CN113758662A - Hydraulic hoist pipe connection leakproofness detecting system - Google Patents
Hydraulic hoist pipe connection leakproofness detecting system Download PDFInfo
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/38—Investigating fluid-tightness of structures by using light
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/04—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
- G01M3/24—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations
- G01M3/243—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations for pipes
- G01M3/246—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using infrasonic, sonic, or ultrasonic vibrations for pipes using pigs or probes travelling in the pipe
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/26—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors
- G01M3/28—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds
- G01M3/2807—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes
- G01M3/2823—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes using pigs or moles traveling in the pipe
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Abstract
A hydraulic hoist pipeline connection sealing detection system comprises a walking device, a detection device and a signal processing device, wherein the detection device is arranged on the walking device; the walking device is used for adhering the inner wall of the pipeline in the hydraulic pipeline and walking with adjustable expansion amplitude; the detection device) comprises a 3D structured light camera, a noise sensor, a vibration sensor and a flow sensor; the detection device is used for collecting data, and coordinate information, deformation, noise, vibration and abnormal flow information of the position where the connection sealing condition in the pipeline occurs are obtained after the collected data are processed and analyzed. The invention aims to provide a technology which can be well applied to detection of the sealing performance of a hydraulic hoist pipeline.
Description
Technical Field
The invention belongs to the technical field of hydraulic pipeline detection, and particularly relates to a technology for detecting the pipeline connection tightness of a hydraulic hoist.
Background
The gate valve hoist applied to ship locks usually adopts a hydraulic transmission type, the dragging of a working gate is realized through the reciprocating motion of a hydraulic piston rod, and the transmission medium of mechanical energy is hydraulic oil. The hydraulic transmission hoist is generally composed of a hydraulic oil pump system and an oil path switching system (control valve group), wherein the motor drives an oil pump to provide mechanical energy for the hoist, and the oil path switching system changes the inlet and outlet directions of hydraulic oil in an oil cylinder to realize the reciprocating motion and braking (holding) of a piston rod. In the three gorges ship lock, the dragging control system and the hydraulic opening and closing system of the miter gate run stably and reliably for nearly two decades, but due to long-time overload running, the working performance is reduced due to the fact that equipment is out of service for a long time, faults occur frequently, and great pressure is brought to safe and stable running of the ship lock, in recent years, the typical fault of hydraulic oil leakage is increased by 70% due to the fact that a high-pressure oil pipe and related connecting parts of the hydraulic opening and closing system are damaged and lose efficacy, normal running of the lock and a valve is greatly influenced, and normal navigation of the ship lock is also blocked.
The hydraulic opening and closing system of the mitre ship lock inverted V-shaped gate is unstable in load due to operation of the gate, particularly when the gate suddenly encounters strong wind and large waves opposite to the load direction, the gate is easy to vibrate due to pressure fluctuation and even pipeline impact caused by out-of-control speed, particularly for adapting to changes of external loads under different submerged water depths, a high-pressure pipeline of the hydraulic opening and closing system bears stress of different degrees, related parts are subjected to structural failures of different degrees such as impact damage or welding part cracking, and the like, and great risks are brought to safe and stable operation of the mitre ship lock. Once the oil pipe beside the oil cylinder or the control valve group and the oil cylinder connecting pipeline have defects to cause a large amount of hydraulic oil to leak, the emergency operation of the miter gate cannot be realized, long-time parking maintenance is required, high requirements on the skill level and labor intensity of maintenance personnel are provided, and great navigation obstacle risks exist. In recent years, servo control and inspection robots based on vision are successfully applied in various fields such as industrial assembly, ocean exploration, medical instruments and the like, and effective determination is made on a specified servo task in a self-adaptive manner according to changes of working environments so as to ensure smooth completion of work. Therefore, it is necessary to design a hydraulic hoist pipeline detection system, so as to realize the automatic remote detection of pipeline corrosion, deformation, cracks, welding seams, fatigue damage or potential defects inside the pipeline, and avoid accidents caused by further damage.
