CN111830032B - Online multi-parameter hydraulic oil intelligent sensor device based on image sensing - Google Patents
Online multi-parameter hydraulic oil intelligent sensor device based on image sensing Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N11/00—Investigating flow properties of materials, e.g. viscosity, plasticity; Analysing materials by determining flow properties
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
- G01N15/0205—Investigating particle size or size distribution by optical means, e.g. by light scattering, diffraction, holography or imaging
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- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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- G01N2015/1024—
Abstract
The on-line multi-parameter hydraulic oil intelligent sensor device based on image sensing comprises a hydraulic system on-site oil product detection subsystem, a computer cloud data processing, storage subsystem and a laboratory data analysis subsystem, wherein the hydraulic system on-site oil product detection subsystem is arranged on a hydraulic pipeline and comprises an image sensor and a detection controller, a lens is arranged between the image sensor and the hydraulic pipeline, an irradiation light source and a thermosensitive material are further arranged in the hydraulic pipeline, one end of the thermosensitive material is fixed, the other end of the thermosensitive material can be observed by the image sensor through the lens, and image data are transmitted into the computer cloud data processing, storage subsystem and the laboratory data analysis subsystem.
Description
Technical Field
The invention relates to the technical field of oil detection, in particular to an online multi-parameter hydraulic oil intelligent sensor device based on image sensing.
Background
A complete hydraulic system consists of five parts, namely power elements, actuators, control elements, auxiliary elements (accessories) and hydraulic oil. Hydraulic systems can be divided into two categories, hydraulic transmission systems and hydraulic control systems. The hydraulic transmission system takes power transmission and movement as main functions. Hydraulic control systems are designed to provide a hydraulic system output that meets certain performance requirements (particularly dynamic performance), and are generally referred to as hydraulic transmission systems. The quality of a hydraulic system depends on the rationality of the system design, the performance of the system components, the pollution protection and treatment of the system, and the last point is particularly important. In recent years, the domestic hydraulic technology is greatly improved, but how to detect the hydraulic system on line in real time, especially detect the oil products in the running process of the hydraulic system, and collect relevant data for big data analysis is not realized.
The existing oil product detection method of the hydraulic system is generally divided into sampling method detection and non-contact method detection. The method has the advantages that various oil properties can be checked in the laboratory, such as hydraulic oil particle inspection, hydraulic oil chemical property inspection, water content inspection and the like, the method for detecting the hydraulic oil in the laboratory has the defects of screening distribution, microscopic generation, an electric induction method, a sedimentation method and the like, the laboratory detection has the defects that a user needs to establish a relatively complex laboratory, special inspectors are required to be equipped, and physical quantities affecting the hydraulic system cannot be checked, such as hydraulic oil temperature, hydraulic oil pressure and other parameters during the operation of the hydraulic system, and the physical parameters are important parameters for judging the operation of the hydraulic oil; the non-contact detection method of hydraulic oil is a laser measurement method, which comprises the steps of irradiating hydraulic oil by a beam of laser through a transparent body, diffracting the laser after passing through the hydraulic oil, converting a diffracted light signal into an electric signal by a photoelectric converter behind the hydraulic oil, calculating the particle diameter of oil product pollutants by using a Stokes principle, counting and finally calculating the pollutant particle number and pollution level of the hydraulic oil, wherein the hydraulic oil particle meter can detect a hydraulic system in real time on site, but in practical application, the particle meter detector can only detect one parameter of the pollutant particles, and can not distinguish air and water particles due to the laser covering principle, so that misjudgment is easy. When detecting other parameters of the hydraulic system in real time, other corresponding sensors, such as a viscosity online detection sensor of hydraulic oil, an online water content monitoring sensor in oil liquid and an online flow monitoring sensor, are also required to be installed, and a detection system, including a computer data acquisition system and a data processing system, is also required to be used. The detection method has the defects that the realization of on-line monitoring and installation of a set of hydraulic system is very complex, the cost of the detection device is very high, and professional technicians are also required during the detection operation. This method is time consuming, laborious and costly to detect the hydraulic system in real time. Many small hydraulic systems or large numbers of mobile equipment with hydraulic systems are difficult or impossible to implement on-line monitoring. The market needs an integrated hydraulic system on-line monitoring device which is simple and convenient to install, maintenance-free and free from maintenance of professional technicians.
Disclosure of Invention
In order to solve the problems, the invention aims to provide an online multi-parameter hydraulic oil intelligent sensor device based on image sensing so as to solve the problems in the oil product detection process.
The technical scheme adopted for solving the technical problems is as follows: the invention discloses an online multi-parameter hydraulic oil intelligent sensor device based on image sensing, which comprises a hydraulic system on-site oil detection subsystem, a computer cloud data processing, storage subsystem and a laboratory data analysis subsystem, wherein the hydraulic system on-site oil detection subsystem is arranged on a hydraulic pipeline and comprises an image sensor and a detection controller, a lens is arranged between the image sensor and the hydraulic pipeline, an irradiation light source and a thermosensitive material are also arranged in the hydraulic pipeline, one end of the thermosensitive material is fixed, the other end of the thermosensitive material can be observed by the image sensor through the lens, and the image data is transmitted into the computer cloud data processing, storage subsystem and the laboratory data analysis subsystem.
