WO2005109253A2 - Procede d'evaluation assistee par ordinateur du pronostic de grandeurs caracteristiques d'un systeme technique realise au moyen d'un modele de pronostic - Google Patents

Procede d'evaluation assistee par ordinateur du pronostic de grandeurs caracteristiques d'un systeme technique realise au moyen d'un modele de pronostic Download PDF

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Publication number
WO2005109253A2
WO2005109253A2 PCT/EP2005/052038 EP2005052038W WO2005109253A2 WO 2005109253 A2 WO2005109253 A2 WO 2005109253A2 EP 2005052038 W EP2005052038 W EP 2005052038W WO 2005109253 A2 WO2005109253 A2 WO 2005109253A2
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Prior art keywords
partial
interval
forecast
determined
parameters
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PCT/EP2005/052038
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German (de)
English (en)
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WO2005109253A3 (fr
Inventor
Benedikte Elbel
Veronika Dunkel
Michael Greiner
David Meintrup
Oliver MÄCKEL
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Siemens Aktiengesellschaft
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Publication of WO2005109253A2 publication Critical patent/WO2005109253A2/fr
Publication of WO2005109253A3 publication Critical patent/WO2005109253A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/008—Reliability or availability analysis

Definitions

  • the invention relates to a method for computer-aided evaluation of the prognosis of parameters of a technical system, which is carried out by means of a prognosis model, as well as a corresponding arrangement and a corresponding computer program dummy product.
  • Forecast model to the determined parameters of the technical system considered on the basis of error deviations.
  • There are also methods for evaluating the suitability of forecasting models U-plot, Prequential Likelihood criterion or hold-out criterion, see e.g. Lyu, M.R .: “Handbook of Software Reliability Engineering", McGraw-Hill, New York, 1995).
  • these also do not evaluate the suitability for long-term forecasts and are also computationally intensive.
  • the object of the invention is therefore to create a method for computer-aided evaluation of a forecast model, with which the suitability of the forecast model for a specific technical system can be checked in a simple manner, in particular also with regard to long-term forecasts.
  • a total time interval with a start point and an end point, in which a plurality of parameters of the technical system have been determined is first divided into several subintervals, each subinterval lying between a subinterval start point and a subinterval end point in the overall interval.
  • a partial adjustment of the forecast model to be evaluated to the parameters determined in the partial interval whereby each partial interval is assigned to a partial adjustment.
  • a forecast accuracy is determined for each partial adjustment, which is a measure of the accuracy of a forecast made with the partial adjustment of one or more parameters of the technical system.
  • those that meet one or more predefined criteria are selected from the partial adjustments.
  • a stability time measure is determined for these selected partial adaptations, which depends on a stability interval that lies essentially between the smallest and the largest partial interval end point of the partial intervals assigned to the selected partial adaptations.
  • Forecast accuracy of the selected adjusted forecast models determines a forecast quality, which represents an evaluation measure for the quality of the forecast carried out with the forecast model.
  • the suitability of the forecast model for long-term forecasts is also taken into account in the quality assessment, because it can be assumed that the longer-term the better the forecast, the more stable it is in the assessment time interval used.
  • the subintervals are nested, i.e. each subinterval begins at the starting point of the total interval, so that subintervals with larger subinterval endpoints always include subintervals with smaller subinterval endpoints.
  • One of the specified criteria when selecting the partial adjustments is that all partial interval endpoints of the partial intervals assigned to the selected partial adjustments valle are temporally successive partial interval endpoints in the total interval and the largest partial interval endpoint of the partial intervals assigned to the selected partial adaptations is the end point of the total interval.
  • a coherent stability time interval is therefore always considered, which always has the end point of the total interval as the end point.
  • the forecast accuracy of a respective partial adjustment is the deviation of one or more parameters forecast using the respective partial adjustment from one or more parameters determined in the total interval and / or one or more parameters forecast using a predetermined partial adjustment.
  • the ascertained accuracy of the forecast of the respective partial adjustment is preferably the deviation of a parameter predicted by means of the respective partial adjustment at the end point of the total interval from a parameter determined at the end point of the total interval and / or from a parameter predicted by means of a predetermined partial adjustment at the end point of the total interval.
  • the predefined partial adaptation is preferably an adaptation which is adapted to all parameters of the overall interval.
  • the deviation of the predicted from the determined parameters is a relative deviation.
  • the predetermined criterion when selecting the partial adjustments consists in the fact that the determined accuracy of the prognosis of a respective partial adjustment is better than a predetermined value. Thus, only if this criterion is met is the respective partial adjustment assigned to the group of selected partial adjustments.
  • a forecast spreading interval is defined as a confidence interval for a chosen statistical significance when forecasting the target quantity.
  • a predefined criterion for a respective partial adjustment is selected such that the criterion is met if a parameter determined at the end point of the total interval lies within the forecasting interval of the respective partial adjustment.
