CN106547265A - A kind of live reliability estimation method and system of track traffic electronic-controlled installation - Google Patents
A kind of live reliability estimation method and system of track traffic electronic-controlled installation Download PDFInfo
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Abstract
The present invention discloses a kind of live reliability estimation method and system of track traffic electronic-controlled installation, and the method step includes：1) the original scene service data of all electronic-controlled installations in train is gathered, and the life-span for failure product in each electronic-controlled installation being calculated according to original scene service data becomes reconciled the truncation life-span of product, acquires the lifetime data of electronic-controlled installation；2) lifetime data of failure product in the lifetime data for getting is fitted according to various different distributions models respectively, the target distribution model of current age data distribution characteristic is determined for compliance with according to fitting result；3) reliability assessment is carried out according to target distribution model to lifetime data；The system includes data acquisition and procession module, Lifetime Distribution Analysis module and reliability assessment module.The present invention can realize the live reliability assessment of electronic-controlled installation, and have the advantages that implementation method simply, Evaluation accuracy and with a high credibility, applied widely.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a method and a system for evaluating the field reliability of an electronic control device of rail transit.
Background
Scientific and technological progress promotes the rapid development of the rail transit industry, and the electronic control device is used as a vehicle-mounted device integrating functions of control, network communication, fault diagnosis and the like, such as an electronic control module, an electronic control system and the like, the reliability of the electronic control device is very important for the safety and the reliability of a train network system, and the requirement of a user on the reliability of the electronic control device is higher and higher.
At present, reliability evaluation research aiming at a rail transit electronic control device is less, and is generally realized based on tests or simulation, the electronic control device generates a large amount of reliability information in the processes of development, test and operation, the use reliability and the service life of a product can be more accurately analyzed depending on field data, the operation reliability information cannot be effectively utilized based on the tests or simulation, and the effective statistical analysis of the reliability information is lacked, so that the evaluation precision and the reliability are not high. The existing field operation reliability evaluation method is usually based on specific type distribution (such as exponential distribution), namely, the evaluation is carried out on the premise that the service life distribution is assumed as the specific type distribution, but the operation working condition of the electronic control device is complex, the error is large when the specific type distribution is directly adopted for evaluation, and an accurate evaluation result cannot be obtained, so that the method cannot be directly applied to the evaluation of the rail transit electronic control device.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the on-site reliability evaluation method and the system of the rail transit electronic control device, which have the advantages of simple implementation method, high evaluation precision and reliability and wide application range.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a field reliability evaluation method for a rail transit electronic control device comprises the following steps:
1) data acquisition and processing: acquiring original field operation data of all target electronic control devices in a train, calculating the service life of a fault product and the end-to-end service life of a good product in each electronic control device according to the original field operation data, and acquiring the service life data of the electronic control devices;
2) and (3) analyzing the life distribution: fitting the service life data of the fault products in the service life data acquired in the step 1) according to a plurality of different distribution models respectively, and determining a target distribution model according with the distribution characteristics of the current service life data according to the fitting result;
3) and (3) reliability evaluation: and carrying out reliability evaluation on the service life data acquired in the step 1) according to the target distribution model determined in the step 2), and evaluating to obtain the reliability of the target electronic control device.
As a further improvement of the method of the present invention, the specific steps of calculating the service life of the faulty product and the end life of the good product in each electronic control device in step 1) are as follows: if the device is a fault product, determining a service life starting calculation point according to original field operation data of the device, and calculating the service life of the device according to the fault time of the device and the determined service life starting calculation point; and if the product is a good product, determining a service life starting calculation point and a truncation time according to the original field operation data of the device, and calculating the truncation service life of the device according to the determined service life starting calculation point and the truncation time.
As a further improvement of the method of the present invention, the specific steps of determining the lifetime initiation calculation point are: and searching according to the priority sequence of the initial operation time of the device, the on-line time of the carrying vehicle type, the delivery time of the carrying vehicle type and the delivery time of the device from the original field operation data, wherein the time which is searched preferentially is used as the service life initial calculation point.
