WO2020215721A1 - Procédé de prédiction de la durée de vie d'un radar laser - Google Patents

Procédé de prédiction de la durée de vie d'un radar laser Download PDF

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Publication number
WO2020215721A1
WO2020215721A1 PCT/CN2019/121211 CN2019121211W WO2020215721A1 WO 2020215721 A1 WO2020215721 A1 WO 2020215721A1 CN 2019121211 W CN2019121211 W CN 2019121211W WO 2020215721 A1 WO2020215721 A1 WO 2020215721A1
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life
product
mtbf
failure
test
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PCT/CN2019/121211
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English (en)
Chinese (zh)
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于成磊
宁智文
刘慧林
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苏州玖物互通智能科技有限公司
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Publication of WO2020215721A1 publication Critical patent/WO2020215721A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/497—Means for monitoring or calibrating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

Definitions

  • the invention relates to a life prediction method of laser radar.
  • the state of a product is generally simplified into two discrete states, normal and failure, and the failure time is used as the object of statistical analysis, that is, the failure time data of the product is obtained through a large sample life test, and then the statistical discrimination method is used to select Appropriate life distribution model is used to describe the failure law of the product, and the parameters of the model are estimated, and finally the reliability of the product life is evaluated through the life distribution model.
  • traditional reliability can be defined as
  • T is the product life
  • n is the number of test samples
  • r(t) is the number of failed samples until time t.
  • lidar products the number of samples that can be used for life testing is limited, that is, n will not be very large; secondly, lidar products are usually high-reliability, long-life products, and within the specified test time It is difficult to get enough failure samples, that is, r(t) tends to zero.
  • the traditional life test only cares about the failure time, and does not care about the specific failure cause of the failure, and does not have much guiding significance for product improvement. Therefore, only using the traditional life test method is not suitable for lidar products.
  • the technical problem to be solved by the present invention is to provide a life prediction method for lidar, which has the ability to determine the reliability feature quantity and design multiple failure criteria based on its performance, and perform the analysis of no failure data, failure data and only degradation data. Life prediction and the establishment of multiple life prediction models for comparison and enhancement of reliability.
  • a method for predicting the life of a lidar includes the following steps:
  • model analysis which specifically includes:
  • the first life prediction model based on the test time is used to predict the life
  • the second life prediction model based on the number of failed products is used to predict the life
  • the third life prediction model includes: collecting the degradation data in the experimental data, predicting the pseudo life through the degradation data, and testing the pseudo life distribution And parameter estimation, the pseudo life index is estimated, and the final predicted life is obtained.
  • the first life prediction model based on test time includes parameter estimation and point estimation, and the parameter estimation includes:
  • the point estimate includes:
  • n is the number of samples
  • t 0 is the life test time
  • compare the size of the MTBF parameter estimation and the MTBF point estimation the product has no obvious degradation or failure
  • the mean time between failures MTBF not failed, not degraded min (MTBF parameter estimation , MTBF point estimation ).
  • the second life prediction model based on the number of failed products includes: respectively counting the number of failed products according to different criteria; calculating the corresponding MTBF failure through the life prediction formula; taking the smallest MTBF failure as the product occurrence Mean time between failures.
  • the criterion includes the average time per frame, the proportion of timeout frames, the proportion of format error frames, the maximum value of random error, the difference from the initial state, the proportion of abnormal values, and the frame miss rate.
  • the life prediction formula includes:
  • MTBF point estimation Among them, r is the total number of related failures in the timed censoring test, and T * is the cumulative related test time.
  • the degradation data in the collected experimental data includes: in the life test, m units of products participate in the life test, and the life test is carried out for a total of T hours, and n criterion matrices (x 1 ,x 2 ,...,x n );
  • the testing and parameter estimation of the pseudo life distribution includes: if the pseudo life distribution of the product is an exponential distribution, the parameter estimation value is: If the pseudo-life distribution of the product is Weibull distribution, the estimated parameter value is
  • the estimating the pseudo life index includes:
  • Exponential distribution life index estimation Estimated Weibull distribution life index:
  • the obtaining of the final life includes: calculating n sets of failure criterion parameters (x 1 , x 2 ,..., x n ) using the above-mentioned algorithms to obtain n sets of life indicators: (MTBF 1 , ⁇ 1 ),(MTBF 2 , ⁇ 2 )...(MTBF n , ⁇ n );
  • MTBF final min (MTBF 1 ,MTBF 2 ,...,MTBF n );
  • the failure criterion in the present invention has the characteristics of low cost, simple and easy to understand, multi-angle response product performance, index calculation based on original data and accurate results; the present invention is based on various aspects of the performance of the reaction product due to the test data analysis
  • the criterion index of the product, the problems found in the analysis process have a very positive effect on perfecting the test environment and improving the performance of the product;
  • the life prediction model considered in the present invention fully considers the situation that will occur in the product test, and gives it suitable for various situations
  • the model, comprehensive consideration of the results of multiple models can make the prediction of the final life more reliable.
  • Figure 1 is a graph showing the changes over time of the indicators of the difference between several products in the present invention and the initial state
  • Figure 2 is a distribution diagram of historical data of the proportion of abnormal values of all products in the present invention.
  • FIG. 3 is a diagram of the distribution of data of the proportion of abnormal values that exceed the threshold and the abnormal value that does not exceed the threshold, and the value that exceeds the threshold in the overall situation of a product in the present invention
  • Figure 4 is a weekly variation curve diagram of the difference between a product in the present invention and the initial state
  • Figure 5 is a combined diagram of the daily change trend of the difference between the same product and the initial state of the present invention.
  • Figure 6 is a visualization diagram of a certain criterion index of the degraded product in the present invention.
  • Figure 7 is a diagram of the time series extrapolation algorithm in the present invention.
  • FIG. 8 is a diagram of the time series prediction result in the present invention.
  • Figure 9 is an exponential distribution diagram of the degraded data set in the present invention.
  • This embodiment discloses a method for predicting the life of lidar, which includes the following steps:
  • Each line of output data scans one frame of data.
  • the information contained in it includes: product model, current status, and each frame
  • the serial number, the time used for each frame, the check code and the measured value and other information, the data of each product for each hour are stored in a separate file;
