CN107478455B - Timing truncation reliability test method suitable for Weibull distribution type product - Google Patents

Timing truncation reliability test method suitable for Weibull distribution type product Download PDF

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CN107478455B
CN107478455B CN201710777965.5A CN201710777965A CN107478455B CN 107478455 B CN107478455 B CN 107478455B CN 201710777965 A CN201710777965 A CN 201710777965A CN 107478455 B CN107478455 B CN 107478455B
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黄洪钟
黄鹏
李彦锋
彭卫文
李贺
胡钧铭
张凯延
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a timing truncation reliability test method suitable for a Weibull distribution type product, which comprises the following steps of: s1, acquiring a shape parameter m of Weibull distribution according to historical data; determining basic parameters of a timing truncation test scheme; s2, determining the allowable failure number c of the test scheme according to the shape parameter m of the Weibull distribution and the basic parameter of the timing truncation test scheme; and S3, determining the total test time T of the test scheme according to the allowable fault number c of the test scheme. The invention can carry out reliability identification on the Weibull distributed product, more scientifically and reasonably determine the test scheme, greatly improve the reliability evaluation level of the product, and improve the design and shaping of the product and the mass production, thereby improving the quality and the reliability of the product.

Description

Timing truncation reliability test method suitable for Weibull distribution type product
Technical Field
The invention relates to the technical field of product reliability tests, in particular to a timing truncation reliability test method suitable for Weibull distributed products.
Background
The reliability test is a general term for various tests performed to understand, evaluate, analyze, and improve the reliability of a product, and is intended to expose defects of the product, provide necessary information for improving the reliability of the product, and finally verify the reliability of the product. In other words, any test related to product failure or failure effects may be considered a reliability test. The reliability of the product is managed and tested in the process of developing the product, particularly plays a crucial role in ensuring that the product meets the reliability requirement of the design, particularly is a reliability identification test which is one of the bases for verifying whether the reliability of the product meets the design requirement and designing and shaping the product and is a qualification certificate provided for a user. The method is generally used for typing identification, is a test before production and provides management information for production decisions.
The reliability identification test method comprises a fixed number truncation test, a timing truncation test and a sequential truncation test, wherein the timing truncation test is the most used test scheme in the reliability identification test, and the reliability identification test method has the advantages that the judgment fault number, the test time and the test cost can be determined before the test, and the management is convenient.
The regular tail-cutting test rule is that n samples are randomly extracted from a batch of products, and the test is stopped when the preset tail-cutting time t is reached. If the total number of faults of the product is r within the specified (0, t) test time, comparing r with the allowable number of faults c, if r is less than or equal to c, the reliability level of the product is qualified, receiving the product, if r is greater than c, the reliability level of the product is unqualified, and rejecting the product.
The timed truncation test is essentially a sampling test, i.e., a test in which a portion of a sample is examined to determine the entire batch of product. Mean life theta is theoretically given0When the average life theta of the product is more than or equal to theta0When the product is qualified, the receiving probability L (theta) is 1; when average life of the product theta<θ0When the product is not qualified, the reception probability L (θ) is 0.
At present, most standards (such as GJB 899A-2009 and MIL-HDBK-781A) at home and abroad are established on the assumption that the working time before the product failure follows the exponential distribution. However, for mechanical products, the lifetime or failure data is mostly subject to weibull distribution. At the moment, a reliability identification test is carried out on a product with fault data subjected to Weibull distribution by using an exponential distribution test scheme, and the result is over conservative.
Disclosure of Invention
Aiming at the defects in the prior art, the timing truncation reliability test method suitable for the Weibull distributed products can scientifically and reasonably determine the test scheme, greatly improve the working efficiency and improve the evaluation level.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the timing tail-truncation reliability test method suitable for Weibull distribution type products comprises the following steps:
s1, acquiring a shape parameter m of Weibull distribution according to historical data; determining basic parameters of a timing truncation test scheme;
s2, determining the allowable failure number c of the test scheme according to the shape parameter m of the Weibull distribution and the basic parameter of the timing truncation test scheme;
and S3, determining the total test time T of the test scheme according to the allowable fault number c of the test scheme.
