CN111562305A - Automobile shock absorber piston defect detection method based on electromagnetic tomography technology - Google Patents
Automobile shock absorber piston defect detection method based on electromagnetic tomography technology Download PDFInfo
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Abstract
The invention discloses a defect detection method of an automobile shock absorber piston based on an electromagnetic tomography technology.A STM32 control chip is in signal connection with a measurement system; the upper computer is in signal connection with the acquisition card; the DDS is in signal connection with the electromagnetic detection sensor through the excitation end circuit; the detection method comprises the following steps: (1) detecting the intact device; (2) detecting the devices with known defect distribution; (3) training a defect imaging mathematical model; acquiring a defect real distribution image of a device with known defect distribution; establishing a deep learning network model; (4) detecting the devices with unknown defect distribution; (5) reconstructing a defect image; and (4) obtaining a high-precision defect distribution image from the low-resolution defect distribution image by using the deep learning network model obtained in the step (3) through representing a mapping relation. The invention can achieve the beneficial effects of reconstructing the defect distribution image of the interior and the surface of the part, realizing the visual measurement of the defects and improving the measurement precision and the measurement speed.
Description
Technical Field
The invention belongs to the technical field of electromagnetic tomography in the electrical detection technology, and particularly relates to a method for detecting defects of an automobile shock absorber piston based on the electromagnetic tomography technology.
Background
The interior of the automobile is composed of various metal parts, and the defects of the surfaces and the interior of the parts can be caused in the production and use processes of the automobile. If the defects are not detected timely and accurately, the overall performance of the automobile is affected, and even potential safety hazards exist.
At present, some existing metal part defect measurement schemes are mostly plane metal part detection or defect detection technologies (such as X-ray, ultrasonic and the like) based on other physical principles, and a detection method and means for special-shaped parts are lacked.
The metal parts in the automobile are typical special-shaped parts, and an effective defect detection method is lacked at present. The traditional CT method has high precision, but has slow speed and certain radiation hazard. The ultrasonic mode needs to paint a coupling agent on the surface of a part and is not suitable for online measurement. At present, the surface defects of the parts are observed manually, and for the internal defects, the internal defects are detected in a sampling destruction mode generally, and the detection precision is low. Although the existing electromagnetic measurement technology is used for detecting metal defects, the detection objects are mostly large and regular metal components.
Disclosure of Invention
The invention provides the method for detecting the defects of the piston of the automobile shock absorber based on the electromagnetic tomography technology aiming at the technical problems in the prior art, and has the advantages of reconstructing the defect distribution images of the inner part and the surface of the part, realizing the visual measurement of the defects and improving the measurement precision and the measurement speed.
In order to solve the technical problems, the invention adopts the technical scheme that: a defect detection method of an automobile shock absorber piston based on an electromagnetic tomography technology,
comprises a detection device; the detection device comprises an STM32 control chip and a measurement system, wherein the STM32 control chip is in signal connection with the measurement system;
the measuring system comprises an electromagnetic detection sensor, an excitation end circuit, a measuring end circuit, a multi-channel gating circuit, a DDS and an upper computer; the upper computer is in signal connection with the acquisition card; the DDS is in signal connection with the electromagnetic detection sensor through the excitation end circuit; the acquisition card is in signal connection with the electromagnetic detection sensor through the measuring end circuit;
the detection method comprises the following steps:
(1) placing the intact device below the electromagnetic detection sensor, detecting to obtain a group of measurement data about the intact device, and transmitting the measurement data to an upper computer;
(2) placing a device with known defect distribution below an electromagnetic detection sensor, detecting to obtain a group of measurement data about the device to be detected, and transmitting the measurement data to an upper computer;
(3) training a defect imaging mathematical model;
acquiring a defect real distribution image of a device with known defect distribution;
subtracting the measurement data obtained in the step (2) from the measurement data obtained in the step (1) to form effective measurement data, and reconstructing an image by a sensitivity coefficient method to obtain a low-resolution defect imaging result; forming a sample set by the low-resolution defect imaging result and the corresponding defect real distribution image, and establishing a deep learning network model;
(4) placing the device with unknown defect distribution below an electromagnetic detection sensor, detecting to obtain a group of measurement data of the device with unknown defect distribution, and transmitting the measurement data to an upper computer;
(5) reconstructing a defect image;
subtracting the measurement data obtained in the step (4) from the measurement data obtained in the step (1) to form effective measurement data, and reconstructing an image by a sensitivity coefficient method to obtain a low-resolution defect distribution image;
and (4) obtaining a high-precision defect distribution image from the low-resolution defect distribution image by using the deep learning network model obtained in the step (3) through representing a mapping relation, and realizing high-precision visual detection of the defects of the devices with unknown defect distribution.
