CN110795863B - Relay service life prediction method based on multi-dimensional design parameters - Google Patents
Relay service life prediction method based on multi-dimensional design parameters Download PDFInfo
- Publication number
- CN110795863B CN110795863B CN201911102421.4A CN201911102421A CN110795863B CN 110795863 B CN110795863 B CN 110795863B CN 201911102421 A CN201911102421 A CN 201911102421A CN 110795863 B CN110795863 B CN 110795863B
- Authority
- CN
- China
- Prior art keywords
- relay
- parameters
- design parameters
- parameter
- service life
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
- G01R31/3271—Testing of circuit interrupters, switches or circuit-breakers of high voltage or medium voltage devices
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing Electric Properties And Detecting Electric Faults (AREA)
Abstract
The invention discloses a relay life prediction method based on multidimensional design parameters, which comprises the steps of firstly analyzing the working principle of a relay product, and obtaining relay life experimental data under different design parameter conditions by a control variable method; then, analyzing and determining sensitive design parameters influencing the service life of the relay; in the process of carrying out a service life experiment, collecting relay working data and determining characteristic parameters representing the service life of a relay; establishing a regression design matrix, taking the characteristic parameters of the service life of the relay as observation vectors, solving parameter vectors, and obtaining a mathematical model of the characteristic parameters relative to the design parameters; taking the change rate of the characteristic parameters as an observation vector, and fitting to obtain a mathematical model of the change rate of the characteristic parameters relative to the design parameters; and finally, combining the two models to obtain a life prediction model. The invention can determine the relation between the service life of the relay and the multi-dimensional design parameters thereof through modeling, and can guide the design of the relay so as to further improve the service life of the relay.
Description
Technical Field
The invention belongs to the technical field of service life prediction, relates to a relay service life prediction method, and particularly relates to a relay service life prediction method based on multidimensional design parameters.
Background
With the rapid development of the fields of new energy, aerospace, aviation, electric vehicles and the like, the requirements of the electromagnetic relay are shifted to high-voltage and high-power levels, the on-off voltage and current of the electromagnetic relay need to be further improved, and meanwhile, the reliability and the service life of the electromagnetic relay need to be guaranteed. At present, the problems of reliability and short service life of the high-power electromagnetic relay become problems to be solved urgently. If the relation between the relay life and the multi-dimensional design parameters of the relay can be searched, the characteristic parameters capable of representing the relay state are determined, and a multi-dimensional design parameter-based relay life prediction method is established, the relay can be guided to be designed to prolong the relay life, meanwhile, the model can be suitable for relay products with different design parameters, the residual life of the relay can be effectively predicted, the replacement of the relay products is guided, and the use reliability of the relay is further improved.
Disclosure of Invention
In order to guide the overall design of the high-power relay, provide a basis for the life prediction of the relay and further improve the reliability and the life of the high-power relay, the invention provides a multi-dimensional design parameter-based relay life prediction method.
