CN115081151B - Method for predicting abrasion degree of push rod of hydraulic electromagnetic directional valve and storage medium - Google Patents
Method for predicting abrasion degree of push rod of hydraulic electromagnetic directional valve and storage medium Download PDFInfo
- Publication number
- CN115081151B CN115081151B CN202211009272.9A CN202211009272A CN115081151B CN 115081151 B CN115081151 B CN 115081151B CN 202211009272 A CN202211009272 A CN 202211009272A CN 115081151 B CN115081151 B CN 115081151B
- Authority
- CN
- China
- Prior art keywords
- push rod
- value
- directional valve
- degree
- reaction
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16K—VALVES; TAPS; COCKS; ACTUATING-FLOATS; DEVICES FOR VENTING OR AERATING
- F16K31/00—Actuating devices; Operating means; Releasing devices
- F16K31/02—Actuating devices; Operating means; Releasing devices electric; magnetic
- F16K31/06—Actuating devices; Operating means; Releasing devices electric; magnetic using a magnet, e.g. diaphragm valves, cutting off by means of a liquid
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Abstract
The invention relates to a method for predicting the abrasion degree of a push rod of a hydraulic electromagnetic directional valve, which comprises the following steps: calculating the aging degree of the electromagnetic coil according to the standard deviation of the input current value and the actual current value when the electromagnetic coil is electrified; determining a first reaction delay time according to the aging degree; determining the vibration coefficient of the current value according to the mean value and the standard deviation of the actual current value; determining a second reaction delay time according to the vibration coefficient; adding the reaction delay time I and the reaction delay time II to obtain a reaction time predicted value; designing a loss function of the wear coefficient prediction recurrent neural network according to the difference value of the real value and the predicted value of the reaction time; acquiring a push rod test data sequence consisting of standard deviation and mean value when the push rod is electrified for multiple times, inputting the push rod test data sequence into a recurrent neural network, and training and outputting an optimal wear coefficient sequence value; on the basis of the existing method, the abrasion degree of the push rod is more accurately predicted by combining the influence of the aging factors of the electromagnetic ring on the speed and the length of the push rod.
Description
Technical Field
The invention relates to the technical field of neural network calculation, in particular to a method for predicting the abrasion degree of a push rod of a hydraulic electromagnetic directional valve and a storage medium.
Background
The electromagnetic valve is industrial equipment for control, is an automatic basic element for controlling fluid and belongs to an actuator. The hydraulic electromagnetic directional valve is one kind of electromagnetic valve, and the push rod in the electromagnetic directional valve is used to push the valve core to move when the armature moves, and the push rod and the valve core are not combined into one body. During continuous movement, the push rod is worn, and after long-term high-frequency reversing, the push rod is likely to be worn and obviously shortened, so that the valve core is not reversed in place, and the normal work of the electromagnetic reversing valve is influenced.
In the prior art, when the abrasion degree of a push rod part in a hydraulic valve control system is predicted, the prediction is generally carried out through reversing frequency and reaction time. The current instability caused by the aging of the electromagnetic coil wire and the accelerated consumption of the service life of the push rod caused by the vibration of the push rod are not considered in the existing method, so that the accurate abrasion degree is difficult to predict. And the neural network can be used for acquiring prediction data according to historical data, so that a neural network-based method for predicting the abrasion degree of the push rod of the hydraulic electromagnetic directional valve can be designed.
Disclosure of Invention
The invention provides a method for predicting the abrasion degree of a push rod of a hydraulic electromagnetic reversing valve and a storage medium, which can predict the service life of the push rod more accurately by combining the increase of the reaction time caused by the weakening of magnetic force caused by the aging of an electromagnetic coil and the abrasion of the push rod caused by the increase of the vibration of the push rod caused by the unstable current value.
