CN106197970B - A kind of bridge rope monitoring method and system based on optimization tensioning string model - Google Patents
A kind of bridge rope monitoring method and system based on optimization tensioning string model Download PDFInfo
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Abstract
The invention discloses a kind of bridge rope monitoring methods based on optimization tensioning string model and system, method to include：It chooses with the wirerope of actual measurement bridge rope same size to build the survival function of newton Gaussian processes, then solution is iterated to the survival function of newton Gaussian processes, it draws the optimal eigenfrequency exponent number of the survival function of newton Gaussian processes and optimal wirerope bending stiffness, string model is finally tensioned according to the result optimizing of iterative solution；Acquire theoretical eigenfrequency；It draws the rumble spectrum of bridge rope, the frequency for meeting optimal eigenfrequency exponent number is then filtered out from obtained rumble spectrum as actual measurement eigenfrequency；Acceleration transducer node determines whether ipc monitor center transmission gathered data according to the ratio size of the difference of actual measurement eigenfrequency and theoretical eigenfrequency and theoretical eigenfrequency and adjusts the sample frequency of acceleration transducer.Error of the present invention is small and can take into account accuracy of analysis and power consumption simultaneously, can be widely applied to bridge monitoring field.
Description
Technical Field
The invention relates to the field of bridge monitoring, in particular to a bridge cable monitoring method and system based on an optimized tensioning string model.
Background
The bridge cable is an important stressed member of bridge structures such as a cable-stayed bridge, a suspension bridge and the like, the cable force value of the bridge cable is an important index for evaluating the state of the bridge, and the measurement of the cable force value of the bridge cable also becomes an important component of a bridge cable monitoring system. At present, a plurality of bridge cable force value measuring methods exist, wherein a frequency spectrum vibration method with the advantages of simplicity and non-destructiveness is widely applied. The frequency spectrum vibration method mainly adopts an acceleration sensor to measure the tension of the bridge cable, obtains the vibration frequency of the bridge cable by obtaining the acceleration of the acceleration sensor under the environmental excitation, and finally obtains the tension of the bridge cable by utilizing a tension string model. In the tensioning string model which is taken as the theoretical basis of the frequency spectrum vibration method, the bending rigidity is a parameter which is difficult to measure, and the bending rigidity has large influence on high-order eigenfrequency, so that the tension of the bridge cable obtained by directly adopting the traditional tensioning string model has larger error.
In the existing bridge cable monitoring system based on a frequency vibration method, each acceleration sensor attached to a bridge cable acquires vibration signals of the bridge cable in a network coverage monitoring area through a wireless sensor network, and transmits data to an upper computer monitoring center for frequency spectrum and cable force analysis. However, the existing bridge cable monitoring system based on the frequency vibration method has the following defects or shortcomings:
(1) The tension of the bridge cable is obtained by directly adopting the traditional tension string model, the influence of bending rigidity is ignored, and the error is large;
(2) The acceleration sensor needs to send all collected signals to an upper computer monitoring center, so that the power consumption of the sensor node is increased; the sampling frequency of the acceleration sensor is a fixed value manually selected, the resolution of the acquired signals is reduced by a low sampling rate, the analysis of data is finally influenced, the power consumption of the sensor node is increased by a high sampling rate, and the analysis accuracy and the power consumption cannot be simultaneously considered.
Disclosure of Invention
In order to solve the above technical problems, the present invention aims to: the bridge cable monitoring method based on the optimized tensioning string model is small in error, capable of simultaneously considering both analysis accuracy and power consumption.
Another object of the present invention is to: the bridge cable monitoring system is small in error, capable of simultaneously considering both analysis accuracy and power consumption and based on the optimized tensioning string model.
The technical scheme adopted by the invention is as follows:
a bridge cable monitoring method based on an optimized tension string model comprises the following steps:
selecting a steel cable with the same specification as the actually measured bridge cable to construct a residual function of the Newton-Gaussian method, then adopting a Jacobi matrix method to carry out iterative solution on the residual function of the Newton-Gaussian method to obtain the optimal eigen frequency order and the optimal bending rigidity of the steel cable of the residual function of the Newton-Gaussian method, and finally optimizing a tension string model according to the result of the iterative solution;
obtaining theoretical eigenfrequency according to the theoretical tension value of the bridge cable and the optimized tension string model;
obtaining a vibration frequency spectrum of the bridge cable according to signals acquired by the nodes of the acceleration sensor, and then screening out frequencies which accord with the optimal eigenfrequency order from the obtained vibration frequency spectrum to be used as actual measurement eigenfrequencies;
and the acceleration sensor node determines whether to send acquired data to an upper computer monitoring center or not and adjusts the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actual measurement eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency so as to balance the power consumption and the analysis accuracy.