  • the stability time measure is the length of the stability interval divided by the length of the total interval, i.e. the stability time measure is a relative value.
  • the forecast quality is calculated using the following formula:
  • Ml is the forecast quality
  • ⁇ the mean value of the forecast accuracy for the selected partial adjustments
  • L the length of the stability interval divided by the length of the total interval.
  • the forecast quality can be calculated using the following formula:
  • M2 is the forecast quality
  • ⁇ the mean of the forecast accuracy for the selected partial adjustments
  • L the Length of the stability interval divided by the length of the total interval.
  • the partial adaptation of the forecast model to the parameters determined in the respective partial interval takes place according to the maximum likelihood method and / or the method of the smallest squares of deviations.
  • the forecast to be evaluated is a reliability forecast, in particular a forecast carried out using a reliability growth model, and the parameters are values which represent the reliability of the technical system.
  • the parameters preferably include the number of total failures of the technical system at the time the respective parameter was determined and / or the average time until a failure of the technical system occurred at the time the respective parameter was determined.
  • the method according to the invention is particularly suitable for a technical system which has processor means on which program means are executed, the evaluated prognosis being a reliability prognosis of the program means.
  • the method according to the invention can be used with different types of forecasting accuracy and / or with different predetermined criteria for the selection of the partial adjustment are carried out, an overall forecast quality being determined from the forecast qualities determined using the different types of forecast accuracy and / or predetermined criteria.
  • this overall forecast quality is a weighted average of the forecast qualities determined using the different types of forecast accuracy and / or specified criteria.
  • the forecast quality can be adjusted depending on the intended use of the forecast model to be evaluated.
  • the forecast quality can be selected on the basis of the forecast targets set by a user of the method.
  • the total time interval used in the method according to the invention is preferably a test and correction phase of the technical system, in which phase the technical system was continuously adapted to improve its reliability.
  • the forecast model evaluated with the method according to the invention thus serves in particular to estimate whether the length of a given test and correction phase of the technical system is sufficient to ensure a certain reliability of the system when it is used later.
  • the method according to the invention can in each case be repeated for a number of different forecast models, as a result of which a plurality of forecast qualities are obtained.
  • the individual forecast qualities can then be compared and the forecast model with the best forecast quality can be used for the forecast of the parameters of the technical system.
  • the invention further relates to an arrangement for computer-aided evaluation of the prognosis of parameters of a technical system carried out by means of a forecast model, the arrangement being designed such that the method according to the invention can be carried out with this arrangement.
  • the invention relates to a computer program product, which in the Memory of a computer can be loaded and includes software code sections with which the inventive method is carried out when the program product is running on the computer.
  • Figure 1 is a diagram showing the sequence of an embodiment of the method according to the invention.
  • FIG. 2 shows a diagram which illustrates the determination of partial adaptations in the method according to the invention.
  • Figure 3 shows a technical arrangement for performing the inventive method.
  • a technical system which has processor means on which program means can be executed.
  • the aim of the method is to evaluate a prognosis that predicts the reliability of the program resources running on the technical system.
  • Reliability parameters of the system were determined beforehand in a total time interval I, which represents a test phase of the technical system, the reliability parameters in the embodiment described here being the cumulative total failure numbers of the technical system determined at a predetermined point in time.
  • the program resources of the technical system were subjected to extensive tests, whereby the errors found in the program resources were corrected.
  • a first step S101 the total interval I is divided into a plurality of subintervals Ii, I 2 ,..., I, the subintervals always starting at the starting point of the total interval I and gradually increasing, so that the last subinterval corresponds to the total interval I.
  • Step S102 partially adjusts a forecast model to be evaluated with the method to the parameters A (t) (t is a point in time in the total interval I and A (t) is the total number of failures at point in time). Any of those known from the prior art can be used
  • Reliability growth model can be used as a forecast model, for example the model by Musa and Okumoto or a logarithmic model. These models include parameters that are adapted in step S102 to the failure numbers A (t) of the technical system in each subinterval Ii, I 2 , ..., I. In this way, a large number of partial adjustments P- ⁇ are obtained.
  • a forecast accuracy ⁇ j_ is determined for each partial adjustment.
  • the forecast accuracy ⁇ ⁇ is the amount of the difference between the number of failures predicted at the end point of the total interval and the number of failures actually determined at the end point divided by the number of failures actually determined at the end point.
  • a ⁇ (t eri ( - ⁇ ) is the number of failures determined with the partial adjustment Pi at the end point t en d of the total interval I and A (t en d) is the actually determined number of failures at the time t en d.
