CN114548311A - Hydraulic equipment intelligent control system based on artificial intelligence - Google Patents
Hydraulic equipment intelligent control system based on artificial intelligence Download PDFInfo
- Publication number
- CN114548311A CN114548311A CN202210191927.2A CN202210191927A CN114548311A CN 114548311 A CN114548311 A CN 114548311A CN 202210191927 A CN202210191927 A CN 202210191927A CN 114548311 A CN114548311 A CN 114548311A
- Authority
- CN
- China
- Prior art keywords
- neural network
- confidence
- confidence coefficient
- pressure data
- state
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- 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
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Mathematical Physics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Medical Informatics (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention relates to the technical field of neural networks, in particular to an intelligent control system of hydraulic equipment based on artificial intelligence. The system is an artificial intelligent optimization operating system, comprising: the device comprises a neural network training module, a neural network application module, a credibility obtaining module and an intelligent control module. The neural network training module is used for training the state judgment neural network and the confidence coefficient neural network; the neural network application module is used for acquiring the confidence coefficient of the pressure data; the confidence level obtaining module is used for screening out the number proportion of high confidence levels in the confidence level sequence; simultaneously analyzing the sequence variation trend, and acquiring the data reliability according to the variation trend and the quantity ratio; acquiring a reliability coefficient, and further acquiring the reliability of the state judgment neural network; and the intelligent control module is used for intelligently controlling the hydraulic equipment according to the result of the state judgment neural network and the corresponding credibility. The embodiment of the invention can adopt the neural network model to detect the leakage state of the hydraulic equipment in time and carry out intelligent regulation.
Description
Technical Field
The invention relates to the technical field of neural networks, in particular to an intelligent control system of hydraulic equipment based on artificial intelligence.
Background
Artificial Neural Networks (ans), also referred to as Neural Networks (NNs) or Connection models (Connection models), are algorithmic mathematical models that Model animal Neural network behavior characteristics and perform distributed parallel information processing. The network achieves the aim of processing information by adjusting the mutual connection relationship among a large number of nodes in the network depending on the complexity of the system.
At present, a neural network model is adopted in a hydraulic control system to adjust parameters or estimate a system state to form a normal state, but the neural network has larger unexplainable performance, and the reliability of a prediction result of the neural network is an uncertain factor in the process of estimating the system state, so that whether an output result of the neural network is accurate or not cannot be judged, further, the intelligent control of hydraulic equipment is not accurate, and the control process is unreasonable.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent hydraulic equipment control system based on artificial intelligence, which adopts the following technical scheme:
one embodiment of the invention provides an intelligent hydraulic equipment control system based on artificial intelligence, which comprises the following modules:
the neural network training module is used for acquiring pressure data of the hydraulic cylinder at each moment and judging a neural network by utilizing a pressure data sequence of a plurality of preset time periods and a corresponding leakage state training state; calculating the confidence corresponding to each pressure data through the system error corresponding to each pressure data, and respectively training a confidence neural network by using the pressure data and the corresponding confidence under different states;
the neural network application module is used for inputting the acquired pressure data into the trained state judgment neural network, outputting a corresponding state, inputting the pressure data into the trained confidence coefficient neural network in the corresponding state, and outputting a corresponding confidence coefficient;
the confidence coefficient obtaining module is used for obtaining a confidence coefficient sequence of a preset time period before the current time, and screening out the number proportion of confidence coefficients larger than a confidence coefficient threshold value; simultaneously analyzing the variation trend of the confidence coefficient sequence, and acquiring the data reliability according to the variation trend and the quantity ratio; converting the confidence sequence before and after screening into a pressure data range, respectively inputting the pressure data range into a state judgment neural network, obtaining a reliability coefficient according to output results of the previous time and the next time, and obtaining the reliability of the state judgment neural network based on the data reliability and the reliability coefficient;
and the intelligent control module is used for evaluating the stability of the intelligent control system of the hydraulic equipment according to the result of the state judgment neural network and the corresponding credibility so as to intelligently control the hydraulic equipment based on the stability.
Preferably, the neural network training module includes:
the confidence coefficient calculation unit is used for acquiring theoretical displacement of a valve core in the hydraulic cylinder corresponding to each pressure data and actual displacement of the hydraulic cylinder, taking a difference value between the theoretical displacement and the actual displacement as the system error, and acquiring the confidence coefficient according to a ratio of the system error to the theoretical displacement; the confidence level is inversely related to the system error.