Preferably, the excitation end circuit comprises a voltage-to-current conversion circuit and a multi-path gating circuit; the measuring end circuit comprises a differential amplifying circuit, a filter circuit and a multi-path gating circuit; the multi-path gating circuit is respectively in signal connection with the electromagnetic detection sensor, the voltage-current conversion circuit and the filter circuit; the differential amplification circuit is in signal connection with the acquisition card; the voltage-current conversion circuit enables the excitation signal to be applied to the excitation coil in the form of current so as to ensure the consistency of the coil excitation signal.
Preferably, the electromagnetic detection sensor is composed of 6-10 coils which are provided with iron cores and have the inner diameter of 1.2mm and are uniformly arranged in an O-shaped mode, the coils are wound by enameled wires with the wire diameter of 0.25mm and have the number of turns of 50, and when parts with different sizes are tested, different sensor arrays need to be designed by comprehensively considering defect information.
Preferably, in the steps (1), (2) and (4), a cyclic excitation cycle measurement mode is used during detection, and l x (l-1) measurement data are obtained in each measurement, wherein l is the number of coils; n is 4 times l, i.e. l coils divide the part into l regions, where each region is in turn divided into 4 sub-regions. After the coil array is measured once, the coil array rotates a certain angle to scan the sub-area corresponding to the next array until the sub-area corresponds to the original detection position.
After one coil is determined to be used as an exciting coil, exciting signals with certain frequency and amplitude are applied to the coil through an exciting end circuit, the rest coils are selected to be used as measuring coils, induction signals are transmitted to an acquisition card after passing through a measuring end circuit, the acquisition card transmits measuring data to an upper computer, and then the upper computer processes and stores the data; and the upper computer judges and detects the defect of the device according to the measurement data.
Preferably, when the device is used for detecting irregular parts, the structure of the parts has irregularity and complexity which are different from the uniform structure of a plate or a rail, and measurement data are inaccurate when coils are positioned at holes of the parts, in the steps (1), (2) and (4), the parts are divided into 1,2 … n areas during detection, after l x (l-1) values are acquired, the relative position between the device and the sensor coil is changed for detecting again, and the action is repeated to enable each area of the parts to measure effective data so as to avoid the situation that the measurement result is inaccurate at the holes, wherein l is the number of the coils.
Preferably, in step (5), the deep learning network model obtained in step (3) is used to represent the mapping relationship between the low-resolution defect image and the defect real distribution image, and the reconstructed low-resolution defect distribution image is input to the mapping relationship to obtain a high-precision defect distribution image.
Compared with the prior art, the invention has the beneficial effects that: aiming at the problem that the existing automobile shock absorber piston cannot realize online automatic measurement due to complex structure, the invention designs the electromagnetic sensor array according to the automobile shock absorber piston structure, reconstructs defect distribution images of the inner part and the surface of a part in a mode of conductivity distribution images through a multi-frequency measurement data sequence, realizes the visual measurement of defects, and improves the measurement precision and the measurement speed.