The purpose of the invention is realized by the following technical scheme:
a relay service life prediction method based on multi-dimensional design parameters comprises the following steps:
the method comprises the following steps: according to the working principle, the manufacturing process and the degradation mechanism of a relay product, determining sensitive design parameters influencing the service life of the relay, namely changing the sensitive design parameters, greatly increasing or reducing the service life of the relay (such as the opening distance of a relay contact, the pre-pressure of an over-travel spring and the magnetic blow magnetic induction intensity);
step two: combining the relay sensitive design parameters determined in the step one by a control variable method, carrying out a relay life test of different parameters of the same series of products, and collecting working data in the test process, wherein the working data comprises relay coil current, main loop current, main contact voltage and relay contact resistance;
step three: processing the working data obtained in the second step to obtain time parameters (such as relay pull-in time, release time, over travel time and rebound time), electric quantity parameters (electric arc quantity on the relay and electric arc quantity on the relay) and energy parameters of the whole life process of the relay, and analyzing and determining parameters which are obvious in change conditions along with the action times of the relay and consistent in change trend as characteristic parameters for representing the service life of the relay;
step four: taking the sensitive design parameters determined in the step one as independent variables of the multiple regression model, expressing the data of the multiple regression model in a matrix form, taking the characteristic parameters as dependent variables, and expressing the data of the multiple regression model by using observation column vectors;
step five: establishing a multiple regression design matrix in a regression model of the characteristic parameters and the sensitive design parameters;
step six: setting a confidence interval, and solving a parameter vector of a regression model of the characteristic parameters and the sensitive design parameters based on the least square principle;
step seven: fitting a multiple regression mathematical model of the characteristic parameters about the sensitive design parameters;
step eight: processing the data obtained in the step two to obtain the change rate of the characteristic parameters, and taking the change rate as the observation column vector in the regression model of the characteristic parameter change rate and the sensitive design parameters;
step nine: establishing a multiple regression design matrix in the characteristic parameter change rate and design parameter regression model;
step ten: setting a confidence interval, and solving the characteristic parameter change rate and the parameter vector of the sensitive design parameter regression model based on the least square principle;
step eleven: fitting to obtain a regression mathematical model of the change rate of the characteristic parameters relative to the sensitive design parameters;
step twelve: and determining a relay life prediction model based on the multi-dimensional design parameters by combining the regression mathematical models obtained in the seventh step and the eleventh step.
Compared with the prior art, the invention has the following advantages:
1. the method can determine the relation between the service life of the relay and the multi-dimensional design parameters thereof through modeling, and can guide the design of the relay so as to further improve the service life of the relay.
2. The relay life prediction model determined by the method based on the multi-dimensional design parameters considers the design parameters of the relay, so that the method can be suitable for relay products of the same series with different design parameters, effectively predicts the residual life of the relay with different design parameters, guides the replacement of the relay products, and further improves the use reliability of the relay.
Drawings
FIG. 1 is a flow chart of a multi-dimensional design parameter-based relay life prediction method according to the present invention.
Fig. 2 shows the result of predicting the opening D of the relay life when the pre-pressure T is 15.6N.
Fig. 3 shows the result of predicting the magnetic blow magnetic induction B for the relay life when the pre-pressure T is 15.6N.
Fig. 4 shows the result of predicting the relay life with respect to the preload T when the contact opening distance D is 2.0 mm.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the protection scope of the present invention.
The invention provides a relay life prediction method based on multidimensional design parameters, which comprises the steps of firstly analyzing the working principle of a relay product, and obtaining relay life experimental data under different design parameter conditions by a control variable method; secondly, analyzing and determining sensitive design parameters influencing the service life of the relay according to the experimental data; meanwhile, in the process of carrying out a service life experiment, data such as relay coil current, main loop current, main contact voltage and the like are collected, and then characteristic parameters representing the service life of the relay are determined; then, the design parameters are used as independent variables, a regression design matrix is established, the service life characteristic parameters of the relay are used as observation vectors, parameter vectors are obtained through a least square method, and a mathematical model of the characteristic parameters relative to the design parameters is obtained; in addition, the change rate of the characteristic parameters is used as an observation vector, and a mathematical model of the change rate of the characteristic parameters relative to the design parameters is obtained through fitting; and finally, combining the two models to obtain a service life prediction model. As shown in fig. 1, the method specifically comprises the following steps:
the method comprises the following steps: taking a series of relay products as an example, because the contact ablation can cause the performance degradation of the relay, the opening distances of the relay contacts are different, and the effective gaps between the moving contact and the fixed contact are different after the ablation, the allowable ablation degrees are different, namely, the performance degradation effects of the relay are different, so that the service life of the relay is greatly influenced, and the relay can be used as one of sensitive design parameters for prolonging the service life; because the pre-pressure of the over-travel spring can determine the contact pressure when the contact is closed, the contact resistance when the contact is closed is further influenced, if the contact resistance is larger, the heating is serious, the ablation degree of the moving contact and the static contact is increased, the service life of the relay is reduced, and the pre-pressure can be used as one of sensitive design parameters for prolonging the service life; because the magnetic blow can accelerate arc extinction, reduce the ablation degree of the contact, and allow the time of arc ablation to be increased, namely the service life of the relay can be prolonged to some extent, the magnetic induction intensity of the magnetic blow can be used as one of sensitive design parameters for prolonging the service life of the relay. The contact opening distances D are respectively set to be 1.6mm, 2.0mm and 2.4mm, the over travel spring pre-pressure T is set to be 12.0N, 13.8N and 15.6N, and the magnetic induction intensities B of magnetic blow are set to be 110mT, 70mT and 14 mT.