  • the relative deviation between the number of default predicted at the end point t en and the number of default can also be used are considered, which was predicted with a partial adjustment adapted to all parameters of the total interval. In the latter case, the sensitivity of the method to random fluctuations in the event of failures at the end of the total interval is significantly reduced.
  • step S104 those partial adaptations are selected that have a forecast accuracy ⁇ i that is smaller than a predetermined maximum value ⁇ . Since the prognosis becomes better the larger the subinterval considered when adapting the forecast model, the subinterval endpoints that are assigned to the selected subadaptations are normally in the vicinity of the end point t ⁇ nd of the total interval I.
  • a stability time measure L is determined in step S105, which depends on a stability interval.
  • the stability interval lies between that
  • the stability time measure is the relative length of the stability interval and can be written mathematically as follows:
  • tk is the smallest partial interval end point of the partial intervals assigned to the selected partial adaptations.
  • a forecast quality M1 is determined in step S106 using the following formula:
  • Ml - 3.003 • (1-LY +0.997 where Ml is the forecast quality, ⁇ is the mean value of the forecast accuracy for the selected adapted forecast models and L is the stability measure of time.
  • the constants 3.003 and 0.997 are chosen so that the term 1 — ⁇ , which becomes greater the higher the accuracy of the forecast, for a stability measure of time, which corresponds to 90% of the length of the total interval, by the value of the denominator, which for large Stability times are less than 1, is still reinforced.
  • the forecasting quality Ml is maximally quartered by the denominator for a short stability interval.
  • the forecast quality can be represented by the following value M2:
  • M2 stands for the forecast quality
  • ⁇ is the mean value of the forecast accuracy for the selected partial adjustments
  • L is the stability time measure defined above.
  • the correction term in the denominator is an expression that is unlimited over the interval [0,1], whereby this expression can have a very large value in the case of a short stability phase and a value close to 1 in the case of a long stability interval. This means that the longer the stability interval, the greater the value M2, and M2 is also suitable for evaluating forecast models for long-term forecasts. By performing the method for a large number of prognostic models, the forecast model can thus be determined that is most suitable for a long-term forecast based on the forecast quality.
  • FIG. 2 shows a diagram which illustrates the adaptation of a forecast model to the parameters of a technical system.
  • the abscissa of the diagram is the time axis t and the ordinate represents the number of total failures A.
  • the total failures A (t) of the technical system measured in a total interval I between t 0 and t e at predetermined times are in the form of Shown measuring points.
  • Interval Ii extends from 0 to tj_, interval I2 from 0 to 2 and interval I3 from 0 to t.3.
  • the failure numbers A (t) in the individual partial intervals are used to adapt the forecast model under consideration. For every interval In . , I2 and I3 thus result in three curves P f ⁇ > 2 unc * 3r which represent a corresponding forecast of the number of failures on the basis of the measured number of failures in the corresponding intervals. As expected, the deviation of the number of failures determined with the forecast P3 from the actual number of failures A (t en d) at the end point t en d is smallest.
  • a maximum percentage deviation ⁇ between the predicted and the actually ascertained number of failures at the end point t e n d is determined and then the partial adaptations of the forecast model are selected in which the deviation of the predicted from the actually ascertained number of failures is less than ⁇ ,
  • the forecast quality can then be determined with the aid of these partial adjustments and the stability time measure described in the preceding.
  • the method according to the invention was used for the forecasting model by Musa and Okumoto and for the forecasting model Log Power carried out.
  • the Model Log Power has a much higher forecast quality than the model from Musa and Okumoto, which is due to the fact that the stability time model of the Log Power model is much longer than the stability time model of the Musa and Okumoto model.
  • the number of total failures was considered as a parameter.
  • other parameters e.g. the average time until a technical system failure occurs.
  • the criterion for selecting a partial adjustment does not have to be fixed by a fixed predetermined value ⁇ . It is also conceivable that a partial adjustment is always selected when the determined parameters of the technical system lie within the forecasting interval of the respective partial adjustment.
  • forecast qualities can be determined for different types of parameters or selection criteria, which can then be combined to form an overall forecast quality according to the forecast goals desired by a user.
  • FIG. 3 shows a technical arrangement with processor means PRZE, on which program means can be executed.
  • the processor means PRZE comprise a processor CPU, a memory MEM and an input / output interface IOS, which is used in different ways via an interface IFC. Output is visible on a monitor MON and / or output on a printer PRT via a graphic interface. An entry is made with the mouse MAS or KEYBOARD.
  • the processor means PRZE also have a data bus BUS, which ensures the connection to the memory MEM, the processor CPU and the input / output interface IOS.
  • additional components can be connected to the data bus BUS, for example additional memories, data memories in the form of a hard disk or a scanner.
  • the technical arrangement can be used as a device for evaluating the prognosis of parameters of a technical system carried out by means of a prognosis model.
  • the computer program product for carrying out the method according to the invention can be loaded into the memory MEM. It is also conceivable that the technical arrangement of FIG. 3 represents the technical system, the parameters of which are predicted using a forecast model, the forecast model being evaluated using the method according to the invention.