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Fig. 1 is a block diagram showing a system configuration of a detection device according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention discloses a defect detection method for an automobile shock absorber piston based on an electromagnetic tomography technology, which is shown in the figure and is based on the defect detection method for the automobile shock absorber piston based on the electromagnetic tomography technology,
comprises a detection device; the detection device comprises an STM32 control chip and a measurement system, wherein the STM32 control chip is in signal connection with the measurement system;
the measuring system comprises an electromagnetic detection sensor, an excitation end circuit, a measuring end circuit, a multi-channel gating circuit, a DDS and an upper computer; the upper computer is in signal connection with the acquisition card; the DDS is in signal connection with the electromagnetic detection sensor through the excitation end circuit; the acquisition card is in signal connection with the electromagnetic detection sensor through the measuring end circuit;
the detection method comprises the following steps:
(1) placing the intact device below the electromagnetic detection sensor, detecting to obtain a group of measurement data about the intact device, and transmitting the measurement data to an upper computer;
(2) placing a device with known defect distribution below an electromagnetic detection sensor, detecting to obtain a group of measurement data about the device to be detected, and transmitting the measurement data to an upper computer;
(3) training a defect imaging mathematical model;
acquiring a defect real distribution image of a device with known defect distribution;
subtracting the measurement data obtained in the step (2) from the measurement data obtained in the step (1) to form effective measurement data, and reconstructing an image by a sensitivity coefficient method to obtain a low-resolution defect imaging result; forming a sample set by the low-resolution defect imaging result and the corresponding defect real distribution image, and establishing a deep learning network model;
(4) placing the device with unknown defect distribution below an electromagnetic detection sensor, detecting to obtain a group of measurement data of the device with unknown defect distribution, and transmitting the measurement data to an upper computer;
(5) reconstructing a defect image;
subtracting the measurement data obtained in the step (4) from the measurement data obtained in the step (1) to form effective measurement data, and reconstructing an image by a sensitivity coefficient method to obtain a low-resolution defect distribution image;
and (4) obtaining a high-precision defect distribution image from the low-resolution defect distribution image by using the deep learning network model obtained in the step (3) through representing a mapping relation, and realizing high-precision visual detection of the defects of the devices with unknown defect distribution.
In this embodiment, the excitation end circuit includes a voltage-to-current conversion circuit and a multi-channel gating circuit; the measuring end circuit comprises a differential amplifying circuit, a filter circuit and a multi-path gating circuit; the multi-path gating circuit is respectively in signal connection with the electromagnetic detection sensor, the voltage-current conversion circuit and the filter circuit; the differential amplification circuit is in signal connection with the acquisition card; the voltage-current conversion circuit enables the excitation signal to be applied to the excitation coil in the form of current so as to ensure the consistency of the coil excitation signal.
In this embodiment, the electromagnetic detection sensor is composed of 6 coils with iron cores and an inner diameter of 1.2mm, which are uniformly arranged in an O-shaped manner, the coils are wound by enameled wires with a wire diameter of 0.25mm and have a number of turns of 50, and different sensor arrays are designed by comprehensively considering defect information when testing parts of different sizes.
In the embodiment, in the steps (1), (2) and (4), a cyclic excitation cyclic measurement mode is used during detection, and 30 pieces of measurement data are obtained in each measurement;
after one coil is determined to be used as an exciting coil, exciting signals with certain frequency and amplitude are applied to the coil through an exciting end circuit, the rest coils are selected to be used as measuring coils, induction signals are transmitted to an acquisition card after passing through a measuring end circuit, the acquisition card transmits measuring data to an upper computer, and then the upper computer processes and stores the data; and the upper computer judges and detects the defect of the device according to the measurement data.
In the embodiment, when the method is used for detecting irregular parts, the structure of the parts has irregularity and complexity which are different from the uniform structure of plates or rails, and measurement data is inaccurate when coils are positioned at holes of the parts, in the steps (1), (2) and (4), the parts are divided into 1,2 … 24 areas during detection, after 30 values are acquired, the relative position between the device and the sensor coil is changed for detecting again, and the action is repeated to enable each area of the parts to measure effective data so as to avoid the situation that the measurement result is inaccurate when the holes are positioned.
In this embodiment, in step (5), the deep learning network model obtained in step (3) is used to represent the mapping relationship between the low-resolution defect image and the defect real distribution image, and the reconstructed low-resolution defect distribution image is input to the mapping relationship, so as to obtain the high-precision defect distribution image.
And designing an electromagnetic sensor array according to the geometric structure of the automobile shock absorber piston. The electromagnetic sensor array can be designed into one or more than one according to the complexity of the part and arranged at different positions of the part. And the electromagnetic field generated by the electromagnetic coil can cover the whole part so as to realize the integral measurement of the part. Each electromagnetic sensor array is composed of a plurality of electromagnetic coils, and the electromagnetic coils are arranged on the smooth plane or cambered surface part of the part. The following description will be given by taking a sensor array composed of 6 coils as an example of a method for detecting a defect on the upper surface of a piston of an automobile shock absorber.
The electromagnetic detection sensor is composed of 6 coils with iron cores which are fixed in a die and evenly distributed at an angle of 60 degrees, the sensor is manufactured in a mode of clockwise winding two circles in each layer, and the consistency of the coils is tested to ensure the validity of measured data.