Further, the method for acquiring the shape parameter m of the weibull distribution from the historical data in step S1 is as follows:
according to a two parameter weibull distribution probability density function:
Figure BDA0001396283660000021
obtaining a likelihood function of fault interval time:
Figure BDA0001396283660000031
and the likelihood functions of the remaining s non-failure data:
Figure BDA0001396283660000032
according to the principle of the maximum likelihood method, further obtaining a maximum likelihood function of a Weibull distribution shape parameter m as follows:
Figure BDA0001396283660000033
according to the maximum likelihood function and the maximum likelihood equation of the Weibull distribution shape parameter m:
Figure BDA0001396283660000034
further obtaining two parameters Weibull distribution parameter maximum likelihood calculation equations:
Figure BDA0001396283660000035
obtaining a Weibull distribution shape parameter m according to the Weibull distribution parameter maximum likelihood calculation equation of the two parameters;
the historical data comprises the number s of samples in the historical use data of similar products or the same product, a scale parameter η, the number k of faults in the test period and corresponding fault interval time t1≤t2≤…≤tkThe truncation time T corresponding to each sample1≤T2≤…≤TsSetting the truncation time tτWherein, the truncation time and the fault interval time corresponding to each sample are less than or equal to the set truncation time tτ。
Further, the basic parameters of the timed truncation test protocol in step S1 include:
number of samples n of product to be tested, production side risk α, use side risk β, lower limit of mean time between failures θ1Checking upper limit of mean time between failures theta0And a discrimination ratio d, where d ═ θ0/θ1。
Furthermore, the values of the producer risk α and the consumer risk β respectively comprise 10%, 20% and 30%, and the value of the identification ratio d comprises 1.5, 2.0 and 3.0.
Further, the method for determining the allowable failure number c according to the shape parameter m of the weibull distribution and the basic parameter of the timing tail-off test scheme in step S2 is as follows:
based on two-parameter Weibull distribution model
Figure BDA0001396283660000041
And
Figure BDA0001396283660000042
obtaining:
F(t)＝1-exp(-λtm)
let t in the above formulam＝x0Obtaining the sample n in the interval (0, x)0]The probability of i faults occurring within is:
Figure BDA0001396283660000043
further, the region (0, x) is obtained0]Reception probability L (θ) of total number of internal failures r equal to or less than allowable number of failures c:
Figure BDA0001396283660000044
point estimation formula based on Weibull distribution
Figure BDA0001396283660000045
Andobtaining:
Figure BDA0001396283660000047
according to the above formula, the receiving probability L (theta) and the product sampling characteristic curve L (theta)0)＝1-α、L(θ1) β, yielding:
Figure BDA0001396283660000048
according to the statistical inference of the poisson process, it is:
Figure BDA0001396283660000051
and obtaining the allowable failure number c of the test scheme:
wherein n is the number of samples, α is the risk of the producer, β is the risk of the user, d is the discrimination ratio, m is the shape parameter of the Weibull distribution, and Γ () is the gamma function;
Figure BDA0001396283660000053
andχ with degree of freedom of 2c +22The upper 1- α and β quantites of distribution.
Further, the method for determining the total test time T of the test scenario according to the allowable number of faults c of the test scenario in step S3 is as follows:
and according to the receiving probability L (theta) that the number r of faults occurring in the n samples within the test tail-ending time t is less than or equal to the allowable number c of faults:
Figure BDA0001396283660000055
obtaining:
Figure BDA0001396283660000056
and (3) obtaining the mathematical expectation of the accumulated failure times according to the failure interval time theta by following a Weibull distribution model:
Figure BDA0001396283660000057
expectation formula according to Weibull distribution model
Figure BDA0001396283660000058
Sum variance formulaObtaining:
Figure BDA0001396283660000062
further obtaining:
the above formula is combined with the product sampling curve L (theta)0) 1- α and L (θ)1) Mean value of β, test tail-off time t for individual product was obtained:
Figure BDA0001396283660000064
further, the total test time was obtained:
T＝nt
wherein n is the number of samples, α is the risk of the producer, β is the risk of the user, d is the identification ratio, and theta1A lower check limit that is the mean time between failures; m is a shape parameter of a Weibull distribution; c is the number of allowed faults; Γ (·) is a gamma function;
Figure BDA0001396283660000065
χ with degree of freedom of 2c +22The upper 1- α and β quantites of distribution.
