EP4066186A1 - Procédé et système d'optimisation de l'entretien de machines industrielles - Google Patents

Procédé et système d'optimisation de l'entretien de machines industrielles

Info

Publication number
EP4066186A1
EP4066186A1 EP21708127.2A EP21708127A EP4066186A1 EP 4066186 A1 EP4066186 A1 EP 4066186A1 EP 21708127 A EP21708127 A EP 21708127A EP 4066186 A1 EP4066186 A1 EP 4066186A1
Authority
EP
European Patent Office
Prior art keywords
load
failure rate
component
measurement data
rate measurement
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
EP21708127.2A
Other languages
German (de)
English (en)
Inventor
Jens Schnittger
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.)
Siemens AG
Siemens Corp
Original Assignee
Siemens AG
Siemens Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens AG, Siemens Corp filed Critical Siemens AG
Publication of EP4066186A1 publication Critical patent/EP4066186A1/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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning

Definitions

  • the invention relates to a computer-implemented method for the numerical optimization of maintenance intervals of at least one component of an industrial machine.
  • the invention also relates to a method for maintaining at least one component of an industrial machine in accordance with the maintenance intervals that were determined by means of the aforementioned optimization method.
  • the invention also relates to a data processing system comprising means for performing the steps of the method for the numerical optimization of maintenance intervals of the type mentioned above.
  • the invention relates to a system comprising at least one industrial machine with at least one component and a data processing system of the type mentioned above.
  • the invention relates to a computer program, comprising send commands which, when the program is executed by a computer, cause the computer to carry out the steps of the method of the above type, and a computer-readable data carrier on which such a computer program is stored, and a data carrier signal transmitted by the computer program of the type mentioned above.
  • a maintenance frequency can be calculated based on experimental statistics or construction information. Often a maximum number of hours is given, which relates to the components of the industrial machine. that the industrial machine may be used without maintenance at the aforementioned constant load.
  • a disadvantage of such maintenance planning is that the wear and tear depends essentially on the load under which the component or the industrial machine is located.
  • this load can depend on a specific usage scenario and vary over time. Therefore, the assumption of a constant load (momentary load) can lead to a considerable overestimation or underestimation of the maintenance intervals. This in turn can lead to an increased failure rate or increased maintenance costs.
  • failure rate is determined by load-independent aging and load-dependent wear.
  • Heuristic failure rate distributions such as Weibull statistics, can be used to model this complicated behavior.
  • the parameters of the failure rate distributions are determined on the basis of failure statistics collected in the past at a given constant load and are no longer changed after they have been determined. As a result, the failure rate distributions determined in this way only apply to this given constant load. If the utilization of the industrial machine is changed, this in turn either leads to increased failures and costly downtimes or to increased maintenance costs.
  • the object of the present invention is to make the use of industrial machines more efficient and at the same time to reduce their maintenance costs.
  • the object is achieved according to the invention with a computer-implemented method of the type mentioned above in that A) the (future or predicted) failure behavior of the at least one component is described by a failure rate pf (t ⁇ S) (“S” for “survival”) .; t is the Time), the failure rate being in the form of a function / of a load integral L (t);
  • the load integral is calculated on the basis of a load of the at least one component that is planned for the future and that varies during operation of the component;
  • At least one maintenance interval is calculated based on the failure behavior calculated (for the future).
  • Step A) can be seen as the definition of a framework that will be used in the further process.
  • the function and the load integral must be determined in the further steps.
  • the function is determined on the basis of the (historical) failure rate measurement data that were recorded at a given, for example constant or piece-wise constant load.
  • step D) the load integral is calculated based on the future-oriented load data relating to the at least one component. It is assumed that the component will be operated under a load that varies (over time) in the future.
  • the maintenance interval is optimized, which is determined on the basis of these (future-oriented) failure statistics.
  • the failure rate measurement data of the component for a given load is understood to mean that measurement data that can be obtained by measuring the failure rate of the component at the given load. Such failure measurements often result in a failure probability with a bathtub-shaped profile.
  • the adaptation / fitting of the failure rate measurement data is understood to mean a compensation calculation, for example regression, maximum likelihood, etc., for failure rate measurement data.
  • load integral is understood to mean an integral of a load function - also called instantaneous load or instantaneous load - over time.
  • the failure rate pf (t ⁇ S) is related by definition to the survival function ps (t) ("S" for "survival”) as follows:
  • the function / is a fixed but arbitrary function that takes (non-negative) real values.
  • Such a dependency of the failure rate on the load integral corresponds to those situations in which the failure probability is determined by the state of wear, the state of wear itself being determined by the cumulative load.
  • the concept of the state of wear is to be understood in a very general sense, where, for example, load-independent aging is also subsumed.
  • L (t) is a vector. If L (t) is a vector, this means that various "load-like" criteria can initially be considered separately, whereby different components of the L (t) vector can describe different criteria, e.g. the torque and speed required for an engine nen.
  • a particularly rapid adaptation / fitting can be made possible if the at least one load is a constant or a piece-wise constant load.
  • load or "load function” is understood to mean an instantaneous load - also known as instantaneous load or momentary tan load.