As a further improvement of the method of the invention, the specific steps of the step 2) are as follows:
2.11) counting the service life data of the fault products in the service life data acquired in the step 1), and fitting the service life data according to a plurality of different distribution models respectively to obtain service life data counting results and fitting results corresponding to the distribution models;
2.22) comparing the fitting result of each distribution model with the statistical result of the service life data, and determining to obtain a target distribution model according with the distribution characteristics of the current service life data according to the comparison result.
As a further improvement of the process of the invention: the histogram of the life data is specifically used as the statistical result of the life data in the step 2.11).
As a further improvement of the method of the present invention, the step 2) further comprises a distribution model checking step, which comprises the following specific steps: sequencing the service life data of the fault products in the service life data acquired in the step 1) in sequence, and generating a probability curve according to a linear relation between failure probability and failure time obtained by conversion of a target distribution model; judging whether the probability curve tends to be linear, if so, judging that the probability curve passes the inspection, and executing the step 3); otherwise, judging that the test is not passed, and returning to execute the step 2) to re-determine the target distribution model
As a further improvement of the method of the present invention, the specific steps of step 3) are:
3.1) carrying out parameter estimation on the service life data obtained in the step 1) according to the target distribution model determined in the step 2) to obtain a parameter estimation value;
and 3.2) solving the reliability function determined by the parameter estimation value to obtain the reliable service life of the electronic control device, and calculating the average service life of the electronic control device by the parameter estimation value and the fault density function corresponding to the target distribution model.
As a further improvement of the process of the invention: in the step 3.1), a maximum likelihood estimation method is specifically adopted for parameter estimation.
As a further improvement of the process of the invention: the distribution model specifically includes an exponential distribution model, a Weibull distribution model, and a lognormal distribution model.
A field reliability evaluation system of a rail transit electronic control device comprises:
the data acquisition and processing module is used for acquiring original field operation data of all target electronic control devices in the train, calculating the service life of a fault product and the end-to-end service life of a good product in each electronic control device according to the original field operation data, and acquiring the service life data of the electronic control devices;
the service life distribution analysis module is used for fitting the service life data of the fault product in the service life data acquired by the service life data acquisition module according to a plurality of different distribution models respectively, and determining a target distribution model according with the distribution characteristics of the current service life data according to the fitting result;
and the reliability evaluation module is used for evaluating the reliability of the service life data acquired by the service life data acquisition module according to the target distribution model determined by the service life distribution analysis module, and evaluating the reliability of the target electronic control device.
Compared with the prior art, the invention has the advantages that:
1) the reliability evaluation is carried out on the basis of the field operation data of the electronic control device, the reliability information of the field operation of the electronic control device is effectively utilized, the evaluation result is more real and accurate compared with the evaluation result of a test and simulation mode, the service life data is obtained by processing the original field operation data and is analyzed, the service life data of the fault product is fitted according to a plurality of different distribution models respectively, a target distribution model which accords with the distribution characteristic of the service life data is determined, the evaluation is carried out by directly utilizing a specific distribution type in comparison with the traditional method, the service life distribution characteristic of the electronic control device can be accurately represented, and therefore the reliability evaluation with high precision and reliability can be realized on the electronic control device in a rail transit vehicle;
2) the invention can determine different service life distribution types aiming at the field operation data of different devices, can be suitable for the evaluation of various electronic control devices in rail transit, and has wide application range and strong universality;
3) the invention makes full use of a large amount of reliability information of the electronic control device for evaluation, and can be suitable for field reliability evaluation of the electronic control device under the condition of large samples, thereby guiding the reliability level control of the large-sample electronic control device;
4) the invention further adopts a data processing mode based on priority to realize the acquisition, the arrangement and the analysis of the original field operation data, can realize the standardized processing of the field reliable information, and can obtain accurate service life data aiming at different information sources as far as possible, thereby greatly improving the utilization rate of the field reliable information and ensuring the precision and the credibility of reliability evaluation.
Drawings
Fig. 1 is a schematic flow chart of an implementation process of the field reliability evaluation method of the rail transit electronic control device according to the embodiment.
Fig. 2 is a schematic flow chart illustrating an implementation process of a field reliability evaluation method of an electronic control device according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating a statistical result and a fitting result of lifetime data according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a probability curve of lifetime data obtained in an embodiment of the present invention.
