WO2013167342A1 - Procédé de traitement assisté par ordinateur de modèles d'un système technique - Google Patents

Procédé de traitement assisté par ordinateur de modèles d'un système technique Download PDF

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Publication number
WO2013167342A1
WO2013167342A1 PCT/EP2013/057675 EP2013057675W WO2013167342A1 WO 2013167342 A1 WO2013167342 A1 WO 2013167342A1 EP 2013057675 W EP2013057675 W EP 2013057675W WO 2013167342 A1 WO2013167342 A1 WO 2013167342A1
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Prior art keywords
quality
criterion
model
criteria
models
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PCT/EP2013/057675
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English (en)
Inventor
Victoria KUSHERBAEVA
Alexander Pyayt
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Siemens Aktiengesellschaft
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Publication of WO2013167342A1 publication Critical patent/WO2013167342A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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"

Definitions

  • the invention refers to a method for computer-aided
  • a corresponding model may be used for abnormal behavior detection of the technical system in condition monitoring. To do so, predicted sensor measurements are compared with real sensor measurements and, in case of a high deviation, an anomaly is detected.
  • the method according to the invention processes several models of a technical system by the use of a computer. Each model is based on a computer- implemented process for
  • each model predicts one or more sensor measurements based on a time series of previous sensor measurements.
  • one or more of the models may be trained based on training data comprising one or more time series of sensor measurements. This is e.g. the case for models based on neural networks .
  • a step a) of the method of the invention several quality criteria for the models of the technical system and a weight for each quality criterion are provided.
  • Each weight is provided.
  • the weights represent a contribution of the respective quality criterion to an overall quality of the models, where the weights are determined based on pairwise comparisons of the quality criteria using intensities of importance according to an Analytic Hierarchy Process.
  • the weights are determined on-line during performing the method.
  • the Analytic Hierarchy Process is well-known from the prior art. E.g., a description of this process can be found in document [5] .
  • the Analytic Hierarchy Process is a method to support decisions, and the above mentioned pairwise
  • step a) of the method according to the invention those alternatives are the quality criteria.
  • a detailed description of the pairwise comparisons can be found in section 4 of document [5] .
  • the intensities of importance used therein are chosen between 1 and 9 and represent the so-called Saaty scale. However, other scales may also be used for representing the intensities of importance. Based on the pairwise comparisons, it is
  • a priority value is assigned to the respective model based on the quality measure for each quality criterion, the priority value increasing with a higher quality according to the quality measure.
  • the priority may correspond directly to the quality measure, i.e. to the quality value is the quality measure.
  • the corresponding quality measure can be defined differently. Below, several examples of quality measures for models are given.
  • a sum over all quality criteria for each model is determined, where the summand for a respective quality criterion is the product of the weight for the respective quality criterion and the priority value for the respective model and quality
  • the sum represents the overall quality of the respective model such that a higher sum corresponds to a higher overall quality.
  • an output including the model with the highest overall quality, i.e. with the highest value of the sum, is generated by the method of the invention.
  • the method of the invention is based on the finding that the pairwise comparisons defined in an Analytic Hierarchy Process may be used in an efficient manner in order to define the contributions of different quality criteria with respect to an overall quality of a model.
  • the method of the invention provides a formal process for choosing an appropriate model for describing a technical system.
  • the method of the invention can be used for models of any technical systems.
  • the technical system which is described by the models is a structure, preferably a building and/or bridge and/or dam, where the sensor measurements are determined by sensors installed at the structure.
  • the sensor measurements are determined by sensors installed at the structure.
  • the sensor is a structure, preferably a building and/or bridge and/or dam, where the sensor measurements are determined by sensors installed at the structure.
  • Pore pressure sensors are particularly used in case that the structure is a dam.
  • an autoregressive process preferably comprise one or more of the following models: an autoregressive process
  • At least one neural network particularly a feed-forward neural network and/or a recurrent neural network.
  • the quality criteria comprise one or more of the following criteria:
  • an error criterion with the quality measure being an error between one or more sensor measurements predicted by the respective model and one or more real sensor measurements, the priority value increasing with a decreasing error, where the error is preferably the root mean squared error and/or the mean absolute error and/or the mean percentage error;
  • an AIC criterion with the quality measure being the Akaike information criterion for the respective model, the priority value increasing with a decreasing Akaike information criterion;
  • an anomaly detection criterion with the quality measure being the success rate or failure rate for correctly determining anomalies based on deviations between one or more sensor measurements predicted by the respective model and one or more real sensor measurements, the priority value increasing with an increasing success rate or decreasing failure rate;