The invention has the beneficial effects that: the method comprises the steps of firstly acquiring shape parameters of Weibull distribution by using similar products or historical use data, determining basic parameters of a timing truncation test, then obtaining the allowable failure number of a test scheme according to the parameters, and finally determining the test time of the test scheme. The reliability identification of the Weibull distributed product by the method can more scientifically and reasonably determine the test scheme, greatly improve the reliability evaluation level of the product, and improve the design and shaping of the product and the mass production, thereby improving the quality and reliability of the product.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the timing truncation reliability test method suitable for the weibull distribution type product comprises the following steps:
s1, acquiring a shape parameter m of Weibull distribution according to historical data; determining basic parameters of a timing truncation test scheme;
s2, determining the allowable failure number c of the test scheme according to the shape parameter m of the Weibull distribution and the basic parameter of the timing truncation test scheme;
and S3, determining the total test time T of the test scheme according to the allowable fault number c of the test scheme.
The method for acquiring the shape parameter m of the weibull distribution from the historical data in step S1 includes:
according to a two-parameter weibull distribution probability density function (two-parameter weibull belongs to one type of weibull):
Figure BDA0001396283660000071
obtaining a likelihood function of fault interval time:
Figure BDA0001396283660000072
and the likelihood functions of the remaining s non-failure data:
Figure BDA0001396283660000081
according to the principle of the maximum likelihood method, further obtaining a maximum likelihood function of a Weibull distribution shape parameter m as follows:
Figure BDA0001396283660000082
according to the maximum likelihood function and the maximum likelihood equation of the Weibull distribution shape parameter m:
Figure BDA0001396283660000083
further obtaining two parameters Weibull distribution parameter maximum likelihood calculation equations:
Figure BDA0001396283660000084
and obtaining a Weibull distribution shape parameter m according to the Weibull distribution parameter maximum likelihood calculation equation of the two parameters.
The historical data comprises the number s of samples in the historical use data of similar products or the same product, a scale parameter η, the number k of faults in the test period and corresponding fault interval time t1≤t2≤…≤tkThe truncation time T corresponding to each sample1≤T2≤…≤TsSetting the truncation time tτWherein, the truncation time and the fault interval time corresponding to each sample are less than or equal to the set truncation time tτ。
In the present example, it is directly assumed that the shape parameter m of the weibull distribution is known, and m is 1.2.
The basic parameters of the timed truncation test protocol in step S1 include:
number of samples n of product to be tested, production side risk α, use side risk β, lower limit of mean time between failures θ1Checking upper limit of mean time between failures theta0And a discrimination ratio d, where d ═ θ0/θ1。
In practice, two types of errors may be made because the entire batch of products is determined by examining a portion of the sample. The first type of error is that a qualified product is mistakenly judged as an unqualified product; the second type of error is to falsely judge the unqualified product as a qualified product. First class of diseaseThe probability of making such errors is said to be a producer risk, generally represented by α, when errors occur, and the probability of making such errors is said to be a consumer risk, generally represented by β, when errors occur, both producer and consumer wish to bear less risk, in which case two life bounds, θ, are given0And theta1(θ0>θ1) When the average life theta of the product is more than or equal to theta0At a large probability, an entire batch of product, L (θ)0) 1- α, and when the average life of product is theta ≦ theta1When, with a small probability, an entire batch of products is received, i.e., L (θ)1) β. from the above conditions, the protocol for the sampling test can be determined.
(1) And for the number of samples n, the GJB 899A-2009 standard states: for reliability tests, at least 2 products per batch should be tested if the user has no other provisions. The recommended sample size is 10% of the product batch, but not more than 20, and the full number of tests (i.e., 100% sampling) is only used in special cases (e.g., due to safety or task completion requirements). I.e. the number n of samples required for the test protocol, is either standardized or agreed upon by the manufacturer and the user.
(2) According to GJB 899A-2009, production formula risk α and use formula risk β are typically 10%, 20% and 30%, and the two types of risks are as close as possible.
(3) Checking lower limit theta of MTBF (mean time between failure) in normal cases1Should be taken as the lowest acceptable value of the product, but the upper limit of the MTBF test θ0The reference value should be selected not depending on the reference value but referring to the reference value.