The method for predicting the abrasion degree of the push rod of the hydraulic electromagnetic directional valve adopts the following technical scheme: the method comprises the following steps:
acquiring a standard deviation of an actual current value in an electromagnetic coil when the electromagnetic directional valve is electrified, and calculating the aging degree of the electromagnetic coil according to an input current value and the standard deviation of the actual current value when the electromagnetic directional valve is electrified;
determining the first reaction delay time of the push rod according to the aging degree;
acquiring a mean value of actual current values in the electromagnetic coil, and determining a vibration coefficient caused by instability of the actual current values according to the mean value and the standard deviation;
determining the reaction delay time II of the push rod according to the vibration coefficient;
adding the reaction delay time I and the reaction delay time II to be used as a predicted value of the reaction time of the push rod;
acquiring a real value of the reaction time of the push rod, and designing a loss function of the recurrent neural network according to a difference value of the real value of the reaction time and the predicted value of the reaction time;
acquiring a push rod test data sequence formed by the standard deviation and the mean value when the power is switched on for multiple times, inputting the push rod test data sequence into the recurrent neural network for training, and outputting a wear coefficient sequence value; verifying the loss function of the cyclic neural network by using the output wear coefficient sequence value, and finishing training for the output wear coefficient sequence value as the optimal wear coefficient sequence value when the loss function is minimum;
acquiring a target data sequence consisting of the standard deviation and the mean value when the electromagnetic directional valve to be tested is subjected to multiple power-on tests; inputting the target data sequence into the trained recurrent neural network, and outputting an optimal wear coefficient sequence value of the push rod to be tested; and determining the residual using times of the push rod according to the optimal wear coefficient sequence value of the push rod to be detected.
The aging degree calculation formula of the electromagnetic coil is as follows:
wherein the content of the first and second substances,
a standard deviation representing the actual current value in the solenoid;
represents the average of the actual current values in the solenoid,
represents an input current value;
indicating the age of the solenoid.
The determining of the first reaction delay time of the push rod according to the aging degree includes:
acquiring the initial distance from a push rod of the electromagnetic directional valve to a liquid return port and the initial speed of the push rod;
calculating the first reaction delay time of the push rod according to the initial distance, the initial speed and the aging degree, wherein the calculation formula of the first reaction delay time is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the initial distance from the push rod to the liquid return port is shown;
representing an initial velocity of the pushrod;
indicating the degree of aging of the solenoid coil;
indicating a reaction delay time of one.
The vibration coefficient is calculated by the following formula:
wherein, the first and the second end of the pipe are connected with each other,
a standard deviation representing an actual current value in the solenoid;
means representing the average of the actual current values in the solenoid;
representing the vibration coefficient.
The calculation formula for determining the second reaction delay time of the push rod according to the vibration coefficient is as follows:
wherein the content of the first and second substances,
represents a vibration coefficient when the current is turned on next time;
indicating the last power-onThe vibration coefficient of (a);
the reaction delay time is two.
The formula for calculating the predicted value of the reaction time of the push rod is as follows:
wherein the content of the first and second substances,
represents a reaction delay time of one;
the reaction delay time is II;
the initial distance from the push rod to the liquid return port is shown;
indicating an initial velocity of the ram;
indicating the degree of aging of the solenoid coil;
represents a vibration coefficient when the current is turned on next time;
representing the vibration coefficient at the last power-on.
The above-mentioned true value of reaction time of obtaining the push rod includes:
acquiring the power-on time of the electromagnetic directional valve and the time of detecting pressure change in the electromagnetic directional valve after power-on;
and calculating the difference value between the pressure change time and the electrifying time, and taking the difference value as the true value of the push rod reaction time in the current test.
The formula for calculating the loss function is:
wherein, the first and the second end of the pipe are connected with each other,
representing the true value of the reaction time;
the reaction time is expressed as a predicted value.
The determining the remaining use times of the push rod according to the optimal wear coefficient sequence value of the push rod to be tested comprises the following steps:
acquiring the standard deviation and the mean value of the actual current value in the electromagnetic coil when the electromagnetic reversing valve to be tested is subjected to the last two-time power-on tests;
calculating the estimated wear rate of the electromagnetic directional valve to be tested during the last power-on test according to the standard deviation and the mean value
；
Obtaining the wear rate greater than the estimated wear rate in the optimal wear coefficient sequence value
The sum of the quantity of all the data is used as the residual using times of the push rod;
the estimated wear rate
The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
represents a vibration coefficient when the secondary power is turned on;
the vibration coefficient at the last energization is shown.