Fig. 5 is a schematic diagram of the reliability curves obtained in the example embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
As shown in fig. 1, the field reliability evaluation method for the electronic control device of rail transit according to the embodiment includes the steps of:
1) data acquisition and processing: acquiring original field operation data of all target electronic control devices in a train, calculating the service life of a fault product and the end-to-end service life of a good product in each electronic control device according to the original field operation data, and acquiring the service life data of the electronic control devices;
2) and (3) analyzing the life distribution: fitting the service life data of the fault product in the service life data acquired in the step 1) according to a plurality of different distribution models respectively, and determining a target distribution model according with the distribution characteristics of the current service life data according to the fitting result;
3) and (3) reliability evaluation: and carrying out reliability evaluation on the service life data acquired in the step 1) according to the target distribution model determined in the step 2), and evaluating to obtain the reliability of the target electronic control device.
The reliability evaluation is carried out on the basis of the field operation data of the electronic control device, the reliability information of the field operation of the electronic control device is effectively utilized, the evaluation result is more real and accurate compared with the evaluation result of a test and simulation mode, the service life data is obtained by processing the original field operation data and is analyzed, the service life data of the fault product is fitted according to a plurality of different distribution models respectively, a target distribution model which accords with the distribution characteristics of the service life data is determined, the evaluation is carried out by directly utilizing a specific distribution type in comparison with the traditional method, the service life distribution characteristics of the electronic control device can be accurately represented, and therefore the reliability evaluation of the electronic control device with high precision and reliability is realized.
In this embodiment, the specific steps of calculating the life of the faulty product and the end-to-end life of the good product in each electronic control device in step 1) are as follows: if the device is a fault product, determining a service life starting calculation point according to original field operation data of the device, and calculating the service life of the device according to the fault time of the device and the determined service life starting calculation point; and if the product is a good product, determining a service life starting calculation point and a truncation time according to the original field operation data of the device, and calculating the truncation service life of the device according to the determined service life starting calculation point and the truncation time.
In this embodiment, the specific steps for determining the life starting calculation point are as follows: and searching from the original field operation data according to the priority sequence of the initial operation time of the device, the on-line time of the carrying vehicle type, the delivery time of the carrying vehicle type and the delivery time of the device, wherein the time which is searched preferentially is used as a service life initial calculation point. Namely, the flow for determining the service life of the electronic control device is specifically as follows:
if the device is a fault product, the specific steps for determining the service life of the device are as follows:
1.11) searching whether a record of the initial running time of the device exists, if so, determining the service life of the device according to the failure time of the device and the initial running time of the device, and otherwise, executing the step 1.12);
1.12) searching whether a record of the online time of the carrying vehicle type exists, and if so, determining the service life of the device according to the failure time of the device and the online time of the carrying vehicle type; otherwise, the step 1.13) is executed;
1.13) searching whether a record of the delivery time of the carried vehicle type exists, and if so, determining the service life of the device according to the failure time of the device and the delivery time of the carried vehicle type; otherwise, the step 1.14) is executed;
1.14) searching whether a record of the device delivery time exists, and if so, determining the service life of the device according to the failure time of the device and the delivery time of the device.
If the product is a good product, the specific steps for determining the corresponding service life are as follows:
1.21) searching whether a record of the initial running time of the device exists, and if so, determining to obtain the end-to-end service life of the device according to the data statistical time and the initial running time of the device; otherwise, the step 1.22) is executed;
1.22) searching whether a record of the on-line time of the vehicle type carried by the device exists, and if so, determining to obtain the tail-ending life of the device according to the data statistics time and the on-line time of the vehicle type carried by the device; otherwise, the step 1.23) is executed;
1.23) searching whether a record of the delivery time of the device-carried vehicle type exists, and if so, determining to obtain the tail-ending service life of the device according to the data statistical time and the delivery time of the device-carried vehicle type; otherwise, the step 1.24) is executed;
1.24) searching whether a record of the device delivery time exists, and if so, determining to obtain the end-cut service life of the device according to the data statistical time and the device delivery time.