The voltage-current conversion circuit adopts a TDA2030 chip to realize voltage-current conversion, so that an excitation signal is applied to an excitation coil in a current mode, and the multi-path selection circuit is controlled by the main control chip to determine the selection of the excitation coil in the sensor array.
The measuring end circuit is composed of a differential amplification circuit, a filter circuit and a multi-path gating circuit, wherein the first two circuits are built by double operational amplifier chips, and the sensing signals are processed to improve the signal-to-noise ratio of the sensing signals.
The STM32 serves as a main control chip of the system and provides control signals for the multi-way gating circuit so as to realize cyclic excitation measurement of the sensor array.
The acquisition card acquires data of the induced voltage and completes data transmission.
And a serial port program for communicating with the main control chip, a measurement data acquisition and storage program and an imaging algorithm program based on deep learning are compiled in the upper computer software to realize image reconstruction.
In the deep learning imaging model training process, imaging electromagnetic measurement data of a part with a known defect position by adopting a sensitivity coefficient algorithm to obtain a defect image with lower resolution, establishing a mapping relation between the low resolution image and a defect real distribution image, and training a defect imaging model; in the testing process, for parts with unknown defect positions, measurement data are collected through a sensor, low-resolution imaging is carried out by adopting a sensitivity coefficient algorithm, and then a high-resolution defect distribution image is reconstructed by combining a defect imaging model, so that accurate visual measurement of defects is realized.
The electromagnetic tomography technology has the working process as follows: a sine wave with the frequency of 50KHz is generated by the signal source module to serve as an excitation signal, the excitation signal passes through the excitation end module and the multi-channel gating module, and then the excitation is applied to the selected excitation coil. According to the principle of electromagnetic induction, a detected sensitive field space generates an excitation magnetic field, and a medium with conductivity in the sensitive field space generates an induction signal in a detection coil on the boundary of the sensitive field space under the action of the excitation magnetic field. The generated induction signals are subjected to data acquisition by an acquisition card through a signal end module and a multi-path gating module and are transmitted to an upper computer, and finally the upper computer reconstructs a defect image through a deep learning electromagnetic tomography mathematical model obtained through training according to the acquired acquisition data to obtain the conductivity of each part of the tested part, and finally three-dimensional image reconstruction of the part is realized.
The method is based on the advantages of high detection speed, high sensitivity, no radiation and no contact of an electromagnetic detection technology, designs the electromagnetic sensor array which accords with the part structure of the automobile shock absorber and a corresponding measurement system, images the defects through a measurement data sequence and an electromagnetic tomography mathematical model established through deep learning, forms a defect image, and realizes the visual measurement of the defects inside and on the surface of the part.
The defect measurement of different depths is realized by the skin depth theory, namely the higher the excitation frequency is, the closer the excitation signal is to the metal surface, so that the information of the metal defect distribution of different depths can be obtained by adjusting the excitation frequency, and further the three-dimensional image of the metal defect distribution is reconstructed.
The invention is not limited to the automobile shock absorber piston, and can further provide an effective method for detecting the defects of other metal special-shaped parts.
The invention realizes the real-time monitoring of the complex device and has the advantages of high-precision identification, low cost, internal monitoring and the like.
The non-destructive detection technology is the most common defect detection technology currently applied, and detects whether the detected object has defects or is not uniformly distributed by using physical signals such as sound, light, electricity, magnetism and the like on the premise of not damaging and influencing the performance of each aspect of the detected object, and deduces the information such as the size, the position, the shape, the property, the quantity and the like of the defects according to the detection signals so as to judge whether the detected object is qualified. The nondestructive detection technology has the advantages of non-destructiveness, comprehensiveness, reliability and the like, and has no substitutable position in the current detection technology.
The present invention has been described in detail with reference to the embodiments, but the description is only illustrative of the present invention and should not be construed as limiting the scope of the present invention. The scope of the invention is defined by the claims. The technical solutions of the present invention or those skilled in the art, based on the teaching of the technical solutions of the present invention, should be considered to be within the scope of the present invention, and all equivalent changes and modifications made within the scope of the present invention or equivalent technical solutions designed to achieve the above technical effects are also within the scope of the present invention. It should be noted that for the sake of clarity, parts of the description of the invention have been omitted where there is no direct explicit connection with the scope of protection of the invention, but where components and processes are known to those skilled in the art.