Further, the step of selecting a steel cable with the same specification as the actually measured bridge cable to construct a residual function of the newton-gaussian method, then performing iterative solution on the residual function of the newton-gaussian method by adopting a jacobian matrix method to obtain an optimal eigen frequency order of the residual function of the newton-gaussian method and an optimal bending stiffness of the steel cable, and finally optimizing a tension string model according to the result of the iterative solution includes:
selecting a steel cable with the same specification as the actually measured bridge cable on a tension tester to perform a tension test, and constructing a residual function of the Newton Gaussian method, wherein the expression of the residual function r of the Newton Gaussian method is as follows:wherein k is an eigenfrequency order, EI is bending stiffness of the steel cable, m is unit mass of the steel cable, L is the length between two fixed test points of the steel cable during a tension test, f is the central frequency of a steel cable vibration frequency spectrum, and N is tension applied to the steel cable;
taking (m, L, f, N) as a known variable and beta = (k, EI) as a variable to be solved, solving the residual function of the Newton Gaussian method by adopting a Jacobi matrix iteration method, and obtaining the optimal eigen frequency order k of the residual function of the Newton Gaussian method o And optimal steel cable bending stiffness EI o ；
Optimum eigenfrequency order k o And optimal wire rope bending stiffness EI o Substituting the tension string model into an optimized tension string model, wherein the expression of the optimized tension string model is as follows:wherein,for the optimum eigenfrequency order k o The corresponding eigenfrequency.
Further, the step of solving the residual function of newton-gaussian method by using (m, L, f, N) as the known variable and β = (k, EI) as the variable to be solved and using jacobian matrix iteration method to obtain the optimal eigenfrequency order of the residual function of newton-gaussian method and the optimal bending stiffness of the steel cable includes:
n residual functions r constructed according to n different tensile tests 1 ,r 2 ,...r n Obtaining a Jacobian matrix of a residual function r, the Jacobian matrix J of the residual function r r The expression of (c) is:
wherein n is not less than 2 and n is an integer,andas a residual function r for the nth run n Partial derivatives of k and EI, respectively;
according to the obtained Jacobian matrix J r Iterating the variable beta to be solved until the variable beta is s+1 And beta s Until the difference is less than the set threshold, finally beta at the end of the iteration s+1 Corresponding k and EI as optimal eigenfrequency order and optimal steel cable bending resistanceStiffness, wherein the formula for iterating the variable β to be solved is:wherein s is iteration number, s is more than or equal to 0 and less than or equal to n, and the initial value of iteration is beta 0 = (1, ei0), EI0 is the minimum estimate of steel cable bending stiffness.
Further, the step of obtaining a vibration frequency spectrum of the bridge cable according to signals collected by the acceleration sensor nodes, and then screening out a frequency which accords with an optimal eigen frequency order from the obtained vibration frequency spectrum as an actually measured eigen frequency includes:
in the acceleration sensor node, adding a Hamming window to the acquired signal to obtain a windowed acquired signal;
carrying out fast Fourier transform on the windowed acquired signal to obtain a vibration frequency spectrum of the bridge cable;
and screening out the frequency which accords with the optimal eigenfrequency order from the obtained vibration frequency spectrum as the actually measured eigenfrequency.
Further, the acceleration sensor node determines whether to send the collected data to the monitoring center of the upper computer and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference value between the actually-measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency so as to balance the power consumption and the analysis accuracy, and the method comprises the following steps:
calculating the ratio of the difference between the actually measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency;
and the acceleration sensor node executes the operation of the corresponding event according to the ratio range of the calculated ratio: if the calculated ratio belongs to the ratio range of the event I, only the actually measured eigenfrequency is sent to the upper computer monitoring center; if the calculated ratio belongs to the ratio range of the second event, the sampling frequency of the acceleration sensor is not changed, and the data acquired by the acceleration sensor is sent to the upper computer monitoring center; and if the calculated ratio belongs to the ratio range of the event III, increasing the sampling frequency of the acceleration sensor and sending the data acquired by the acceleration sensor to the upper computer monitoring center.
