CN110245379A - A kind of sealed type electromagnetic relay failure Identification of Mechanism method - Google Patents
A kind of sealed type electromagnetic relay failure Identification of Mechanism method Download PDFInfo
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- G—PHYSICS
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- 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
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- G06F30/20—Design optimisation, verification or simulation
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01H—ELECTRIC SWITCHES; RELAYS; SELECTORS; EMERGENCY PROTECTIVE DEVICES
- H01H50/00—Details of electromagnetic relays
- H01H50/54—Contact arrangements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention of this reality is related to a kind of sealed type electromagnetic driving switch electric appliance failure mechanism method of discrimination.This method is simple and easy, applied widely, at low cost, accuracy rate is high, can remove the disturbing factors such as arc erosion, without to tested electricity after device begin to speak processing, without high-power microscope, specific step is as follows: the main points performance degradation supplemental characteristic in 1. acquisition relay life cycles, the data set that collected data matrix is differentiated as electromagnetic relay failure mechanism.2. being filtered using the data set that FIR (Finite Impulse Response, FIR) high-pass filter obtains test, the randomness for removing arc erosion and material transfer is interfered caused by data collection system.3. extracting the optimal combination parameter that can distinguish relay failure mechanism from the data set after noise reduction filtering using Bayes Discriminatory Method.4. being differentiated by optimal combination parameter to relay failure mechanism using random forests algorithm.
Description
The technical field is as follows:
the invention relates to a method for judging a failure mechanism of a sealed electromagnetic relay.
Background art:
the contact failure of the electromagnetic relay is mainly caused by arcing generated in the processes of attracting and releasing contacts of the electromagnetic relay, and the existing method for judging the failure mechanism of the relay comprises a posterior observation method, a dynamic contact resistance monitoring method and a principal component analysis method. The post observation method is generally to perform a complete life test on the tested relay, open the shell after the relay fails, and determine the failure mechanism by observing the distribution condition of the contact surface material by means of a high power microscope and other equipment. The method has high accuracy, high cost and narrow application range, and can not be finished due to the limitation of objective conditions; although the dynamic contact resistance monitoring method realizes the on-line judgment of the relay failure mechanism, only a single performance degradation parameter is analyzed and other performance degradation parameters influencing the relay failure mechanism are ignored, so that the judgment accuracy and the universality are insufficient; the principal component analysis method is a data feature extraction method based on linear transformation of original feature data, and the relay performance degradation parameter data are nonlinear data, and the nonlinear attributes are ignored after the conversion by the principal component analysis method, so that certain influence is caused on the judgment accuracy.
The invention content is as follows:
the invention aims to provide a method for judging a failure mechanism of a sealed electromagnetic relay. The above object is achieved by the following specific steps:
the method comprises the following steps: collecting six key performance degradation parameter Data of overtravel time, bounce time, contact resistance, pull-in time, release time and arcing time in a typical relay life cycle by utilizing a developed relay reliability life test system, and collecting matrix Datan×6The data set is used as a data set for judging the contact failure mechanism of the electromagnetic relay;
step two: considering that contact arcing and randomness of material transfer can bring certain interference to a data acquisition system, an FIR (Finite Impulse Response) high-pass filter is adopted to carry out noise reduction filtering processing on a data set obtained by a test;
step three: and extracting the optimal identification combination parameters capable of distinguishing the failure mechanism of the relay from the data set after noise reduction and filtering by adopting a Bayesian discrimination method. When P (C) is satisfiedi|X)>P(CjIf | X) (i is not less than 1, j is not more than m, and j is not equal to i), the sample X to be classified in the data set after noise reduction processing is set to { a ═ a1,a2,...,anIs classified into class Ci(i is more than or equal to 1 and less than or equal to m), and the Bayes theorem shows that:
wherein, P (C)i)＝P(Cj),(Ci,CjI ≠ j), namely: the probability of each class in the dataset is equal, pair P (C)j| X) maximized are:
the Bayesian algorithm is provided with the following conditional attributes which are independent of each other:
wherein,Siis represented by CiThe number of instances of the samples in the training set, S representing the total number of samples in the training set, the model can be expressed as:
probability P (a)1|Ci),P(a2|Ci),...,P(an|Ci) May be estimated from training samples, wherein:
wherein,representative Attribute AkThe function of the gaussian density of (a),respectively, the standard deviation and the mean thereof.
To be classified for relay failure mechanismSample X, calculate each class CiC (C) conditional probabilityi)P(X|Ci). When P (C) is satisfiedi|X)>P(CjWhen | X) (i is more than or equal to 1, m is more than or equal to j, and j is not equal to i), the sample X to be classified is divided into CiA category. The discrimination ability of each degradation parameter for the contact failure mechanism is shown in table 1, and the larger the discrimination ability value is, the stronger the discrimination ability of the corresponding degradation parameter for the contact failure mechanism is. The best discrimination combination parameter for differentiating relay failure mechanism is overtravel time and bounce time.