Claims (7)
1. The method for detecting the defects of the automobile shock absorber piston based on the electromagnetic tomography technology is characterized by comprising a detection device; the detection device comprises an STM32 control chip and a measurement system, wherein the STM32 control chip is in signal connection with the measurement system;
the measuring system comprises an electromagnetic detection sensor, an excitation end circuit, a measuring end circuit, a multi-channel gating circuit, a DDS and an upper computer; the upper computer is in signal connection with the acquisition card; the DDS is in signal connection with the electromagnetic detection sensor through the excitation end circuit; the acquisition card is in signal connection with the electromagnetic detection sensor through the measuring end circuit;
the detection method comprises the following steps:
(1) placing the intact device below the electromagnetic detection sensor, detecting to obtain a group of measurement data about the intact device, and transmitting the measurement data to an upper computer;
(2) placing a device with known defect distribution below an electromagnetic detection sensor, detecting to obtain a group of measurement data about the device to be detected, and transmitting the measurement data to an upper computer;
(3) training a defect imaging mathematical model;
acquiring a defect real distribution image of a device with known defect distribution;
subtracting the measurement data obtained in the step (2) from the measurement data obtained in the step (1) to form effective measurement data, and reconstructing an image by a sensitivity coefficient method to obtain a low-resolution defect imaging result; forming a sample set by the low-resolution defect imaging result and the corresponding defect real distribution image, and establishing a deep learning network model;
(4) placing the device with unknown defect distribution below an electromagnetic detection sensor, detecting to obtain a group of measurement data of the device with unknown defect distribution, and transmitting the measurement data to an upper computer;
(5) reconstructing a defect image;
subtracting the measurement data obtained in the step (4) from the measurement data obtained in the step (1) to form effective measurement data, and reconstructing an image by a sensitivity coefficient method to obtain a low-resolution defect distribution image;
and (4) obtaining a high-precision defect distribution image from the low-resolution defect distribution image by using the deep learning network model obtained in the step (3) through representing a mapping relation, and realizing high-precision visual detection of the defects of the devices with unknown defect distribution.
2. The method for detecting the piston defect of the automobile shock absorber based on the electromagnetic tomography technology as claimed in claim 1, wherein the excitation end circuit comprises a pressure-current conversion circuit and a multi-way gating circuit; the measuring end circuit comprises a differential amplifying circuit, a filter circuit and a multi-path gating circuit; the multi-path gating circuit is respectively in signal connection with the electromagnetic detection sensor, the voltage-current conversion circuit and the filter circuit; the differential amplification circuit is in signal connection with the acquisition card.
3. The method for detecting the piston defect of the automobile shock absorber based on the electromagnetic tomography technology as claimed in claim 1, wherein the electromagnetic detection sensor is composed of 6-10 coils which are provided with iron cores and have inner diameters of 1.2mm and are uniformly arranged in an O-shaped manner, the coils are wound by enameled wires with wire diameters of 0.25mm, and the number of turns of the enameled wires is 50.
4. The method for detecting the piston defect of the automobile shock absorber based on the electromagnetic tomography technology as claimed in the claims 1-3, wherein in the steps (1) (2) (4), a cyclic excitation cycle measurement mode is used during the detection, and each measurement obtains l x (l-1) measurement data, wherein l is the number of coils;
after one coil is determined to be used as an exciting coil, exciting signals with certain frequency and amplitude are applied to the coil through an exciting end circuit, the rest coils are selected to be used as measuring coils, induction signals are transmitted to an acquisition card through the measuring end circuit, the acquisition card transmits measured data to an upper computer, and then the upper computer processes and stores the data.
5. The method for detecting the piston defect of the automobile shock absorber based on the electromagnetic tomography technology as claimed in the claims 1-3, wherein in the steps (1), (2) and (4), the part is divided into 1,2 … n areas during the detection, after l x (l-1) data is acquired, the relative position between the device and the sensor coil is changed to detect again, and the action is repeated to enable each area of the part to measure effective data, wherein l is the number of the coils.
6. The method for detecting the piston defect of the automobile shock absorber based on the electromagnetic tomography technology as claimed in claim 1, wherein in the step (5), the deep learning network model obtained in the step (3) is used for representing the mapping relation between the low-resolution defect image and the defect real distribution image, and the reconstructed low-resolution defect distribution image is input to the mapping relation to obtain the high-precision defect distribution image.
7. The method for detecting the defect of the piston of the automobile shock absorber based on the electromagnetic tomography technology as claimed in claim 5, wherein n is 4 times of l.
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