Preferably, the neural network training module includes:
and the confidence coefficient neural network training unit is used for acquiring the state of the pressure data sequence through the state judgment neural network, distinguishing the pressure data sequence into different groups according to different states, and training the confidence coefficient neural network in the group of states by using the pressure data and the corresponding confidence coefficient of each group respectively.
Preferably, the reliability obtaining module includes:
and the quantity proportion acquisition unit is used for acquiring the confidence coefficient threshold value by carrying out threshold segmentation on the confidence coefficient sequence, screening out the confidence coefficient quantity which is greater than the confidence coefficient threshold value in the confidence coefficient sequence, and taking the ratio of the confidence coefficient quantity to the quantity of all elements of the confidence coefficient sequence as the quantity proportion.
Preferably, the reliability obtaining module includes:
and the variation trend analysis unit is used for establishing a coordinate system by taking the time as an abscissa and taking the confidence coefficient corresponding to the pressure data at each time as an ordinate, acquiring coordinate points of the confidence coefficient sequence in the coordinate system, acquiring principal component direction vectors of the coordinate points, judging the increasing and decreasing variation trend according to the direction of the principal component direction vectors, and acquiring the variation amplitude according to the included angle between the principal component direction vectors and the horizontal direction.
Preferably, the reliability obtaining module includes:
the data reliability obtaining unit is used for obtaining the data reliability according to the change amplitude and the number ratio; when the principal component direction vector is in an increasing trend, the variation amplitude and the data reliability are in a positive correlation relationship; when the principal component direction vector is in a decreasing trend, the variation amplitude and the data reliability are in a negative correlation relationship.
Preferably, the reliability obtaining module includes:
the confidence coefficient acquisition unit is used for converting the confidence coefficient sequence into a pressure data range, inputting the pressure data range into the state judgment neural network, and outputting a corresponding first state; setting elements which are not greater than the confidence coefficient threshold value in the confidence coefficient sequence to zero to obtain a screening sequence, converting the screening sequence into a pressure data range, inputting the pressure data range into the state judgment neural network, and outputting a corresponding second state; and acquiring the reliability coefficient according to whether the first state and the second state are consistent.
Preferably, the intelligent control module includes:
the stability evaluation unit is used for stabilizing the intelligent control system of the hydraulic equipment when the output result of the state judgment neural network is normal and the reliability is greater than the credibility threshold; otherwise, the intelligent control system of the hydraulic equipment is unstable and needs to be intelligently adjusted.
The embodiment of the invention at least has the following beneficial effects:
and calculating the confidence of the state judgment network according to the output result of the confidence network, further evaluating the reliability of the state judgment network, and evaluating the leakage result of the current hydraulic equipment by using the judgment result and the reliability of the state judgment network so as to regulate the pressure of the hydraulic equipment. According to the embodiment of the invention, the neural network model can be adopted to detect the leakage state of the hydraulic equipment in time, and corresponding early warning response can be made through intelligent control, so that the sensitivity of the system is improved, and the state of the system is predicted and evaluated more accurately.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of 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 other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a system block diagram of an artificial intelligence based intelligent control system for a hydraulic device according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a position servo system of the valve-controlled hydraulic cylinder.
Detailed Description
In order to further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, the structure, the features and the effects of the hydraulic device intelligent control system based on artificial intelligence according to the present invention are provided with the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of the intelligent control system of the hydraulic equipment based on artificial intelligence in detail with reference to the accompanying drawings.
Referring to fig. 1, a system block diagram of an artificial intelligence based hydraulic device intelligent control system according to an embodiment of the present invention is shown, where the system includes the following modules:
the system comprises a neural network training module 100, a neural network application module 200, a credibility obtaining module 300 and an intelligent control module 400.
The neural network training module 100 is configured to acquire pressure data of the hydraulic cylinder at each moment, and judge a neural network by using a pressure data sequence of a plurality of preset time periods and a corresponding leakage state training state; and calculating the confidence corresponding to the pressure data according to the system error corresponding to each pressure data, and respectively training a confidence neural network by using the pressure data in different states and the corresponding confidence.
Specifically, the neural network training module 100 includes a pressure data obtaining unit 110, a state judgment neural network training unit 120, a confidence calculating unit 130, and a confidence neural network training unit 140.
The pressure data acquisition unit 110 is used to acquire pressure data of the hydraulic cylinder at each time.
Pressure data at each moment inside the hydraulic cylinder is obtained by a pressure sensor installed near the hydraulic pump.