Further, the image sensor is a CCD camera.
Further, the lens is a micro lens.
Further, the heat sensitive material is a heat sensitive metal strip.
Further, the computer cloud data processing and storing subsystem comprises a data acquisition and storage module, a data processing and analysis module and a data release module.
Further, the laboratory data analysis subsystem uses expert system software for analysis.
Further, the image sensor comprises a sensor base, an oil inlet channel is formed in one end of the sensor base, an oil outlet channel is formed in the other end of the sensor base, a module base is installed on the sensor base in a matched mode, a through hole communicated with the oil inlet channel and the oil outlet channel is formed in the module base, a thermosensitive metal strip with one fixed end is installed in the through hole, an LED light source and a light-transmitting plate are correspondingly arranged at the lower side of the thermosensitive metal strip, a microscope lens and a CCD module which are vertically arranged are installed on the upper side of the thermosensitive metal strip, the microscope lens and the CCD module are installed in the module base, a control module is further installed on the module base, and data, a power interface and an antenna interface are formed on the control module.
Further, an annular gap valve core is arranged at the initial end of the oil outlet channel, and a valve core limiting screw is also arranged in the oil outlet channel.
The invention has the positive effects that: the invention relates to an image sensing-based online multi-parameter hydraulic oil intelligent sensor device, which is characterized in that in a hydraulic system, related characteristics are obtained through a related sensor: physical indexes such as pollutant granularity, flow, temperature, and attribute of pollutant particles. And then, timely transmitting related PCs and control centers, and summarizing the cloud data center by using the mobile phone APP. And then relevant data are arranged, and an expert analysis system carries out health diagnosis on the hydraulic system through means such as a mathematical algorithm, comparison and the like to obtain relevant conclusions and provide relevant engineering design and service personnel. The method can realize real-time digital monitoring, establish an early warning system, predict the health state of the hydraulic system through expert system big data analysis, estimate the service lives of the hydraulic pump, the hydraulic valve, the oil cylinder, the sealing element and the like, and pre-judge faults, thereby ensuring the effective operation of the hydraulic system. This approach overcomes the drawbacks of the current user's need to build a hydraulic laboratory and test with a skilled technician or the need for a complex and expensive detection system.
Drawings
FIG. 1 is a schematic diagram of the structure of the present invention;
FIG. 2 is a schematic diagram of the hydraulic system field oil detection subsystem;
FIG. 3 is a schematic diagram of the structure of an image sensor;
FIG. 4 is an enlarged partial schematic view of I in FIG. 3;
FIG. 5 is a schematic diagram of the calculation of particle size using a progressive scan method;
FIG. 6 is a schematic diagram of the calculation of contaminant content;
FIG. 7 is a schematic diagram of a first bitmap image obtained by a computer through image decompression processing to obtain two consecutive bitmap images from a color CCD;
FIG. 8 is a schematic diagram of a second bit map image obtained by a computer through image decompression processing to obtain two consecutive bit map images by a color CCD;
FIG. 9 is a schematic diagram of an image bitmap screen formed by combining the two images of FIGS. 7 and 8;
FIG. 10 is a schematic view of a concentric annular gap;
FIG. 11 is a schematic diagram of the principle of hydraulic oil temperature detection;
FIG. 12 is a two-dimensional graphical result of a particle image experiment;
FIG. 13 is a schematic view of a three-dimensional envelope volume;
FIG. 14 is a schematic illustration of a water-in-oil droplet pretreatment;
FIG. 15 is a schematic diagram of the boundary of a determination of water droplets;
FIG. 16 is a schematic diagram of the scanning method to determine the number of widths of the contaminant in the x-direction of each row;
FIG. 17 is a two-dimensional graphical result of a particle image experiment;
FIG. 18 is a schematic view of a three-dimensional envelope volume;
FIG. 19 is a schematic diagram showing the results of plotting the moisture content of each of the different emulsion states in the same three dimensions;
fig. 20 is a schematic diagram of structural connection of a hydraulic system detection computer cloud platform system.
In the figure, a CCD module 2 micro-lens 3 module base 4 sensor base 5 oil inlet channel 6 thermosensitive metal strip 7LED light source 8 light-transmitting plate 9 annular gap valve core 10 valve core limiting screw 11 oil outlet channel 12 control module 13 data and power interface 14 antenna interface 15 hydraulic pipeline 16 hydraulic system on-site oil product detection subsystem 17 computer cloud data processing and storage subsystem 18 laboratory data analysis subsystem 19 detection controller 20 lens 21 irradiates light source 22 thermosensitive material 23 image sensor.
Detailed Description
The invention discloses an on-line multi-parameter hydraulic oil intelligent sensor device based on image sensing, which is shown in fig. 1 and comprises a hydraulic system on-site oil product detection subsystem 16, a computer cloud data processing and storing subsystem 17 and a laboratory data analysis subsystem 18, wherein the hydraulic system on-site oil product detection subsystem 16 is arranged on a hydraulic pipeline 15. As shown in fig. 2, the on-site oil product detection subsystem 16 of the hydraulic system comprises an image sensor 23 and a detection controller 19, a lens 20 is installed between the image sensor 23 and the hydraulic pipeline 15, an irradiation light source 21 and a heat-sensitive material 22 are also installed in the hydraulic pipeline 15, one end of the heat-sensitive material 22 is fixed, the other end of the heat-sensitive material 22 can be observed by the image sensor 23 through the lens 20, and image data is transmitted into the computer cloud data processing and storing subsystem 17 and the laboratory data analysis subsystem 18.