For example, patent document No. 2019112349904 discloses a full-automatic submarine pipeline inspection robot for ocean engineering, which comprises a walking vehicle body and a camera body frame, wherein a camera is arranged on the walking vehicle body, a camera body is arranged on the camera body frame, the camera body frame is fixedly arranged on the surface of the walking vehicle body through a base, a wheel-type and crawler-type combined mode is adopted as the advancing power of the walking vehicle body, and the walking wheel and the crawler are independently driven by a stepping motor, so that the walking vehicle body has enough power to walk in a submarine pipeline, a 360-degree panoramic camera is arranged on the walking vehicle body, when the walking vehicle body walks in the submarine pipeline, the 360-degree panoramic camera is used for shooting the condition inside the pipeline, when the problems of corrosion, deformation, cracks and the like occur inside the submarine pipeline, the camera body is used for shooting the condition inside the submarine pipeline, realize the dynamic and static combination of 360 degree panoramic camera and camera body in the shooting process.
The prior art cannot be directly applied to detection of the pipeline tightness of the hydraulic hoist.
Disclosure of Invention
The invention aims to provide a technology which can be well applied to detection of the sealing performance of a hydraulic hoist pipeline.
A hydraulic hoist pipeline connection sealing detection system comprises a walking device, a detection device arranged on the walking device and a signal processing device;
the walking device is used for adhering the inner wall of the pipeline in the hydraulic pipeline and walking with adjustable expansion amplitude;
the detection device comprises a 3D structured light camera, a noise sensor, a vibration sensor and a flow sensor;
the detection device is used for collecting data, and coordinate information, deformation, noise and vibration abnormal information of the position where the connection sealing condition in the pipeline occurs are obtained after the collected data are processed and analyzed.
The walking device comprises a driving device, a supporting device, an elastic device and an adjusting device;
the supporting device is used as a carrier for mounting and supporting the detection device; the adjusting device is used for adaptively adjusting the unfolding amplitude of the walking device according to the inner diameter of the hydraulic pipeline; the elastic device ensures that the walking device is reliably attached to the pipe wall; the detection device is fixedly arranged on the walking device and comprises a 3D structured light camera, a noise sensor, a vibration sensor and a flow sensor; the control device comprises a control module, a communication module, an image processing module, a data processing module and a detection output module.
The driving device is arranged at the tail end of the supporting device, an elastic device is arranged between the driving device and the supporting device, and an adjusting device is arranged in the supporting device;
the detection device is fixedly arranged on the supporting device of the walking device and comprises a 3D structured light camera, a noise sensor, a vibration sensor and a flow sensor;
the signal processing device comprises a control module, a communication module, an image processing module, a data processing module and a detection output module;
the image processing module mainly comprises a preprocessing unit, a quick identification unit and a positioning unit;
the 3D structured light camera, the noise sensor, the vibration sensor and the flow sensor in the detection device are respectively connected with the background server through the communication module in the signal processing device, detected picture, sound wave, strain and flow data information are processed by the image processing module and the data processing module, are output through the detection output module and are transmitted to the background server through the communication module, and the background server analyzes and judges the data to obtain position coordinate information, deformation, noise and vibration abnormal information of the sealing condition of the internal connection of the pipeline.
A method for detecting the connection tightness of a hydraulic hoist pipeline comprises the following steps:
step 1: the 3D structure camera collects images and preprocesses the collected images;
step 2: carrying out graying processing on the preprocessed image, establishing a template image set according to a set precision rotation model diagram, extracting a template image set outline, and carrying out binarization;
and step 3: the detection device rapidly identifies image information obtained by detection processing in the moving process of the walking device along the pipeline, and meanwhile, the noise sensor, the vibration sensor and the flow sensor acquire and process noise sound waves, strain and flow information of a specific position area, and perform data processing, drawing and output detection graphs;
and 4, step 4: judging whether the target area has a sealing defect or not according to the detection image obtained in the step 3, if so, calculating the corner position by using the minimum surrounding moment or the minimum surrounding triangle, and acquiring an angle and a center for positioning;
and 5: and 4, obtaining a plurality of groups of positioning results, calculating the speed of the walking device corresponding to the plurality of groups of positioning data according to the moment when the image is obtained before positioning, and calculating the accurate speed of the walking device by using a least square method.