The state judgment neural network training unit 120 is configured to train a state judgment neural network using the pressure data sequences of a plurality of preset time periods and the corresponding leakage states.
In historical data, selecting a plurality of pressure data sequences of preset time periods, simultaneously acquiring corresponding leakage states as labels, labeling event properties, using 0 and 1 as label data, using 0 to indicate that leakage does not occur and using 1 to indicate that leakage occurs, and training a classification network through a cross entropy loss function to obtain a state judgment neural network.
The confidence coefficient calculation unit 130 is configured to obtain a theoretical displacement of a valve element in the hydraulic cylinder corresponding to each pressure data and an actual displacement of the hydraulic cylinder, use a difference value between the theoretical displacement and the actual displacement as a system error, and obtain a confidence coefficient according to a ratio of the system error to the theoretical displacement; the confidence level is inversely related to the system error.
According to the principle of the prior art valve-controlled hydraulic cylinder, the system sends a command signal U, as shown in figure 2rThen the control signal is generated by the controller and the servo amplifier to control the displacement X of the valve core of the servo valvevThe displacement X of the piston rod of the hydraulic cylinder is controlled by the displacement of the valve core of the servo valvepDisplacement of piston rod XpObtaining a displacement feedback signal U after the detection of the displacement sensorfThen and the command signal UrCompared to produce a deviation UeAnd substituted into the controller to regulate the displacement of the piston rod.
Fig. 2 is from the paper "neural network based valve-controlled hydraulic cylinder system fault diagnosis-smelling middle flying", see page 24 of the paper.
Under the action of thrust, the hydraulic cylinder sends a command signal UrAnd displacement signal U of displacement sensorfThere is an error, so that it is often necessary to follow the command signal UrCorresponding theoretical piston rod displacement XvActual displacement X from the piston rodpThe difference value of (a) is used for adjusting the piston rod, but an error still exists after the adjustment, and the magnitude of the error is taken as a system error delta-Xv-XpI.e. the difference between the theoretical displacement and the real displacement of the piston rod. Meanwhile, the internal pressure change and the position of the piston rod are in positive correlation, so that the pressure error can be further represented by calculating the displacement error.
The piston rod displacement value and the pressure value change along with the change of the leakage amount, and the larger the system error delta is, the smaller the reliability of the displacement data is. Obtaining confidence coefficients through system errors and theoretical displacement, wherein the larger the error is, the smaller the confidence coefficient is, and the formula for obtaining the confidence coefficient corresponding to the pressure data is as follows:
where s represents confidence, Δ represents system error, and XvIndicating the theoretical displacement of the piston rod corresponding to the command data.
The smaller the system error proportion is, the larger the confidence coefficient is, and the relationship between the confidence coefficient and the system error is simulated through an inverse proportion function.
The confidence neural network training unit 140 is configured to obtain the state of the pressure data sequence through the state judgment neural network, divide the pressure data sequence into different groups according to different states, and train the confidence neural network in the group of states by using the pressure data of each group and the corresponding confidence.
Inputting historical data into a trained state judgment neural network, acquiring the state of a pressure data sequence, respectively taking the data with leakage and the data with leakage as a group, respectively training confidence coefficient neural networks, taking the pressure data at each moment as a training set when training the confidence coefficient neural networks, taking the corresponding confidence coefficient as a label to train the confidence coefficient neural networks, and taking a loss function as a cross entropy loss function. And finally obtaining a first confidence degree neural network of the non-leakage state and a second confidence degree neural network of the leakage state after training.
The neural network application module 200 is configured to input the acquired pressure data into the trained state judgment neural network, output a corresponding state, input the pressure data into the trained confidence neural network in the corresponding state, and output a corresponding confidence.
Specifically, pressure data are collected in real time, a pressure data sequence of a preset time period before the current time is used as the pressure sequence of the current time, and the pressure data sequence is input into a trained state judgment neural network to obtain an output result. And determining which confidence coefficient neural network is selected to carry out confidence coefficient calculation of data according to the output result of the state judgment neural network. If the output result of the stability neural network is 0, indicating that the system has no leakage event, selecting a first confidence coefficient neural network to calculate the confidence coefficient; and if the output result of the stability neural network is 1, indicating that a leakage event occurs, selecting a second confidence coefficient neural network for performing confidence coefficient calculation.
It should be noted that, since the system error is smaller than the theoretical displacement, and the obtained confidence level value is smaller than 1, after the confidence level is obtained, all the obtained confidence levels are normalized according to the maximum confidence level value, and the value range is adjusted to [0,1 ].