Wherein the image sensor 23 may be an existing CCD camera, the lens 20 may be an existing micro-lens 2, and the heat sensitive material 22 may be an existing heat sensitive metal strip 6.
The hydraulic system field oil detection subsystem 16 connected with the hydraulic pipeline 15 is shown in fig. 2, in the device, a hydraulic oil channel is provided, two ends of the channel are provided with a hydraulic oil inflow interface and a hydraulic oil outflow interface, an observation hole is formed in the middle of the channel, the observation hole is encapsulated by a transparent body A, the hydraulic oil is ensured not to flow out of the observation hole, and the transparent body A has certain pressure resistance capability. A light reflecting plate B is arranged at the bottom of a viewing hole, a light source is arranged on the side of the viewing hole, a microscope is arranged above the viewing hole, a CCD is arranged on an upper end viewing mirror of the microscope, the magnification of the microscope can be arbitrarily selected, light emitted by the light source irradiates flowing hydraulic oil through a transparent body A of the viewing hole, then the hydraulic oil passes through the reflecting plate B in a reflecting mode and then passes through the transparent body A to enter an objective lens of the microscope, so that an amplified flowing hydraulic oil picture in a hydraulic channel can be seen on the viewing mirror of the microscope, a color CCD is arranged at the upper end of the viewing mirror of the microscope, the flowing hydraulic oil picture is converted into a video electric signal through the CCD, the video electric signal can be displayed on a display screen through a computer, and the video electric signal can be remotely transmitted through the Internet.
The other structure of the device is that the reflecting plate B in the device is removed and replaced by a transparent body B, a luminous light source is arranged below the transparent body B, other structures are unchanged, the light source passes through the transparent body B, passes through hydraulic oil and then passes through the transparent body A to enter the objective lens of the microscope, and thus, the hydraulic oil picture in the hydraulic oil channel can be observed in the observation mirror of the microscope.
A thermosensitive telescopic material C is arranged on the reflecting plate or the transparent body B, one end of the thermosensitive material is fixed on the wall of the hydraulic oil channel, and the other end of the thermosensitive material is aligned with the objective lens of the microscope and is in a free state. The free end of the thermosensitive telescopic material can be seen by the observation mirror of the microscope, and when the temperature of the hydraulic oil changes, the length of the free end of the thermosensitive material observed by the microscope changes and is transmitted to a CCD picture.
The CCD video picture is transmitted to a computer memory, and a plurality of parameters of the hydraulic system for online detection of physical parameters can be obtained through image processing, namely, the particle number of pollutants, the flow rate of hydraulic oil, the material property of the pollutants of the hydraulic oil, and the temperature of the hydraulic oil are 1, 2. The CCD image information uses color bitmap information including the image scanning array and gray and chromaticity values for each scanning pixel. And the detection device controller performs preliminary arrangement compression on the CCD image data and then transmits the image data to the computer cloud platform database through wireless communication or the Internet.
The computer cloud data processing and storing subsystem 17 is used for acquiring, storing and processing the acquired data of each hydraulic system through a public network system or a wireless communication network system by utilizing the existing public computer cloud system, and is responsible for issuing oil product detection data, namely, a complete hydraulic system detection product table is established in the computer cloud system, and thousands of hydraulic systems sleeved at different places can be monitored on line by utilizing the platform through the data acquisition module. The computer cloud data processing and storing subsystem consists of three modules: the system comprises a data acquisition and storage module, a data processing and analysis module and a data release module.
The laboratory data analysis subsystem 18 performs data analysis on the image sensing data detected from the on-site hydraulic system, and after the sensor senses the physical parameters of the oil product of the on-site hydraulic system, the laboratory also performs analysis on the image sensing data by using a software expert system.
The expert system has three ways of data entry: 1. the pressure, temperature and pollution particle characteristic parameters of a hydraulic system are acquired by an on-site sensor of the computer cloud platform, and the water content of hydraulic oil is acquired; 2. the input data of the user mainly comprise parameters which cannot be acquired by a sensor of the on-site oil product detection subsystem and are important for system state analysis, such as the original design flow, pressure, the code number of hydraulic oil products and the like of the system; 3. the input data of laboratory technicians mainly comprise characteristic parameter time interval selection, missing or incomplete data in a database, and the laboratory is required to perform experimental verification or simulation and input result data and other data requiring manual intervention.
The expert system derives and compares the data according to the input data, characteristic parameters and the like according to a preset condition program and expert knowledge database data of the expert system, derives result data conforming to rules or conditions from the result data, realizes the detection of the running state of the hydraulic system, realizes the diagnosis when the hydraulic system is abnormal, and realizes the prediction and diagnosis of the early failure of the hydraulic system.