In step 1, specifically, calibrating a 3D structure camera through an image processing module, converting an image coordinate system into a pixel coordinate system, converting coordinates of any point in space under the image coordinate system into coordinate values of the camera coordinate system, constructing a high-speed visual observer by using the camera, and designing the observer by using a maximum correlation entropy Kalman filtering algorithm; the preprocessing mainly comprises the steps of denoising an image before image feature extraction, observing an image feature vector containing noise by using an observer, and processing the image by combining a field mean value method, a median filtering method and a Gaussian filtering algorithm.
In step 2, the template image is collected from the images with the sealing defects, such as cracks and welding seams, and the target geometric shape features, color features and texture features are utilized to perform feature extraction on the target in the image, so as to obtain target feature information;
the method comprises the steps of establishing a matching template based on a template matching algorithm of geometric features, carrying out similarity judgment on the matching template by extracting area, edge, corner point, curvature, image moment and gradient feature information of a target object in an image, solving a translation matrix and a rotation matrix of the matching template, and mapping the relation between the image obtained by calibration and a world coordinate system into the world coordinate system so as to obtain the position and posture identification of the walking device at the current quick movement time.
In step 3, when data processing is carried out on the collected noise sound wave, strain and flow information, random interference signals and noise are removed by utilizing a wavelet packet decomposition combined denoising method based on the combination of kurtosis test and wavelet threshold algorithm; the method comprises the steps of performing kurtosis inspection on noise and vibration information, determining an influence interval of impulse noise, performing wavelet decomposition on signals in the interval, performing threshold processing on coefficients of the signals, reconstructing processed wavelet packet coefficients, and performing wavelet inverse transformation by using the processed decomposition coefficients to obtain denoised signals. For the collected flow signal, the energy characteristics of the signal are described by intercepting the flow signal for a certain time period and using the power spectral density in the time period, so that the energy concentration frequency band of the flow leakage signal is obtained.
In step 3, the method specifically comprises the following steps:
1) detecting kurtosis, using kurtosis as characteristic index to represent pulse signal in vibration signal, and acquiring data as ai(i ═ 1,2,3,.., n), and the kurtosis calculation formula is:
where k is the kurtosis coefficient, n is the total number of samples of the signal, aiIs the ith sampling point, mu and sigma are the mean value and standard deviation of the acquired data respectively; equally dividing the original acquisition signal into Q sections, wherein each section is L, and when L is a determined value, obtaining a series of kurtosis values { k } according to the formulamQ, 1,2, · m; the series of kurtosis values are counted to obtain the variation degree C under the condition of the L valuevmWhich is defined as
In the formula, σ (k)m) Is the standard deviation of the kurtosis of each data segment,
is the mean value of the kurtosis of each segment of data, CvmThe larger the difference between impulse noise and non-impulse signal, and CvmThe smaller the difference between the impulse noise and the non-impulse signal is, the smaller the difference between the impulse noise and the non-impulse signal is;
selecting an L value corresponding to the maximum value of the degree of variation, dividing the signal, and then finding out a kurtosis value exceeding a threshold value according to a result, wherein the time zone in which the pulse noise occurs can be determined in a corresponding time period;
2) the selection of the threshold and the threshold function have two more key problems in the noise elimination process, namely the selection sum of the thresholdThe selection of threshold function, the selection mode of fixed threshold, it produces an extreme value of minimum mean square error, its calculation method of threshold is:
counting;
after the threshold is found, the wavelet coefficients are processed by a threshold function:
in the formula, N is the signal length, σ is the mean square error of the signal, λ is the threshold, sgn (×) is the sign of the function, which is 0 when the absolute value of the wavelet coefficient is smaller than the threshold λ, except that the threshold λ is smaller than the absolute value of the wavelet coefficient, the threshold is subtracted from both.