Further, the ratio range of the event one is [0, 15%), and the event one sets the sampling frequency of the acceleration sensor to 200Hz; the value range of the second event is [15%,30% ]; the value range of the event III is (30%, 100%), and the sampling frequency of the acceleration sensor is increased to 1000Hz by the event III.
The other technical scheme adopted by the invention is as follows:
a bridge cable monitoring system based on an optimized tension string model comprises the following modules:
the tensioning string model optimizing module is used for selecting a steel cable with the same specification as the actually measured bridge cable to construct a residual function of the Newton-Gaussian method, then performing iterative solution on the residual function of the Newton-Gaussian method by adopting a Jacobi matrix method to obtain the optimal eigen frequency order and the optimal bending rigidity of the steel cable of the residual function of the Newton-Gaussian method, and finally optimizing a tensioning string model according to the result of the iterative solution;
the theoretical eigenfrequency calculation module is used for solving the theoretical eigenfrequency according to the theoretical tension value of the bridge cable and the optimized tensioning string model;
the actual measurement eigenfrequency acquisition module is used for obtaining a vibration frequency spectrum of the bridge cable according to signals acquired by the acceleration sensor nodes, and then screening frequencies which accord with the optimal eigenfrequency order from the obtained vibration frequency spectrum to be used as actual measurement eigenfrequencies;
and the acceleration sensor node determining and adjusting module is used for determining whether to send the acquired data to the upper computer monitoring center or not and adjusting the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actually-measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency by the acceleration sensor node so as to balance the power consumption and the analysis accuracy.
Further, the tensioned string model optimization module includes:
the construction unit is used for selecting a steel cable with the same specification as the actually measured bridge cable on the tensile testing machine to carry out tensile test, and constructing a residual function of the Newton Gaussian method, wherein the expression of the residual function r of the Newton Gaussian method is as follows:
wherein k is an eigenfrequency order, EI is bending stiffness of the steel cable, m is unit mass of the steel cable, L is the length between two fixed test points of the steel cable during a tension test, f is the central frequency of a steel cable vibration frequency spectrum, and N is tension applied to the steel cable;
the iteration unit is used for solving a residual function of the Newton Gaussian method by using (m, L, f, N) as a known variable and beta = (k, EI) as a variable to be solved by adopting a Jacobi matrix iteration method to obtain an optimal eigenfrequency order of the residual function of the Newton Gaussian method and an optimal bending stiffness of the steel cable;
substitution unit for substituting the optimal eigenfrequency order k o And optimal wire rope bending stiffness EI o Substituting the tension string model into an optimized tension string model, wherein the expression of the optimized tension string model is as follows:
wherein,and the eigenfrequency corresponding to the optimal eigenfrequency order ko.
Further, the iteration unit includes:
a Jacobian matrix acquisition subunit for acquiring n residual functions r constructed according to n times of different tensile tests 1 ,r 2 ,...r n Obtaining a Jacobian matrix of a residual function r, the Jacobian matrix J of the residual function r r The expression of (a) is:
wherein n is not less than 2 and n is an integer,andas a residual function r of the n-th experiment n Partial derivatives of k and EI, respectively;
an iteration subunit for obtaining a Jacobian matrix J according to the obtained Jacobian matrix r Iterating the variable beta to be solved until the variable beta is reached s+1 And beta s Until the difference is less than the set threshold, finally beta at the end of the iteration s+1 And taking the corresponding k and EI as an optimal eigenfrequency order and an optimal bending rigidity of the steel cable, wherein the formula for iterating the variable beta to be solved is as follows:
wherein s is the number of iterations, s is greater than or equal to 0 and less than or equal to n, and the initial value of the iteration is beta 0 = (1, ei0), EI0 is the minimum estimate of steel cable bending stiffness.