  • the criterion includes the average time per frame: extract the measurement time of each frame and analyze the average time of each device to monitor whether the motor speed is stable;
  • Percentage of timeout frames The proportion of frames whose measurement time exceeds a certain threshold (reflection of the lower bound of the motor speed that can be received) for each frame. This indicator also reflects the performance of the motor;
  • Proportion of format error frames The data transmission equipment has a certain error rate, this indicator can reflect the error situation of the data in the transmission stage;
  • the size of random error is an important aspect to measure whether the measurement product is stable. This indicator calculates the maximum value of random error in the output value and gives a threshold. If the random error exceeds the given threshold, we will treat it as Make degradation data samples;
  • Proportion of outliers This indicator measures whether the concentration of measurement data meets the standard. A high proportion of outliers indicates a large divergence of data;
  • Frame missing rate Use the frame serial number in the output information of the product to get this indicator. If the serial number is not continuous, it indicates that there is a frame missing. This indicator reflects the effectiveness of the data transmission process in a continuous working state;
  • the categories of failure criteria can be continuously improved and accumulated in the course of multiple tests, so as to gradually achieve the goal of comprehensively describing product performance;
  • a computer program is used to calculate the indicators corresponding to each data file according to the above definition and store them in the database. All indicators reflect the performance of the product, and its monitoring and analysis can understand the performance of the product in time. Among them, the indicator that can reflect the product life is used as the failure criterion to predict the life.
  • the original data is processed to remove abnormalities, and then according to the definition of the criterion, the corresponding criterion index value is calculated: average time per frame, proportion of timeout frames, proportion of error format frames, random error
  • the maximum value, the difference from the initial state, the proportion of abnormal values, the missing frame rate, etc. are stored in the database; after a long time of operation, the data generated has multiple dimensions: the criterion category dimension, the product dimension, and the time dimension. Focus on analysis from a single dimension or a combination of multiple dimensions respectively;
  • Analysis frequency You can do trend visualization and analysis every 24 hours or one week of data collection to observe the trend changes of each indicator.
  • Analysis method analysis can be done from both the overall and individual perspectives
  • the visualization methods commonly used by our company for individual analysis include: 1) The distribution diagram of a certain indicator of a certain device that exceeds the threshold and the part that does not exceed the threshold. The distribution characteristics of the two parts of data can be seen separately.
  • Figure 3 shows a product The distribution of abnormal values that exceed the threshold and the proportion of data that does not exceed the threshold, and the value that exceeds the threshold account for the overall situation, where yes means less than or equal to the corresponding threshold, that is, the distribution of data that does not exceed the threshold, and no means exceeding The distribution of threshold data; 2) The change curve of a certain indicator of a device every week, and further classify the time dimension to analyze whether the change trend of the data is related to the time after classification, as shown in Figure 4 The weekly change curve of the difference from the initial state, Figure 5 shows the daily change trend combination chart of the same product from the initial state;
  • Criterion is used to predict product life:
  • model analysis including:
  • Degradation definition before the end of the life test is reached, once a device does not exceed the threshold due to its own performance, but approaches the threshold, it is considered that the product is degraded;
  • the first life prediction model based on the test time is used to predict the life including parameter estimation and point estimation.
  • the parameter estimation includes:
  • the point estimate includes:
  • the device After determining the threshold for each criterion, if there is a device that exceeds the threshold during the test, the device is recorded as a failed device. If the product fails, the second life prediction model based on the number of failed products is used to predict the life. Including: count the number of failed products according to different criteria; calculate the corresponding MTBF failure through the life prediction formula; take the smallest MTBF failure as the mean time between failures of the product;
  • the device After determining the threshold of each criterion, if there is a device that exceeds the threshold during the test, the device is recorded as a failed device;
  • the life prediction formula includes:
  • r is the total number of related failures in the timed censoring test
  • T * is the cumulative related test time
  • the third life prediction model based on the amount of degradation is used to predict the life.
  • Figure 6 shows the visualization of a degraded product in a certain criterion index (example).
  • the third life prediction model includes: collecting degradation data in the experimental data, predicting pseudo life through the degradation data, testing the pseudo life distribution and parameter estimation, estimating the pseudo life index, and obtaining the final predicted life;
  • the degradation data in the collection of experimental data includes: in the life test, it is determined through visual analysis that there are m products degraded (the degree of degradation of each product may be inconsistent, but they should all tend to threshold changes), and The life test is carried out for a total of T hours, and n criterion matrices (x 1 , x 2 ,..., x n ) can be obtained, n ⁇ 7, and n is 7 by enumerating the 7 criteria above;
  • the prediction of pseudo life through degradation data includes: the M- th product, the judgment matrix of x n when the life test time is T is because For degradation data, it must have a trend. Under this premise, when When there is periodicity, use the time series model to predict.
  • Figure 7 shows the time series extrapolation algorithm. The ordinate value can be seen from the figure. As time goes by, it has a trend and a certain periodicity, so we can use the time series model to predict.
  • Figure 8 shows the time series forecast results, and gives the corresponding confidence interval.
  • the testing and parameter estimation of the pseudo life distribution includes: for a certain criterion, composing the pseudo life data of all products into a sample, and verifying the distribution type of the sample. If the pseudo-life distribution of the product is an exponential distribution, the estimated parameter value is: If the pseudo-life distribution of the product is Weibull distribution, the estimated parameter value is
  • the degradation data set obeys the exponential distribution, as shown in Figure 9, the exponential distribution parameter estimation method should be used to estimate the product life;
  • the estimation of the pseudo life index includes:
  • Exponential distribution life index estimation The life prediction obtained from the above degradation data with a sample size of 50 is as follows:
  • the derivation of the final life includes: the 7 groups of failure criterion parameters (x 1 , x 2 ,..., x 7 ) are used to calculate the 7 groups of life indexes: (MTBF 1 , ⁇ 1 ), (MTBF 2 , ⁇ 2 )...(MTBF 7 , ⁇ 7 );
  • MTBF final min (MTBF 1 , MTBF 2 ,..., MTBF 7 ), because the criterion definition itself reacts differently to product performance, and some criteria may show obvious degradation levels. Some are not, so the life results predicted by each criterion may be quite different. The minimum value of all the criterion prediction results is used to reduce the influence of this difference;