3) And power spectral density calculation, wherein an autocorrelation function of a signal describes the correlation degree of a random signal, namely the value of the acquired flow signal x (t) at different moments, the statistical characteristics of the signal under time domain characteristics are represented, before the power spectrum is calculated, the autocorrelation function of the intercepted signal is firstly obtained, and then Fourier transform is carried out on the autocorrelation function to obtain the power spectrum of the signal.
Wherein x (t) represents the flow signal, the convolution operator, x*(. tau.) denotes conjugation, Sx(ω) represents the power spectral density, R, of the signalx(τ) represents the power spectrum, and t is time.
Compared with the prior art, the invention has the following technical effects:
1) the invention provides a detection and control system for automatically monitoring the pipeline connection tightness of a hydraulic hoist, which realizes the self-adaptive detection of the defects of pipelines with different diameters, provides technical guarantee for the safe and stable operation of the ship lock hydraulic hoist and realizes the automatic detection of the state of ship lock equipment;
2) the invention provides a method for detecting and processing multiple characteristics of images, sound waves, strain and flow in a specific area by using a 3D structured light camera, a noise sensor, a vibration sensor and a flow sensor, and establishing scientific tightness judging means such as pipeline leakage and defects, and the like, thereby providing a reference for intelligent multi-characteristic observation of a hydraulic hoist pipeline;
3) the tightness detection system constructed based on the visual servo and artificial intelligence algorithm provides a certain technical scheme for a walking perception integrated intelligent detection and control system in a special environment, and reduces the problem of pipeline tightness which is difficult to directly observe perception into an intelligent method and an induction means, so that the system has popularization and practical values.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a control flow diagram of the method of the present invention;
FIG. 3 is a flowchart of the operation of the image processing module of the present invention;
FIG. 4 is a diagram illustrating a model creation process according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an image preprocessing process according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a fast recognition process according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of coordinate system transformation in an initialization process according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating distortion correction during initialization according to an embodiment of the present invention.
Detailed Description
The embodiment selects a hydraulic hoist of a three gorges continuous five-stage ship lock valve as an implementation object to specifically implement and explain the technical scheme of the invention.
A hydraulic hoist pipeline connection sealing detection system comprises a hardware part, a driving device, a supporting device, an elastic device and an adjusting device, wherein the hardware part comprises the driving device and the detecting device which are used for driving and supporting; the detection device is fixedly arranged on the walking device and mainly comprises a 3D structured light camera, a noise sensor, a vibration sensor and a flow sensor; the signal processing part comprises a communication module, an image processing module, a detection output module and a power supply module.
The walking devices are uniformly distributed along the circumferential direction, the driving device is arranged at the tail end of the supporting device, the elastic device is arranged between the driving device and the supporting device, and the adjusting device is arranged in the supporting device. All the device structures are made of oil-resistant and anti-corrosion materials.
Detection device fixed set up in on running gear's the strutting arrangement, including 3D structure light camera, noise inductor, vibration inductor and flow sensor, 3D structure light camera passes through electric wire electric connection with noise inductor, vibration inductor and flow sensor, and by power module supplies power.
The signal processing part comprises a control module, a communication module, an image processing module, a detection output module and a power supply module, wherein the communication module receives and transmits data with a background server of the detection system. The image processing module mainly comprises a preprocessing unit, a quick identification unit and a positioning unit.
The 3D structured light camera, the noise sensor, the vibration sensor and the flow sensor in the detection device are respectively connected with the background server through the communication module in the software module, detected picture, sound wave, strain and flow data information is output through the detection output module after being processed by the image processing module and the data processing module to be transmitted to the background server through the communication module, and the background server analyzes and judges the data to obtain position coordinate information, deformation, noise and vibration abnormal information of connection sealing conditions such as internal deformation, cracks and welding seams of the pipeline.