The field reliability information source of the rail transit electronic control device is generally low in quality, and if the situation that the device investment time cannot be determined, only fault data, missing random deleted data, normal working product service life data and the like exist, the situation cannot be directly used as evaluation data. According to the method, the acquisition, the arrangement and the analysis of the original field operation data are realized, the standardized processing of the field reliable information can be realized, meanwhile, the accurate service life data can be obtained as far as possible aiming at different information sources, and the accuracy and the reliability of reliability evaluation are ensured.
The method comprises the steps of firstly collecting original field operation data of an electronic control device from the online operation time of the urban rail transit vehicle to the data statistics time for analysis. For the failure data of the failure product, the getting-on time, the failure processing end time, the failure product receiving time, the repair product sending time, and the like can be obtained, and the information source condition of the failure data is specifically processed according to table 1 to calculate the service life (running time) of the device.
Table 1: and (6) processing field fault data.
For the fault-free tail-truncation data of the fault-free normal operation device, the information source condition in the data is specifically processed according to the table 2 so as to calculate the tail-truncation service life of the device.
Table 2: and (4) processing on-site fault-free data.
Considering the influence of too short tail-end time on the evaluation result, the embodiment specifically defines the device life data with short operation time (specifically <1 year) as invalid data, and finally obtains the total life data of the electronic control device after removing the invalid data from the acquired data.
The statistical data of the overall life in the specific embodiment of the present invention are shown in table 3 below.
Table 3: electronic control device life data.
In this embodiment, the specific steps of step 2) are as follows:
2.11) counting the service life data of the fault products in the service life data acquired in the step 1), and fitting the service life data according to a plurality of different distribution models respectively to obtain service life data counting results and fitting results corresponding to the distribution models;
2.22) comparing the fitting result of each distribution model with the statistical result of the life data respectively, and determining to obtain a target distribution model according with the distribution characteristics of the current life data according to the comparison result.
According to the embodiment, on the basis that the service life data are fitted according to various different distribution models, the fitting results of the distribution models are compared with the service life data statistical results, the distribution model which is most consistent with the current service life data distribution characteristics can be determined quickly and accurately, and the accuracy and the reliability of reliability evaluation are ensured.
In this embodiment, the histogram of the lifetime data is specifically used as the statistical result of the lifetime data in step 2.11). The method comprises the steps of drawing a histogram by using life data of a fault product, fitting according to a plurality of specified distribution models, comparing the fitting result of each distribution model with the histogram of the life data, and selecting the most consistent distribution model as a target distribution model.
In this embodiment, step 2) further includes a step of inspecting the distribution model, and the specific steps are as follows: sequencing the service life data of the fault products in the service life data acquired in the step 1), and generating a probability curve according to the linear relation between the failure probability and the failure time obtained by the conversion of the target distribution model; judging whether the probability curve tends to be linear, if so, judging that the test is passed, and executing the step 3); otherwise, judging that the test is not passed, and returning to execute the step 2) to determine the target distribution model again.
In this embodiment, first, a target distribution model is preliminarily determined according to a fitting result of each distribution model, and then the determined distribution model is tested, where the specific process of the test is as follows:
sequencing n life data in sequence: x (1) is more than or equal to x (2) is more than or equal to … and is less than or equal to x (n);
secondly, transforming the currently determined distribution model to obtain a linear relation between failure probability and failure time;
sequentially tracing the n life data on probability paper one by one according to the coordinates output in the step II to obtain a probability curve of the life data;
judging whether the probability curve tends to be linear, namely each point is approximate to a straight line, indicating that the current service life data comes from the distribution totality of the current distribution model, namely the current distribution model accords with the distribution characteristic of the current service life data, otherwise, the distribution model is required to be determined again if the current distribution model does not accord with the distribution characteristic of the current service life data.
In this embodiment, the specific steps of step 3) are as follows:
3.1) carrying out parameter estimation on the service life data obtained in the step 1) according to the target distribution model determined in the step 2) to obtain a parameter estimation value;
and 3.2) solving the reliability function determined by the parameter estimation value to obtain the reliable service life of the electronic control device, and calculating the average service life of the electronic control device by the parameter estimation value and the fault density function corresponding to the target distribution model.
In this embodiment, the maximum likelihood estimation method is specifically adopted in step 3.1) to perform parameter estimation, and the parameter estimation is simple to implement and has high precision.