A storage medium on which a hydraulic solenoid directional valve push rod wear degree prediction program is stored, the hydraulic solenoid directional valve push rod wear degree prediction program, when executed by a processor, implementing the steps of the hydraulic solenoid directional valve push rod wear degree prediction method. The beneficial effects of the invention are:
when the service life of the push rod is predicted in the prior art, the service life is generally calculated by using the reversing frequency, the abrasion degree of the push rod at each time is the same by default, but the abrasion degree of the push rod at each time is different, and the later period is faster. In the prior art, the service life of the push rod is predicted according to initial performance calculation, the static calculation is performed, only time factors are considered, the abrasion environments are different every time, the dynamic change is performed in practical application, and therefore large errors exist in calculation.
The invention provides a neural network-based method for predicting the abrasion degree of a push rod of a hydraulic electromagnetic directional valve, which can realize real-time calculation of the service life of the push rod part in the hydraulic electromagnetic directional valve, and can realize more accurate prediction of the service life of the push rod by combining the increase of the reaction time caused by the weakening of magnetic force caused by the aging of an electromagnetic coil in the hydraulic electromagnetic directional valve and the damage of the push rod caused by the increase of the vibration of the push rod caused by unstable current.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of the general steps of an embodiment 1 of the method for predicting the degree of wear of a push rod of a hydraulic solenoid directional valve according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
an embodiment of the method for predicting the degree of wear of the push rod of the hydraulic electromagnetic directional valve, as shown in fig. 1, includes:
s1, obtaining a standard deviation of an actual current value in an electromagnetic coil when the electromagnetic reversing valve is electrified, and calculating the aging degree of the electromagnetic coil according to an input current value and the standard deviation of the actual current value when the electromagnetic reversing valve is electrified.
When the discrete degree of the actual current value of the electromagnetic coil in the electromagnetic directional valve relative to the input current value is obtained, the standard deviation of the actual current value is calculated, and the discrete degree of the actual current value can be reflected through the standard deviation. When the actual current value is measured, an ammeter is connected to the electromagnetic coil, and the instability degree of the current value measured by the ammeter is used as the aging degree of the electromagnetic coil.
The aging degree calculation formula of the electromagnetic coil is as follows:
wherein the content of the first and second substances,
a standard deviation representing the actual current value in the solenoid;
electromagnetic wire for representationThe average of the actual current values in the loop,
representing an input current value;
indicating the age of the solenoid.
And S2, determining the first reaction delay time of the push rod according to the aging degree.
In the electromagnetic reversing valve, when a control system provides driving current, the magnetic field of the electromagnetic coil and the lower yoke iron act to generate thrust for moving the magnetic core, a push rod fixed on the magnetic core is pushed out, an ejector block in the pilot valve is pushed to move, a working port in the pilot valve is communicated with a high-pressure port, and a liquid return port is closed. When the control system cuts off the driving current of the electromagnetic valve, the electromagnetic coil discharges inductance energy through the internal discharge circuit, the push rod resets under the action of the spring force, and the working port in the pilot valve is communicated with the liquid return port.
When the electromagnetic reversing valve is electrified, the push rod is subjected to electromagnetic thrust to close the liquid return port, and the pressure in the electromagnetic reversing valve changes after the push rod closes the liquid return port. The time is required for closing the liquid return port by the push rod, and the initial distance from the push rod to the liquid return port before the push rod is tested is a fixed value, so the reaction time of the push rod mainly depends on the length of the push rod and the movement speed of the push rod.
The moving speed of the push rod mainly depends on the aging degree of the electromagnetic coil, and the larger the aging degree is, the smaller the magnetic force of the electromagnetic coil is, the slower the moving speed of the push rod is. The delay time of the reaction time caused by the decrease of the moving speed of the push rod is denoted by t 1.
