensors).
  • sensors for example vibration sensors
  • Wheel bearing damage, gear damage, and the like can be predicted.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un procédé (1 ; 10 ; 40) servant à définir des cycles d'utilisation restante d'un dispositif de production (20 ; 73) sur la base de l'usure. Le procédé comprend les étapes consistant à : définir (2 ; 11 ; 41) une première valeur d'état d'un paramètre de maintenance d'une partie du dispositif de production (20 ; 73) sur la base de données de capteur d'un capteur pour le dispositif de production ; et définir (3 ; 16 ; 49) un cycle d'utilisation futur de la partie du dispositif de production (20 ; 73), pour lequel le paramètre de maintenance présente une deuxième valeur d'état fixée, le cycle d'utilisation futur étant défini sur la base de la première valeur d'état et sur la base d'un modèle de dégradation stochastique discret.
EP20712294.6A 2019-04-23 2020-03-16 Procédé servant à définir des cycles d'utilisation restante, circuit de définition de cycle d'utilisation restante, dispositif de définition de cycle d'utilisation restante Pending EP3959571A1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE102019002890 2019-04-23
DE102020200051.4A DE102020200051A1 (de) 2019-04-23 2020-01-06 Verfahren zum Bestimmen von Restnutzungszyklen, Restnutzungszyklusbestimmungsschaltung, Restnutzungszyklusbestimmungsvorrichtung
PCT/EP2020/057149 WO2020216530A1 (fr) 2019-04-23 2020-03-16 Procédé servant à définir des cycles d'utilisation restante, circuit de définition de cycle d'utilisation restante, dispositif de définition de cycle d'utilisation restante

Publications (1)

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EP3959571A1 true EP3959571A1 (fr) 2022-03-02

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CN (1) CN114008549A (fr)

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103488881B (zh) * 2013-09-06 2017-05-17 中国人民解放军第二炮兵工程大学 一种不确定退化测量数据下的设备剩余寿命预测方法
US9846978B1 (en) * 2016-06-15 2017-12-19 Ford Global Technologies, Llc Remaining useful life estimation of vehicle component
CN107145645B (zh) * 2017-04-19 2020-11-24 浙江大学 带不确定冲击的非平稳退化过程剩余寿命预测方法
CN107145720B (zh) * 2017-04-19 2020-05-12 浙江大学 连续退化和未知冲击共同作用下的设备剩余寿命预测方法
CN108629073B (zh) * 2018-03-14 2019-05-03 山东科技大学 一种多模式的退化过程建模及剩余寿命预测方法
CN109343505A (zh) * 2018-09-19 2019-02-15 太原科技大学 基于长短期记忆网络的齿轮剩余寿命预测方法

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