After the distribution model of the current life data is determined, the corresponding distribution function, the fault density function and the reliability function can be determined. In the embodiment, the field operation process is approximated to a fixed number truncation process with replacement, and the distribution parameters are estimated by using a maximum likelihood estimation method; obtaining a reliability function corresponding to the distribution according to the parameter estimation result, solving the time t in the reliability function when the reliability R is known to obtain the reliable service life tR(ii) a And substituting the estimated parameters into a fault density function, solving the mathematical expectation of time t to obtain the average service life E (T), and evaluating the reliability state of the electronic device by using the reliability and the reliable service life as evaluation indexes.
The present invention is further described below by taking three examples of distribution models including an exponential distribution model, a Weibull distribution model (two-parameter Weibull distribution model), and a lognormal distribution model.
The exponential distribution can represent the life distribution after eliminating the early failure, and at the moment, the product is in the accidental failure period in the life cycle, and the failure rate function is a constant; the shape parameter m of Weibull distribution has different values and can represent different stages of the life cycle of a product, when m is less than 1, the failure rate function is monotonically decreased, the product is in an early failure period, when m is 1, the Weibull distribution is exponential distribution, when m is more than 1, the failure rate function is monotonically increased, and the product is in a loss failure period; the failure rate function of the lognormal distribution starts from zero and rises first and then falls. The distribution characteristics and parameter estimation process are as follows:
(ii) exponential distribution
The exponentially distributed fault density function is:
f(t)＝λe-λt(1)
the exponential distribution parameter is estimated according to the following formula (2):
weibull distribution
The fault density function for the Weibull distribution is:
weibull distribution parameters were estimated according to the following equations (4), (5):
wherein, solving the transcendental equation of the formula (4) about m, and substituting the formula (I) to obtain the estimated value of eta.
③ lognormal distribution
The fault density function for a lognormal distribution is:
the Weibull distribution parameters were estimated according to the following equation (7):
wherein,
as shown in fig. 2, a specific process for implementing field reliability evaluation of the electronic control device in this embodiment is as follows:
the method comprises the following steps: data acquisition and processing
Acquiring original field operation data (field fault information) of the electronic control device through a quality data monitoring center, wherein the original field operation data comprises fault data and fault-free tail data; processing original field operation data, determining the vehicle-loading time, the fault time and the operation time of a fault product for a fault product, determining the total number of fault-free products and the vehicle-loading time of the fault-free products for a fault-free product (good product), and obtaining available data to form a reliability information base after the processing is finished; and searching the priority sequence of each electronic control device based on the initial running time, the loading vehicle type on-line time, the loading vehicle type delivery time and the device delivery time in a reliability information base, taking the time which is searched preferentially as a service life starting point, and calculating the service life or the end-to-end service life of each device to obtain service life data of the electronic control device.
Step two: and (5) analyzing the life distribution.
In the embodiment, reliability and reliable service life are used as evaluation indexes, after the service life data are obtained, exponential distribution, Weibull distribution and logarithmic normal distribution are fitted to the service life data of the fault product, a histogram is drawn, the results of the fitted probability density function curve and the service life data histogram are shown in FIG. 3, and the comparison result in the graph shows that the failure probability density function of Weibull distribution is most consistent with field service life data, and the service life distribution of the electronic control device is preliminarily determined to be Weibull distribution.
Step three: and (5) checking the life distribution.
And generating a probability curve on probability paper according to the life data of the fault product based on the linear relation between the failure probability and the failure time of Weibull distribution. The resulting Weibull probability curve is shown in fig. 4, where it can be seen that the data points are substantially aligned, and thus the life of the electronic control unit is considered to follow a two-parameter Weibull distribution Wei (m, η). I.e. the fault density function, is shown in equation (3).
Step four: and (6) reliability evaluation.