  • a duration of validity criterion with the quality measure being the duration of validity of predictions of the respective model, the priority value increasing with an increasing duration of validity;
  • one or more quality criteria from said several quality criteria are quantitative criteria with quality measures which are calculated without using expert knowledge.
  • the error criterion, the coefficient of determination criterion, the AIC criterion, the anomaly detection criterion and the duration of validity criterion are usually quantitative criteria where predetermined formulas exist in order to calculate the quality measures.
  • one or more quality criteria from said several quality criteria may also be qualitative criteria with quality measures which are determined using expert knowledge.
  • the above computational complexity criterion is usually a qualitative criterion.
  • the quality measures for one or more predetermined criteria from said several criteria, and particularly from the above mentioned
  • the alternatives are now the models and not the quality criteria.
  • the intensities of importance are based on expert knowledge. As described below, this expert knowledge may be specified by a user via a user interface, i.e. a user can determine the corresponding intensities of importance for the models. This may also be the case for the pairwise
  • step a) comparisons defined in step a) .
  • a user may specify the corresponding
  • intensities of importance for the quality criteria via a user interface .
  • the method generates a user interface for enabling a user to at least partially define the models and/or the quality criteria and/or the intensities of importance of the Analytic Hierarchy Process for the quality criteria and/or the intensities of importance of the Analytic Hierarchy Process for the models.
  • the selection of the appropriate model can be adjusted based on user preferences.
  • the output generated in step d) includes the overall quality for the model with the highest sum and/or a ranking of the models with respect to their overall quality, where the ranking preferably includes the overall qualities for each model. According to this variant, more information despite the model with the highest quality is generated.
  • the output generated in step d) can be stored in a
  • the output is provided on a
  • corresponding user interface e.g. a monitor
  • the method determines for pairwise comparisons performed in step a) of claim 1 and/or performed for the above described predetermined criteria an inconsistency measure, the
  • inconsistency measure being preferably output in case that it exceeds a predetermined threshold.
  • the determination of such an inconsistency measure is known from the prior art and e.g. described in section 6 of document [5] .
  • the method calculates a sensitivity measure for the model which is output in step d) by varying the intensities of importance of the Analytic Hierarchy Process as defined in claim 1 and/or the intensity of importance of the Analytic Hierarchy Process used for the above defined predetermined criteria.
  • the definition of such a sensitivity measure lies within the knowledge of a skilled person.
  • the sensitivity measure is presented to a user via a user interface.
  • the invention also refers to a computer program product directly loadable into the internal memory of a digital computer, comprising software code portions for performing the method of the invention or one or more preferred embodiments of the method of the invention when the product is run on a computer.
  • the invention refers to a computer program for controlling a computer to perform the method of the invention or one or more preferred embodiments of the method of the invention .
  • the Analytic Hierarchy Process defines a goal G in a first uppermost hierarchy HI, the goal being the choice of the best alternative in general.
  • the corresponding alternatives are different models Ml, M2 , M3 , M4 which are arranged in the lowest hierarchy level H3 of the hierarchical structure of Fig. 1.
  • Each model describes a technical system by a computer- implemented process which predicts sensor measurements of one or more parameters of the technical system.
  • the technical system is a structure, particularly a bridge, building or dam, where the sensor measurements refer to signals of sensors installed at the structure.
  • those sensors may be pore pressure sensors (e.g. installed in a dam) and/or inclination sensors and/or displacement sensors.
  • the models are used for anomaly detection in the technical system. To do so, sensor measurements predicted by the models based on previous measured parameters and compared with the actual real sensor measurements occurring in the corresponding structure. If there is a high deviation in the model output and the real sensor measurements, an anomaly will be detected by the model. Hence, the corresponding model will be used in an on- line monitoring of the technical system and provides an alarm in case that an anomaly is detected.
  • Model Ml refers to an autoregressive model
  • model M2 to an autoregressive moving average model
  • model M3 to a feed- forward neural network
  • model M4 to a recurrent neural network. All those models are well-known from the prior art. Evidently, a different number of models and other models may also be used in the method of the invention.
  • Criterion CI refers to the duration of validity
  • criterion C2 to the Akaike information criterion (see document [6] )
  • criteria C3 to success/failure in finding anomalies during previous time
  • criterion C4 to the root mean squared error between predicted and real sensor measurements
  • criterion C5 to the coefficient of determination
  • criterion C6 to the computational complexity.
  • Each criterion expresses a quality which can be determined for each of the models Ml to M4.
  • the quality of a respective model is higher when the duration of validity is longer, the Akaike information criterion is smaller, the success rate in finding anomalies is higher or the failure rate is lower, the root mean squared error is lower, the coefficient of determination is lower and the computational complexity is lower.