Step two: and combining the relay sensitive design parameters determined in the step one by a control variable method, carrying out a relay life experiment with the same series of products and different parameters, wherein the experimental scheme is shown in table 1, and the current of a relay coil, the current of a main loop and the voltage of a main contact are collected in the experimental process.
Table 1 orthogonal experimental protocol table
Step three: and C, processing the data obtained in the step two to obtain parameters such as overtravel time, pull-in time, arcing electric quantity, arcing energy and the like in the whole service life process of the relay, and comparing to find that the variation condition of the overtravel time along with the action times of the relay is obvious, and for each design parameter combination in the experimental scheme, the variation trend of the overtravel time along with the action times of the relay is similar, so that the overtravel time is selected as a characteristic parameter for representing the service life of the relay.
Step four: taking the design parameters of the contact opening distance D, the over travel spring pre-pressure T and the magnetic induction intensity B of magnetic blowing determined in the step one as independent variables, taking the over travel time as an observation vector Y, and expressing the parameters in a matrix form as shown in a formula (1):
step five: establishing a multiple regression design matrix in the regression model of the overtravel time and the design parameters D, B, T, wherein the multiple regression design matrix is shown as the formula (2):
X＝[1 D T T2 B] (2)。
step six: setting a confidence interval to be 90%, and solving a parameter vector beta of the regression model based on the least square principle as follows:
step seven: the mathematical model of the characteristic parameter overtravel time t about the sensitive design parameter D, B, T is obtained by fitting:
t＝-2652.1-38.3D-15.5T+429.3T2-0.3B (4)。
step eight: processing the data obtained in the step two to obtain the change rate of the overtravel time of the characteristic parameter, and determining the change rate of the overtravel time and an observation column vector Y in the design parameter D, B, T regression model as follows:
Y＝[0.00192 0.00187 0.00155 0.00138 0.00127 0.00125 0.000813 0.000827 0.00082]′ (5)。
step nine: establishing a multiple regression design matrix in the overtravel time change rate and design parameter D, B, T regression model, as shown in formula (6):
X＝[1 D] (6)。
step ten: setting a confidence interval to be 90%, and solving a parameter vector beta of the regression model based on the least square principle as follows:
step eleven: the mathematical model of the overtravel time change rate k of the characteristic parameter about the sensitive design parameter D, B, T obtained by fitting is as follows:
k＝0.0037-0.0012D (8)。
step twelve: combining equation (4) and equation (8) can obtain a relay life prediction model based on design parameters:
the result of predicting the life of the relay with respect to the opening distance D from equation (9) is shown in fig. 2, with the over-travel spring preload T set to 15.6N.
Fig. 3 shows the result of predicting the life of the relay with respect to the magnetic blow-out magnetic induction B from equation (9) by setting the over-travel spring preload T to 15.6N.
The result of predicting the relay life with respect to the preload T from the equation (9) is shown in fig. 4, with the contact opening D set to 2.0 mm.