Estimating the service life distribution parameters of the electronic control device by adopting a maximum likelihood estimation method for the service life data obtained in the step one, wherein the likelihood function of two parameters Weibull distribution Wei (m, eta) is as follows:
taking logarithm from two sides of the equation (8) to obtain a log-likelihood function:
and respectively solving the partial derivatives of the parameters m and eta, and making the partial derivatives zero to obtain an equation set:
the estimated values of the parameters m and η obtained by solving the equation set (10) are:
then the reliability function of the electronic control device under the weibull distribution condition obtained from the parameter estimation value is:
the reliable life is:
from the above estimation formula of the reliable life, and focusing on the life level at the reliability degrees of 0.9 and 0.5, it is possible to obtain:
when the reliability is 0.9, the reliability service life of the electronic control device is calculated to be 321 days, which is slightly less than 11 months;
② when the reliability is 0.5, the median life of the product is 2803 days, which is about 7.68 years.
From the estimation of the average lifetime, the average lifetime can be found as:
i.e., the MTBF of the device was 4273 days, which was about 11.71 years.
Fig. 5 shows a reliability curve of the electronic control device obtained in the present embodiment, and the reliability curve represents a change trend of the usage reliability of the device with time.
Step five: and (6) analyzing an evaluation result.
As shown in fig. 5, the reliability of the electronic control device in this embodiment decreases rapidly in the early stage and tends to be stable in the later stage, which indicates that the product has more early failures. Furthermore, the shape parameter m of the weibull distribution is 0.86938<1, which also indicates that the product is currently in an early failure stage. Therefore, the screening condition before the product leaves the factory can be enhanced, and the early failure caused by the manufacturing process can be reduced, so that the inherent reliability level of the product can be improved.
The distribution model in this embodiment is an exponential distribution model, a Weibull distribution model, and a lognormal distribution model, and certainly, other types of distribution models may be set according to actual needs to further improve the evaluation accuracy.
The field reliability evaluation system of the rail transit electronic control device in the embodiment comprises:
the data acquisition and processing module is used for acquiring original field operation data of all target electronic control devices in the train, calculating the service life of a fault product and the end-to-end service life of a good product in each electronic control device according to the original field operation data, and acquiring the service life data of the electronic control devices;
the service life distribution analysis module is used for fitting the service life data of the fault product in the service life data acquired by the service life data acquisition module according to a plurality of different distribution models respectively, and determining a target distribution model according with the distribution characteristics of the current service life data according to the fitting result;
and the reliability evaluation module is used for evaluating the reliability of the service life data acquired by the service life data acquisition module according to the target distribution model determined by the service life distribution analysis module, and evaluating the reliability of the target electronic control device.
The evaluation system of the present embodiment is a system corresponding to the above evaluation method, and the principle thereof is consistent with the above method.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (10)
1. A field reliability evaluation method for a rail transit electronic control device is characterized by comprising the following steps:
1) data acquisition and processing: acquiring original field operation data of all target electronic control devices in a train, calculating the service life of a fault product and the end-to-end service life of a good product in each electronic control device according to the original field operation data, and acquiring the service life data of the electronic control devices;
2) and (3) analyzing the life distribution: fitting the service life data of the fault products in the service life data acquired in the step 1) according to a plurality of different distribution models respectively, and determining a target distribution model according with the distribution characteristics of the current service life data according to the fitting result;
3) and (3) reliability evaluation: and carrying out reliability evaluation on the service life data acquired in the step 1) according to the target distribution model determined in the step 2), and evaluating to obtain the reliability of the target electronic control device.
2. The on-site reliability evaluation method for the rail transit electronic control devices as claimed in claim 1, wherein the specific steps of calculating the service life of the fault product and the end-to-end service life of the good product in each electronic control device in the step 1) are as follows: if the device is a fault product, determining a service life starting calculation point according to original field operation data of the device, and calculating the service life of the device according to the fault time of the device and the determined service life starting calculation point; and if the product is a good product, determining a service life starting calculation point and a truncation time according to the original field operation data of the device, and calculating the truncation service life of the device according to the determined service life starting calculation point and the truncation time.
3. The on-site reliability evaluation method of the rail transit electronic control device according to claim 2, wherein the specific steps of determining the life starting point are as follows: and searching according to the priority sequence of the initial operation time of the device, the on-line time of the carrying vehicle type, the delivery time of the carrying vehicle type and the delivery time of the device from the original field operation data, wherein the time which is searched preferentially is used as the service life initial calculation point.