In expert systems, it is essential to build a relatively complete expert knowledge database. Expert knowledge databases are a combination of long-term experience accumulation and expertise and achievements of the expert. The expert knowledge database contains the components, technical characteristics and the like of a typical hydraulic circuit; technical parameters, structural compositions, technical characteristics, main manifestations and characteristics of faults and the like of various types of hydraulic elements, hydraulic accessories and hydraulic oil products; there are also data on the material characteristics, shape characteristics, surface characteristic appearance, etc. of the contaminant particles. In the use process of the expert knowledge database, the discovered deviation data is corrected in time, and the discovered missing data is supplemented in time, which is one of the functions of the expert system.
After the expert system receives the pressure, temperature, pollution particle characteristic parameters, the water content of the hydraulic oil and the viscosity of the hydraulic oil, which are acquired by the on-site sensor of the computer cloud platform, the expert system respectively processes the data to form a general system state conclusion, wherein the conclusion mainly is the change condition of the pressure, temperature and pollutant particles of the hydraulic system in a time period, and whether the current system is abnormal is found by comparing the change condition with an expert knowledge database of the expert system. Outputting the current state result of the hydraulic system and the prejudging of the change trend of the state of the hydraulic system for users to use.
If the user wishes to obtain more comprehensive working conditions of the hydraulic system, the expert system can deduce the detailed state analysis result of the specific hydraulic system through program deduction and calculation by inputting necessary characteristic hydraulic system data by the user. The method comprises the changing conditions of pressure, temperature and flow parameters of the hydraulic system in the same time area, the changing conditions of pollutant particles and the changing conditions of the working efficiency of the hydraulic system in the time area; the components that may be in question and the cause of the problem are identified for the occurrence of the fault. And outputting a detailed result of the current state of the hydraulic system, and predicting the change trend of the state of the hydraulic system for a user to use by using elements and reasons for generating faults.
Thus, a complete hydraulic system detection computer cloud platform system is established, and thousands of hydraulic systems in different places can be monitored on line by using the platform through the data acquisition module. The user only needs to install a set of hydraulic system on-site detection subsystem to finish the health detection of the hydraulic system, so that the investment scale is greatly saved, and especially the health detection of the mobile hydraulic system such as the hydraulic system of engineering construction machinery, the hydraulic system in the ship running process, the hydraulic system of forging metallurgy and the like is very convenient and quick.
Further, as shown in fig. 3 and 4, the image sensor 23 includes a sensor base 4, an oil inlet channel 5 is provided at one end of the sensor base 4, an oil outlet channel 11 is provided at the other end of the sensor base 4, a module base 3 is mounted on the sensor base 4 in a matching manner, a through hole communicated with the oil inlet channel 5 and the oil outlet channel 11 is provided in the module base 3, a thermal metal strip 6 with one fixed end is mounted in the through hole, an LED light source 7 and a light transmitting plate 8 are correspondingly provided at the lower side of the thermal metal strip 6, a vertically arranged micro lens 2 and a CCD module 1 are mounted at the upper side of the thermal metal strip 6, the micro lens 2 and the CCD module 1 are mounted in the module base 3, a control module 12 is further mounted on the module base 3, and a data and power interface 13 and an antenna interface 14 are provided on the control module 12. An annular gap valve core 9 is arranged at the initial end of the oil outlet channel 11, and a valve core limiting screw 10 is also arranged in the oil outlet channel 11.
The micro lens 2 of the detection sensor is fixed on the module base 3, and the objective lens is positioned above the oil passing channel of the sensor and is contacted with the flowing detected oil; the CCD module 1 is also arranged on the base module, and an image sensor on the CCD module is arranged right above the ocular lens of the microscope and can receive image signals from the microscope. A thermosensitive metal strip 6 and a reflecting plate 8 for detecting temperature are arranged below a microscope objective, an annular gap valve core 9 is arranged at the initial end of an oil outlet channel 11, a certain gap is formed between the cylindrical surface of the valve core and the oil outlet channel, and annular gap flow is formed when hydraulic oil flows through the annular gap flow. The reflector and the valve core limiting screw 10 simultaneously play a role in limiting the movement of the valve core along the oil passage. An LED light source 7 for illumination is arranged between the oil inlet channel 5 and the oil outlet channel and on the side wall of the oil channel below the microscope objective lens. The module base is fixed to the sensor base 4 by screws. A control module 12 is also mounted on top of the module base. The control module is provided with an interface 13 for power and data communication to the sensor and an antenna interface 14 for wireless data communication.
The invention discloses a method for realizing on-line health monitoring by a device, which is realized by the following technical scheme:
firstly, performing field detection on a hydraulic system through an image sensor to obtain detection data of the hydraulic system, secondly, transmitting the obtained detection data to a data cloud center for data processing and storage, and finally, performing data arrangement, analysis and comparison in a laboratory data analysis system to perform health diagnosis on the hydraulic system;
the detection data of the hydraulic system is one or more of hydraulic oil viscosity, hydraulic oil temperature, pollutant granularity and pollutant material property.