The confidence level obtaining module 300 is configured to obtain a confidence level sequence of a preset time period before the current time, and screen out a number proportion of confidence levels larger than a confidence level threshold; meanwhile, analyzing the variation trend of the confidence coefficient sequence, and acquiring the data reliability according to the variation trend and the number ratio; the confidence sequence before and after screening is converted into a pressure data range to be respectively input into the state judgment neural network, a reliability coefficient is obtained according to the output results of the previous time and the next time, and the reliability of the state judgment neural network is obtained based on the data reliability and the reliability coefficient.
Specifically, the reliability obtaining module 300 includes a number ratio obtaining unit 310, a variation trend analyzing unit 320, a data reliability obtaining unit 330, a data reliability obtaining unit 340, a reliability coefficient obtaining unit 350, and a reliability evaluating unit 360.
The number proportion obtaining unit 310 is configured to obtain a confidence threshold by performing threshold segmentation on the confidence sequence, screen out the confidence number greater than the confidence threshold in the confidence sequence, and use a ratio of the confidence number to the number of all elements of the confidence sequence as a number proportion.
And performing threshold segmentation on the obtained confidence coefficient sequence by a maximum inter-class variance method to obtain an optimal confidence coefficient threshold, screening out confidence coefficients larger than the confidence coefficient threshold from the confidence coefficient sequence as high confidence coefficients, and obtaining the number proportion r of the number of the high confidence coefficients in the confidence coefficient sequence, wherein the larger the number proportion r is, the more the high confidence coefficients in the confidence coefficient sequence are, the more accurate the judgment result of the state judgment neural network is.
And the variation trend analysis unit 320 is configured to establish a coordinate system by using the time as an abscissa and using the confidence corresponding to the pressure data at each time as an ordinate, acquire coordinate points of the confidence sequence in the coordinate system, acquire principal component direction vectors of the coordinate points, judge an increasing and decreasing variation trend of the principal component direction vectors according to directions of the principal component direction vectors, and acquire a variation amplitude of the principal component direction vectors according to an included angle between the principal component direction vectors and a horizontal direction.
And acquiring coordinates of the coordinate points, acquiring principal component directions of the data by using a PCA algorithm, and acquiring K principal component directions, wherein each principal component direction is a 2-dimensional unit vector and corresponds to a characteristic value. The principal direction of the data is the direction of the principal component with the largest eigenvalue, and the direction of the maximum projection variance of the data, that is, the main distribution direction of the data is represented as a vector, and the angle between the vector and the horizontal direction is represented as α.
And judging the change trend of the sequence according to the angle interval distribution of the included angle between the principal component direction vector and the horizontal direction, wherein if the included angle alpha between the principal component direction vector and the horizontal direction is between 0 and 90 degrees, the confidence coefficient is an increasing trend. The confidence is decreasing if the principal component direction vector falls between-90 deg. -0 deg. to the horizontal.
Taking the ratio of the absolute value of the included angle alpha between the principal component direction vector and the horizontal direction to 90 degrees as the variation amplitude t:
a data reliability obtaining unit 330, configured to obtain data reliability according to the variation amplitude and the number ratio; when the direction vector of the principal component is in an increasing trend, the variation amplitude and the data reliability are in positive correlation; when the principal component direction vector is in a decreasing trend, the variation amplitude and the data reliability are in a negative correlation relationship.
Forming a binary group by the variation trend and the variation amplitude of the principal component direction vector, wherein the first element represents the increasing or decreasing trend, 0 represents the decreasing trend, and 1 represents the increasing trend; the second element is the magnitude of the variation t, i.e., the magnitude of the trend. The representation of the doublet is (0, t) or (1, t).
For example: (1, 0.9), indicating an increasing tendency and the increasing amplitude is 0.9. The closer the magnitude of the change is to 1, the faster the rate of change of the confidence of the data is illustrated.
And a data reliability obtaining unit 340 for obtaining data reliability according to the variation trend and the number ratio.
When the trend of change is increasing, 1+ t is taken as the increasing proportion of the number proportion r, and the data reliability u of the data sequence is (1+ t) multiplied by r; when the trend of change is decreasing, 1-t is taken as the decreasing proportion of the number proportion r, and the data reliability u of the data sequence is (1-t) × r.
The confidence coefficient under the ideal condition is 1, on the premise of increasing the trend, the larger the increasing trend is, the more quickly the confidence coefficient can be increased to the ideal trend, and the greater the data reliability is; on the premise of reducing the trend, the larger the reducing trend is, the farther the confidence coefficient is from the ideal trend, and the data reliability is lower.