4. The on-site reliability assessment method for the rail transit electronic control device according to claim 3, wherein the specific steps of the step 2) are as follows:
2.11) counting the service life data of the fault products in the service life data acquired in the step 1), and fitting the service life data according to a plurality of different distribution models respectively to obtain service life data counting results and fitting results corresponding to the distribution models;
2.22) comparing the fitting result of each distribution model with the statistical result of the service life data, and determining to obtain a target distribution model according with the distribution characteristics of the current service life data according to the comparison result.
5. The on-site reliability evaluation method for the rail transit electronic control device according to claim 4, wherein the histogram of the life data is used as the statistical result of the life data in the step 2.11).
6. The on-site reliability assessment method for the rail transit electronic control device according to claim 4, wherein the step 2) further comprises a distributed model checking step, and the specific steps are as follows: sequencing the service life data of the fault products in the service life data acquired in the step 1) in sequence, and generating a probability curve according to a linear relation between failure probability and failure time obtained by conversion of a target distribution model; judging whether the probability curve tends to be linear, if so, judging that the probability curve passes the inspection, and executing the step 3); otherwise, judging that the test is not passed, and returning to execute the step 2) to determine the target distribution model again.
7. The on-site reliability assessment method for the rail transit electronic control device according to any one of claims 1 to 6, wherein the specific steps of the step 3) are as follows:
3.1) carrying out parameter estimation on the service life data obtained in the step 1) according to the target distribution model determined in the step 2) to obtain a parameter estimation value;
and 3.2) solving the reliability function determined by the parameter estimation value to obtain the reliable service life of the electronic control device, and calculating the average service life of the electronic control device by the parameter estimation value and the fault density function corresponding to the target distribution model.
8. The on-site reliability assessment method for rail transit electronic control devices according to claim 7, characterized in that in step 3.1), the maximum likelihood estimation method is specifically adopted for parameter estimation.
9. The on-site reliability evaluation method of the rail transit electronic control device according to any one of claims 1 to 6, characterized in that: the distribution model specifically includes an exponential distribution model, a Weibull distribution model, and a lognormal distribution model.
10. A field reliability evaluation system of a rail transit electronic control device is characterized by comprising:
the data acquisition and processing module is used for acquiring original field operation data of all target electronic control devices in the train, calculating the service life of a fault product and the end-to-end service life of a good product in each electronic control device according to the original field operation data, and acquiring the service life data of the electronic control devices;
the service life distribution analysis module is used for fitting the service life data of the fault product in the service life data acquired by the service life data acquisition module according to a plurality of different distribution models respectively, and determining a target distribution model according with the distribution characteristics of the current service life data according to the fitting result;
and the reliability evaluation module is used for evaluating the reliability of the service life data acquired by the service life data acquisition module according to the target distribution model determined by the service life distribution analysis module, and evaluating the reliability of the target electronic control device.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108595847A (en) * | 2018-04-27 | 2018-09-28 | 公安部天津消防研究所 | A kind of appraisal procedure for gas extinguishing system reliability |
| CN109766217A (en) * | 2018-12-20 | 2019-05-17 | 北京梧桐车联科技有限责任公司 | A kind of vehicle system fault repairing method and device |