The processing calculation method for the image signal comprises the following steps:
transmitting CCD video pictures to a computer memory, and calculating a plurality of parameters of hydraulic oil on-line detection physical parameters in a hydraulic system through image digital processing: (1) Viscosity of hydraulic oil, particle number and particle size of pollutants, temperature of hydraulic oil, material property of hydraulic oil pollutants and water content of hydraulic oil are all 3, and the water content of hydraulic oil is 5.
Example 1
When the detection data of the hydraulic system are particle number and particle size, the hydraulic system is processed according to the following steps:
the method for counting and calculating the size of the pollutant particles is obtained by an image processing method, and is divided into two steps, wherein the first step is an image preprocessing step, firstly, a gray threshold D0 of an image is set (the gray threshold is adjusted by general software to realize the determination of Pengbird), because hydraulic oil is a light-transmitting liquid pollutant and is a shading body, gray levels displayed by the pollutant and the hydraulic oil in the image are different, in a bitmap of a color CCD, each pixel has four parameters R (red), G (green), B (blue) and gray D, and the gray level is taken as the gray threshold D0 of the pollutant pixel with the gray level more than or equal to the gray level. And (3) setting the pixel value larger than the gray threshold value D0 in the original image as D0 and setting the pixel value smaller than the gray threshold value D0 to zero, so that an image with only two gray scales after image processing can be obtained.
In the second step, the size of the particles is calculated by using a progressive scanning method, as shown in fig. 5, a pixel point of a CCD is used as a minimum particle unit of the contaminant, the gray matrix of the whole image bitmap is D (m, n), m is the number of abscissas (x-direction) of the whole bitmap, and n is the number of ordinates (y-direction) of the whole bitmap. Scanning is carried out by adopting a scanning method, wherein the initial y direction is 1, the X direction is gradually increased by 1, when the gray level of the point scanned to the X1 is D0, the point is marked as a pollutant, then the X coordinate is increased by 1, if the gray level value is D0, the X coordinate is continuously increased by 1 until the gray level value of the scanning point is 0, the boundary point of the pollutant is reached, the accumulated number in the X direction is the width of the pollutant in the first row of the matrix D (m, n) and is stored in an xn memory. Then adding 1 in the y direction, judging whether the gray value of each pollutant pixel point corresponding to the previous row is 0, judging whether the gray value of the adjacent pixel point of the point is 0, if so, marking the point as the boundary of the pollutant in the row, and accumulating the width of the pollutant in the row; the y direction is increased by 1.
The method is looped until there are no pixels in the row with gray level value D0 threshold. The principle of completing the scanning is that the gray scale of each pixel point with the gray scale value of D0 threshold value is judged to the gray scale of the pixel point adjacent to the left and right, if D0 is used, the continuous contamination is indicated, and if 0 is used, the boundary of the contamination is indicated;
and accumulating and calculating the width number of the pollutants in the x direction of each row, taking the maximum value xz, and accumulating the maximum value yz of the pollutants in the y direction. The two are then maximized to the maximum diameter of the contaminant, e.g., the following plot, where xz=6, yz=5,
taking the maximum xz=6, the diameter of the contaminant is 6 (pixels), and the size of each pixel is determined by adding the magnification of the microscope to the matrix pixels of the CCD.
When the scanning of the pixel point of the pollutant is completed, the gray value of the corresponding pixel point is set to 0, and then the scanning of the next pollutant is started by the same method until the gray values of the image matrix D (m, n) are all 0. The calculation of the particle number and particle size of the pollutant in the picture is completed after all the cycles.
Calculation of the contaminant content as shown in fig. 6, the volume of the hydraulic oil corresponding to the image matrix D (m, n) is taken as a unit, and different image matrices Di (m, n) are recorded and calculated along with the flow of the hydraulic oil, and each image matrix Di (m, n) is not overlapped. And the pollutant particle number of each different picture and the volume of the hydraulic oil are accumulated to calculate the pollutant content, and along with the increase of the accumulated number, the calculation accuracy of the pollutant particle number content is improved.
Content of contaminants = number of contaminant particles (cumulative number)/volume cumulative number of hydraulic oil;
example 2
When the detection data of the hydraulic system is the viscosity of hydraulic oil, the processing is carried out according to the following steps:
the computer obtains two continuous bitmap pictures obtained by the color CCD through image decompression processing, as shown in fig. 7 and 8, the images are stored in a matrix form in T (m, n), ti (m, n), m represents the positions of columns in each row, n represents the positions of rows in each column, m can be called x-axis, n can be called y-axis, and each pixel has four parameter values of R (red), G (green), B (blue) and gray D.