Acquiring an initial distance from a push rod to a liquid return port and an initial speed of the push rod before a power-on test of the electromagnetic directional valve;
calculating the first reaction delay time of the push rod at the current time according to the initial distance, the initial speed and the aging degree, wherein the calculation formula of the first reaction delay time is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the initial distance from the push rod to the liquid return port is shown;
representing an initial velocity of the pushrod;
indicating the degree of aging of the solenoid coil;
indicating a reaction delay time of one.
And S3, acquiring a mean value of actual current values in the electromagnetic coil, and determining a vibration coefficient caused by instability of the actual current values according to the mean value and the standard deviation.
Wherein, the length of push rod is decided by the impaired degree of push rod, and the impaired main two reasons that have of push rod: 1) Damage due to commutation frequency 2) vibration damage due to instability of the magnetic force of the electromagnetic coil. The electromagnetic coil is aged to cause unstable current and unstable magnetic force, so that the vibration degree of the push rod is increased, and the push rod is abraded.
The calculation formula of the vibration coefficient is as follows:
wherein, the first and the second end of the pipe are connected with each other,
a standard deviation representing the actual current value in the solenoid;
means representing the average of the actual current values in the solenoid;
representing the vibration coefficient.
And S4, determining the second reaction delay time of the push rod according to the vibration coefficient.
The calculation formula for determining the second reaction delay time of the push rod according to the vibration coefficient is as follows:
wherein the content of the first and second substances,
represents a vibration coefficient when the secondary power is turned on;
representing the vibration coefficient at the last power-on;
the reaction delay time is II; the magnetic force is reduced due to the reduction of current, the hydraulic machine environment often has large vibration in actual work, the larger the magnetic force is, the larger the capacity of resisting external influence force is, and the magnetic force is small.
And S5, adding the reaction delay time I and the reaction delay time II to be used as a reaction time predicted value of the push rod.
The formula for calculating the predicted value of the reaction time of the push rod is as follows:
wherein the content of the first and second substances,
represents a reaction delay time of one;
the reaction delay time is II;
the initial distance from the push rod to the liquid return port is shown;
indicating an initial velocity of the ram;
indicating the degree of aging of the solenoid coil;
represents a vibration coefficient when the current is turned on next time;
representing the vibration coefficient at the last power-on.
S6, obtaining a real value of the reaction time of the push rod, and designing a loss function of the recurrent neural network according to a difference value of the real value of the reaction time and the predicted value of the reaction time.
Acquiring the power-on time of the electromagnetic directional valve and the time of detecting pressure change in the electromagnetic directional valve after power-on; calculating the difference value between the pressure change time and the power-on time, and taking the difference value as the true value of the push rod reaction time in the current test;
when the power is on, the push rod is subjected to electromagnetic thrust to close the liquid return port, and the pressure in the electromagnetic reversing valve changes after the push rod closes the liquid return port. The time is required for the push rod to close the liquid return port, and the initial distance from the push rod to the liquid return port before the push rod is tested is a fixed value, so the reaction time of the push rod mainly depends on the length of the push rod and the movement speed of the push rod. The time for energizing the electromagnetic directional valve at each test is measured at each test. The time for detecting the pressure change in the electromagnetic directional valve after the power is on is also measured in each test.
Designing a loss function of the recurrent neural network according to the difference value of the true value of the reaction time and the predicted value of the reaction time, wherein the calculation formula of the loss function is as follows:
wherein the content of the first and second substances,
representing the true value of the reaction time;
the reaction time is expressed as a predicted value.
S7, a push rod test data sequence consisting of the standard deviation and the mean value is obtained when multiple times of power-on are conducted, the push rod test data sequence is input into the recurrent neural network for training, and a wear coefficient sequence value is output; and verifying the loss function of the recurrent neural network by using the output wear coefficient sequence value, and finishing training for the output wear coefficient sequence value as the optimal wear coefficient sequence value when the loss function is minimum.
The learning modes of the recurrent neural network are various, and the learning rate of the recurrent neural network is set to enable the true value of the reaction time to be gradually close to the predicted value of the reaction time.