  • cumulative load, or load integral denotes an instantaneous load that is integrated over time. For example, with a constant load 1, the result is 1 * t for the load integral, but periodic load functions, for example, are also conceivable.
  • the accuracy of the adaptation it can be useful if the failure rate measurement data of the component are provided for two or more predetermined, preferably different loads. For example, there can be different constant loads.
  • the function / (x) is designed as an at least one-parameter family of preferably continuous probability distributions, in particular as a multi-parameter, for example three-parameter modified Weibull distribution
  • the failure rate is designed as a function / of a load integral L (t) (see equation (3)), so that in the example described, in which the function / is the three-parameter modified Weibull distribution Equation (4) is formed, x is equal to the cumulative load L (t).
  • the parameters a, b and l are load-dependent according to the formula given, whereas a, b 'and l' are independent of 1; Their values can be determined when fitting to the specific measurement data.
  • the parameters of the function / (x) are determined using the maximum likelihood method.
  • the load integral or the load function can, for example, define a degradation state of the component of the industrial machine.
  • aging effects, fatigue, wear, etc. can be taken into account.
  • the load integral can be calculated, for example, on the basis of a future work plan of the industrial machine, for example, in which the (planned) load on the component of the machine is described.
  • the load integral L (t) can be calculated / determined for the future and inserted into equation (3) in order to obtain the statistics of the failure behavior for the Calculating the future and thus being able to forecast future failure rates.
  • At least one maintenance interval or several, preferably all maintenance intervals for the at least one component of an industrial machine can now be optimized numerically.
  • One or more statistical variables can (but do not have to) be calculated from the failure rates as required in order to calculate the optimized maintenance intervals or maintenance interval lengths based on these.
  • the production within one or more industrial plants can be controlled / optimized accordingly by having an operator or a system described below manually or using industrial machine (s) within the industrial plant (s) at a time calculated according to the optimized maintenance intervals stops automatically, followed by the maintenance process.
  • the failure rate measurements of the components that lead to the failure rate measurement data are typically carried out at specified maintenance interval lengths - also in the aforementioned method for numerical optimization of maintenance intervals.
  • the specified (non-optimized) maintenance interval lengths can all be the same - e.g. specified / prescribed by the manufacturer of the component.
  • the optimized maintenance intervals are load-dependent.
  • the failure rate measurements can be recorded with at least one predetermined load and with predetermined maintenance interval lengths.
  • the (load-dependent) maintenance intervals are preventive maintenance intervals, in particular cost-optimized preventive maintenance intervals.
  • a cost-optimized preventive maintenance interval T opt can be calculated, for example, using the following equation (see, for example, "Ingenierie de la maintenance: De la conception 1 'exploitation d'un bien”. Jean-Claude Francastei; Dunod, L'Usine antibiotic 2009 ( 2nd edition), Chapter 5.3:
  • Cp and Cu are maintenance costs that arise in the event of a planned or unplanned maintenance event
  • the term maintenance interval of a component is understood to mean a time interval between the commissioning of the component and the first scheduled / preventive maintenance or between two scheduled maintenance. This type of maintenance should not be confused with a repair, since a repair is corrective maintenance.
  • G) further failure rate measurement data are collected for the at least one optimized maintenance interval, and E) steps A to F are repeated, the failure rate measurement data in step A additionally including the further failure rate measurement data.
  • the optimization of the maintenance interval lengths can be successively further improved by including the failure rate measurement data, which were collected for the maintenance interval lengths that have already been optimized, as input failure rate measurement data.
  • the object of the invention is also achieved with a method for maintaining at least one component of an industrial machine in that at least one, preferably several, in particular all maintenance intervals are optimized as described above for the at least one component of the industrial machine and the at least one component is maintained according to the optimized maintenance intervals.
  • the object of the invention is also achieved with a data processing system which comprises means for executing the steps of the computer-implemented method described above for optimizing maintenance intervals of at least one component of an industrial machine.
  • the object of the invention is also achieved with a computer program which comprises commands which, when the program is executed by a computer, cause the computer to execute the program Carry out steps of the aforementioned method for optimizing maintenance intervals.
  • This computer program can be part of the data processing system insofar as it can be stored and executed on a computer, for example a server, of the data processing system.
  • the object of the invention is also achieved with a system comprising at least one industrial machine with at least one component and a data processing system described above.
  • the data processing system is designed to exchange data with the at least one industrial machine, in particular to collect or obtain failure rate measurement data of the component for at least one predetermined load and to implement the optimized maintenance intervals on the industrial machine.
  • the system is automated and is set up, for example, to automatically switch off the corresponding component and / or the industrial machine when maintenance intervals are exceeded (scheduled maintenance not carried out).
  • FIG. 2 shows a flow chart of a method for optimizing maintenance intervals.
  • FIG. 1 shows in particular a data processing system DVS which interacts with several industrial machines IM1, IM2, IM3 and can exchange data.
  • the data processing System DVS several modules - for example a first M1, a second M2 and a third module M3 - comprise, each of which can fulfill a number of specific tasks.
  • the data processing system DVS can comprise a combination of software and hardware components.
