CN202533411U - System for detecting object partial impedance changes - Google Patents
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Abstract
The utility model discloses a system for detecting object partial impedance changes. The system classifies by using different object partial impedances and different response signals after knocking and combining with a mode identification method. The system can be used for detecting bolt tightening torque and damage states of composite materials. The system is simple in equipment, simple and practical in method, accurate in detecting result and capable of achieving various classifications.
Description
Technical Field
The utility model relates to a detect local impedance change system of object, more specifically relate to detect the system of bolt elasticity degree and detection combined material damage based on the classifier is strikeed in the control.
Background
Various devices often use bolts to connect components. Although common, the degree of fastening directly affects the quality of the whole device. If the bolts at certain key parts are loosened, even serious safety accidents can be caused. In order to ensure the connection quality of the bolt, the parameters such as the friction coefficient, the maximum torque and the like of the bolt need to be determined by adopting a standard detection method, and the tightening torque of the bolt also needs to be strictly regulated for some precision equipment. The tightness degree of the bolt is likely to change due to the influence of external factors such as vibration, impact and temperature change during the use of the equipment. It is therefore desirable to use a non-destructive inspection method to assess the actual condition of the bolted joint.
The tightness of the bolts directly affects the local mechanical resistance. In actual engineering, an experienced inspector often judges whether a bolt is loosened or not according to sound changes by using a knocking method, but a manual knocking method for detecting damage too much depends on the experience of the inspector, so that the method cannot be widely applied to actual engineering; moreover, the experience of each inspector is different, so that the detection result has larger difference and poor stability.
In the prior art, theoretical analysis of a method for identifying the damage caused by tapping considers that different sounds are generated in different areas of tapping because the tapping force of different areas changes, so that the damage of a structure can be identified according to the width of a time domain curve of the tapping force or the area ratio under a frequency domain curve. For this reason, a theoretical explanation was also made using a series spring model: it is believed that the occurrence of damage results in a reduction of the local stiffness of the structure, thereby locally softening the structure. However, the detection result of the method is not ideal enough, and the bolt tightening torque or the structural damage state cannot be identified quantitatively.
Nondestructive detection, that is, non-destructive means is adopted to detect the defects affecting the use and the positions of the defects by using the techniques of sound, light, electricity, heat, magnetism, ray and the like in the materials and the components, such as pores, inclusions, cracks, delamination and the like. At present, the nondestructive testing method of the composite material mainly comprises ultrasonic testing, thermal imaging testing and the like. Each technique has its specific application range and advantages and disadvantages, and a single method is difficult to realize for all types of defects, and usually requires a combination of multiple methods.
Therefore, it is desirable to provide a practical object state detection method, which can quickly detect the tightness state and/or the material damage state of the bolt in real time, and has certain accuracy and stability.
SUMMERY OF THE UTILITY MODEL
To the above problem, the utility model provides a system based on controlled knocking and classifier detect the local impedance change of object, through treating the knock force that detects the object and apply invariable, extract characteristic information from the response signal who waits to detect the object to combine the mode identification method to realize nondestructive test, not only can carry out qualitative discernment to the local anomaly of object, but also can carry out quantitative discernment to the local anomaly of object.
The utility model provides a system for detecting the local impedance change of an object based on controlled knocking and a classifier, which comprises a knocking device, a sensor and a signal processing part; wherein:
the knocking device is used for applying a fixed knocking force to the object to be tested;
a sensor for sensing a response signal transmitted to the sensor by a target area struck by an object; the sensor is one of a speed sensor, an acceleration sensor or a displacement sensor;
a signal processing component that receives the response signal sensed by the sensor and processes it to detect an impedance state of the object target area, the signal processing component comprising:
the spectrogram processing device is used for carrying out conversion processing on the response signal sensed by the sensor so as to obtain a signal spectrogram of the response signal and intercepting spectrogram data of the signal frequency spectrum in a specific frequency band; and
and an impedance state determination device which detects an object impedance state based on the spectrogram data output by the classifier and the spectrogram processing device.