TABLE 1 Distinguishing Capacity values of degradation parameters for contact failure mechanisms
Step four: and judging the contact failure mechanism of the relay by adopting a random forest algorithm and combining parameters of the overtravel time and the bounce time. Sampling samples in the data set after noise reduction and filtering processing by using a replaced sampling method, constructing a proper decision tree for each sampling sample to form a random forest model, voting the test samples by using the constructed decision trees, wherein the category with the most votes is the category to which the test sample belongs. The random forest measures the degree that the average correct classification number exceeds the average wrong classification number in the model by using a marginal function, the larger the value of the marginal function is, the better the classification effect is represented, and the definition of the marginal function is shown in formula (6).
MR(x,y)＝Iθ(f(x,θ)＝y)-maxj＝yIθ(f(x,θ)＝j) (6)
The generalization error of the random forest model in the two-dimensional space can be represented by formula (7).
PE＝px,y(MR(x,y)<0) (7)
When the decision tree is large enough, the random forest classification model obeys the law of large numbers, and the convergence condition of the random variable in the model can be represented by formula (8). Formula (8) shows that the random forest has good expansibility, and no overfitting phenomenon occurs along with the expansion of the subtrees.
The invention has the beneficial effects that:
the invention relates to a failure mechanism judging method of a sealed electromagnetic relay, which judges failure mechanisms (bridging failure, abrasion failure and pollution failure) of collected test data by utilizing a developed relay reliability life test system without performing cavity opening treatment on a tested relay and without using a high-power microscope.
Description of the drawings:
FIG. 1 is a block diagram of the hardware system of the present invention.
FIG. 2 is a software system block diagram of the present invention.
Fig. 3 is a control circuit schematic of the present invention.
Fig. 4 is a schematic diagram of the contact monitoring circuit of the present invention.
Fig. 5 is a schematic diagram of the degraded data acquisition circuit of the present invention.
Fig. 6 is a schematic diagram of the power supply circuit of the present invention.
Fig. 7 is a schematic view of the contact surface morphology in the normal state and in three different failure mechanism states of the present invention.
FIG. 8 is a graph of the discrimination accuracy of different subtrees of the random forest of the present invention.
FIG. 9 is a graph showing the discriminating effect of the random forest 5 subtree of the present invention.
FIG. 10 is a graph of the discrimination effect of the random forest 10 subtrees of the present invention.
FIG. 11 is a graph showing the discrimination effect of subtrees of the random forest 20 according to the present invention.
FIG. 12 is a graph of the discrimination effect of subtrees of the random forest 50 of the present invention.
The specific implementation mode is as follows:
example 1:
the invention relates to a method for judging a failure mechanism of a sealed electromagnetic relay. The above object is achieved by the following specific steps:
the method comprises the following steps: collecting six key performance degradation parameter Data of overtravel time, bounce time, contact resistance, pull-in time, release time and arcing time in a typical relay life cycle by utilizing a developed relay reliability life test system, and collecting matrix Datan×6The data set is used as a data set for judging the contact failure mechanism of the electromagnetic relay;
step two: considering that contact arcing and randomness of material transfer can bring certain interference to a data acquisition system, an FIR (Finite Impulse Response) high-pass filter is adopted to carry out noise reduction filtering processing on a data set obtained by a test;
step three: and extracting the optimal identification combination parameters capable of distinguishing the failure mechanism of the relay from the data set after noise reduction and filtering by adopting a Bayesian discrimination method. When P (C) is satisfiedi|X)>P(CjIf | X) (i is not less than 1, j is not more than m, and j is not equal to i), the sample X to be classified in the data set after noise reduction processing is set to { a ═ a1,a2,...,anIs classified into class Ci(i is more than or equal to 1 and less than or equal to m), and the Bayes theorem shows that:
wherein, P (C)i)＝P(Cj),(Ci,CjI ≠ j), namely: the probability of each class in the dataset is equal, pair P (C)j| X) maximized are:
the Bayesian algorithm is provided with the following conditional attributes which are independent of each other:
wherein,Siis represented by CiThe number of instances of the samples in the training set, S representing the total number of samples in the training set, the model can be expressed as:
probability P (a)1|Ci),P(a2|Ci),...,P(an|Ci) May be estimated from training samples, wherein:
wherein,representative Attribute AkThe function of the gaussian density of (a),respectively, the standard deviation and the mean thereof.