The invention also discloses a method for detecting the connection tightness of the hydraulic hoist pipeline, which comprises the following steps,
step 1: after the control module receives a remote start instruction, the 3D structure camera starts to collect video images, wirelessly transmits the obtained video images to the image processing module through the communication module, correspondingly preprocesses and analyzes image information, and filters to eliminate interference and noise;
step 2: performing graying processing according to the acquired template image, rotating the model diagram according to the set precision to establish a template diagram set, extracting the outline of the template diagram set, and loading the template diagram set into a model register of a control module after binaryzation;
and step 3: the method comprises the steps of quickly identifying image information obtained by detection processing in the process that a walking device moves along a pipeline, collecting and processing noise sound waves, strain and flow information of a specific position area by a noise sensor, a vibration sensor and a flow sensor, processing by a data processing module, transmitting to a detection output module by a communication module, drawing and outputting a detection graph;
and 4, step 4: according to the quick identification result in the step 3, roughly cutting the target area, extracting the outline and judging whether the sealing defects such as cracks, welding seams and the like exist, if the defects exist, calculating the angular point position by using the minimum surrounding moment or the minimum surrounding triangle, acquiring the angle and the center, and positioning;
and 5: calculating the speed of the walking device of the plurality of groups of positioning data according to the plurality of groups of positioning results and the moment when the image is obtained before positioning, calculating the accurate speed of the walking device by using a least square method, and loading the accurate speed into a sending register of a control module;
as shown in fig. 2, after the control module receives a start instruction, the control module initializes, establishes communication, starts multiple threads after initialization is successful, synchronously starts a walking device control thread, a 3D structured light camera thread, a vibration and noise sensor control thread, a flow sensor control thread, and a camera start-stop control thread, respectively initializes the corresponding control threads, after the camera starts, captures an image according to the camera, receives the image, processes the image, and sends a result to a server, the walking device controls according to the result of image processing, the subsequent vibration and noise sensor control thread and the flow sensor control thread can start after the walking device stops and the camera start signal is set to zero, the camera start signal is set to zero after detection is completed, and the camera is closed. After the camera start-stop control thread is initialized, a camera start-up required signal is determined according to a signal received by the control end, and all detected image, sound wave, strain and flow data information are processed by the image processing module and the data processing module to a certain extent and are output by the detection output module.
In the step 1, the method also comprises the following steps: the image processing module calibrates the 3D structure camera, converts an image coordinate system into a pixel coordinate system, and converts coordinates of any point in space under the image coordinate system into coordinate values of the camera coordinate system. The method comprises the steps of constructing a high-speed visual observer by using a camera, designing the observer by using a maximum correlation entropy Kalman filtering algorithm, carrying out denoising processing on an image before image feature extraction, observing an image feature vector containing noise by using the observer, and processing the image by combining a field mean value method, a median filtering method and a Gaussian filtering algorithm.
In the step 2, the template image is collected from the existing images of the cracks, the welding seams and other sealing defects, and the target geometric shape features, the color features and the texture features are utilized to extract the features of the target in the image, so that the target feature information is obtained.
The method comprises the steps of establishing a matching template based on a template matching algorithm of geometric features, carrying out similarity judgment on the matching template by extracting area, edge, corner point, curvature, image moment and gradient feature information of a target object in an image, solving a translation matrix and a rotation matrix of the matching template, and mapping the relation between the image obtained by calibration and a world coordinate system into the world coordinate system so as to obtain the position and posture identification of the walking device at the current quick movement time.
In step 3, the data processing module mainly eliminates random interference signals and noise by using a wavelet packet decomposition combined denoising method based on the combination of kurtosis inspection and wavelet threshold algorithm, the method carries out kurtosis inspection on original signals collected by a noise and vibration sensor, determines an influence interval of impulse noise, reconstructs processed wavelet packet coefficients by carrying out wavelet decomposition on the signals in the interval and carrying out threshold processing on the coefficients, and carries out wavelet inverse transformation on the processed decomposition coefficients to obtain denoised signals. For the collected flow signal, the energy characteristics of the signal are described by intercepting the flow signal for a certain time period and using the power spectral density in the time period, so that the energy concentration frequency band of the flow leakage signal is obtained.