  • the first module Ml can be set up, for example, to receive data, e.g. failure rate measurement data, from the industrial machines IM1, IM2, IM3 (step B), to store and process them.
  • the first module M1 can be set up to fit the failure rate measurement data and to determine a function / function on the basis of this calculation (step C).
  • the failure rate measurement data can have a profile with a bath tub-shaped profile.
  • the first module Ml can fit the bathtub-shaped curves with a three-parameter modified Weibull distribution and determine the three parameters a ', b' and l 'which define the three-parameter modified Weibull distribution.
  • the three-parameter modified Weibull distribution is particularly well suited for the case when operating conditions correspond to a constant load or a piece-wise constant load.
  • the failure rate measurement data of the component are provided for two or more predetermined, preferably different loads.
  • the data processing system DVS can - here by means of the first module Ml - increase the accuracy of the adjustment method, e.g. the fitting, because more measurement data are available and, for example, determine the parameters a ', b' and l 'more precisely.
  • the second module M2 can be set up, for example, to generate a load integral L (t) that is used when calculating the Failure behavior of the components of the industrial machines IM1, IM2, IM3 - i.e. in step E - should be used to calculate and thus to execute step D.
  • the (future) work plans for calculating a time-dependent load and the load integral can, for example, be designed by the third module M3 and made available to the second module M2 or transmitted to the second module M2.
  • the third module M3 determines work plans, for example information about the use of the industrial machines IM1, IM2, IM3 for the coming week / two weeks / the coming month.
  • the work plans can be determined on the basis of specified (not yet optimized) maintenance intervals.
  • the third module M3 then sends these (non-optimized) work plans to the second module M2 so that the second module M2 can calculate the time-dependent load and the load integral L (t) (execution of step D).
  • the rule to calculate the failure rate pf (t ⁇ S) as a function of / from the load integral L (t) can, for example, be present in the second module M2, e.g. be stored.
  • the function / for the rule present in the second module M2 is not known a priori (rule with an unknown function, which must first be defined).
  • the function / is determined / determined in the present example by means of the first module Ml and transmitted to the second module M2, e.g. in the form of three parameters a ', b' and l '.
  • the second module M2 can then carry out step E and calculate the failure rate pf (t ⁇ S) using the function / and the load integral L (t) (Eq. (3)).
  • Equation (3) applies to any load integral L (t) and thus to any operating conditions of the industrial machines IM1, IM2, IM3 that vary over time. Based on the now calculated failure behavior, maintenance intervals can be optimized (step F). This can, for example, be carried out by means of the second module M2.
  • the third module M3 takes maintenance intervals into account when designing the work plans.
  • the third module M3 can design work plans as a function of the optimized maintenance intervals that the third module M3 can receive from the second module M2 (get made available or can get transmitted).
  • the third module M3 is preferably also set up to implement the work or production plans (including maintenance plans) designed (based on the optimized maintenance intervals) by, for example, controlling the industrial machines IM1, IM2, IM3 accordingly or a corresponding de Control of the industrial machines IM1, IM2, IM3 initiated. This can be done either automatically or with the help of an operator.
  • modules Ml, M2, M3 can be in the form of hardware and / or software, e.g. can be designed as parts of a software program.
  • the data processing system DVS can, for example, make the user of the industrial machines IM1, IM2, IM3 aware of an upcoming maintenance, for example by signaling this to the user.
  • the signaling can take place in the form of repetitive audio and / or video signals, where the frequency of the signals can be increased as the upcoming maintenance date approaches.
  • further failure rate measurements can be carried out after the optimized maintenance plan has been implemented obtain further failure rate measurement data (step G).
  • the further failure rate measurement data can serve to improve the optimization by repeating steps A to F, the further failure rate measurement data being used as part of the input failure rate measurement data in step A (step E).
  • the production of an automated industrial plant can be optimized based on the optimized maintenance intervals. This can be done, for example, by implementing (optimized) work plans determined with the aid of the optimized maintenance intervals.
  • the transfer can be done automatically by a system superordinate to the industrial plant, for example, or with the help of operating personnel.
  • FIG. 2 shows flow charts of a computer-implemented method for optimizing maintenance intervals.
  • a failure behavior is described by a failure rate, the failure rate being designed as a function of a load integral.
  • step B failure rate measurement data of the component are provided for at least one predetermined load.
  • step C the function is determined by fitting the failure rate measurement data.
  • step D the load integral is calculated (for example on the basis of a future operating plan).
  • step E the failure behavior is calculated based on the specified function and the calculated load integral.
  • step F an optimized maintenance interval derived therefrom is calculated.
  • step G further failure rate measurements for the at least one optimized maintenance interval can be carried out in step G in order to obtain further failure rate measurement data, steps A to F being repeated in step E, the failure rate measurement data additionally including the further failure rate measurement data in step A.
  • the invention relates to methods and systems in which the maintenance of components and thus maintenance of one or more industrial plants can be optimized. It will be understood that changes and / or additions of parts can be made to the methods and systems described above without departing from the field and scope of the present invention. It will also be apparent that, while the invention has been described with reference to specific examples, one skilled in the art should certainly be able to obtain many other corresponding forms of methods and systems having the characteristics set forth in the claims and thus all fall within the scope of protection established thereby.