A confidence coefficient obtaining unit 350, configured to convert the confidence sequence into a pressure data range, input the state judgment neural network, and output a corresponding first state; setting elements not greater than the confidence coefficient threshold value in the confidence coefficient sequence to zero to obtain a screening sequence, converting the screening sequence into a pressure data range input state judgment neural network, and outputting a corresponding second state; and obtaining a reliability coefficient according to whether the first state and the second state are consistent.
And converting the confidence coefficient sequence into a pressure data range in an equal proportion according to the value of the pressure data, using the range as the input of the state judgment neural network, outputting a first state, wherein if the output state is no leakage, the first state is 0, and if the output state is a leakage state, the first state is 1.
And converting the screening sequence into a pressure data range in the same equal proportion, using the pressure data range as the input of the state judgment neural network, outputting a second state, wherein if the output state is no leakage, the second state is 0, and if the output state is a leakage state, the second state is 1.
If the first state and the second state are the same, the data with small confidence coefficient has small influence on the result reliability, namely the output result of the state judgment neural network is accurate, and the reliability coefficient v is 1; otherwise, the accuracy of the output result of the state judgment neural network is low, and the reliability coefficient v is 0.8 at the moment.
The reliability evaluation unit 360 is configured to determine the reliability of the neural network based on the data reliability and the reliability coefficient acquisition state.
And taking the product of the confidence coefficient and the reliability coefficient of the state judgment neural network as the reliability of the state judgment neural network. The higher the reliability is, the more accurate the judgment result of the state judgment neural network is.
And the intelligent control module 400 is used for evaluating the stability of the intelligent control system of the hydraulic equipment according to the result of the state judgment neural network and the corresponding credibility so as to intelligently control the hydraulic equipment based on the stability.
Specifically, the intelligent control module 400 includes a stability evaluation unit 410.
The stability evaluation unit 410 is configured to, when the state determines that the output result of the neural network is normal and the reliability is greater than the confidence threshold, stabilize the intelligent control system of the hydraulic device; otherwise, the intelligent control system of the hydraulic equipment is unstable and needs to be intelligently adjusted.
When the output result of the state judgment neural network is normal, namely the state is not leaked, the reliability of the state judgment neural network is used as a correction parameter of the result, and if the reliability of the state judgment neural network is greater than a reliability threshold value, the intelligent control system of the hydraulic equipment is stable and operates normally; if the reliability of the state judgment neural network is not greater than the reliability threshold value, it is indicated that although the output result of the state judgment neural network is normal, the reliability of the result is not high enough, leakage may be slight at the moment, and the system is not identified, so that timely adjustment is needed, the internal pressure of the hydraulic cylinder is gradually reduced to stop the operation of the hydraulic equipment, and serious leakage is avoided.
When the output result of the state judgment neural network is abnormal, namely a leakage state, if the reliability of the state judgment neural network is greater than a credible threshold, the output result of the neural network is accurate, hydraulic pressure leakage exists at the moment, intelligent control needs to be carried out on hydraulic equipment, the internal pressure of a hydraulic cylinder is gradually reduced to stop the hydraulic equipment from running, and meanwhile, early warning needing manual maintenance is carried out; if the reliability of the state judgment neural network is not greater than the credible threshold value, the hydraulic leakage is indicated, parts of a hydraulic system are possibly damaged, and the operation needs to be stopped immediately and maintained.
As an example, in the embodiment of the present invention, the value of the confidence threshold is 0.8.
In summary, the embodiment of the present invention includes a neural network training module 100, a neural network application module 200, a reliability obtaining module 300, and an intelligent control module 400.