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  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Debugging And Monitoring (AREA)

Abstract

L'invention concerne un procédé d'évaluation assistée par ordinateur du pronostic de grandeurs caractéristiques (A(t)) d'un système technique réalisé au moyen d'un modèle de pronostic, consistant à diviser un intervalle de temps total (I) en plusieurs intervalles partiels (I1, I2, ...., I), une pluralité de grandeurs caractéristiques (A(ti)) du système ayant été déterminées dans l'intervalle de temps total (I). Pour chaque intervalle partiel (I1, I2, ...., I), une adaptation partielle (Pi) du modèle de pronostic est réalisée sur les grandeurs caractéristiques (A(ti)) déterminées dans l'intervalle partiel (I1, I2, ...., I) de manière à affecter chaque intervalle partiel (I1, I2, ...., I) à une adaptation partielle (Pi). Une précision de pronostic (i) est enfin déterminée pour chaque adaptation partielle (Pi). Les adaptations partielles remplissant un ou plusieurs critères définis sont sélectionnées et une grandeur temporelle de stabilité (L) est déterminée pour les adaptations partielles (Pi) sélectionnées, ladite grandeur temporelle de stabilité dépendant d'un intervalle de stabilité situé essentiellement entre le plus petit et plus grand point terminal des intervalles partiels (I1, I2, ...., I) affectés aux adaptations partielles (Pi) sélectionnées. La grandeur temporelle de stabilité (L) et les précisions de pronostic (i) des adaptations partielles (Pi) sélectionnées servent ensuite à déterminer une qualité de pronostic (M1; M2) constituant une grandeur d'évaluation pour la qualité du pronostic réalisé au moyen du modèle de pronostic.
PCT/EP2005/052038 2004-05-05 2005-05-04 Procede d'evaluation assistee par ordinateur du pronostic de grandeurs caracteristiques d'un systeme technique realise au moyen d'un modele de pronostic WO2005109253A2 (fr)