1) Detecting kurtosis, using kurtosis as characteristic index to represent pulse signal in vibration signal, and acquiring data as ai(i ═ 1,2,3,.., n), and the kurtosis calculation formula is:
where k is the kurtosis coefficient, n is the total number of samples of the signal, aiIs the ith sample point and μ and σ are the mean and standard deviation of the collected data, respectively. Equally dividing the original acquisition signal into Q sections, wherein each section is L, and when L is a determined value, obtaining a series of kurtosis values { k } according to the formulamQ, 1, 2. The series of kurtosis values are counted to obtain the variation degree C under the condition of the L valuevmWhich is defined as
In the formula, σ (k)m) Is the standard deviation of the kurtosis of each data segment,
is the mean value of the kurtosis of each data segment. CvmThe larger the difference between impulse noise and non-impulse signal, and CvmSmaller is indicative of less difference between impulse noise and non-impulse signal. Selecting maximum value of variance to correspond toThe signal is divided, and then the time zone of the pulse noise can be determined by finding out the peak value exceeding the threshold value according to the result and the corresponding time period.
2) The selection of the threshold and the threshold function have two more key problems in the noise elimination process, namely, the selection of the threshold and the selection of the threshold function, and the selection mode of the fixed threshold, which generates an extreme value of the minimum mean square error. The threshold value calculation method comprises the following steps:
after the threshold is obtained, the wavelet coefficients are processed by a threshold function, and the noise reduction results obtained by using different threshold functions are different. Threshold function:
in the formula, N is the signal length, σ is the mean square error of the signal, λ is the threshold, sgn (×) is the sign of the function, which is 0 when the absolute value of the wavelet coefficient is smaller than the threshold λ, except that the threshold λ is smaller than the absolute value of the wavelet coefficient, the threshold is subtracted from both.
3) And power spectral density calculation, wherein an autocorrelation function of a signal describes the correlation degree of a random signal, namely the value of the acquired flow signal x (t) at different moments, the statistical characteristics of the signal under time domain characteristics are represented, before the power spectrum is calculated, the autocorrelation function of the intercepted signal is firstly obtained, and then Fourier transform is carried out on the autocorrelation function to obtain the power spectrum of the signal.
Wherein x (t) represents the flow signal, the convolution operator, x*(. tau.) denotes conjugation, Sx(omega) meterPower spectral density, R, of the signalx(τ) represents the power spectrum, and t is time.
In this embodiment, the initialization process includes distortion correction, and the image coordinate system is converted into a pixel coordinate system:
converting the coordinates of any point in the space under the image coordinate system into coordinate values of a camera coordinate system:
wherein: u, v are the horizontal and vertical coordinates of any point in the image plane, u0，v0The horizontal and vertical coordinates of the center point of the image plane are respectively, X and Y are respectively the horizontal and vertical coordinates of any point in the camera coordinate plane, R is a rotation matrix (3 degrees of freedom) under a right-hand system, t is a translation matrix under the right-hand system, the coordinates of any point P in space under the camera coordinate system are (X, Y and z), and the coordinates under the world coordinate system are (X, Y and z)w,yw,zw)。
Claims (8)
1. The hydraulic hoist pipeline connection tightness detection system is characterized by comprising a walking device (1), a detection device (2) arranged on the walking device (1) and a signal processing device (3);
the walking device (1) is used for attaching the inner wall of the pipeline in the hydraulic pipeline and walking with adjustable expansion amplitude;
the detection device (2) comprises a 3D structured light camera, a noise sensor, a vibration sensor and a flow sensor;
the detection device (2) is used for collecting data, and coordinate information, deformation, noise and vibration abnormal information of a part where the connection sealing condition in the pipeline occurs are obtained after the collected data are processed and analyzed.