Claims (4)
1. A relay service life prediction method based on multi-dimensional design parameters is characterized by comprising the following steps:
the method comprises the following steps: determining sensitive design parameters influencing the service life of the relay according to the working principle, the manufacturing process and the degradation mechanism of a relay product;
step two: combining the relay sensitive design parameters determined in the step one by a control variable method, carrying out a relay life test of different parameters of the same series of products, and collecting working data in the test process;
step three: processing the working data obtained in the step two to obtain a time parameter, an electric quantity parameter and an energy parameter of the whole life process of the relay, and analyzing and determining a parameter which has obvious change condition along with the action times of the relay and has consistent change trend as a characteristic parameter for representing the life of the relay;
step four: taking the sensitive design parameters determined in the step one as independent variables of the multiple regression model, expressing the data of the multiple regression model in a matrix form, taking the characteristic parameters as dependent variables, and expressing the data of the multiple regression model by using observation column vectors;
step five: establishing a multiple regression design matrix in the regression model of the characteristic parameters and the sensitive design parameters:
X＝[1 D T T2 B]；
d is the contact opening distance, T is the over travel spring pre-pressure, and B is the magnetic induction intensity of magnetic blow;
step six: setting a confidence interval, and solving a parameter vector of a regression model of the characteristic parameters and the sensitive design parameters based on the least square principle;
step seven: fitting a multiple regression mathematical model of the characteristic parameters with respect to the sensitive design parameters:
t＝-2652.1-38.3D-15.5T+429.3T2-0.3B；
t is the characteristic parameter overtravel time;
step eight: processing the working data obtained in the step two to obtain the change rate of the characteristic parameters, and using the change rate as the observation column vector in the regression model of the characteristic parameter change rate and the sensitive design parameters;
step nine: establishing a multiple regression design matrix in the characteristic parameter change rate and design parameter regression model:
X＝[1 D]；
step ten: setting a confidence interval, and solving the characteristic parameter change rate and the parameter vector of the sensitive design parameter regression model based on the least square principle;
step eleven: fitting to obtain a regression mathematical model of the change rate of the characteristic parameters relative to the sensitive design parameters;
step twelve: and determining a relay life prediction model based on multi-dimensional design parameters by combining the regression mathematical model obtained in the seventh step and the eleventh step:
2. the method of claim 1, wherein the operational data includes relay coil current, main loop current, main contact voltage data, relay contact resistance.
3. The method of claim 1, wherein the time parameters include relay pull-in time, release time, over travel time, and bounce time.
4. The method of claim 1, wherein the parameters of power include power-on arcing power of the relay and power-off arcing power of the relay.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911102421.4A CN110795863B (en) | 2019-11-12 | 2019-11-12 | Relay service life prediction method based on multi-dimensional design parameters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911102421.4A CN110795863B (en) | 2019-11-12 | 2019-11-12 | Relay service life prediction method based on multi-dimensional design parameters |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110795863A CN110795863A (en) | 2020-02-14 |
| CN110795863B true CN110795863B (en) | 2022-02-18 |
Family
ID=69444267
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911102421.4A Active CN110795863B (en) | 2019-11-12 | 2019-11-12 | Relay service life prediction method based on multi-dimensional design parameters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110795863B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111597754B (en) * | 2020-04-16 | 2023-08-04 | 广东省智能制造研究所 | Fault prediction method and device for shielding door, terminal equipment and storage medium |