Three pollutants are taken as marked points a, b and c in the image of the figure (1-3-1), and the coordinates of the three points in the image bitmap are respectively: a (xa, ya), b (xb, yb), c (xc, yc), xa, xb, xc are the abscissa of the three markers a, b, c, respectively, and the coordinates of the contaminant markers a1, b1, c1 at the image bitmap are: a1 (xa 1, ya 1), b1 (xb 1, yb 1), c1 (xc 1, yc 1), xa1, xb1, xc1 are the abscissa of the three markers a1, b1, c1, respectively. The two image bitmaps of fig. 7 and 8 are combined to form an image bitmap picture, as shown in fig. 9, the connection lines a, a1, the connection lines b, b1 and the connection lines c, c1 are running tracks of the pollutants a, b and c respectively, the transverse coordinate (x direction) difference of the running tracks of the three pollutant marks a, b and c is taken respectively,
Δxa= (xa 1-xa), Δxb= (xb 1-xb), Δxc= (xc 1-xc), then taking the average value Δx= (Δxa+Δxb+Δxc)/3,
the Δx can be used as the moving distance of hydraulic oil in a ring gap in the time difference Δt from the image 1 to the image 2, the moving speed v=Δx/Δt of the hydraulic oil, the oil seal cross-sectional area s=pi R2-pi R2 (the large circle reduces the circle area) of the hydraulic oil, and the flow q=v×s of the hydraulic oil in unit time is finally calculated.
According to the hydrodynamic formula of hydraulic oil:
flow rate of hydraulic oil through annular gap
Pressure difference
Viscosity of hydraulic oil
S is the oil seal sectional area through which hydraulic oil flows;
d is the diameter of the annular gap hole;
delta is the gap amount;
the other fixed values are designed by structural dimensions, and are marked in the concentric annular gap schematic diagram of fig. 10.
Example 3
When the detection data of the hydraulic system is the temperature of hydraulic oil, the hydraulic oil is processed according to the following steps:
one end of a thermosensitive material wire is fixed on the wall of the hydraulic oil channel, and the other end is aligned with the objective lens of the microscope, so that the thermosensitive material wire is in a free state. The free end of the thermosensitive telescopic material can be seen by a microscope, the temperature scale is marked at the bottom end of the picture by using computer software, when the temperature of hydraulic oil changes, the length of the free end of the thermosensitive material filament observed by the microscope changes, and the free end of the thermosensitive material filament can stay on different temperature scales along with the temperature change. And further reflects the real-time temperature of the hydraulic oil in the field as shown in fig. 11.
The variable quantity of the thermosensitive telescopic material filaments in the CCD picture is used as the reference quantity of the temperature change of hydraulic oil.
Hydraulic oil temperature t=reference temperature t0+Δl is the temperature coefficient of thermal expansion of the thermosensitive material.
Example 4
When the detection data of the hydraulic system is the attribute of the pollutant material, the method comprises the following steps:
the detection of the property of the hydraulic oil pollutants adopts an image detection method, the chromatographic analysis is carried out by using pollutant images captured by a color CCD, and the material properties of the pollutants are analyzed to determine whether the pollutants are rubber, copper or stainless steel. As well as its content and size.
Chromatographic analysis method: in the process of digital conversion of color CCD signals, four parameter values, R (red), G (green), B (blue) and gray scale are used for each pixel point, so when a certain pollutant is analyzed by an image (the method 2 is detected), the pixels of the pollutant can form an array table, as shown in the table 1:
TABLE 1
The analysis method adopts a comparison method: the same sensing device was set up in the laboratory with different particles dissolved in the hydraulic oil, for example particles of the gasket rubber dissolved in the hydraulic seed, and a set of image signals containing the rubber particles were obtained by the sensing device, forming a set of standard rubber contaminant image arrays, as shown in table 2.
TABLE 2
According to the chromaticity theory and the ambiguity theory, the chromaticity of a substance is basically consistent, the chromaticity change is continuous, and the pixel chromaticity array of the rubber pollutant obtained through experiments is an array which continuously changes within a certain range, that is,
r (red) is a continuously variable array within a certain range,
g (green) is a continuously variable array over a range,
r (blue) is a continuously variable array within a certain range,
r, G, B is formed into three two-dimensional coordinates, R, G two-dimensional coordinate graph, R, B two-dimensional coordinate graph and G, B two-dimensional coordinate graph, each experimental data point is drawn to form an experimental envelope line, a series of experimental data can be obtained by repeating various rubber particle image experiments to form an experimental envelope line, and the experimental envelope line is shown in fig. 12.
The three two-dimensional patterns are combined to form a three-dimensional envelope pattern, called experimental data R, G, B chromaticity envelope, as shown in fig. 13.
The three-dimensional graph is used for judging boundary conditions of the on-site collected pollutants, the pixels of the pollutants are formed into an array table 1 and also drawn into a chromaticity envelope body taking R, G, B as a three-axis, and if the chromaticity envelope body is in the experimental data R, G, B chromaticity envelope body, the pollutants are judged to be rubber pollutants.
By setting up a copper contaminant R, G, B chromaticity envelope and a steel contaminant R, G, B chromaticity envelope in the same manner, it is possible to distinguish whether the contaminants are copper or steel, respectively.
The property of the hydraulic oil pollutant is judged to be a characteristic which is not available in the existing field detection hydraulic oil sensor, the property of the pollutant is judged to be an important index for judging the field hydraulic system, and if the rubber particles in the pollutant are increased, the fact that the sealing rubber of the hydraulic system is worn or damaged can cause unstable power pressure of the hydraulic system or deviate from a design value is indicated. If copper contamination increases, this indicates that the sliding parts of the hydraulic system are worn or damaged. If the contamination of the steel increases, this indicates that the steel body of the system is subject to wear or damage. The detection of these three pollutants provides the necessary conditions and basis for the expert system to diagnose the operation of the hydraulic system in the field.