2. The system according to claim 1, characterized in that the walking means (1) comprises drive means (1-1), support means (1-2), elastic means (1-3) and adjustment means (1-4);
the supporting device (1-2) is used as a carrier for installing and supporting the detection device (2); the adjusting device (1-4) is used for adaptively adjusting the unfolding amplitude of the walking device (1) according to the inner diameter of the hydraulic pipeline; the elastic device (1-3) ensures that the walking device (1) is reliably attached to the pipe wall; the detection device (2) is fixedly arranged on the walking device (1), and the detection device (2) comprises a 3D structured light camera, a noise sensor, a vibration sensor and a flow sensor; the control device (3) comprises a control module (3-1), a communication module (3-2), an image processing module (3-3), a data processing module (3-4) and a detection output module (3-5).
3. The system according to claim 2, characterized in that the driving device (1-1) is arranged at the end of the supporting device (1-2), an elastic device (1-3) is arranged between the driving device (1-1) and the supporting device (1-2), and an adjusting device (1-4) is arranged in the supporting device (1-3);
the detection device (2) is fixedly arranged on a supporting device (1-2) of the walking device (1), and the detection device (2) comprises a 3D structured light camera (2-1), a noise sensor (2-2), a vibration sensor (2-3) and a flow sensor (2-4);
the signal processing device (3) comprises a control module (3-1), a communication module (3-2), an image processing module (3-3), a data processing module (3-4) and a detection output module (3-5);
the image processing module (3-3) mainly comprises a preprocessing unit (3-3-1), a quick identification unit (3-3-2) and a positioning unit (3-3-3);
the 3D structured light camera (2-1), the noise sensor (2-2), the vibration sensor (2-3) and the flow sensor (2-4) in the detection device (2) are respectively connected with the background server through the communication module (3-2) in the signal processing device (3), detected data information of pictures, sound waves, strain and flow is processed through the image processing module (3-3) and the data processing module (3-4), output through the detection output module (3-5) and transmitted to the background server through the communication module (3-2), and the background server analyzes and judges the data to obtain coordinate information, deformation, noise and vibration abnormal information of the position where the sealing condition of the internal connection of the pipeline occurs.
4. The method for detecting the connection tightness of the hydraulic hoist pipeline is characterized by comprising the following steps of:
step 1: the 3D structure camera collects images and preprocesses the collected images;
step 2: carrying out graying processing on the preprocessed image, establishing a template image set according to a set precision rotation model diagram, extracting a template image set outline, and carrying out binarization;
and step 3: the detection device rapidly identifies image information obtained by detection processing in the moving process of the walking device along the pipeline, and meanwhile, the noise sensor, the vibration sensor and the flow sensor acquire and process noise sound waves, strain and flow information of a specific position area, and perform data processing, drawing and output detection graphs;
and 4, step 4: judging whether the target area has a sealing defect or not according to the detection image obtained in the step 3, if so, calculating the corner position by using the minimum surrounding moment or the minimum surrounding triangle, and acquiring an angle and a center for positioning;
and 5: and 4, obtaining a plurality of groups of positioning results, calculating the speed of the walking device corresponding to the plurality of groups of positioning data according to the moment when the image is obtained before positioning, and calculating the accurate speed of the walking device by using a least square method.
5. The method according to claim 4, wherein in step 1, specifically, the 3D structure camera is calibrated through the image processing module, the image coordinate system is converted into a pixel coordinate system, the coordinates of any point in space under the image coordinate system are converted into coordinate values of the camera coordinate system, the camera is used for constructing the high-speed visual observer, and the observer is designed by adopting a maximum correlation entropy Kalman filtering algorithm; the preprocessing mainly comprises the steps of denoising an image before image feature extraction, observing an image feature vector containing noise by using an observer, and processing the image by combining a field mean value method, a median filtering method and a Gaussian filtering algorithm.