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Abstract

L'invention concerne un procédé de prédiction de la durée de vie d'un radar laser, comprenant les étapes suivantes consistant à : effectuer un test de durée de vie sur un produit, et obtenir des données expérimentales du test de durée de vie ; après le test de durée de vie, analyser les données expérimentales, déterminer si le produit présente une dégradation ou une défaillance évidente ou non, et effectuer une analyse par modèle, plus précisément, si le produit ne présente pas de dégradation ni de défaillance évidente, prédire la durée de vie à l'aide d'un premier modèle de prédiction de durée de vie basé sur le temps de test ; si le produit subit une défaillance, prédire la durée de vie à l'aide d'un deuxième modèle de prédiction de durée de vie basé sur le nombre de produits défaillants ; si le produit subit une dégradation, prédire la durée de vie à l'aide d'un troisième modèle de prédiction de durée de vie basé sur la quantité de dégradation ; et le troisième modèle de prédiction de durée de vie comprenant : la collecte de données de dégradation dans les données expérimentales, la prédiction d'une pseudo-durée de vie au moyen des données de dégradation, la réalisation d'une inspection et d'une estimation de paramètres sur une distribution de pseudo-durée de vie, et l'estimation d'un indice de pseudo-durée de vie pour obtenir la durée de vie finale. Le procédé peut être utilisé pour la prédiction de durée de vie du radar laser.
PCT/CN2019/121211 2019-04-25 2019-11-27 Procédé de prédiction de la durée de vie d'un radar laser WO2020215721A1 (fr)

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CN115081200A (zh) * 2022-06-13 2022-09-20 北京理工大学 复杂设备的加速因子及失效边界域分析方法
CN115795898A (zh) * 2022-12-12 2023-03-14 中国人民解放军陆军装甲兵学院 一种服务器硬盘的再制造最佳寿命评估方法及***
CN116228045B (zh) * 2023-05-09 2023-09-01 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） 基于性能退化的产品可靠性薄弱环节评估方法和装置
CN116228045A (zh) * 2023-05-09 2023-06-06 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） 基于性能退化的产品可靠性薄弱环节评估方法和装置
CN116878857A (zh) * 2023-09-07 2023-10-13 中国船舶集团有限公司第七一九研究所 一种船用中温橡胶挠性接管的加速寿命试验方法及***
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CN117236748B (zh) * 2023-09-08 2024-03-26 中国人民解放军63863部队 一种基于组件成败型的可修产品可靠性估计方法

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