The input value of the recurrent neural network is represented by
) A composed push rod test data sequence in which
Represents the standard deviation of the actual current value in the solenoid,
representing the average of the actual current values in the solenoid. Will be composed of
) Inputting the formed push rod test data sequence into a recurrent neural network, minimizing the difference between a true value and a predicted value through iterative training, and finally outputting an optimal wear coefficient sequence value.
S8, acquiring a target data sequence consisting of the standard deviation and the mean value when the electromagnetic directional valve to be tested is subjected to multiple power-on tests; inputting the target data sequence into the trained recurrent neural network, and outputting an optimal wear coefficient sequence value of the push rod to be tested; and determining the residual using times of the push rod according to the optimal wear coefficient sequence value of the push rod to be detected.
(iii) obtaining the time of multiple power-on tests of the electromagnetic directional valve to be tested
) And inputting the target data sequence into a circulating neural network, and outputting the optimal wear coefficient sequence value of the push rod of the electromagnetic directional valve to be tested.
When the residual using times of the push rod is determined according to the optimal wear coefficient sequence value, the standard deviation and the mean value of the actual current value in the electromagnetic coil during the last two times of power-on tests of the electromagnetic directional valve to be tested are obtained; calculating the estimated wear rate of the electromagnetic directional valve to be tested during the last power-on test according to the standard deviation and the mean value
；
Estimating wear rate
The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
represents a vibration coefficient when the secondary power is turned on;
represents the vibration coefficient at the last energization.
Obtaining theGreater than said estimated wear rate in the value of the sequence of optimum wear coefficients
The sum of the total data is used as the remaining using times of the push rod.
For example: if the optimal wear coefficient sequence value is obtained as follows:
{
}。
if the wear rate is estimated
Is located at
And
then the wear rate is estimated
If the value of (A) is less than (B), the value of the obtained optimal wear coefficient sequence is greater than the estimated wear rate
The overall data sequence of (a) is as follows:
{
}，
then it will be driven from
~ 
The sum of the total data is used as the remaining use times of the push rod.
One storage medium is a memory, and a hydraulic electromagnetic directional valve push rod wear degree prediction program is stored in the memory. And when the hydraulic electromagnetic directional valve push rod abrasion degree prediction program is executed by the processor, the method for predicting the abrasion degree of the hydraulic electromagnetic directional valve push rod is realized.
In summary, the present invention provides a method for predicting the wear degree of a push rod of a hydraulic solenoid directional valve and a storage medium, which can predict the life of the push rod more accurately by combining the increase of the push rod due to the shock increase of the push rod caused by the unstable current and the increase of the magnetic force caused by the aging of the solenoid coil.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. The method for predicting the abrasion degree of the push rod of the hydraulic electromagnetic directional valve is characterized by comprising the following steps:
acquiring a standard deviation of an actual current value in an electromagnetic coil when the electromagnetic directional valve is electrified, and calculating the aging degree of the electromagnetic coil according to an input current value and the standard deviation when the electromagnetic directional valve is electrified;
the aging degree calculation formula of the electromagnetic coil is as follows:
wherein, the first and the second end of the pipe are connected with each other,
a standard deviation representing an actual current value in the solenoid;
represents the average of the actual current values in the solenoid,
represents an input current value;
indicating the degree of aging of the solenoid coil;
determining a first reaction delay time of the push rod according to the aging degree, comprising the following steps:
acquiring an initial distance from a push rod of the electromagnetic directional valve to a liquid return port and an initial speed of the push rod;
calculating the first reaction delay time of the push rod according to the initial distance, the initial speed and the aging degree, wherein the calculation formula of the first reaction delay time is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the initial distance from the push rod to the liquid return port is shown;
indicating an initial velocity of the ram;
indicating the degree of aging of the solenoid coil;