(4) The discrimination ratios d are usually selected to be 1.5, 2.0, 3.0. In the experimental statistical protocol, the upper limit θ is examined0Checking lower limit theta1And the discrimination ratio d, if only two of the three parameters are determined, the other will be determined accordingly.
In the present example, the number n of product samples is 8, the decision risk α is β is 20%, and the MTBF test limit of the product is θ1500h, 1.5.
The method for determining the allowable fault number c according to the shape parameter m of the weibull distribution and the basic parameters of the timing tail-off test scheme in the step S2 is as follows:
based on two-parameter Weibull distribution model
Figure BDA0001396283660000101
And
Figure BDA0001396283660000102
obtaining:
F(t)＝1-exp(-λtm)
let t in the above formulam＝x0Obtaining the sample n in the interval (0, x)0]The probability of i faults occurring within is:
Figure BDA0001396283660000103
further, the region (0, x) is obtained0]Reception probability L (θ) of total number of internal failures r equal to or less than allowable number of failures c:
Figure BDA0001396283660000104
the point estimation formula of the above formula according to Weibull distribution
Figure BDA0001396283660000105
And
Figure BDA0001396283660000106
obtaining:
Figure BDA0001396283660000107
according to the above formula, the receiving probability L (theta) and the product sampling characteristic curve L (theta)0)＝1-α、L(θ1) β, yielding:
Figure BDA0001396283660000108
the above equation is derived from statistical inference of the poisson process:
Figure BDA0001396283660000109
and obtaining the allowable failure number c of the test scheme:
Figure BDA0001396283660000111
wherein n is the number of samples, α is the risk of the producer, β is the risk of the user, d is the identification ratio, and theta1A lower check limit that is the mean time between failures; m is a shape parameter of a Weibull distribution; c is the number of allowed faults; Γ (·) is a gamma function;
Figure BDA0001396283660000112
χ with degree of freedom of 2c +22The upper 1- α and β quantites of distribution.
In the present example, the decision risk α is β is 20%, the discrimination ratio d is 1.5, and the shape parameter m is 1.2, since the allowable failure number c must be an integer, the solution for solving the allowable failure number formula is to calculate c is 0,1,2, … …
Figure BDA0001396283660000113
Will calculate the result with dmAnd (4) comparing values, and selecting the closest c. In the present example, when c is 11, the result is the closest. The allowable failure number c of the test scheme is taken as 11.
The method for determining the total test time T of the test scheme according to the allowable fault number c of the test scheme in the step S3 is as follows:
and according to the receiving probability L (theta) that the number r of faults occurring in the n samples within the test tail-ending time t is less than or equal to the allowable number c of faults:
Figure BDA0001396283660000114
obtaining:
Figure BDA0001396283660000115
and (3) obtaining the mathematical expectation of the accumulated failure times according to the failure interval time theta by following a Weibull distribution model:
expectation formula according to Weibull distribution model
Figure BDA0001396283660000121
Sum variance formula
Figure BDA0001396283660000122
Obtaining:
Figure BDA0001396283660000123
further obtaining:
Figure BDA0001396283660000124
combined with the product sampling curve L (theta)0) 1- α and L (θ)1) Mean value of β, test cut-off time for individual product:
Figure BDA0001396283660000125
further, the total test time was obtained:
T＝nt
wherein n is the number of samples, α is the risk of the producer, β is the risk of the user, d is the identification ratio, and theta1A lower check limit that is the mean time between failures; m is a shape parameter of a Weibull distribution; c is the number of allowed faults; Γ (·) is a gamma function;
Figure BDA0001396283660000126
χ with degree of freedom of 2c +22Distributed byUpper 1- α and β quantites.
In the present example, the number n of the product samples is 8, the decision risk α is β is 20%, and the MTBF test limit of the product is θ1When the discrimination ratio d is 1.5, the shape parameter m is 1.2, and the allowable failure number c is 11, the trial tail-off time T is 979h, and the total trial time T is 7832h can be calculated.