Preferably, the object is a composite material and the impedance state represents a damage state of the composite material.
Preferably, the object is a bolt, and the impedance state represents a tightness state of the bolt.
Specifically, the knocking device comprises a vibration exciter, a plurality of character frames connected with the vibration exciter, and a gasket arranged at the bottom of each character frame.
The sensor can be arranged on a knocked object and directly senses a response signal of the knocked object. However, since it is cumbersome to place the sensor on the knocked object, it is preferable to integrate the sensor on the knocking device.
Specifically, the classifier is a support vector machine classifier. The classifier can be a support vector machine, a Bayes classifier, a BP neural network classifier, a decision tree algorithm, or a simple vector comparison index, etc. The classifier is preferably a support vector machine classifier because the support vector machine method has a strict statistical learning theoretical basis and has many advantages in solving the problems of small samples, nonlinearity and high-dimensional pattern recognition.
The utility model provides a system based on classifier detects local impedance of object and changes, the equipment of adoption is convenient for use, and operating procedure is fairly simple, after having training the classifier under the condition of abundant sampling sample, can not only simple and convenient rapid confirm when detecting the combined material state whether the position that detects has the damage, can classify the type of damage moreover.
Drawings
FIG. 1 is a simplified mechanical model of the knock detection method of the present invention;
fig. 2 is a schematic view of a knocking device and a sensor according to an embodiment of the present invention;
fig. 3 is a schematic view of a system for detecting a tightness state of a bolt according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a power spectral density curve of a group of test signals for detecting a tightness state of a bolt provided by an embodiment of the present invention;
fig. 5 is a graph of the relationship between the bolt tightening torque and the sensitive peak frequency in the power spectrum in the embodiment of the present invention;
fig. 6 is a schematic view of a composite material state detection system according to another embodiment of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific embodiments.
The equivalent stiffness and friction coefficient of the bolt are not only related to material properties, geometry, but also vary with bolt tightening torque. The equivalent stiffness and damping of a composite material are related not only to the material properties, geometry of the composite material, but also to the type, extent, etc. of damage to the composite material. Therefore, to accurately describe the mechanical behavior of the bolt or the composite material under the action of the knocking force, a very complicated nonlinear dynamic analysis must be involved. And the purpose of the utility model is to detect the degree of tightness of bolt or combined material's damage state, so only strive for the response sensitive to bolt tightening moment or combined material damage state.
In the present invention, the local impedance of the bolt or composite material (mainly including equivalent stiffness, also including damping and equivalent mass) is a function of the bolt tightening torque or the type of damage to the composite material, while the stiffness of the rapping device is constant. As shown in FIG. 1, the system of the object to be measured is simplified to a spring-mass system with a mass m1Spring rate k1(T) is a function of the tightening torque T. Further assume mass m2Has a vertical rigidity k2. Respectively making the displacement of the knocking device and the object system to be measured u by taking the static balance position of the mass block as a reference state1And u2The following governing equation can be established:
m1ü1+(k1+k2)u1-k2u2＝0
m2ü2-k2u1+k2u2＝F (1)
although damping exists in a practical system, the damping effect is not considered in the formula (1) because the damping solution is only concerned about forced vibration under a steady state. It is assumed that the tapping force is a superposition of several simple harmonic components:wherein A isi、ωiAnd
the amplitude, circular frequency and initial phase of the ith order force, respectively, and t represents time. At a given tightening torque T, it is easy to(1) The ith order response of the rapping device is obtained by the equation:
wherein,
and
is the natural circular frequency of the system:
the acceleration of the rapping device is:
as can be seen from the equation (4), if the knocking force is included and included
And
the close frequency components are in the acceleration power spectrum density chart of the knocking device
Andtwo spikes occur. K when the impedance state of the object to be measured changes1(T) varies, and the positions of the two peaks in the power spectral density map vary according to equation (3); according to equation (4), the peak amplitude also changes.