represents a reaction delay time of one;
obtaining the average value of the actual current value in the electromagnetic coil, and determining the vibration coefficient caused by the instability of the actual current value according to the average value and the standard deviation, wherein the calculation formula of the vibration coefficient is as follows:
wherein, the first and the second end of the pipe are connected with each other,
a standard deviation representing an actual current value in the solenoid;
means representing the average of the actual current values in the solenoid;
representing the vibration coefficient;
and determining the second reaction delay time of the push rod according to the vibration coefficient by the calculation formula:
wherein the content of the first and second substances,
represents a vibration coefficient when the current is turned on next time;
representing the vibration coefficient at the last power-on;
representing a reaction delay time of two;
adding the reaction delay time I and the reaction delay time II to be used as a predicted value of the reaction time of the push rod;
acquiring a real value of the reaction time of the push rod, and designing a loss function of the recurrent neural network according to a difference value of the real value of the reaction time and the predicted value of the reaction time;
acquiring a push rod test data sequence formed by the standard deviation and the mean value when the power is switched on for multiple times, inputting the push rod test data sequence into the recurrent neural network for training, and outputting a wear coefficient sequence value; verifying the loss function of the recurrent neural network by using the output wear coefficient sequence value, and finishing training for the output wear coefficient sequence value as the optimal wear coefficient sequence value when the loss function is minimum;
acquiring a target data sequence consisting of the standard deviation and the mean value when the electromagnetic directional valve to be tested is subjected to multiple power-on tests; inputting the target data sequence into the trained recurrent neural network, and outputting an optimal wear coefficient sequence value of the push rod to be tested; and determining the residual using times of the push rod according to the optimal wear coefficient sequence value of the push rod to be detected.
2. The method for predicting the wear degree of the push rod of the hydraulic electromagnetic directional valve according to claim 1, wherein the predicted reaction time value of the push rod is calculated by the following formula:
wherein the content of the first and second substances,
represents a reaction delay time of one;
the reaction delay time is II;
the initial distance from the push rod to the liquid return port is shown;
indicating an initial velocity of the ram;
indicating the degree of aging of the solenoid coil;
represents a vibration coefficient when the current is turned on next time;
represents the vibration coefficient at the last energization.
3. The method for predicting the abrasion degree of the push rod of the hydraulic electromagnetic directional valve according to claim 1, wherein the step of obtaining the real value of the reaction time of the push rod comprises the following steps:
acquiring the power-on time of the electromagnetic directional valve and the time of detecting pressure change in the electromagnetic directional valve after power-on;
and calculating the difference value between the pressure change time and the electrifying time, and taking the difference value as the true value of the push rod reaction time in the current test.
4. The method for predicting the degree of wear of a push rod of a hydraulic solenoid directional valve according to claim 1, wherein the loss function is calculated by the formula:
wherein, the first and the second end of the pipe are connected with each other,
representing the true value of the reaction time;
the predicted value of the reaction time is shown.
5. The method for predicting the abrasion degree of the push rod of the hydraulic electromagnetic directional valve according to claim 1, wherein the step of determining the residual using times of the push rod according to the optimal abrasion coefficient sequence value of the push rod to be tested comprises the following steps:
acquiring the standard deviation and the mean value of the actual current value in the electromagnetic coil during the last two times of power-on tests of the electromagnetic reversing valve to be tested;
calculating the estimated wear rate of the electromagnetic directional valve to be tested during the last power-on test according to the standard deviation and the mean value
；
Obtaining the wear rate greater than the estimated wear rate in the optimal wear coefficient sequence value
The sum of the quantity of all the data is used as the residual using times of the push rod;
the estimated wear rate
The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
represents a vibration coefficient when the secondary power is turned on;
representing the vibration coefficient at the last power-on.