That is, the experimental protocol determined by the examples of the present invention is:
carrying out a timing truncation test with the test time of 979h on 8 sampled products, and if the fault number r is less than or equal to c which is 11, determining that the reliability level of the series of products is qualified, and receiving the products; and if r > c is 11, the reliability level of the series of products is considered to be unqualified, and the products are rejected.
In summary, the invention firstly utilizes similar products or historical use data to acquire shape parameters of Weibull distribution and determine basic parameters of the timing truncation test, then obtains the allowable failure number of the test scheme according to the parameters, and finally determines the test time of the test scheme. The reliability identification of the Weibull distributed product by the method can more scientifically and reasonably determine the test scheme, greatly improve the reliability evaluation level of the product, and improve the design and shaping of the product and the mass production, thereby improving the quality and reliability of the product.

Claims (2)

1. A timing truncation reliability test method suitable for Weibull distribution type products is characterized by comprising the following steps:
s1, acquiring a shape parameter m of Weibull distribution according to historical data; determining basic parameters of a timing truncation test scheme;
s2, determining the allowable failure number c of the test scheme according to the shape parameter m of the Weibull distribution and the basic parameter of the timing truncation test scheme;
s3, determining the total test time T of the test scheme according to the allowable fault number c of the test scheme;
the method for acquiring the shape parameter m of the weibull distribution from the historical data in step S1 includes:
according to a two parameter weibull distribution probability density function:
Figure FDA0002215876550000011
obtaining a likelihood function of fault interval time:
Figure FDA0002215876550000012
and the likelihood functions of the remaining s non-failure data:
Figure FDA0002215876550000013
according to the principle of the maximum likelihood method, further obtaining a maximum likelihood function of a Weibull distribution shape parameter m as follows:
Figure FDA0002215876550000014
according to the maximum likelihood function and the maximum likelihood equation of the Weibull distribution shape parameter m:
Figure FDA0002215876550000015
further obtaining two parameters Weibull distribution parameter maximum likelihood calculation equations:
Figure FDA0002215876550000021
obtaining a Weibull distribution shape parameter m according to the Weibull distribution parameter maximum likelihood calculation equation of the two parameters;
the historical data comprises the number s of samples in the historical use data of similar products or the same product, a scale parameter η, the number k of faults in the test period and corresponding fault interval time t1≤t2≤…≤tkThe truncation time T corresponding to each sample1≤T2≤…≤TsSetting the truncation time tτWherein, the truncation time and the fault interval time corresponding to each sample are less than or equal to the set truncation time tτ；
The basic parameters of the timed truncation test protocol in step S1 include:
number of samples n of product to be tested, production side risk α, use side risk β, lower limit of mean time between failures θ1Checking upper limit of mean time between failures theta0And a discrimination ratio d, where d ═ θ0/θ1；
The production formula risk α and the use formula risk β respectively comprise 10%, 20% and 30%, and the discrimination ratio d comprises 1.5%, 2.0 and 3.0;
the method for determining the allowable fault number c according to the shape parameter m of the weibull distribution and the basic parameters of the timing tail-off test scheme in the step S2 is as follows:
based on two-parameter Weibull distribution model
Figure FDA0002215876550000022
Andobtaining:
F(t)＝1-exp(-λtm)
let t in the above formulam＝x0Obtaining the sample n in the interval (0, x)0]The probability of i faults occurring within is:
Figure FDA0002215876550000024
further, the region (0, x) is obtained0]Reception probability L (θ) of total number of internal failures r equal to or less than allowable number of failures c:
Figure FDA0002215876550000031
point estimation formula based on Weibull distribution
Figure FDA0002215876550000032
And
Figure FDA0002215876550000033
obtaining:
according to the above formula, the receiving probability L (theta) and the product sampling characteristic curve L (theta)0)＝1-α、L(θ1) β, yielding:
Figure FDA0002215876550000035
the above equation is derived from statistical inference of the poisson process:
Figure FDA0002215876550000036
and obtaining the allowable failure number c of the test scheme:
wherein n is the number of samples, α is the risk of the producer, β is the risk of the user, d is the discrimination ratio, m is the shape parameter of the Weibull distribution, and Γ () is the gamma function;
Figure FDA0002215876550000038
χ with degree of freedom of 2c +22The upper 1- α and β quantites of distribution.