Further, the acceleration sensor node determination and adjustment module comprises:
the calculation unit is used for calculating the ratio of the difference value of the actually measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency;
and the execution event determining unit is used for executing the operation of the corresponding event according to the ratio range of the calculated ratio by the acceleration sensor node: if the calculated ratio belongs to the ratio range of the event I, only the actually measured eigenfrequency is sent to the upper computer monitoring center; if the calculated ratio belongs to the ratio range of the second event, the sampling frequency of the acceleration sensor is not changed, and the data acquired by the acceleration sensor is sent to the upper computer monitoring center; and if the calculated ratio belongs to the ratio range of the event III, increasing the sampling frequency of the acceleration sensor and sending the data acquired by the acceleration sensor to the upper computer monitoring center.
The method of the invention has the beneficial effects that: the Newton Gaussian method is adopted to optimize the traditional tension string model, the optimal eigenfrequency order and the bending rigidity of the steel cable are solved, the influence of the bending rigidity is considered, the bending rigidity can be accurately obtained, and the error is small; the acceleration sensor node determines whether to send the acquired data to the upper computer monitoring center or not and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actual measurement eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency, so that the condition of continuously sending sampling signals is avoided, the condition of low analysis accuracy is avoided, and the analysis accuracy and the power consumption can be considered at the same time.
The system of the invention has the advantages that: the Newton Gaussian method is adopted to optimize the traditional tension string model, the optimal eigenfrequency order and the bending rigidity of the steel cable are solved, the influence of the bending rigidity is considered, the bending rigidity can be accurately obtained, and the error is small; the acceleration sensor node determines whether to send the acquired data to the upper computer monitoring center or not and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actually measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency, so that the condition of continuously sending sampling signals is avoided, the condition of low analysis accuracy is avoided, and the analysis accuracy and the power consumption can be considered simultaneously.
Drawings
FIG. 1 is an overall flow chart of a bridge cable monitoring method based on an optimized tension string model according to the present invention;
fig. 2 is a flowchart of the algorithm according to the first embodiment.
Detailed Description
Referring to fig. 1, a bridge cable monitoring method based on an optimized tension string model includes the following steps:
selecting a steel cable with the same specification as the actually measured bridge cable to construct a residual function of the Newton-Gaussian method, then adopting a Jacobi matrix method to carry out iterative solution on the residual function of the Newton-Gaussian method to obtain the optimal eigen frequency order and the optimal bending rigidity of the steel cable of the residual function of the Newton-Gaussian method, and finally optimizing a tension string model according to the result of the iterative solution;
obtaining theoretical eigenfrequency according to the theoretical tension value of the bridge cable and the optimized tension string model;
obtaining a vibration frequency spectrum of the bridge cable according to signals acquired by the nodes of the acceleration sensor, and then screening out frequencies which accord with the optimal eigenfrequency order from the obtained vibration frequency spectrum to be used as actual measurement eigenfrequencies;
the acceleration sensor node determines whether to send collected data to the monitoring center of the upper computer and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actually-measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency so as to balance power consumption and analysis accuracy.
Further as a preferred embodiment, the step of selecting a steel cable with the same specification as the actually measured bridge cable to construct a residual function of the newton-gaussian method, then performing iterative solution on the residual function of the newton-gaussian method by using a jacobian matrix method to obtain an optimal eigen frequency order and an optimal bending stiffness of the steel cable of the residual function of the newton-gaussian method, and finally optimizing the tension string model according to the iterative solution result includes:
selecting a steel cable with the same specification as an actually measured bridge cable on a tension tester to carry out a tension test, and constructing a residual function of the Newton-Gaussian method, wherein the expression of the residual function r of the Newton-Gaussian method is as follows:the method comprises the following steps that k is an eigenfrequency order, EI is bending rigidity of a steel cable, m is unit mass of the steel cable, L is the length between two fixed test points of the steel cable during a tension test, f is the central frequency of a steel cable vibration frequency spectrum, and N is tension applied to the steel cable;
taking (m, L, f, N) as a known variable and beta = (k, EI) as a variable to be solved, adopting a Jacobian matrix iteration method to solve the residual function of the Newton Gaussian method, and obtaining the optimal eigen frequency order k of the residual function of the Newton Gaussian method o And optimal wire rope bending stiffness EI o ；
The optimal eigenfrequency order k o And optimal steel cable bending stiffness EI o Substituting the tension string model into an optimized tension string model, wherein the expression of the optimized tension string model is as follows:wherein,for the optimum eigenfrequency order k o The corresponding eigenfrequency.