The actual structure is much more complex than the model shown in fig. 1, and other spikes may appear in the acceleration power spectral density map of the knock force. When the impedance state of the object to be measured changes, the frequency corresponding to these non-sensitive peaks does not change significantly, but the amplitude may change. In addition, if m2＞＞m1，k2＞＞k1Then, then
And also
It is likely to exceed the frequency band covered by the tapping force and thus not appear in the acceleration power spectral density map. Thus, in an acceleration power spectral density map of an actual knocking device, only the position and the amplitude of one peak are sensitive to the impedance state of the object to be detected, and the other peaks only have some changes of the amplitude along with the impedance state of the object to be detected.
According to the analysis, the correlation exists between the shape of the acceleration power spectral density curve of the knocking device and the impedance state of the object to be detected. If the frequency band of the curve contains
The relationship is more compact and can be used to detect the impedance state of the object to be detected, that is, the tightening torque of the bolt or the damage state of the composite material.
Fig. 2 shows a schematic connection diagram of a knocking device and a sensor, wherein the knocking device comprises a vibration exciter 201, a frame 202 and a washer 203. The frame 202 is preferably a steel structure and is connected to the lower side of the exciter 201. The gasket 203 is provided with a rubber limiting sheet and is connected to the lowest end of the vibration exciter 201.
And a sensor, specifically an accelerometer 204, connected to the knocking device, wherein the accelerometer 204 is disposed at a joint between the frame 202 and the vibration exciter 201, and is used for acquiring an acceleration signal of the knocking device.
As an embodiment of the utility model, utilize the knocking device that fig. 2 shows to strike and wait to detect the part, through accelerometer 204 detects the response signal that the part produced after being strikeed has the close relation between local impedance change and this response signal according to the object, differentiates the object state based on the mode identification method. The utility model provides a method for detecting object state based on classifier specifically includes following step:
a. the component is struck with a fixed striking force using a striking device.
b. The response signal, which is an acceleration signal, transmitted by the component to the rapping device is sensed with an accelerometer 204 integrated on the rapping device.
c. And carrying out conversion processing on the acceleration signal to obtain a signal spectrogram of the acceleration signal, and intercepting spectrogram data of the signal frequency spectrum in a specific frequency band. Specifically, the spectrogram data is preferably a power spectral density map (power spectral density) estimated by fourier transform, AR model, Welch method, maximum entropy, or the like.
In the training phase of the classifier
d. Repeating the steps a-c on the target area in each impedance state respectively for the objects of which the target area is in the 1 st to the Nth impedance states so as to obtain a plurality of training spectrogram data of the target area in the specific frequency band in the impedance state, wherein N is an integer larger than 1;
e. and providing each impedance state and the plurality of training spectrogram data in the impedance state to the classifier, and training the classifier. The classifier can be support vector machine classifier, Bayes classifier, BP neural network classifier, decision tree algorithm or simple vector comparison index etc. because the support vector machine method has strict statistics learning theory basis, has many advantages in the aspect of solving small sample, nonlinearity and high dimension pattern recognition problem, consequently the utility model discloses the preferred support vector machine classifier that adopts.
-in the detection phase of the classifier
f. B, acquiring detection spectrogram data of the target area in the specific frequency band under the impedance state to be detected by the object with the target area in the impedance state to be detected through the steps a-c;
g. and providing the detection spectrogram data under the impedance condition to be detected for the classifier so as to classify the impedance condition to be detected into one of the 1 st to the Nth impedance states.
When the object is a bolt, the impedance state represents a tightness state of the bolt, and further, the tightness state may be a tightening torque of the bolt.
When the object is a composite material, the impedance state represents a damage state of the composite material, and further, the damage state may be delamination, volume type defect, crack, or the like.
As another embodiment of the present invention, fig. 3 shows that the present invention provides a system for detecting the tightness of a bolt based on a classifier, including: the device comprises a knocking device 31, a sensor 32 integrated on the knocking device 31, and a signal processing part 34, wherein the signal processing part 34 comprises a spectrogram processing device 35 and a tightness state determining device 36. The part to be detected is a bolt 33. Wherein the knocking device 31 and the sensor 32 are shown in detail in fig. 2.