Example 5
When the detection data of the hydraulic system is the water content of the hydraulic oil, the hydraulic oil is processed according to the following steps:
the index of the water content of the hydraulic oil is an important index of the hydraulic oil, water exists in the hydraulic oil in two states, one is in a suspension state, the suspension state is a water-in-oil drop state, the water-in-oil state is caused by the same surface tension of the water, and the detection method of the state is the same as the method of calculating the number of pollutant particles and the particle size, and the pretreatment method of the image is different.
The first step, the image preprocessing method for detecting the water drop content of the oil pocket is to set the gray value D1 of the water drop pixel, and the gray value of the oil pocket is smaller than the gray value of the oil, so that two conditions of the oil pocket are judged, one is round in shape, and the gray value is smaller than the gray value D1 of the hydraulic oil. The gray value of the pixel with the gray value smaller than D1 is zero, and the gray values of the rest pixels are set to D1, so that an image with only two gray values after image processing can be obtained, as shown in a schematic diagram of FIG. 14 after water-in-oil droplet pretreatment.
In the second step, the size of the water drop is calculated by using a progressive scanning method, as shown in fig. 15, a pixel point of one CCD is taken as a minimum particle unit of the water drop, the gray matrix of the whole image bitmap is D (m, n), m is the number of abscissas (x direction) of the whole bitmap, and n is the number of ordinates (y direction) of the whole bitmap. Scanning is carried out by adopting a scanning method, wherein the initial y direction is 1, the X direction is gradually increased by 1, when the gray level of the point scanned to the X1 is 0, the point is marked as a water drop, then the X coordinate is increased by 1, if the gray level value is 0, the X-direction coordinate is continuously increased by 1 until the gray level value of the scanning point is D1, the boundary point of the water drop is reached, and the accumulated number in the X direction is the width of the water drop in the first row of the matrix D (m, n) and is stored in an xn memory. Then adding 1 in the y direction, judging whether the gray value is 0 at each water drop pixel point corresponding to the previous row, judging whether the gray of the adjacent pixel point of the point is D1 at the same time, if so, marking that the point is the boundary of the water drop in the row, and accumulating the width of the water drop in the row; the y-direction is incremented by 1 and the above method is cycled until there are no pixels in the row with a gray scale value of 0. The principle of the scanning is that the gray scale of each pixel point with the gray scale value of 0 threshold value is judged for the gray scale of the pixel point adjacent to the left pixel point and the right pixel point, if the gray scale value is 0, the water drops are continuous, and if the gray scale value is D1, the boundary of the water drops is indicated.
And accumulating and calculating the width number of the pollutants in the x direction of each row, taking the maximum value xz, and accumulating the maximum value yz of the pollutants in the y direction. The maximum value is again determined as the maximum diameter of the contaminant, for example, in fig. 16, xz=6, yz=5, and xz=6 are determined by scanning, the diameter of the contaminant is 6 (pixel point), and the size of each pixel point is determined by adding the magnification of the microscope to the matrix pixel point of the CCD.
When the contaminant pixel is scanned, the gray value of the corresponding pixel is set as D1, and then the scanning of the next water drop image is started by the same method until the gray values of the image matrix D (m, n) are all D1. The calculation of the number of the water drop particles and the water drop size of the picture is completed after all the cycles.
The water content calculation in the suspended state of the water-in-oil form is based on the volume of the hydraulic oil corresponding to the image matrix D (m, n), and different image matrices Di (m, n) are recorded and calculated along with the flow of the hydraulic oil, and each image matrix Di (m, n) is not overlapped. The water content in the suspension state in the hydraulic oil is calculated by accumulating the water drop particle numbers of different pictures and calculating the ratio of the sum of the water drop volumes to the volume of the hydraulic oil, and the calculation accuracy of the water content in the suspension state in the hydraulic oil is improved along with the increase of the accumulated number.
Water content in suspension in hydraulic oil = water droplet accumulation addend/volume accumulation addend of hydraulic oil.
The second state of water in the hydraulic oil is an emulsified state, and the chromaticity of the hydraulic oil changes with the increase of the emulsified amount of the water content in the hydraulic oil, and the chromaticity is generally gradually whitened, so that the preferred method for checking the water content of the hydraulic oil is a visual inspection method. The method for detecting the water content of the water in the emulsified state of the hydraulic oil is the same as the attribute detection method of pollutants, and a chromaticity comparison method is adopted. Firstly, performing image preprocessing, namely taking an original image matrix T (m, n) to perform image preprocessing, removing pixels of pollutants and water drops in the image matrix, wherein the rest pixels are pixels of hydraulic oil, and arranging parameters of the pixels of the hydraulic oil into an array table by using four parameter values, R (red), G (green), B (basket) and gray D, namely the pixels of each hydraulic oil in the digital conversion process of a color CCD signal: table 3 was formed.
TABLE 3 Table 3
The analysis method adopts a comparison method: the same sensing device is arranged in a laboratory, hydraulic oil with different emulsification state water contents is used as a standard sample, and an image sensor is used for sampling to form a pollution image parameter array with fixed emulsification state water contents. Starting with 0.1% emulsified water content, each time the 0.1% emulsified water content was increased to a scale level, 50 image parameter arrays were experimentally formed from 0.1% to 5% emulsified water content, each image parameter array being as shown in table 4.