Specifically, the neural network training module is used for acquiring pressure data of the hydraulic cylinder at each moment, and judging the neural network by using pressure data sequences of a plurality of preset time periods and corresponding leakage state training states; calculating the confidence corresponding to the pressure data according to the system error corresponding to each pressure data, and respectively training a confidence neural network by using the pressure data in different states and the corresponding confidence thereof; the neural network application module is used for inputting the acquired pressure data into the trained state judgment neural network, outputting a corresponding state, inputting the pressure data into the trained confidence coefficient neural network in the corresponding state, and outputting a corresponding confidence coefficient; the confidence coefficient acquisition module is used for acquiring a confidence coefficient sequence of a preset time period before the current time, and screening out the number proportion of confidence coefficients larger than a confidence coefficient threshold value; meanwhile, analyzing the variation trend of the confidence coefficient sequence, and acquiring the data reliability according to the variation trend and the number ratio; converting confidence sequence before and after screening into pressure data ranges, respectively inputting the pressure data ranges into a state judgment neural network, obtaining a reliability coefficient according to output results of the previous time and the next time, and obtaining the reliability of the state judgment neural network based on data reliability and the reliability coefficient; the intelligent control module is used for evaluating the stability of the intelligent control system of the hydraulic equipment according to the result of the state judgment neural network and the corresponding credibility so as to intelligently control the hydraulic equipment based on the stability. The embodiment of the invention can adopt the neural network model to detect the leakage state of the hydraulic equipment in time, can make corresponding early warning response through intelligent control, improves the sensitivity of the system, and can predict and evaluate the state of the system more accurately.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (8)
1. Hydraulic equipment intelligence control system based on artificial intelligence, its characterized in that, this system includes the following module:
the neural network training module is used for acquiring pressure data of the hydraulic cylinder at each moment and judging a neural network by utilizing a pressure data sequence of a plurality of preset time periods and a corresponding leakage state training state; calculating the confidence corresponding to each pressure data through the system error corresponding to each pressure data, and respectively training a confidence neural network by using the pressure data and the corresponding confidence under different states;
the neural network application module is used for inputting the acquired pressure data into the trained state judgment neural network, outputting a corresponding state, inputting the pressure data into the trained confidence coefficient neural network in the corresponding state, and outputting a corresponding confidence coefficient;
the confidence coefficient obtaining module is used for obtaining a confidence coefficient sequence of a preset time period before the current time, and screening out the number proportion of confidence coefficients larger than a confidence coefficient threshold value; simultaneously analyzing the variation trend of the confidence coefficient sequence, and acquiring the data reliability according to the variation trend and the quantity ratio; converting the confidence sequence before and after screening into a pressure data range, respectively inputting the pressure data range into a state judgment neural network, obtaining a reliability coefficient according to output results of the previous time and the next time, and obtaining the reliability of the state judgment neural network based on the data reliability and the reliability coefficient;
and the intelligent control module is used for evaluating the stability of the intelligent control system of the hydraulic equipment according to the result of the state judgment neural network and the corresponding credibility so as to intelligently control the hydraulic equipment based on the stability.
2. The artificial intelligence based hydraulic device intelligence control system of claim 1, wherein the neural network training module comprises:
the confidence coefficient calculation unit is used for acquiring theoretical displacement of a valve core in the hydraulic cylinder corresponding to each pressure data and actual displacement of the hydraulic cylinder, taking a difference value between the theoretical displacement and the actual displacement as the system error, and acquiring the confidence coefficient according to a ratio of the system error to the theoretical displacement; the confidence level is inversely related to the system error.
3. The artificial intelligence based hydraulic device intelligence control system of claim 1, wherein the neural network training module comprises:
and the confidence coefficient neural network training unit is used for acquiring the state of the pressure data sequence through the state judgment neural network, dividing the pressure data sequence into different groups according to different states, and training the confidence coefficient neural network in the group of states by using the pressure data and the corresponding confidence coefficient of each group respectively.
4. The artificial intelligence based hydraulic device intelligence control system of claim 1, wherein the confidence level acquisition module includes:
and the quantity proportion acquisition unit is used for performing threshold segmentation on the confidence coefficient sequence to acquire the confidence coefficient threshold, screening out the confidence coefficient quantity which is greater than the confidence coefficient threshold in the confidence coefficient sequence, and taking the ratio of the confidence coefficient quantity to the quantity of all elements of the confidence coefficient sequence as the quantity proportion.
5. The artificial intelligence based hydraulic device intelligence control system of claim 1, wherein the confidence level acquisition module includes:
and the variation trend analysis unit is used for establishing a coordinate system by taking the time as an abscissa and taking the confidence coefficient corresponding to the pressure data at each time as an ordinate, acquiring coordinate points of the confidence coefficient sequence in the coordinate system, acquiring principal component direction vectors of the coordinate points, judging the increasing and decreasing variation trend according to the direction of the principal component direction vectors, and acquiring the variation amplitude according to the included angle between the principal component direction vectors and the horizontal direction.
6. The artificial intelligence based hydraulic device intelligence control system of claim 5, wherein the confidence level acquisition module includes:
the data reliability obtaining unit is used for obtaining the data reliability according to the change amplitude and the number ratio; when the principal component direction vector is in an increasing trend, the variation amplitude and the data reliability are in a positive correlation relationship; when the principal component direction vector is in a decreasing trend, the variation amplitude and the data reliability are in a negative correlation relationship.