| CN111027988A (en) * | 2019-12-05 | 2020-04-17 | 山东中车风电有限公司 | Product reliability pre-estimation system based on truncated data |
| CN111523705A (en) * | 2020-04-02 | 2020-08-11 | 茂盟（上海）工程技术股份有限公司 | Method for predicting physical life of distributed energy station equipment |
| CN111679955A (en) * | 2020-08-11 | 2020-09-18 | 北京东方通软件有限公司 | Monitoring diagnosis and snapshot analysis system for application server |
| CN111814360A (en) * | 2020-08-24 | 2020-10-23 | 中国人民解放军海军航空大学青岛校区 | Method for evaluating use reliability of airborne electromechanical equipment |
| CN112257215A (en) * | 2019-07-02 | 2021-01-22 | 中车株洲电力机车研究所有限公司 | Maximum likelihood estimation solving method and system for product life distribution parameters |
| CN112379198A (en) * | 2020-11-05 | 2021-02-19 | 华润电力技术研究院有限公司 | Method and system for evaluating residual life of equipment |
| CN112966093A (en) * | 2021-03-04 | 2021-06-15 | 北京航空航天大学 | Method, device, equipment and medium for improving electronic product |
| CN113468039A (en) * | 2021-08-30 | 2021-10-01 | 深圳荣耀智能机器有限公司 | Reliability evaluation method and related equipment |
| CN118570190A (en) * | 2024-07-24 | 2024-08-30 | 常州路航轨道交通科技有限公司 | Rail defect measurement and identification system and method based on machine vision |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070274304A1 (en) * | 2006-05-23 | 2007-11-29 | Lycette William H | Fail rate method for fast measurement of equipment reliabiliy |
| CN102103658A (en) * | 2010-12-03 | 2011-06-22 | 北京航空航天大学 | Practical electronic product service life evaluation model parameter high-accuracy extraction method |
| CN104933323A (en) * | 2015-07-10 | 2015-09-23 | 北京航空航天大学 | Method for evaluating reliability by fusing success/failure data and failure time data of product |
| CN105718722A (en) * | 2016-01-18 | 2016-06-29 | 中国人民解放军国防科学技术大学 | Product reliability estimation method based on time-truncated life testing data |
-
2016
- 2016-11-01 CN CN201610936093.8A patent/CN106547265B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070274304A1 (en) * | 2006-05-23 | 2007-11-29 | Lycette William H | Fail rate method for fast measurement of equipment reliabiliy |
| CN102103658A (en) * | 2010-12-03 | 2011-06-22 | 北京航空航天大学 | Practical electronic product service life evaluation model parameter high-accuracy extraction method |
| CN104933323A (en) * | 2015-07-10 | 2015-09-23 | 北京航空航天大学 | Method for evaluating reliability by fusing success/failure data and failure time data of product |
| CN105718722A (en) * | 2016-01-18 | 2016-06-29 | 中国人民解放军国防科学技术大学 | Product reliability estimation method based on time-truncated life testing data |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108595847A (en) * | 2018-04-27 | 2018-09-28 | 公安部天津消防研究所 | A kind of appraisal procedure for gas extinguishing system reliability |
| CN109766217A (en) * | 2018-12-20 | 2019-05-17 | 北京梧桐车联科技有限责任公司 | A kind of vehicle system fault repairing method and device |
| CN112257215A (en) * | 2019-07-02 | 2021-01-22 | 中车株洲电力机车研究所有限公司 | Maximum likelihood estimation solving method and system for product life distribution parameters |
| CN111027988A (en) * | 2019-12-05 | 2020-04-17 | 山东中车风电有限公司 | Product reliability pre-estimation system based on truncated data |
| CN111027988B (en) * | 2019-12-05 | 2023-07-14 | 山东中车风电有限公司 | Product reliability prediction system based on truncated data |
| CN111523705A (en) * | 2020-04-02 | 2020-08-11 | 茂盟（上海）工程技术股份有限公司 | Method for predicting physical life of distributed energy station equipment |
| CN111679955A (en) * | 2020-08-11 | 2020-09-18 | 北京东方通软件有限公司 | Monitoring diagnosis and snapshot analysis system for application server |
| CN111814360A (en) * | 2020-08-24 | 2020-10-23 | 中国人民解放军海军航空大学青岛校区 | Method for evaluating use reliability of airborne electromechanical equipment |
| CN112379198A (en) * | 2020-11-05 | 2021-02-19 | 华润电力技术研究院有限公司 | Method and system for evaluating residual life of equipment |
| CN112966093A (en) * | 2021-03-04 | 2021-06-15 | 北京航空航天大学 | Method, device, equipment and medium for improving electronic product |
| CN113468039A (en) * | 2021-08-30 | 2021-10-01 | 深圳荣耀智能机器有限公司 | Reliability evaluation method and related equipment |
| CN113468039B (en) * | 2021-08-30 | 2021-12-17 | 深圳荣耀智能机器有限公司 | Reliability evaluation method and related equipment |
| CN118570190A (en) * | 2024-07-24 | 2024-08-30 | 常州路航轨道交通科技有限公司 | Rail defect measurement and identification system and method based on machine vision |
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