Further, as a preferred embodiment, the step of solving the residual function of the newton-gaussian method by using (m, L, f, N) as a known variable and β = (k, EI) as a variable to be solved and using a jacobian matrix iteration method to obtain an optimal eigenfrequency order of the residual function of the newton-gaussian method and an optimal bending stiffness of the steel cable includes:
n residual functions r constructed according to n different tension tests 1 ,r 2 ,...r n Obtaining a Jacobian matrix of a residual function r, the Jacobian matrix J of the residual function r r The expression of (c) is:
wherein n is not less than 2 and n is an integer,andas a residual function r of the n-th experiment n Partial derivatives of k and EI, respectively;
according to the obtained Jacobian matrix J r Iterating the variable beta to be solved until the variable beta is s+1 And beta s Until the difference is less than the set threshold, beta finally ending with iteration s+1 The corresponding k and EI are used as the optimal eigenfrequency order and the optimal bending rigidity of the steel cable, wherein the formula for iterating the variable beta to be solved is as follows:wherein s is iteration number, s is more than or equal to 0 and less than or equal to n, and the initial value of iteration is beta 0 = (1, EI0), EI0 is the minimum estimate of the bending stiffness of the steel cable.
Further as a preferred embodiment, the step of obtaining a vibration spectrum of the bridge cable according to the signal acquired by the node of the acceleration sensor, and then screening a frequency which meets an optimal eigen frequency order from the obtained vibration spectrum as an actually measured eigen frequency includes:
in the acceleration sensor node, adding a Hamming window to the acquired signal to obtain a windowed acquired signal;
carrying out fast Fourier transform on the windowed acquired signal to obtain a vibration frequency spectrum of the bridge cable;
and screening out the frequency which accords with the optimal eigenfrequency order from the obtained vibration frequency spectrum as the actually measured eigenfrequency.
Further as a preferred embodiment, the acceleration sensor node determines whether to send the collected data to the upper computer monitoring center and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference between the measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency, so as to balance the power consumption and the analysis accuracy, and the method includes the following steps:
calculating the ratio of the difference between the actually measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency;
and the acceleration sensor node executes the operation of the corresponding event according to the ratio range of the calculated ratio: if the calculated ratio belongs to the ratio range of the event I, only the actually measured eigenfrequency is sent to an upper computer monitoring center; if the calculated ratio belongs to the ratio range of the second event, the sampling frequency of the acceleration sensor is not changed, and data acquired by the acceleration sensor are sent to the upper computer monitoring center; and if the calculated ratio belongs to the ratio range of the event III, improving the sampling frequency of the acceleration sensor and sending the data acquired by the acceleration sensor to an upper computer monitoring center.
Further as a preferred embodiment, the ratio range of the event one is [0, 15%), and the event one sets the sampling frequency of the acceleration sensor to 200Hz; the value range of the second event is [15%,30% ]; the sampling frequency of the acceleration sensor is increased to 1000Hz by the event III, wherein the value range of the event III is (30%, 100%).
Referring to fig. 1, a bridge cable monitoring system based on an optimized tension string model includes the following modules:
the tensioning string model optimizing module is used for selecting a steel cable with the same specification as the actually measured bridge cable to construct a residual function of the Newton-Gaussian method, then performing iterative solution on the residual function of the Newton-Gaussian method by adopting a Jacobi matrix method to obtain the optimal eigen frequency order and the optimal bending rigidity of the steel cable of the residual function of the Newton-Gaussian method, and finally optimizing a tensioning string model according to the result of the iterative solution;
the theoretical eigenfrequency calculation module is used for solving the theoretical eigenfrequency according to the theoretical tension value of the bridge cable and the optimized tensioning string model;
the actual measurement eigenfrequency acquisition module is used for obtaining a vibration frequency spectrum of the bridge cable according to signals acquired by the acceleration sensor node, and then screening out the frequency which accords with the optimal eigenfrequency order from the obtained vibration frequency spectrum to be used as the actual measurement eigenfrequency;
and the acceleration sensor node determining and adjusting module is used for determining whether to send the acquired data to the upper computer monitoring center or not and adjusting the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actually-measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency by the acceleration sensor node so as to balance the power consumption and the analysis accuracy.