7. The artificial intelligence based hydraulic device intelligence control system of claim 1, wherein the confidence level acquisition module includes:
the confidence coefficient acquisition unit is used for converting the confidence coefficient sequence into a pressure data range, inputting the pressure data range into the state judgment neural network, and outputting a corresponding first state; setting elements which are not greater than the confidence coefficient threshold value in the confidence coefficient sequence to zero to obtain a screening sequence, converting the screening sequence into a pressure data range, inputting the pressure data range into the state judgment neural network, and outputting a corresponding second state; and acquiring the reliability coefficient according to whether the first state and the second state are consistent.
8. The artificial intelligence based hydraulic device intelligence control system of claim 1, wherein the intelligence control module comprises:
the stability evaluation unit is used for stabilizing the intelligent control system of the hydraulic equipment when the output result of the state judgment neural network is normal and the reliability is greater than the credibility threshold; otherwise, the intelligent control system of the hydraulic equipment is unstable and needs to be intelligently adjusted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210191927.2A CN114548311B (en) | 2022-02-28 | 2022-02-28 | Hydraulic equipment intelligent control system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210191927.2A CN114548311B (en) | 2022-02-28 | 2022-02-28 | Hydraulic equipment intelligent control system based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114548311A true CN114548311A (en) | 2022-05-27 |
| CN114548311B CN114548311B (en) | 2022-12-02 |
Family
ID=81660790
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210191927.2A Active CN114548311B (en) | 2022-02-28 | 2022-02-28 | Hydraulic equipment intelligent control system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114548311B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116503229A (en) * | 2023-06-27 | 2023-07-28 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipe network inspection method, internet of things system and storage medium |
| CN116717734A (en) * | 2023-08-10 | 2023-09-08 | 牧马人(山东)勘察测绘集团有限公司 | Data monitoring method for steady-state operation of natural gas pipeline |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010055303A (en) * | 2008-08-27 | 2010-03-11 | Denso It Laboratory Inc | Learning data management device, learning data management method and air-conditioner for vehicle, and control device of apparatus |
| CN109919302A (en) * | 2019-02-28 | 2019-06-21 | 新华三技术有限公司 | A kind of neural network training method and device |
| WO2020030722A1 (en) * | 2018-08-09 | 2020-02-13 | Iee International Electronics & Engineering S.A. | Sensor system including artificial neural network configured to perform a confidence measure-based classification or regression task |
| CN110848896A (en) * | 2019-11-12 | 2020-02-28 | 深圳孚沃德斯科技有限公司 | Intelligent energy-saving control system and method for air conditioner cooling system based on neural network |
| CN110874685A (en) * | 2018-09-04 | 2020-03-10 | 中国计量科学研究院 | Intelligent electric energy meter running state distinguishing method and system based on neural network |
| US20200410691A1 (en) * | 2017-12-01 | 2020-12-31 | Koninklijke Philips N.V. | Segmentation system for segmenting an object in an image |
| US20210064988A1 (en) * | 2019-02-20 | 2021-03-04 | Dalian University Of Technology | Reliability calculation method of the thermal error model of a machine tool based on deep neural network and the monte carlo method |
| CN112991418A (en) * | 2021-03-09 | 2021-06-18 | 北京地平线信息技术有限公司 | Image depth prediction and neural network training method and device, medium and equipment |
| CN113872328A (en) * | 2021-09-26 | 2021-12-31 | 国网安徽省电力有限公司铜陵供电公司 | Transformer substation remote intelligent inspection method and system based on neural network |
| US20220004863A1 (en) * | 2020-07-01 | 2022-01-06 | International Business Machines Corporation | Confidence classifiers for diagnostic training data |
-
2022
- 2022-02-28 CN CN202210191927.2A patent/CN114548311B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010055303A (en) * | 2008-08-27 | 2010-03-11 | Denso It Laboratory Inc | Learning data management device, learning data management method and air-conditioner for vehicle, and control device of apparatus |
| US20200410691A1 (en) * | 2017-12-01 | 2020-12-31 | Koninklijke Philips N.V. | Segmentation system for segmenting an object in an image |
| WO2020030722A1 (en) * | 2018-08-09 | 2020-02-13 | Iee International Electronics & Engineering S.A. | Sensor system including artificial neural network configured to perform a confidence measure-based classification or regression task |