  • the criteria CI to C6 have already been explained before and, thus, will not be
  • Each of the criteria CI to C6 is associated with a
  • intensities of importance on a predetermined scale are assigned to each pair of criteria expressing which criterion of the pair is regarded as more important. If a criterion is compared with itself, the intensity of importance "1" is assigned to this pair. In case that the first criterion of a pair is more important than the second criterion, a value higher than 1 is assigned to this pair. For the reciprocal pair where the first and second criteria are interchanged, the reciprocal value of the intensity of importance is assigned to this pair . Based on the above assignment, a matrix is built where the rows refer to the first criteria of the pairs and the columns to the second criteria of the pairs. Based on this matrix, the (principal) eigenvector of the matrix is determined. A component of the eigenvector represents a priority value with respect to the criterion of the row in the matrix
  • the weights wl, w2, w6 refer to those eigenvector components/priority values determined by the pairwise comparisons according to the Analytic Hierarchy Process.
  • a user interface is generated in the
  • Fig. 1 enabling a user and particularly an expert to define corresponding intensities of importance. This can be done in a qualitative manner, i.e. the user needs not specify the corresponding intensities of importance but only has to judge in a qualitative manner which criterion out of a pair he regards as more important, e.g. by defining a position in a bar visualized on a monitor of a corresponding user interface.
  • each of the models Ml to M4 is evaluated with respect to each of the quality criteria CI to C6.
  • corresponding quality measures of the criteria are determined for each model.
  • the quality measure is the duration of validity.
  • the quality measure is the Akaike information criterion represented by a
  • the quality measure is the success rate or failure rate of anomaly detection.
  • the quality measure is the root mean squared error.
  • the quality measure is the
  • the quality measure is the computational complexity.
  • the criteria CI to C5 are quantitative criteria for which a corresponding quality measure can be calculated straight forward without using any expert knowledge. Contrary to that, the criterion C6 referring to the computational complexity is a qualitative criterion which needs expert knowledge to be determined.
  • pairwise comparisons according to an Analytic Hierarchy Process are used to obtain the quality measure of the computational complexity for each method.
  • pairwise comparisons of the models are made and corresponding intensities of importance are assigned to each compared pair of models.
  • a higher intensity of importance shall reflect that the first model of the corresponding pair is more important (i.e. has a lower computational complexity) than the second model of the pair.
  • the intensities of importance are determined by a user via a user interface provided by the method.
  • a priority value is determined which can be regarded as a quality measure of the criterion C6.
  • the priority value corresponds to a component of the eigenvector of the matrix of the pairwise comparisons.
  • IInn oorrddeerr ttoo ddeetteerrmmiinnee aann oovveerraallll qquuaalliittyy ooff tthhee rreessppeeccttiivvee mmooddeell,
  • aa ssuumm oovveerr aallll ccrriitteerriiaa iiss ccaallccuullaatteedd ffoorr eeaacchh mmooddeell.
  • the model with the highest value of the sum is output by the method, e.g. visualized on a monitor of a corresponding user interface.
  • This model refers to the model 35 with the highest overall quality and, thus, gives the user the information which model he shall use.
  • the other models with the respective values of the sums are also output via the user interface or a ranking of the models according to the values of the sums is shown on the user interface.
  • the consistency ratio explained in this section can be regarded as a corresponding measure of inconsistency.
  • the inconsistency i.e. the consistency ratio
  • the user may be requested to once again define intensities of importance for the pairwise comparisons.
  • the stability of the decisions based on the intensities of importance input by the user may be determined, e.g. by slightly varying the intensities of importance chosen by the user and evaluating if a high change in the final result occurs. In case that a high change occurs, this can be regarded as an instable decision and a corresponding information may be given to the user by the user interface.
  • the invention as described in the foregoing is based on both qualitative criteria and quantitative criteria. However, the invention may also be implemented by only using either quantitative criteria or qualitative criteria. Furthermore, the corresponding intensities of importance need not be determined on-line by a user via a user interface.
  • the invention as described in the foregoing has a number of advantages.
  • the invention combines the Analytic Hierarchy Process with models of a technical system in order to select the best model for describing the technical system.
  • the models describe structures and are used for structural health monitoring where anomalies can be detected based on deviations between predicted sensor measurements and real sensor measurements .
  • the problem of determining the best model is decomposed in subtasks of several quality criteria, where each quality criterion is analyzed separately.
  • the method of the invention enables to work with qualitative expert knowledge based on the
  • the method processes quantitative information obtained from numerical experiments with the models.
  • the invention also estimates inconsistencies in the judgments of a user and provides information with respect to the stability of the decisions made by the user.
  • the method of the invention solves a decision-making problem of choosing the best model for a technical system that fits several predefined criteria.
  • the decision-making process is adaptive depending on the preference of criteria and results of data experiments.