For the sample X to be classified of the relay failure mechanism, calculating each class CiC (C) conditional probabilityi)P(X|Ci). When P (C) is satisfiedi|X)>P(CjWhen | X) (i is more than or equal to 1, m is more than or equal to j, and j is not equal to i), the sample X to be classified is divided into CiA category. The discrimination ability of each degradation parameter for the contact failure mechanism is shown in table 1, and the larger the discrimination ability value is, the stronger the discrimination ability of the corresponding degradation parameter for the contact failure mechanism is. The best discrimination combination parameter for differentiating relay failure mechanism is overtravel time and bounce time.
TABLE 1 Distinguishing Capacity values of degradation parameters for contact failure mechanisms
Step four: and judging the contact failure mechanism of the relay by adopting a random forest algorithm and combining parameters of the overtravel time and the bounce time. Sampling samples in the data set after noise reduction and filtering processing by using a replaced sampling method, constructing a proper decision tree for each sampling sample to form a random forest model, voting the test samples by using the constructed decision trees, wherein the category with the most votes is the category to which the test sample belongs. The random forest measures the degree that the average correct classification number exceeds the average wrong classification number in the model by using a marginal function, the larger the value of the marginal function is, the better the classification effect is represented, and the definition of the marginal function is shown in formula (6).
MR(x,y)＝Iθ(f(x,θ)＝y)-maxj＝yIθ(f(x,θ)＝j) (6)
The generalization error of the random forest model in the two-dimensional space can be represented by formula (7).
PE＝px,y(MR(x,y)<0) (7)
When the decision tree is large enough, the random forest classification model obeys the law of large numbers, and the convergence condition of the random variable in the model can be represented by formula (8). Formula (8) shows that the random forest has good expansibility, and no overfitting phenomenon occurs along with the expansion of the subtrees.
Example 2:
a method for judging failure mechanism of sealed electromagnetic relay. And a distributed control mode is adopted, and six key performance degradation parameter data of overtravel time, bounce time, contact resistance, attraction time, release time and arcing time in the whole life cycle of 8 tested relays are synchronously monitored and collected and stored. The hardware system comprises a control circuit, a relay contact monitoring circuit, a degradation data acquisition circuit and a power supply circuit. The hardware system block diagram is shown in fig. 1. The software system adopts a data acquisition control DAQ developed by the Hua company, and is compiled by VB6.0 software based on the Windows XP system environment. A software system block diagram is shown in fig. 2. When the tested relay is in contact failure, the system can be immediately stopped, the current action times and states of the contacts can be recorded, and meanwhile, an alarm is given out. The system is designed according to GB/T15510-:
(1) coil excitation voltage: the range is direct current 0-100V, and the rated voltage is +/-10%;
(2) a load power supply: the rated voltage is +/-10 percent, and the upper limit is direct current 80A/100V;
(3) the number of the monitoring relays is as follows: 8, only one of the active ingredients is added;
(4) detecting parameters: overtravel time, bounce time, contact resistance, pull-in time, release time and arcing time;
(5) and (3) measuring precision: 1 mu s;
(6) the action frequency is as follows: the concentration is selectable within the range of 30-600 times/min;
(7) monitoring start-stop time: the time period after 40% of the time after the coil of the tested relay is electrified and after 40% of the time after the coil of the tested relay is powered off;
(8) and (3) data storage: the data can be manually stored in real time in the test process, and can be automatically stored at intervals.
Example 3:
designing a control circuit:
an IPC-610L porphyry industrial personal computer is used as an upper computer, and a Modbus communication protocol is adopted to issue high and low level instructions to coil pins Y0-Y7 of a lower computer controller (DVP-32EH desktop PLC) so as to complete the attraction and separation of relay contacts. And the two PLCs are controlled in a centralized way by utilizing a Yutai UT-891-USB to 485 serial port communication cable. The schematic diagram is shown in fig. 3.
Designing a contact monitoring circuit:
and the synchronous monitoring and storage of the data of six key performances of over travel time, bounce time, contact resistance, pull-in time, release time and arcing time are realized.
The contact current and the coil current are voltage-converted by using sampling resistors of 1 omega and 0.1 omega/50W. When the contact of the relay to be tested fails, the load power supply is cut off by opening the switch K1, and the schematic diagram is shown in FIG. 4.
Designing a degradation data acquisition circuit:
the signal data monitored by the contact monitoring circuit is received by the porphyry ADAM-3968 adapter plate, and the received data is transmitted to the porphyry PCI-1747U high-performance data acquisition card by the porphyry PCL-10168 communication cable, and the schematic diagram is shown in FIG. 5.
Designing a power supply circuit:
the principle diagram of the power supply circuit is shown in fig. 6, and the power supply circuit adopts LRS-350-5 and LRS-350-12 switching power supplies produced by Taiwan Mingxi corporation of China to respectively supply power to a common pin (C0-C3) of a DVP-32EH Taida PLC and a coil of a tested relay.