According to the utility model discloses a measuring method, with four bolts that length is 94mm, diameter is 8mm, fixes two aluminium strips on ground rail. A square wave signal is delivered to the exciter 201 for generating the striking force. The period of the square wave is set to 1 second, so that the free vibration signal with higher frequency can be sufficiently attenuated between two times of knocking. A total of 20 sets of tests were performed, and in each set of tests, the tightening torque of the bolt to be tested was adjusted to five states in sequence with a torque wrench: 0Nm, 5Nm, 10Nm, 205Nm and 20 Nm. Wherein the bolt is in the loose state when tightening torque is 0Nm, and the bolt is in the tight state when tightening torque is 20 Nm. In each state, the tapping device described in fig. 2 was caused to tap the bolt 33 with the same force for 30 seconds while its acceleration signal was recorded with the accelerometer 204. Thus, there are 12 acceleration signals as detection samples in each state, and there are 60 detection samples in total.
Then, the acceleration signal is input into the spectrogram processing device 25 for processing, and a signal spectrogram of the acceleration signal is obtained. In particular, the spectral plot is preferably a power spectral density. In order to ensure that the acquired data is the response of forced vibration in a steady state, the middle continuous 20-second data is intercepted from the acceleration signal of each detection sample, and the power spectral density curve of the data is obtained by an autoregressive (autoregressive) method. The frequency range of the power spectral density curve comprises a frequency range sensitive to tightness of the bolt, and preferably, the power spectral density comprises at least a part with a frequency value equal to a natural circular frequency of a system consisting of the knocking device and the bolt.
As can be seen from a comparison of the power spectral density curves of the first set of test signals in FIG. 5, spikes typically occur near 400Hz, 550Hz, 625Hz, and 710Hz in the power spectral density curves. In the fully loosened state of the bolt, the spike at around 400Hz disappears completely. With the increase of the bolt tightening torque, the position of the peak around 400Hz shifts to the high frequency direction, and the amplitude changes greatly. In addition, when the tightness degree of the bolt is changed, the positions of the other three peaks are basically unchanged, but the amplitudes of the three peaks are changed. It can be seen that the peaks around 400Hz are sensitive to the tightness of the bolt, corresponding to ω 1 in the simplified mechanical model shown in fig. 1, and the remaining peaks should be the natural frequencies of the aluminum block system itself. Therefore, the power spectral density map contains at least a portion with a frequency of 400 Hz. In the present embodiment, the power spectral density map is selected from a frequency range of 350Hz to 750 Hz. Furthermore, aiming at different bolts to be tested or different experimental conditions, the selected frequency range is adjusted according to the actual situation, so that the frequency range sensitive to the tightness of the bolts is included.
The power spectral densities of all 12 groups of acceleration signals (total 60 samples) are taken as training samples, and the bolt state corresponding to each corresponding acceleration signal is taken as an expected output and is provided for a support vector machine to perform learning training. The bolt states are five states of 0Nm, 5Nm, 10Nm, 205Nm, and 20 Nm.
Then, 8 groups of acceleration signals (total 40 samples) are obtained again through the same steps as the 12 groups of acceleration signals, the 8 groups of acceleration signals are processed through a spectrogram processing device 35 to obtain the power spectral densities thereof, the power spectral densities of the 8 groups of acceleration signals are input into the trained support vector machine as test samples to be tested, and the output identification result is the bolt state corresponding to the 8 groups of signals. The recognition results are shown in table 1. It can be seen from table 1 that the bolt loose state can be completely correctly identified; the 5Nm state is completely not correctly recognized; and the larger the tightening torque is, the higher the accuracy of the recognition result is.