6. A storage medium having stored thereon a hydraulic solenoid directional valve pushrod wear degree prediction program which, when executed by a processor, implements the steps of the hydraulic solenoid directional valve pushrod wear degree prediction method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211009272.9A CN115081151B (en) | 2022-08-23 | 2022-08-23 | Method for predicting abrasion degree of push rod of hydraulic electromagnetic directional valve and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211009272.9A CN115081151B (en) | 2022-08-23 | 2022-08-23 | Method for predicting abrasion degree of push rod of hydraulic electromagnetic directional valve and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115081151A CN115081151A (en) | 2022-09-20 |
| CN115081151B true CN115081151B (en) | 2022-11-04 |
Family
ID=83244717
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211009272.9A Active CN115081151B (en) | 2022-08-23 | 2022-08-23 | Method for predicting abrasion degree of push rod of hydraulic electromagnetic directional valve and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115081151B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2298718A1 (en) * | 1999-02-19 | 2000-08-19 | Sebastien Hardy | Brake wear managing system |
| CN113219334A (en) * | 2021-05-06 | 2021-08-06 | 南京航空航天大学 | Wallboard molded surface loading state early warning method based on push rod loading current |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103557204B (en) * | 2013-11-12 | 2016-02-03 | 北京理工大学 | A kind of hydraulicdirectional control valve contamination wear and Environment restore test stand |
| DE102019220501A1 (en) * | 2019-12-21 | 2021-06-24 | Robert Bosch Gesellschaft mit beschränkter Haftung | Method for controlling a hydraulic cylinder of a work machine |
-
2022
- 2022-08-23 CN CN202211009272.9A patent/CN115081151B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2298718A1 (en) * | 1999-02-19 | 2000-08-19 | Sebastien Hardy | Brake wear managing system |
| CN113219334A (en) * | 2021-05-06 | 2021-08-06 | 南京航空航天大学 | Wallboard molded surface loading state early warning method based on push rod loading current |
Non-Patent Citations (1)
| Title |
|---|
| 基于BP神经网络的连杆衬套磨损量预测;曹存存等;《组合机床与自动化加工技术》;20160825(第08期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115081151A (en) | 2022-09-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6234584B2 (en) | Calculation method of physical variable in positive displacement pump | |
| EP1167725B2 (en) | Engine valve drive control apparatus and method | |
| CA2920224C (en) | Method for determining hydraulic parameters in a displacement pump | |
| Liu et al. | Study on the control methods of a water hydraulic variable ballast system for submersible vehicles | |
| US11243269B2 (en) | System and method for spool fault detection of solenoid valves using electrical signature | |
| CN115081151B (en) | Method for predicting abrasion degree of push rod of hydraulic electromagnetic directional valve and storage medium | |
| Daigle et al. | Improving computational efficiency of prediction in model-based prognostics using the unscented transform | |
| Oates et al. | Piezoelectric hydraulic pump system dynamic model | |
| US20230052987A1 (en) | System and method for solenoid valve optimization and measurement of response deterioration | |
| AU2020242695B2 (en) | Methods for controlling and diagnosing an actuator for a low power system | |
| CN114265368B (en) | Combined state self-adaptive estimation method for aero-engine servo control system | |
| CN114428462A (en) | Method, equipment and medium for dynamically controlling controlled system based on MPC algorithm | |
| CA2922313C (en) | Method for improving metering profiles of displacement pumps | |
| CN107703761B (en) | Estimation method for viscous characteristic parameter of pneumatic regulating valve | |
| CN113325886A (en) | Valve hysteresis compensation method and device, electronic equipment and storage medium | |
| Aborobaa et al. | Sensorless Position Estimating and Transition time Identifying for the spool of a High Speed on/off Solenoid Valve | |
| US6332436B1 (en) | Method for the control of electromagnetic actuators for the actuation of intake and exhaust valves in internal combustion engines | |
| Vonderbank et al. | Energetic Investigation of Common Pilot Operation and the Energy Saving Potential of Electromechanical Valve Actuators | |
| CN117108600B (en) | Pressure control method and system for pressure control valve in hydraulic system | |
| US20240151317A1 (en) | Method for switching an electromagnetically actuated hydraulic valve and hydraulic valve | |
| Breidi et al. | Monitoring digital technologies in hydraulic systems using CUSUM control charts | |
| Noergaard et al. | Experimental validation of modelled fluid forces in fast switching hydraulic on/off valves | |
| Schoenau et al. | Utilization of Artificial Neural Networks in the Control, Identification and Condition Monitoring of Hydraulic Systems—An Overview | |
| CN117799590A (en) | Brake pressure control method, brake pressure control system, brake device and medium for rolling stock | |
| Du et al. | A garch modeling approach for constant speed drive residual life predicting of aircraft generator |
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 |