TABLE 4 Table 4
According to the chromaticity theory and the ambiguity theory, the chromaticity of the fixed water content in the emulsified state is basically consistent, and as the pixel points are many after the pretreatment of the image matrix T (m, n), the R (red), G (green), B (blue) and gray D parameters of each pixel point are non-coincident within a certain range, the pixel chromaticity array of the fixed water content in the emulsified state can be obtained through experiments and is a continuous change array within a certain range, that is,
r (red) is a continuously variable array within a certain range,
g (green) is a continuously variable array over a range,
r (blue) is a continuously variable array within a certain range,
r, G, B is formed into three two-dimensional coordinates, R, G two-dimensional coordinate graph, R, B two-dimensional coordinate graph and G, B two-dimensional coordinate graph, each experimental data point is drawn to form an experimental envelope line, a series of experimental data can be obtained by repeating various rubber particle image experiments, and an experimental envelope line is formed, as shown in fig. 17.
The three two-dimensional patterns are combined to form a three-dimensional envelope pattern, which is called experimental data R, G, B chromaticity envelope, as shown in fig. 18.
The three-dimensional graph is a continuously-changing three-dimensional envelope body, which is obtained by judging the boundary condition of fixed water content in the field collection emulsion state and drawing the water content in different emulsion states in the same three-dimensional mode, and is shown in the following figure 19.
And comparing the image parameter three-dimensional envelope formed by the hydraulic oil image array table 3 of the emulsified water content acquired on site with the upper chart to obtain the corresponding emulsified water content.
And adding the water content calculated in the first part of suspension state to the water content obtained by comparing the water content with the emulsified state to obtain the total water content of the field detection hydraulic oil.
Hydraulic oil water content=suspended water content+emulsified water content.
The technical scheme of the invention is not limited to the scope of the embodiments of the invention. The technical content that is not described in detail in the invention is known in the prior art.
Claims (1)
1. An online multi-parameter hydraulic oil intelligent sensor device based on image sensing is characterized in that: the system comprises a hydraulic system field oil product detection subsystem (16), a computer cloud data processing and storing subsystem (17) and a laboratory data analysis subsystem (18), wherein the hydraulic system field oil product detection subsystem (16) is arranged on a hydraulic pipeline (15), the hydraulic system field oil product detection subsystem (16) comprises an image sensor (23) and a detection controller (19), a lens (20) is arranged between the image sensor (23) and the hydraulic pipeline (15), an irradiation light source (21) and a thermosensitive material (22) are also arranged in the hydraulic pipeline (15), one end of the thermosensitive material (22) is fixed, the other end of the thermosensitive material (22) can be observed through the lens (20), and image data is transmitted into the computer cloud data processing and storing subsystem (17) and the laboratory data analysis subsystem (18); the lens (20) is a microscope lens (2);
the image sensor (23) is a CCD camera; the heat sensitive material (22) is a heat sensitive metal strip (6);
the image sensor (23) comprises a sensor base (4), an oil inlet channel (5) is formed in one end of the sensor base (4), an oil outlet channel (11) is formed in the other end of the sensor base (4), a module base (3) is mounted on the sensor base (4) in a matched mode, a through hole communicated with the oil inlet channel (5) and the oil outlet channel (11) is formed in the module base (3), a heat-sensitive metal strip (6) with one fixed end is mounted in the through hole, an LED light source (7) and a light-transmitting plate (8) are correspondingly arranged at the lower side position of the heat-sensitive metal strip (6), a microscope lens (2) and a CCD module (1) which are vertically arranged are mounted on the upper side of the heat-sensitive metal strip (6), a control module (12) is mounted on the module base (3), and a data and power interface (13) and an antenna interface (14) are formed on the control module (12);
the laboratory data analysis subsystem (18) adopts expert system software for analysis, an annular gap valve core (9) is arranged at the starting end of the oil outlet channel (11), and a valve core limiting screw (10) is also arranged in the oil outlet channel (11);
the hydraulic system on-site oil product detection subsystem (16) is provided with a hydraulic oil channel, two ends of the channel are provided with a hydraulic oil inflow interface and a hydraulic oil outflow interface, an observation hole is formed in the middle of the channel and is encapsulated by a transparent body A, a light reflecting plate B is arranged at the bottom of the observation hole, a light source is arranged at the side of the observation hole, a microscope is arranged above the observation hole, a color CCD is arranged at the upper end of the microscope observation mirror, a thermosensitive telescopic material C is arranged on the light reflecting plate B, one end of the thermosensitive material is fixed on the wall of the hydraulic oil channel, and the other end of the thermosensitive material is aligned with an objective lens of the microscope and is in a free state;
the computer cloud data processing and storing subsystem (17) is composed of three modules: the system comprises a data acquisition and storage module, a data processing and analysis module and a data release module; a complete hydraulic system detection product table is established in the computer cloud system, and thousands of hydraulic systems in different places are monitored on line by using the data acquisition module for the platform.
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