6. The method according to claim 4, wherein in step 2, the template image is collected from an image with a sealing defect, such as a crack or a welding seam, and the target in the image is subjected to feature extraction by using the geometric feature, the color feature and the texture feature of the target, so as to obtain target feature information;
establishing a matching template, performing similarity judgment on the matching template by extracting area, edge, corner point, curvature, image moment and gradient characteristic information of a target object in an image, solving a translation matrix and a rotation matrix of the matching template, and mapping the relation between the image obtained by calibration and a world coordinate system into the world coordinate system so as to obtain the position and posture identification of the walking device at the current moment of rapid movement.
7. The method according to claim 4, wherein in step 3, when data processing is performed on the collected noise sound wave, strain and flow information, random interference signals and noise are removed by using a wavelet packet decomposition combined denoising method based on a combination of kurtosis test and wavelet threshold algorithm; the method comprises the steps of performing kurtosis inspection on noise and vibration information, determining an influence interval of impulse noise, performing wavelet decomposition on signals in the interval, performing threshold processing on coefficients of the wavelet decomposition, reconstructing processed wavelet packet coefficients, performing wavelet inverse transformation by using the processed decomposition coefficients to obtain denoised signals, intercepting flow signals of a certain time length, and describing energy characteristics of the signals by using power spectral density in the time to obtain an energy concentration frequency band of flow leakage signals.
8. The method according to claim 7, characterized in that in step 3, it comprises in particular the steps of:
1) detecting kurtosis, using kurtosis as characteristic index to represent pulse signal in vibration signal, and acquiring data as ai(i ═ 1,2,3,.., n), and the kurtosis calculation formula is:
where k is the kurtosis coefficient, n is the total number of samples of the signal, aiIs the ith sampling point, mu and sigma are the mean value and standard deviation of the acquired data respectively; equally dividing the original acquisition signal into Q sections, wherein each section is L, and when L is a determined value, obtaining a series of kurtosis values { k } according to the formulamQ, 1,2, · m; the series of kurtosis values are counted to obtain the variation degree C under the condition of the L valuevmWhich is defined as
In the formula, σ (k)m) Is the standard deviation of the kurtosis of each data segment,
is the mean value of the kurtosis of each segment of data, CvmThe larger the difference between impulse noise and non-impulse signal, and CvmThe smaller the difference between the impulse noise and the non-impulse signal is, the smaller the difference between the impulse noise and the non-impulse signal is;
selecting an L value corresponding to the maximum value of the degree of variation, dividing the signal, and then finding out a kurtosis value exceeding a threshold value according to a result, wherein the time zone in which the pulse noise occurs can be determined in a corresponding time period;
2) the selection of the threshold and the threshold function have two more key problems in the noise elimination process, namely, the selection of the threshold and the selection of the threshold function, the selection mode of the fixed threshold, and the extreme value of the minimum mean square error generated by the selection mode of the fixed threshold, wherein the calculation method of the threshold is as follows:
after the threshold is found, the wavelet coefficients are processed by a threshold function:
in the formula, N is the signal length, sigma is the mean square error of the signal, lambda is a threshold, sgn (×) is a function sign, when the absolute value of the wavelet coefficient is smaller than the threshold lambda, the wavelet coefficient is 0, and when the difference is that the threshold lambda is smaller than the absolute value of the wavelet coefficient, the wavelet coefficient is subtracted by the threshold lambda;
3) calculating power spectral density, wherein an autocorrelation function of a signal describes the correlation degree of a random signal, namely an acquired flow signal x (t), at different moments, the statistical characteristics of the signal under time domain characteristics are represented, before calculating a power spectrum, the autocorrelation function of an intercepted signal is firstly obtained, and then Fourier transform is carried out on the autocorrelation function to obtain the power spectrum of the signal:
wherein x (t) represents the flow signal, the convolution operator, x*(. tau.) denotes conjugation, Sx(ω) represents the power spectral density, R, of the signalx(τ) represents the power spectrum, and t is time.
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