TABLE 1 identification of bolt tightening torque
FIG. 5 compares the frequency of the sensitive spikes in the power spectral density curves of all 20 sets of acceleration signals as a function of bolt tightening torque; this spike disappeared when the bolt was fully loosened, so it was not involved in the comparison. It can be seen from fig. 6 that when the tightening torque is 5Nm, the dispersibility of the data is very large, and the dispersibility of the data gradually decreases as the torque increases. This is because the process of each set of experiments was measured starting with the bolt completely loosened and increasing tightening torque gradually. When the bolts are loose, the randomness of the various contact and friction conditions is greater. And the torque range of the torque wrench used in the experiment is 4 Nm-20 Nm, and the accuracy is not as high when the torque is small as when the torque is large. The recognition results in table 1 can thus be understood: the accuracy increases with increasing tightening torque.
In the embodiment, the number of the bolt states is five, the number of the identification results is five correspondingly, not only can the bolt be identified to be screwed or loosened, but also the screwing torque of the bolt can be identified quantitatively, which cannot be achieved by the manual knocking damage identification method. Of course, according to the actual engineering requirements, the five bolt tightening torques can be divided into two bolt states: tightened and not tightened. For example, a tightening torque of 0 to 10Nm is regarded as not tightened, and a tightening torque of more than 10Nm is regarded as tightened.
The above is only an embodiment of the present invention. In fact, as the number of detection samples provided to the support vector machine increases, the recognition accuracy of the support vector machine increases accordingly. The present embodiment is schematically illustrated with only a small number of samples, and the specific number of samples to be used and the accuracy of the recognition result cannot be used to limit the protection scope of the present invention.
As another embodiment of the present invention, fig. 6 is a schematic diagram of a system for detecting damage to a composite material based on a classifier, including: the device comprises a knocking device 71, a sensor 72 integrated on the knocking device 71, and a signal processing part 74, wherein the signal processing part 74 comprises a spectrogram processing device 75 and a damage state determining device 76. The component to be inspected is a composite material 73. Wherein, the knocking device 31 can be a knocking device shown in fig. 2, and the sensor is preferably an accelerometer 204 shown in fig. 2, and the connection relationship with the knocking device is also shown in fig. 2. Of course, the sensor 72 may also be a speed sensor or a displacement sensor.
The method for detecting the damage state of the composite material based on the classifier comprises the following steps:
a. the composite material 73 is rapped with a fixed force by means of the rapping device 71.
b. The response signal transmitted to the rapping device 71 by the target area being rapped by the composite material is sensed with a sensor 72, wherein said sensor 72 is an accelerometer 204 as shown in fig. 2 and said response signal is an acceleration signal. Of course, the sensor 72 may also be a speed sensor or a displacement sensor, and the corresponding response signal is a speed signal or a displacement signal.
c. The response signal sensed by the sensor 72 is transformed by the spectrogram processing device 75 to obtain a signal spectrogram of the response signal, and spectrogram data of the signal spectrum in a specific frequency band is intercepted. Specifically, the response signal is an acceleration signal, and preferably, a power spectral density map of the acceleration signal is obtained and estimated by fourier transform, an AR model, a Welch method, or a maximum entropy method. The specific frequency band is a frequency band sensitive to the state of the composite material in the power spectral density diagram.
d. And (c) respectively repeating the steps a-c on the composite material in each state to obtain a plurality of training spectrogram data of the composite material in the specific frequency band in the state for the composite material in the normal state and in various damage states. Specifically, the damage state may be a fault, a volume defect, a crack, or the like.
e. And providing each state and the plurality of training spectrogram data in the state to the classifier, and training the classifier. The classifier can be support vector machine classifier, Bayes classifier, BP neural network classifier, decision tree algorithm or simple vector comparison index etc. because the support vector machine method has strict statistics learning theory basis, has many advantages in the aspect of solving small sample, nonlinearity and high dimension pattern recognition problem, consequently the utility model discloses the preferred support vector machine classifier that adopts.
f. And c, acquiring the detection spectrogram data of the composite material in the specific frequency band in the state to be detected through the steps a-c.
g. And providing the detection spectrogram data under the condition to be detected to the classifier, wherein the output identification result is the state of the composite material.
Based on the fact that the impedance of the composite material in the normal state is different from that of the composite material in the damaged state, and the response signals generated after the composite material is knocked are different, the state of the composite material is detected and classified by combining a pattern recognition method, whether the composite material is damaged or not can be distinguished, and the damage type of the composite material can be distinguished under the condition that a sampling sample is sufficient.
The system described in the above embodiment is based on the correlation between the shape of the acceleration power spectral density curve of the knocking device and the impedance state of the object to be measured. In fact, similar correlation relationships also exist between the shapes of other spectrograms of the acceleration signal of the knocking device and the impedance state of the object to be detected. Further, similar correlation exists between other spectrograms such as a power density spectrogram of the speed signal or the displacement signal of the knocking device and the impedance state of the object to be detected. Therefore, according to the utility model discloses, the spectrogram of knocking device's acceleration, speed or displacement signal all can be used for detecting the impedance state change of object, all can be used for detecting bolt elasticity state or combined material damage state promptly.
Specifically, the accelerometer 204 in the above embodiments may be replaced by a displacement sensor or a velocity sensor, and the collected and processed response signal may be a displacement signal or a velocity signal.
Further, in the above embodiment, the sensor is integrated on the tapping device. It will be appreciated by those skilled in the art that the sensor may also be placed on the tapped bolt or near the tapped area of the composite material.
The utility model provides a method and system based on classifier detection local impedance changes, the equipment of adoption is convenient for use, and the operating procedure is fairly simple, and when having under the condition of abundant sampling sample to train the classifier after examine can be simple and convenient rapid classify to the detection state to can carry out multiclass classification.
Above only the preferred embodiment of the present invention, the protection scope of the present invention is not limited to the above embodiments, all belong to the technical solution of the present invention under the thought. It should be noted that various modifications and decorations can be made by those skilled in the art without departing from the principle of the present invention.
Claims (7)
1. A system for detecting local impedance changes in an object, the system comprising a tapping device, a sensor and a signal processing component; it is characterized in that the preparation method is characterized in that,
the knocking device is used for applying a fixed knocking force to the object to be tested;
a sensor for sensing a response signal transmitted to the sensor by a target area struck by an object; the sensor is a speed sensor, an acceleration sensor or a displacement sensor;
a signal processing component that receives the response signal sensed by the sensor and processes it to detect an impedance state of the object target area, the signal processing component comprising:
the spectrogram processing device is used for carrying out conversion processing on the response signal sensed by the sensor so as to obtain a signal spectrogram of the response signal and intercepting spectrogram data of the signal frequency spectrum in a specific frequency band; and
and an impedance state determination device which detects an object impedance state based on the spectrogram data output by the classifier and the spectrogram processing device.
2. The system of claim 1, wherein the object is a composite material and the impedance state represents a damage state of the composite material.
3. The system of claim 1, wherein the object is a bolt and the impedance state represents a tightness state of the bolt.
4. The system according to any one of claims 1 to 3, wherein the knocking device comprises an exciter, a frame connected with the exciter, and a gasket arranged at the bottom of the frame.
5. The system of claim 4, wherein the sensor is a velocity sensor, an acceleration sensor, or a displacement sensor.
6. The system of claim 5, wherein the classifier is a support vector machine classifier.
7. The system of claim 1, wherein the sensor is integrated on the tapping device.
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| CN201220026428XU CN202533411U (en) | 2012-01-19 | 2012-01-19 | System for detecting object partial impedance changes |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108225887A (en) * | 2017-12-14 | 2018-06-29 | 中国特种飞行器研究所 | Bolt class standard part corrosion detecting method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108225887A (en) * | 2017-12-14 | 2018-06-29 | 中国特种飞行器研究所 | Bolt class standard part corrosion detecting method |
| CN108225887B (en) * | 2017-12-14 | 2020-09-22 | 中国特种飞行器研究所 | Corrosion detection method for bolt standard parts |
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