As mentioned above, the high temperature and arcing generated in the relay closing and releasing process can erode the contact material, causing the material transfer between the contacts, so that the contact pressure on the contact surface, the contact gap and the contact overtravel change, and the contact failure of different types can be caused along with the gradual accumulation of the material transfer. Fig. 7 is a schematic diagram of the contact surface morphology in the normal state and three different failure mechanism states of the relay. It is assumed that the contact material in each section is transferred from the lower stationary contact to the upper moving contact. The normal state is shown in fig. 7(a), the contact pressure of the surfaces of the contact pair is relatively uniform, and the contact clearance and the contact overtravel do not exceed the normal range; due to the accumulation of high temperature and arcing between contacts, contact materials can splash and evaporate in a wider range towards opposite contact spots, so that the contact overtravel is reduced, the contact gap is increased, when the contact overtravel reaches a limit value, the contact pair can not complete contact after the relay is closed, and the 'abrasion' failure occurs, as shown in fig. 7 (b); the contact gap is caused to decrease when the contact material accumulates to form one or more stabilizing projections in an area of the surface of the counterpart contact. When the limit is reached, the contact gap is completely filled, resulting in contact of the contact pair when the relay is opened, and "bridging" failure occurs, as shown in fig. 7 (c); when the total amount and direction of contact material acceptance and loss are about the same, the contact gap and contact over travel are substantially within the normal range, but the surfaces of the contact pairs are severely ablated, various contaminants are deposited, and the stationary contacts are completely "burned through" when the limit condition is reached, and a "contamination" failure occurs, as shown in fig. 7 (d). The contact failure judging effect of the density electromagnetic relay contact is shown in figures 8-12.
Claims (1)
1. A failure mechanism judging method for a sealed electromagnetic drive switch electric appliance comprises the following specific steps:
the method comprises the following steps: collecting six key performance degradation parameter Data of overtravel time, bounce time, contact resistance, pull-in time, release time and arcing time in a typical relay life cycle by utilizing a developed relay reliability life test system, and collecting matrix Datan×6The data set is used as a data set for judging the contact failure mechanism of the electromagnetic relay;
step two: considering that contact arcing and randomness of material transfer can bring certain interference to a data acquisition system, an FIR (Finite Impulse Response) high-pass filter is adopted to carry out noise reduction filtering processing on a data set obtained by a test;
step three: and extracting the optimal identification combination parameters capable of distinguishing the failure mechanism of the relay from the data set after noise reduction and filtering by adopting a Bayesian discrimination method. When P (C) is satisfiedi|X)>P(CjIf | X) (i is not less than 1, j is not more than m, and j is not equal to i), the sample X to be classified in the data set after noise reduction processing is set to { a ═ a1,a2,...,anIs classified into class Ci(i is more than or equal to 1 and less than or equal to m), and the Bayes theorem shows that:
wherein, P (C)i)＝P(Cj),(Ci,CjI ≠ j), namely: the probability of each class in the dataset is equal, pair P (C)j| X) maximized are:
the Bayesian algorithm is provided with the following conditional attributes which are independent of each other:
wherein,Siis represented by CiThe number of instances of the samples in the training set, S representing the total number of samples in the training set, the model can be expressed as:
probability P (a)1|Ci),P(a2|Ci),...,P(an|Ci) May be estimated from training samples, wherein:
wherein,representative Attribute AkThe function of the gaussian density of (a),respectively, the standard deviation and the mean thereof.
For the sample X to be classified of the relay failure mechanism, calculating each class CiC (C) conditional probabilityi)P(X|Ci). When P (C) is satisfiedi|X)>P(CjWhen | X) (i is more than or equal to 1, m is more than or equal to j, and j is not equal to i), the sample X to be classified is divided into CiA category.
Step four: and judging the contact failure mechanism of the relay by adopting a random forest algorithm and combining parameters of the overtravel time and the bounce time. Sampling samples in the data set after noise reduction and filtering processing by using a replaced sampling method, constructing a proper decision tree for each sampling sample to form a random forest model, voting the test samples by using the constructed decision trees, wherein the category with the most votes is the category to which the test sample belongs. The random forest measures the degree that the average correct classification number exceeds the average wrong classification number in the model by using a marginal function, the larger the value of the marginal function is, the better the classification effect is represented, and the definition of the marginal function is shown in formula (6).
MR(x,y)＝Iθ(f(x,θ)＝y)-maxj＝yIθ(f(x,θ)＝j) (6)
The generalization error of the random forest model in the two-dimensional space can be represented by formula (7).
PE＝px,y(MR(x,y)<0) (7)
When the decision tree is large enough, the random forest classification model obeys the law of large numbers, and the convergence condition of the random variable in the model can be represented by formula (8). Formula (8) shows that the random forest has good expansibility, and no overfitting phenomenon occurs along with the expansion of the subtrees.
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Application publication date: 20190917 |