2. The timing tail reliability test method for Weibull distributed products as claimed in claim 1, wherein the method for determining the total test time T of the test scheme according to the allowable failure number c of the test scheme in step S3 is:
and according to the receiving probability L (theta) that the number r of faults occurring in the n samples within the test tail-ending time t is less than or equal to the allowable number c of faults:
Figure FDA0002215876550000041
obtaining:
Figure FDA0002215876550000042
and (3) obtaining the mathematical expectation of the accumulated failure times according to the failure interval time theta by following a Weibull distribution model:
expectation formula according to Weibull distribution model
Figure FDA0002215876550000044
Sum variance formula
Figure FDA0002215876550000045
Obtaining:
Figure FDA0002215876550000046
further obtaining:
combined with the product sampling curve L (theta)0) 1- α and L (θ)1) Mean value of β, test tail-off time t for individual product was obtained:
Figure FDA0002215876550000048
further, the total test time was obtained:
T＝nt
wherein n is the number of samples, α is the risk of the producer, β is the risk of the user, d is the identification ratio, and theta1A lower check limit that is the mean time between failures; m is a shape parameter of a Weibull distribution; c is the number of allowed faults; Γ (·) is a gamma function;
Figure FDA0002215876550000051
χ with degree of freedom of 2c +22The upper 1- α and β quantites of distribution.
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CN108241909B (en) * 2018-01-24 2022-04-26 长安大学 Method for predicting remanufacturing time of mechanical equipment
CN109344472B (en) * 2018-09-17 2023-02-07 中国人民解放军海军工程大学 Method for estimating reliability parameters of mechanical and electrical components
CN110069750A (en) * 2019-04-12 2019-07-30 电子科技大学 Non-precision analysis method based on four parameters of Weibull
CN110096820B (en) * 2019-05-07 2020-08-07 中国人民解放军国防科技大学 Method for predicting residual life of k/n (G) system when unit life obeys Weibull distribution
CN112052577B (en) * 2020-08-28 2022-09-06 中国航发贵阳发动机设计研究所 Determination method for specified value evaluation time of reliability index of aircraft engine
CN112485018B (en) * 2020-11-18 2021-09-10 西安电子科技大学 Mechanical equipment energy consumption abnormity detection method based on mechanism data fusion
CN112883590B (en) * 2021-03-22 2022-10-18 中国人民解放军国防科技大学 System reliability verification test scheme determination method
CN112733088B (en) * 2021-03-30 2021-06-18 中国人民解放军国防科技大学 Expert information-based Weibull product reliability acceptance scheme design method
CN114970120B (en) * 2022-05-09 2023-10-24 中国人民解放军海军工程大学 System and method for determining life test scheme of exponential life type electronic part
CN114580985B (en) * 2022-05-09 2022-08-05 中国人民解放军海军工程大学 Quality detection method and system for electronic product
CN114897349B (en) * 2022-05-09 2023-09-05 中国人民解放军海军工程大学 Success-failure type sequential sampling test scheme determining system and method
CN115825793A (en) * 2022-11-25 2023-03-21 楚能新能源股份有限公司 Power battery calendar life identification test method

Family Cites Families (7)

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Publication number Priority date Publication date Assignee Title
JP2002090267A (en) * 2000-09-21 2002-03-27 Mitsui Eng & Shipbuild Co Ltd Failure diagnosis method
US6996493B1 (en) * 2004-02-11 2006-02-07 The United States Of America As Represented By The Secretary Of The Navy Programmed computation of predicted pressure loads on marine vessels
CN102981452B (en) * 2012-12-28 2015-04-01 吉林大学 Method for modeling and evaluating reliability of three types of functional components of numerical control machine tool
CN104239694B (en) * 2014-08-28 2016-11-23 北京交通大学 The failure predication of a kind of municipal rail train bogie and condition maintenarnce method
CN104462757B (en) * 2014-11-03 2017-10-31 南京航空航天大学 Weibull distribution reliability Sequential Compliance Methods based on Monitoring Data
CN106443221B (en) * 2015-08-04 2019-01-15 北京电子工程总体研究所 A kind of reliability determination method for electronic product
CN105894133B (en) * 2016-05-12 2017-04-26 华中科技大学 Wind turbine component maintenance and spare part demand forecasting method

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