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DE200410022142 DE102004022142B4 (de) 2004-05-05 2004-05-05 Verfahren zur rechnergestützten Bewertung der mittels eines Prognosemodells durchgeführten Prognose von Kenngrößen eines technischen Systems

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1865514A1 (fr) * 2006-06-06 2007-12-12 Westinghouse Electric Company LLC Procédé d'analyse d'interaction pastille-gaine
RU2480828C1 (ru) * 2011-10-14 2013-04-27 Общество С Ограниченной Ответственностью "Лайфстайл Маркетинг" Способ прогноза целевого показателя событий по неограниченному количеству характеристик
RU2530308C1 (ru) * 2013-04-23 2014-10-10 Открытое акционерное общество "Федеральная гидрогенерирующая компания-РусГидро" Автоматизированный универсальный диагностический комплекс для управления безопасностью и надежностью гидротехнических сооружений гидроэлектростанций и иных объектов на всех стадиях их жизненного цикла
RU2665231C1 (ru) * 2017-11-29 2018-08-28 Владимир Сергеевич Пахомов Способ и система формирования вариантов стратегии долгосрочного планирования мероприятий по обеспечению требуемого состояния сложной организационно-технической системы
RU2668487C2 (ru) * 2015-12-21 2018-10-01 Федеральное государственное казенное военное образовательное учреждение высшего образования "Военный учебно-научный центр Военно-Морского Флота "Военно-морская академия им. Адмирала Флота Советского Союза Н.Г. Кузнецова" Система информационной поддержки принятия управленческих решений для обслуживающего персонала судовой энергетической установки

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1865514A1 (fr) * 2006-06-06 2007-12-12 Westinghouse Electric Company LLC Procédé d'analyse d'interaction pastille-gaine
KR101344626B1 (ko) 2006-06-06 2013-12-26 웨스팅하우스 일렉트릭 컴퍼니 엘엘씨 펠릿-피복재 상호작용 분석 방법
RU2480828C1 (ru) * 2011-10-14 2013-04-27 Общество С Ограниченной Ответственностью "Лайфстайл Маркетинг" Способ прогноза целевого показателя событий по неограниченному количеству характеристик
RU2530308C1 (ru) * 2013-04-23 2014-10-10 Открытое акционерное общество "Федеральная гидрогенерирующая компания-РусГидро" Автоматизированный универсальный диагностический комплекс для управления безопасностью и надежностью гидротехнических сооружений гидроэлектростанций и иных объектов на всех стадиях их жизненного цикла
RU2668487C2 (ru) * 2015-12-21 2018-10-01 Федеральное государственное казенное военное образовательное учреждение высшего образования "Военный учебно-научный центр Военно-Морского Флота "Военно-морская академия им. Адмирала Флота Советского Союза Н.Г. Кузнецова" Система информационной поддержки принятия управленческих решений для обслуживающего персонала судовой энергетической установки
RU2665231C1 (ru) * 2017-11-29 2018-08-28 Владимир Сергеевич Пахомов Способ и система формирования вариантов стратегии долгосрочного планирования мероприятий по обеспечению требуемого состояния сложной организационно-технической системы

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