| CN112329194B (en) * | 2020-07-01 | 2022-04-22 | 河北工业大学 | Railway signal relay effective time parameter determination method based on mathematical model |
| CN112098833B (en) * | 2020-09-18 | 2023-10-13 | 株洲国创轨道科技有限公司 | Relay service life prediction method, system, medium and equipment |
| CN114252731B (en) * | 2021-12-13 | 2024-04-05 | 广西电网有限责任公司桂林供电局 | Relay action characteristic evaluation method and device based on multiple parameters |
| CN118039417B (en) * | 2024-03-29 | 2024-08-02 | 浙江西海电气有限公司 | Design method, equipment and medium for moving contact assembly of molded case circuit breaker |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7196435B2 (en) * | 2003-04-03 | 2007-03-27 | Goodrich Corporation | Solid state relay and method of operating the same |
| CN101344575A (en) * | 2008-08-20 | 2009-01-14 | 哈尔滨工业大学 | Relay reliability service life experiment system based on dynamic characteristic |
| CN106446317B (en) * | 2016-06-01 | 2020-02-07 | 河北工业大学 | Method for predicting storage life of sealed relay based on mathematical model |
| CN106202988A (en) * | 2016-10-11 | 2016-12-07 | 哈尔滨工业大学深圳研究生院 | The Stepwise multiple-regression model of a kind of predictive disease life cycle and application |
-
2019
- 2019-11-12 CN CN201911102421.4A patent/CN110795863B/en active Active
Non-Patent Citations (2)
| Title |
|---|
| 弹用电磁继电器贮存退化试验及其寿命预测方法;王召斌 等;《北京航空航天大学学报》;20161231;第42卷(第12期);全文 * |
| 航天继电器接触失效表征参数的确定与分析;孟彦辰 等;《电器与能效管理技术》;20140830;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110795863A (en) | 2020-02-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110795863B (en) | Relay service life prediction method based on multi-dimensional design parameters | |
| Karetta et al. | Simulation of the gasdynamic and electromagnetic processes in low voltage switching arcs | |
| CN107680835A (en) | A kind of breaker arcing contact ablation state evaluating method based on neutral net | |
| CN111079299A (en) | Method for calculating closing bounce electric contact mechanical property of electromagnetic relay under capacitive load | |
| CN109061459A (en) | The fault determination method and device of on-load voltage regulating switch, storage medium, processor | |
| CN109670687A (en) | A kind of mass analysis method based on particle group optimizing support vector machines | |
| CN112698196B (en) | High-voltage switch mechanical characteristic monitoring device | |
| Ruempler et al. | Low-voltage circuit breaker arc simulation including contact arm motion | |
| Feng et al. | Simulations on arc surfaced C-shaped armatures for round-like bore railguns | |
| Zhou et al. | Experimental study on arc behaviors of a bridge-type contact when opening a resistive load in the range of from 280VDC to 730 VDC | |
| Qiu et al. | Electrostatic field feature selection technique for breakdown voltage prediction of sphere gaps using support vector regression | |
| Bo et al. | Simulation of arc dwelling behavior during bridge-type contacts opening process for high-power relay | |
| Kirschbaum et al. | Prognostics for electromagnetic relays using deep learning | |
| Keidar et al. | Model of a diffuse column vacuum arc with cathode jets burning in parallel with a high-current plasma column | |
| Sawa et al. | Fundamental characteristics of arc extinction by magnetic blow-out at DC voltages (< 500V) II | |
| CN209591908U (en) | SF6 breaker air blowing type arc-chutes | |
| Shin et al. | Arc modeling to predict arc extinction in low-voltage switching devices | |
| Filipic et al. | Preliminary numerical experiments in multiobjective optimization of a metallurgical production process | |
| CN113687222B (en) | SF (sulfur hexafluoride)6Method and system for evaluating state of arc contact of circuit breaker | |
| CN115728627A (en) | Electric sound contact fault is judgement system in advance | |
| Han et al. | Study on factors influencing the characteristics of arc in DC contactors | |
| Almurr et al. | Numerical investigations on the electric arc behavior upon contact opening in a low-voltage switch under the effect of external magnetic field | |
| Mcbride | The verification of a computational model of arc motion using an arc imaging system | |
| Wileman et al. | A prognostic framework for electromagnetic relay contacts | |
| Wang et al. | Investigation on Arc Asynchronous Characteristics of the Double Bridge-Type Contacts in DC Relays |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |