CN111458129A - High-precision online detection system for cantilever beam of crane - Google Patents

Abstract

The invention provides an on-line detection system for a high-precision crane cantilever beam, which utilizes N vibration sensors to perform fault diagnosis on the crane cantilever beam. N vibration sensors are evenly distributed on the cantilever beam frame, and the distance between each two vibration sensors is equal. The output ends of the N vibration sensors are connected to the input end of the signal processing module, and the signal processing module filters the received vibration signals. , the high-precision detection of the vibration signal is realized through the delay circuit, the addition circuit, etc. In addition, the fault diagnosis of the crane cantilever beam is realized by the variation range between the vibration signals collected by the N vibration sensors. Combining hardware and The software makes the detection more convenient and accurate.

Description

A high-precision crane cantilever beam online detection system

technical field

The invention relates to the field of intelligent testing, in particular to an on-line detection system for a high-precision crane cantilever beam.

Background technique

The geometric structure and load situation of the crane cantilever beam are relatively complex. The traditional design mainly determines its structural parameters based on experience, without accurate strength monitoring. In order to meet the reliability requirements, a larger safety factor is usually taken, resulting in material waste and cost. improve.

In the prior art, there are many algorithms and theories for the optimal design of the crane cantilever beam structure. Genetic Algorithm (GA), as a global optimization search algorithm, has the characteristics of not being limited by the search space and not solving the continuity. It is a general framework for solving optimization problems of complex systems, and it is very robust to the kinds of problems. However, the existing research on genetic algorithm is mainly based on the optimization method of continuous variables, and the crane cantilever beam mostly uses I-beam, and the design variables are often discrete variables. If the discrete results are obtained by adjusting the continuous variable optimization method, it is necessary to check Its feasibility and reliability, it is very likely that a feasible discrete solution cannot be obtained. Therefore, the crane cantilever beam detection system used in the prior art generally has low precision and complicated calculation.

SUMMARY OF THE INVENTION

Therefore, in order to overcome the above problems, the present invention provides an on-line detection system for a high-precision crane cantilever beam, which uses N vibration sensors to perform fault diagnosis on the crane cantilever beam. N vibration sensors are evenly distributed on the cantilever beam frame, and the distance between each two vibration sensors is equal. The output ends of the N vibration sensors are connected to the input end of the signal processing module, and the signal processing module filters the received vibration signals. , the high-precision detection of the vibration signal is realized through the delay circuit, the addition circuit, etc. In addition, the fault diagnosis of the crane cantilever beam is realized by the variation range between the vibration signals collected by the N vibration sensors. Combining hardware and The software makes the detection more convenient and accurate.

The high-precision crane cantilever beam online detection system provided by the present invention includes N vibration sensors, wherein N is a natural number greater than or equal to 2, the crane cantilever beam includes a cantilever beam frame body and a cantilever beam bracket, and the cantilever beam bracket is arranged on the cantilever beam frame body In between, N vibration sensors are evenly distributed on the cantilever beam frame, and the distance between every two vibration sensors is equal. The N vibration sensors transmit the collected vibration signals to the signal processing module for signal processing and then transmit them to the microprocessor. The microprocessor performs fault diagnosis on the crane cantilever beam according to the received vibration signals.

Specifically, the N vibration sensors transmit the collected vibration signals to the signal processing module for signal processing and then transmit them to the microprocessor. The microprocessor performs fault diagnosis on the crane cantilever beam according to the received vibration signals. The diagnosis steps are as follows :

Step S1: marking the N vibration sensors, marking sequentially from left to right or from right to left, and sequentially transmitting vibration signals to the microprocessor according to the size of the marking serial number;

Step S2: the microprocessor checks the received vibration signals, if the number of vibration signals received by the microprocessor in a preset sampling period is not equal to N, then return to step S1, otherwise go to step S3;

Step S3: the microprocessor marks the received N vibration signals as Z(1), Z(2)...Z(N);

Step S4: The microprocessor processes two adjacent vibration signals to obtain S(1), S(2)...S(N-1), where S(1)=丨Z(1)-Z( 2) 丨, S(2)=丨Z(2)-Z(3)丨…S(N-1)=丨Z(N-1)-Z(N)丨;

Step S5: compare the obtained S(1), S(2)...S(n-1) with the preset threshold value respectively, if there is a situation greater than the preset threshold value, the microprocessor controls the alarm device to issue an alarm signal, wherein , the preset threshold is A|Z(1)-Z(n)|/(N-1), A is a self-defined parameter.

Specifically, in step S5, the obtained S(1), S(2)...S(n-1) are respectively compared with the preset thresholds, and if there is a situation greater than the preset thresholds, the microprocessor controls the alarm device to issue an alarm signal, where the preset threshold is A丨Z(1)-Z(n)丨/(N-1), and A is a self-defined parameter; if the hoisting device under the crane cantilever is under no-load condition, then let A= 1. If the hoisting device under the cantilever beam of the crane is under load, let A=G1/G2, where G1 is the weight of the cantilever beam of the crane, and G2 is the load weight of the hoisting device.

Specifically, after the microprocessor-controlled alarm device sends out an alarm signal, the staff inspects the cantilever beam of the crane. A safety rope is arranged on one side of the cantilever beam frame. The safety rope passes through the safety rope fixing hole of the safety rope fixing device. The fixing device is fixedly connected to one side of the cantilever beam frame. The worker slides the protective rope to the safety rope through the protective buckle. The protective buckle is equipped with a force sensor, which is used to monitor the tension signal between the safety rope and the protective buckle. , and transmit the detected tension signal to the microprocessor, and the microprocessor evaluates the safety performance of the safety rope according to the received tension signal.

Specifically, the microprocessor evaluates the safety performance of the safety rope according to the received pulling force signal. If the pulling force signal received by the microprocessor is greater than the preset pulling force threshold, the microprocessor sends an alarm signal to prompt the staff to stop the maintenance. Check whether the protective buckle and the safety rope are stuck. The preset tension threshold is 1/5 of the maximum tension that the safety rope can carry.

Specifically, the signal processing module includes a delay circuit, a multiplication circuit, a first addition circuit, a second addition circuit, a third addition circuit and a subtraction circuit, wherein the vibration signal collected by the kth vibration sensor is x[ k], x[k] goes through the delay circuit, the multiplication circuit, the first addition circuit, the second addition circuit in turn, and then outputs to the subtraction circuit, the output signal is y1, x[k] goes through the delay circuit, the multiplication circuit, the first addition circuit in turn After the circuit is output to the third addition circuit, the output signal is y2, then the vibration signal x[k] collected by the kth vibration sensor is divided into two channels after the signal processing module, one is y1, the other is y2, The signal processing module transmits two signals to the microprocessor.

Specifically, there are N delay circuits, where N is an integer greater than 1, the serially connected delay elements L generate N delayed signals for x[k], and i represents an integer ranging from 1 to N-1, then the first The delay amount D _i+1 corresponding to the i+1 delayed signal is greater than the delay amount Di corresponding to the i-th delayed signal. The multiplication circuit is used to multiply the i-th delayed signal by the i-th weight to generate the i-th delayed signal. As a result of the weighting, the first adding circuit adds x[k] and the delayed signals whose i is an even number to generate the first sum value S1, and the second adding circuit adds the delayed signals whose i is an odd number to generate a second sum value S2, the third summation circuit adds the first summation value S1 and the second summation value S2, and the addition result y1 is the first filtered signal, and the subtraction circuit subtracts the second summation value S2 from the first summation value S1, which is The subtraction result y2 is the second filter signal, the first filter signal y1 and the second filter signal y2 are transmitted to the microprocessor, and the microprocessor obtains the average value of the first filter signal y1 and the second filter signal y2, and calculates the average value of the first filter signal y1 and the second filter signal y2. The value is recorded as the sampled value Z(k) of the kth vibration sensor.

Compared with the prior art, the present invention has the following beneficial effects:

(1) The high-precision crane cantilever beam online detection system provided by the present invention utilizes N vibration sensors to perform fault diagnosis on the crane cantilever beam. N vibration sensors are evenly distributed on the cantilever beam frame, and the distance between each two vibration sensors is equal. The output ends of the N vibration sensors are connected to the input end of the signal processing module, and the signal processing module filters the received vibration signals. , the high-precision detection of the vibration signal is realized through the delay circuit, the addition circuit, etc. In addition, the fault diagnosis of the crane cantilever beam is realized by the variation range between the vibration signals collected by the N vibration sensors. Combining hardware and The software makes the detection more convenient and accurate.

(2) The high-precision crane cantilever beam online detection system provided by the present invention is also inventive in that the present invention uses a multi-point monitoring method to monitor the vibration uniformity of the crane cantilever beam. The vibration of the cantilever beam will deviate from the normal value, and the normal value is determined according to the signals collected by other vibration sensors, which realizes dynamic monitoring and greatly improves the monitoring accuracy. Noise and vibration signals further improve the monitoring accuracy.

Description of drawings

Fig. 1 is the structure diagram of the high-precision crane cantilever beam online detection system of the present invention;

Fig. 2 is the structure diagram of the protection device of the high-precision crane cantilever beam of the present invention;

Fig. 3 is the structure diagram that the staff of the present invention performs maintenance on the cantilever beam of the crane;

4 is a schematic diagram of the high-precision crane cantilever beam online detection system of the present invention;

FIG. 5 is a block diagram of the operation of the signal processing module of the present invention.

Reference number:

1- Cantilever beam; 2- Operation room; 3- Cantilever beam frame body; 4- Cantilever beam support; 5- Hoisting device; 6- Crane support column; 7- Safety rope; 8- Safety rope fixing device; 9- Safety rope Fixing hole; 10-protection rope; 11-protection buckle.

Detailed ways

The high-precision crane cantilever beam online detection system provided by the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

As shown in Figures 1-2, the high-precision crane cantilever beam online detection system provided by the present invention includes N vibration sensors, where N is a natural number greater than or equal to 2, and the crane cantilever beam 1 includes a cantilever beam frame body 3 and a cantilever beam support 4. The cantilever beam bracket 4 is arranged between the cantilever beam frame bodies 3, and N vibration sensors are evenly distributed on the cantilever beam frame body 3. The distance between each two vibration sensors is equal, and the output ends of the N vibration sensors are connected to the signal processing module. The output end of the signal processing module is connected to the input end of the microprocessor, and the N vibration sensors transmit the collected vibration signals to the signal processing module for signal processing, and then transmit them to the microprocessor. Perform fault diagnosis on the crane cantilever beam 1 according to the received vibration signal.

In the above embodiment, the high-precision crane cantilever beam online detection system provided by the present invention utilizes N vibration sensors to perform fault diagnosis on the crane cantilever beam. N vibration sensors are evenly distributed on the cantilever beam frame, and the distance between each two vibration sensors is equal. The output ends of the N vibration sensors are connected to the input end of the signal processing module, and the signal processing module filters the received vibration signals. , the high-precision detection of the vibration signal is realized through the delay circuit, the addition circuit, etc. In addition, the fault diagnosis of the crane cantilever beam is realized by the variation range between the vibration signals collected by the N vibration sensors. Combining hardware and The software makes the detection more convenient and accurate.

Preferably, the N vibration sensors transmit the collected vibration signals to the signal processing module for signal processing and then transmit them to the microprocessor, and the microprocessor performs fault diagnosis on the crane cantilever beam 1 according to the received vibration signals. Proceed as follows:

Step S1: marking the N vibration sensors, marking sequentially from left to right or from right to left, and sequentially transmitting vibration signals to the microprocessor according to the size of the marking serial number;

Step S2: the microprocessor checks the received vibration signals, if the number of vibration signals received by the microprocessor in a preset sampling period is not equal to N, then return to step S1, otherwise go to step S3;

Step S3: the microprocessor marks the received N vibration signals as Z(1), Z(2)...Z(N);

Step S4: The microprocessor processes two adjacent vibration signals to obtain S(1), S(2)...S(N-1), where S(1)=丨Z(1)-Z( 2) 丨, S(2)=丨Z(2)-Z(3)丨…S(N-1)=丨Z(N-1)-Z(N)丨;

Step S5: compare the obtained S(1), S(2)...S(n-1) with the preset threshold value respectively, if there is a situation greater than the preset threshold value, the microprocessor controls the alarm device to issue an alarm signal, wherein , the preset threshold is A|Z(1)-Z(n)|/(N-1), A is a self-defined parameter.

Preferably, in step S5, the obtained S(1), S(2)...S(n-1) are respectively compared with the preset thresholds, and if there is a situation greater than the preset thresholds, the microprocessor controls the alarm device to send out Alarm signal, wherein, the preset threshold is A|Z(1)-Z(n)|/(N-1), A is a self-defined parameter; Let A=1, if the hoisting device 5 under the crane cantilever beam 1 is under load, then let A=G1/G2, where G1 is the weight of the crane cantilever beam 1, and G2 is the load weight of the hoisting device 5.

As shown in Figure 3-4, after the microprocessor-controlled alarm device sends out an alarm signal, the staff inspects the cantilever beam 1 of the crane. A safety rope 7 is arranged on one side of the cantilever beam frame body 3, and the safety rope 7 passes through the safety rope fixing device The safety rope fixing hole 9 of the The sensor, the force sensor is used to monitor the tension signal between the safety rope 7 and the protective buckle 11, and transmit the detected tension signal to the microprocessor, and the microprocessor evaluates the safety performance of the safety rope 7 according to the received tension signal .

Preferably, the microprocessor evaluates the safety performance of the safety rope 7 according to the received pulling force signal, and if the pulling force signal received by the microprocessor is greater than the preset pulling force threshold, the microprocessor sends an alarm signal to prompt the staff to stop During maintenance, check whether the protective buckle 11 and the safety rope 7 are stuck. The preset tension threshold is 1/5 of the maximum tension that the safety rope 7 can bear.

The microprocessor is arranged in the operating room 2, and the alarm device is also arranged in the operating room 2. In addition, the high-precision crane cantilever beam online detection system provided by the present invention also includes an expansion port, which can add sensor devices and microprocessors through the expansion port. For example, adding an image sensor to detect the image information of the cantilever beam 1 of the jack, etc.

Preferably, the signal processing module includes a delay circuit, a multiplication circuit, a first addition circuit, a second addition circuit, a third addition circuit and a subtraction circuit, wherein the vibration signal collected by the kth vibration sensor is x [k], x[k] goes through the delay circuit, the multiplication circuit, the first addition circuit, the second addition circuit in turn, and then output to the subtraction circuit, the output signal is y1, x[k] goes through the delay circuit, the multiplication circuit, the first After the addition circuit is output to the third addition circuit, the output signal is y2, then the vibration signal x[k] collected by the kth vibration sensor is divided into two channels after the signal processing module, one is y1 and the other is y2 , the signal processing module transmits two signals to the microprocessor.

Preferably, there are N delay circuits, where N is an integer greater than 1, the serially connected delay elements L generate N delayed signals for x[k], and i represents an integer ranging from 1 to N-1, then The delay amount Di ₊₁ corresponding to the i+1-th delayed signal is greater than the delay amount Di corresponding to the i-th delayed signal. The multiplying circuit is used to multiply the i-th delayed signal by the i-th weight to generate the i-th delayed signal. As a result of the i weighting, the first addition circuit adds x[k] and the delayed signals of which i is an even number to generate a first sum value S1, and the second addition circuit adds the delayed signals of which i is an odd number to generate a second sum value S1 The summation value S2, the third summation circuit adds the first summation value S1 and the second summation value S2, and the addition result y1 is the first filtered signal, and the subtraction circuit subtracts the second summation value S2 from the first summation value S1, The subtraction result y2 is the second filter signal, the first filter signal y1 and the second filter signal y2 are transmitted to the microprocessor, and the microprocessor obtains the average value of the first filter signal y1 and the second filter signal y2, and calculates the average value of the first filter signal y1 and the second filter signal y2. This value is recorded as the sampling value Z(k) of the kth vibration sensor.

To further illustrate, the signal processing module 100 includes a delay circuit 101, a multiplication circuit 102, a first addition circuit 103, a second addition circuit 104, a third addition circuit 105, and a subtraction circuit 106, wherein the kth vibration The vibration signal collected by the sensor is x[k], and x[k] passes through the delay circuit 101, the multiplication circuit 102, the first addition circuit 103, and the second addition circuit 104 in turn, and then is output to the subtraction circuit 106, and the output signal is y1, x[ k] passes through the delay circuit 101, the multiplying circuit 102, and the first adding circuit 103 in sequence, and then is output to the third adding circuit 105, and the output signal is y2, then the vibration signal x[k] collected by the kth vibration sensor passes through the signal processing module 100 The output signal is divided into two channels, one is y1 and the other is y2.

5 is a schematic diagram of a signal processing module. The signal processing module 100 includes a delay circuit 101 , a multiplying circuit 102 , a first adding circuit 103 , a second adding circuit 104 , a third adding circuit 105 and a subtracting circuit 106 . There are N delay circuits 101, N is an integer greater than 1, and the delay elements L connected in series generate N delayed signals for x[k]. The signal processing module 100 in FIG. 5 takes N=24 as an example, i Represents an integer ranging from 1 to N-1, then the delay amount Di ₊₁ corresponding to the i+1-th delayed signal is greater than the delay amount Di corresponding to the i-th delayed signal, and the multiplying circuit 102 is used to convert the The i-th delayed signal is multiplied by the i-th weight to generate the i-th weighted result. During actual testing, the delay amount and the weight are conventional choices made by those skilled in the art according to specific test conditions. The first adding circuit 103 adds x[k] and twelve delayed signals where i is an even number to generate a first sum value S1, and the second adding circuit 104 adds the other twelve delayed signals where i is an odd number. Add to generate the second sum value S2, the third adding circuit 105 adds the first sum value S1 and the second sum value S2, the addition result y1 is the first filtered signal, and the subtraction circuit 106 subtracts the first sum value S1 The second sum value S2 is removed, and the subtraction result y2 is the second filtered signal.

The first filtered signal y1 and the second filtered signal y2 are transmitted to the microprocessor, and the microprocessor obtains the average value of the first filtered signal y1 and the second filtered signal y2, and records this value as the sampling of the kth vibration sensor value Z(k).

Therefore, the present invention uses a multi-point monitoring method to monitor the vibration uniformity of the crane cantilever beam. At the structural instability, the vibration of the crane cantilever beam will deviate from the normal value, and the normal value is determined according to the signals collected by other vibration sensors, so as to realize In addition, the use of signal processing module can effectively filter out noise and vibration signals caused by high-altitude environmental factors, further improving the monitoring accuracy.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described with reference to the preferred embodiments of the present invention, those of ordinary skill in the art should Various changes in the above and in the details may be made therein without departing from the spirit and scope of the present invention as defined by the appended claims.

Claims (7)

1. The high-precision crane cantilever beam online detection system is characterized by comprising N vibration sensors, wherein N is a natural number greater than or equal to 2, the crane cantilever beam (1) comprises cantilever beam frame bodies (3) and cantilever beam supports (4), the cantilever beam supports (4) are arranged between the cantilever beam frame bodies (3), the N vibration sensors are uniformly distributed on the cantilever beam frame bodies (3), the distance between every two vibration sensors is equal, the output ends of the N vibration sensors are connected with the input end of a signal processing module, the output end of the signal processing module is connected with the input end of a microprocessor, the N vibration sensors transmit collected vibration signals to the signal processing module for signal processing and then transmit the vibration signals to the microprocessor, and the microprocessor carries out fault diagnosis on the crane cantilever beam (1) according to the received vibration signal.

2. The high-precision online detection system for the cantilever beam of the crane according to claim 1, wherein the N vibration sensors transmit collected vibration signals to the signal processing module for signal processing and then to the microprocessor, the microprocessor performs fault diagnosis on the cantilever beam (1) of the crane according to the received vibration signals, and the diagnosis steps are as follows:

step S1: marking the N vibration sensors, sequentially marking the vibration sensors from left to right or from right to left, and sequentially transmitting vibration signals to the microprocessor according to the sizes of the marking serial numbers;

step S2: the microprocessor checks the received vibration signals, if the number of the vibration signals received by the microprocessor in a preset sampling period is not equal to N, the step is returned to S1, otherwise, the step is carried out to S3;

step S3: the microprocessor marks the received N vibration signals as Z (1), Z (2) … Z (N);

step S4: the microprocessor processes two adjacent vibration signals to obtain S (1) and S (2) … S (N-1), wherein S (1) = Z (1) -Z (2), S (2) = Z (2) -Z (3) … S (N-1) = Z (N-1) -Z (N);

step S5: comparing the obtained S (1) and S (2) … S (N-1) with preset threshold values respectively, and if the obtained S (1) and S (2) … S (N-1) are greater than the preset threshold values, controlling an alarm device to send out an alarm signal by the microprocessor, wherein the preset threshold values are A I Z (1) -Z (N) I/(N-1), and A is a self-defined parameter.

3. The high-precision crane cantilever beam online detection system of claim 2, wherein the obtained S (1) and S (2) … S (N-1) are respectively compared with a preset threshold in step S5, and if the obtained S (1) and S (2) S (N-1) are greater than the preset threshold, the microprocessor controls the alarm device to send out an alarm signal, wherein the preset threshold is ai Z (1) -Z (N) i/(N-1), and a is a self-defined parameter; and if the hoisting device (5) under the crane cantilever beam (1) is in an unloaded condition, making A =1, and if the hoisting device (5) under the crane cantilever beam (1) is in a loaded condition, making A = G1/G2, wherein G1 is the weight of the crane cantilever beam (1) and G2 is the load weight of the hoisting device (5).

4. The high-precision online detection system for the cantilever beam of the crane according to claim 2, wherein after the microprocessor controls the alarm device to send out an alarm signal, a worker overhauls the cantilever beam (1), a safety rope (7) is arranged on one side in the cantilever beam frame body (3), the safety rope (7) passes through a safety rope fixing hole (9) of a safety rope fixing device (8), the safety rope fixing device (8) is fixedly connected with one side in the cantilever beam frame body (3), the worker slidably connects a self-protection rope (10) to the safety rope (7) through a protection buckle (11), the protection buckle (11) is provided with a force sensor, and the force sensor is used for monitoring a tension signal between the safety rope (7) and the protection buckle (11) and transmitting the detected tension signal to the microprocessor, and the microprocessor evaluates the safety performance of the safety rope (7) according to the received tension signal.

5. The high-precision crane cantilever beam online detection system according to claim 4, wherein the microprocessor evaluates the safety performance of the safety rope (7) according to the received tension signal, and if the tension signal received by the microprocessor is greater than a preset tension threshold, the microprocessor sends an alarm signal to prompt a worker to stop maintenance and check whether the protection buckle (11) and the safety rope (7) are stuck or not, wherein the preset tension threshold is 1/5 at which the safety rope (7) can bear the maximum tension.

6. The system as claimed in claim 2, wherein the signal processing module comprises a delay circuit, a multiplication circuit, a first addition circuit, a second addition circuit, a third addition circuit and a subtraction circuit, wherein the vibration signal collected by the kth vibration sensor is x [ k ], x [ k ] passes through the delay circuit, the multiplication circuit, the first addition circuit and the second addition circuit in sequence and then is output to the subtraction circuit, the output signal is y1, x [ k ] passes through the delay circuit, the multiplication circuit and the first addition circuit in sequence and then is output to the third addition circuit, the output signal is y2, and then the output signal of the vibration signal x [ k ] collected by the kth vibration sensor after passing through the signal processing module is divided into two paths, one path is y1, and the other path is y2, and the signal processing module transmits two paths of signals to the microprocessor.

7. The high accuracy crane cantilever online detection system of claim 6, wherein there are N of said delay circuits, N is an integer greater than 1, and the number of serially connected delay elements L is x [ k ]]Generating N delayed signals, i represents an integer ranging from 1 to N-1, and the delay amount D corresponding to the i +1 th delayed signal_i+1Greater than the delay amount Di corresponding to the ith delayed signal, the multiplication circuit is used for multiplying the ith delayed signal by the ith weight to generate the ith weighted result, and the first addition circuit is used for multiplying x [ k ]]And the delayed signals with i being an even number are added to generate a first sum value S1, the delayed signals with i being an odd number are added to generate a second sum value S2, the first sum value S1 and the second sum value S2 are added to generate an addition result y1 of the first sum value S2, the subtraction circuit subtracts the second sum value S2 from the first sum value S1 of the subtraction result y2 of the second sum value S2 of the third sum value S2 of the third sum value S, the first filtered signal y1 and the second filtered signal y2 of the third sum value S1 of the second sum value S3552 of the third sum value S, and the third sum value y2 of the third sum value S1 of the third sum value S2 of the third sum.

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