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Abstract

L'invention porte sur un procédé de traitement assisté par ordinateur de modèles (M1, M2, M3, M4) d'un système technique, chaque modèle (M1, M2, M3, M4) étant basé sur un processus mis en œuvre par ordinateur pour prédire des mesures de capteur. Le procédé comprend l'étape consistant à fournir plusieurs critères de qualité (C1, C2, …, C6) pour les modèles (M1, M2, M3, M4) et un poids (w1, w2, …, w6) pour chaque critère de qualité (C1, C2, …, C6), chaque poids (w1, w2, …, w6) représentant une contribution du critère de qualité respectif (C1, C2, …, C6) à une qualité globale des modèles (M1, M2, M3, M4), les poids (w1, w2, …, w6) étant déterminés en fonction de comparaisons par paire des critères de qualité (C1, C2, …, C6) à l'aide d'intensités d'importance conformément à un processus de hiérarchie analytique. En outre, une mesure de qualité du modèle respectif (M1, M2, M3, M4) relativement au critère correspondant (C1, C2, …, C6) est déterminée pour chaque modèle (M1, M2, M3, M4) et pour chaque critère de qualité (C1, C2, …, C6), une valeur de priorité (p11, p12, …, p46) étant attribuée au modèle respectif (M1, M2, M3, M4) sur la base de la mesure de qualité pour chaque critère de qualité (C1, C2, …, C6). En outre, une somme (s1, s2, s3, s4) sur tous les critères de qualité (C1, C2, …, C6) pour chaque modèle (M1, M2, M3, M4) est déterminée, le terme de somme pour un critère de qualité respectif (C1, C2, …, C6) étant le produit du poids (w1, w2, …, w6) pour le critère de qualité respectif (C1, C2, …, C6) et de la valeur de priorité (p11, p12, …, p46) pour le modèle respectif (M1, M2, M3, M4) et le critère de qualité respectif (C1, C2, …, C6), la somme (s1, s2, s3, s4) représentant la qualité globale du modèle respectif (M1, M2, M3, M4). Enfin, une sortie est générée qui comprend le modèle (M1, M2, M3, M4) ayant la plus haute qualité globale.
PCT/EP2013/057675 2012-05-07 2013-04-12 Procédé de traitement assisté par ordinateur de modèles d'un système technique WO2013167342A1 (fr)

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CN108332696A (zh) * 2018-01-27 2018-07-27 中国地质科学院探矿工艺研究所 滑坡监测方法选择方法
CN108446563A (zh) * 2018-02-09 2018-08-24 桂林电子科技大学 一种基于模糊层次分析法的ics信息安全评估方法
CN109062245A (zh) * 2018-07-19 2018-12-21 杭州电子科技大学 一种无人机地面站系统软件的可靠性智能分配方法
CN109598412A (zh) * 2018-11-02 2019-04-09 国网河北省电力有限公司雄安新区供电公司 面向园区能源管理的评价方法及终端设备
CN116227941A (zh) * 2023-05-06 2023-06-06 湖南百舸水利建设股份有限公司 一种调水工程的风险模拟计算评估方法及系统

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108332696A (zh) * 2018-01-27 2018-07-27 中国地质科学院探矿工艺研究所 滑坡监测方法选择方法
CN108446563A (zh) * 2018-02-09 2018-08-24 桂林电子科技大学 一种基于模糊层次分析法的ics信息安全评估方法
CN109062245A (zh) * 2018-07-19 2018-12-21 杭州电子科技大学 一种无人机地面站系统软件的可靠性智能分配方法
CN109062245B (zh) * 2018-07-19 2021-06-01 杭州电子科技大学 一种无人机地面站系统软件的可靠性智能分配方法
CN109598412A (zh) * 2018-11-02 2019-04-09 国网河北省电力有限公司雄安新区供电公司 面向园区能源管理的评价方法及终端设备
CN116227941A (zh) * 2023-05-06 2023-06-06 湖南百舸水利建设股份有限公司 一种调水工程的风险模拟计算评估方法及系统

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