| CN110874685A (en) * | 2018-09-04 | 2020-03-10 | 中国计量科学研究院 | Intelligent electric energy meter running state distinguishing method and system based on neural network |
| US20210064988A1 (en) * | 2019-02-20 | 2021-03-04 | Dalian University Of Technology | Reliability calculation method of the thermal error model of a machine tool based on deep neural network and the monte carlo method |
| CN109919302A (en) * | 2019-02-28 | 2019-06-21 | 新华三技术有限公司 | A kind of neural network training method and device |
| CN110848896A (en) * | 2019-11-12 | 2020-02-28 | 深圳孚沃德斯科技有限公司 | Intelligent energy-saving control system and method for air conditioner cooling system based on neural network |
| US20220004863A1 (en) * | 2020-07-01 | 2022-01-06 | International Business Machines Corporation | Confidence classifiers for diagnostic training data |
| CN112991418A (en) * | 2021-03-09 | 2021-06-18 | 北京地平线信息技术有限公司 | Image depth prediction and neural network training method and device, medium and equipment |
| CN113872328A (en) * | 2021-09-26 | 2021-12-31 | 国网安徽省电力有限公司铜陵供电公司 | Transformer substation remote intelligent inspection method and system based on neural network |
Non-Patent Citations (1)
| Title |
|---|
| 张超然，吕余海: "基于置信度神经网络的机载武器攻击区拟合算法", 《弹箭与制导学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116503229A (en) * | 2023-06-27 | 2023-07-28 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipe network inspection method, internet of things system and storage medium |
| CN116503229B (en) * | 2023-06-27 | 2023-09-26 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipe network inspection method, internet of things system and storage medium |
| CN116717734A (en) * | 2023-08-10 | 2023-09-08 | 牧马人(山东)勘察测绘集团有限公司 | Data monitoring method for steady-state operation of natural gas pipeline |
| CN116717734B (en) * | 2023-08-10 | 2023-10-20 | 牧马人(山东)勘察测绘集团有限公司 | Data monitoring method for steady-state operation of natural gas pipeline |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114548311B (en) | 2022-12-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114548311B (en) | Hydraulic equipment intelligent control system based on artificial intelligence | |
| CN111596604A (en) | Intelligent fault diagnosis and self-healing control system and method for engineering equipment based on digital twinning | |
| CN111444953B (en) | Sensor fault monitoring method based on improved particle swarm optimization algorithm | |
| CN115016276B (en) | Intelligent water content adjustment and environment parameter Internet of things big data system | |
| CN108549866B (en) | Remote sensing airplane identification method based on dense convolutional neural network | |
| CN109695533B (en) | Automatic intelligent setting method for neutral position of speed regulator hydraulic servo system | |
| CN114397043B (en) | Multi-point temperature intelligent detection system | |
| CN115654208B (en) | Adjusting valve opening monitoring method and system based on image recognition | |
| CN112685857B (en) | Bearing residual life prediction method based on transfer migration | |
| CN114036506A (en) | Method for detecting and defending false data injection attack based on LM-BP neural network | |
| CN116865343B (en) | Model-free self-adaptive control method, device and medium for distributed photovoltaic power distribution network | |
| CN111673026B (en) | Online control method and control system for pressing process of forging press | |
| CN116664846B (en) | Method and system for realizing 3D printing bridge deck construction quality monitoring based on semantic segmentation | |
| CN110889646B (en) | Method for checking correctness of operating parameters of generator set adapting to working condition change | |
| CN111863153A (en) | Method for predicting total amount of suspended solids in wastewater based on data mining | |
| CN116628836A (en) | Data-driven supercritical airfoil performance prediction method, system and medium | |
| CN114397809B (en) | Intelligent control system for material weighing big data detection and packaging | |
| CN114880930A (en) | Entity equipment digital twin modeling simulation prediction method based on online learning | |
| CN110045716B (en) | Method and system for detecting and diagnosing early fault of closed-loop control system | |
| CN114415503A (en) | Temperature big data thing networking detects and intelligent control system | |
| CN108764337A (en) | Underwater robot propeller fault degree discrimination method based on relative grey correlative degree boundary constraint | |
| CN112115640A (en) | Control valve viscosity detection method based on transfer learning | |
| CN112116003B (en) | Self-adaptive dynamic fault diagnosis method and system for complex sensor network | |
| CN112748717B (en) | Actuator fault diagnosis method based on evidence fusion | |
| CN117330208B (en) | Sensor temperature compensation method, system and storage medium based on MoblieNet neural network and bilinear interpolation |
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 |