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Abstract

L'invention concerne un procédé mis en œuvre par ordinateur pour optimiser numériquement des intervalles de service pour au moins un composant d'une machine industrielle (IM1, IM2, IM3), le composant étant exposé à une charge dépendant du temps. A) Le comportement de défaillance est décrit par un taux de défaillance, le taux de défaillance étant sous la forme d'une fonction d'une intégrale de charge ; B) des données de mesure de taux de défaillance du composant sont fournies pour au moins une charge prédéfinie ; C) la fonction est définie au moyen d'un ajustement des données de mesure de taux de défaillance ; D) l'intégrale de charge est calculée ; E) le comportement de défaillance est calculé sur la base de la fonction prédéfinie et de l'intégrale de charge calculée ; F) le comportement de défaillance calculé est pris en tant que base pour calculer au moins un intervalle de service optimisé.
EP21708127.2A 2020-02-26 2021-02-09 Procédé et système d'optimisation de l'entretien de machines industrielles Pending EP4066186A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP20159493.4A EP3872721A1 (fr) 2020-02-26 2020-02-26 Procédés et systèmes d'optimisation de l'entretien de machines industrielles
PCT/EP2021/053060 WO2021170392A1 (fr) 2020-02-26 2021-02-09 Procédé et système d'optimisation de l'entretien de machines industrielles

Publications (1)

Publication Number Publication Date
EP4066186A1 true EP4066186A1 (fr) 2022-10-05

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EP20159493.4A Withdrawn EP3872721A1 (fr) 2020-02-26 2020-02-26 Procédés et systèmes d'optimisation de l'entretien de machines industrielles
EP21708127.2A Pending EP4066186A1 (fr) 2020-02-26 2021-02-09 Procédé et système d'optimisation de l'entretien de machines industrielles

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US (1) US20230195099A1 (fr)
EP (2) EP3872721A1 (fr)
CN (1) CN115151929A (fr)
WO (1) WO2021170392A1 (fr)

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Publication number Publication date
WO2021170392A1 (fr) 2021-09-02
EP3872721A1 (fr) 2021-09-01
CN115151929A (zh) 2022-10-04
US20230195099A1 (en) 2023-06-22

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