Further as a preferred embodiment, the tensioned string model optimization module comprises:
the construction unit is used for selecting a steel cable with the same specification as the actually measured bridge cable on the tensile testing machine for a tensile test, and constructing a residual function of a Newton Gaussian method, wherein the expression of the residual function r of the Newton Gaussian method is as follows:
wherein k is an eigenfrequency order, EI is bending stiffness of the steel cable, m is unit mass of the steel cable, L is the length between two fixed test points of the steel cable during a tension test, f is the central frequency of a steel cable vibration frequency spectrum, and N is tension applied to the steel cable;
the iteration unit is used for solving the residual function of the Newton Gaussian method by using the Jacobi matrix iteration method by taking (m, L, f and N) as a known variable and beta = (k, EI) as a variable to be solved so as to obtain the optimal eigenfrequency order of the residual function of the Newton Gaussian method and the optimal bending stiffness of the steel cable;
substitution unit for substituting the optimal eigenfrequency order k o And optimal wire rope bending stiffness EI o Substituting the tension string model into the tension string model to generate an optimized tension string model, wherein the expression of the optimized tension string model is as follows:
wherein,the order ko of the eigenfrequency is the optimum eigenfrequency.
Further as a preferred embodiment, the iteration unit includes:
a Jacobian matrix acquisition subunit for acquiring n residual functions r constructed according to n different tensile tests 1 ,r 2 ,…r n Obtaining a Jacobian matrix of a residual function r, the Jacobian matrix J of the residual function r r The expression of (a) is:
wherein n is not less than 2 and n is an integer,andas a residual function r for the nth run n Partial derivatives of k and EI, respectively;
an iteration subunit for obtaining a Jacobian matrix J according to the obtained Jacobian matrix r Iterating the variable beta to be solved until the variable beta is s+1 And beta s Until the difference is less than the set threshold, finally beta at the end of the iteration s+1 The corresponding k and EI are used as the optimal eigenfrequency order and the optimal bending rigidity of the steel cable, wherein the formula for iterating the variable beta to be solved is as follows:
wherein s is iteration number, s is more than or equal to 0 and less than or equal to n, and the initial value of iteration is beta 0 = (1, ei0), EI0 is the minimum estimate of steel cable bending stiffness.
Further as a preferred embodiment, the acceleration sensor node determination and adjustment module includes:
the calculation unit is used for calculating the ratio of the difference value of the actually measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency;
and the execution event determining unit is used for executing the operation of the corresponding event according to the ratio range of the calculated ratio by the acceleration sensor node: if the calculated ratio belongs to the ratio range of the event I, only the actually measured eigenfrequency is sent to the upper computer monitoring center; if the calculated ratio belongs to the ratio range of the second event, the sampling frequency of the acceleration sensor is not changed, and the data acquired by the acceleration sensor is sent to the upper computer monitoring center; and if the calculated ratio belongs to the ratio range of the event III, increasing the sampling frequency of the acceleration sensor and sending the data acquired by the acceleration sensor to the upper computer monitoring center.
The invention is further explained and illustrated in the following description with reference to the figures and the specific embodiments thereof.
Example one
Referring to fig. 2, a first embodiment of the present invention:
aiming at the problems that the prior art has large error and cannot simultaneously consider analysis accuracy and power consumption, the invention provides a brand-new bridge cable monitoring method which optimizes a traditional tension string model by considering the influence of bending rigidity and can intelligently adjust the sampling frequency of a sensor node and send collected data to an upper computer. The flow of the bridge cable monitoring algorithm of the invention is shown in fig. 2, and mainly comprises the following processes:
and (I) extracting theoretical eigenfrequency.
The process of extracting the theoretical eigenfrequency can be further subdivided into:
(1) And (3) performing a tension test on the steel cable with the same specification as the actually measured bridge cable on a tension tester, and constructing a residual function r of a Newton Gaussian method:
k is the order of the eigenfrequency, EI is the bending rigidity of the steel cable, m is the unit mass of the steel cable, L is the length between two testing fixed points of the steel cable, f is the central frequency of the vibration frequency spectrum, and N is the tensile force applied to the steel cable. Here, β = (k, EI) as an unknown variable, and (m, L, f, N) as a known variable.
(2) N residual functions (n) are constructed by n different tensile tests> = 2), and then from an estimated minimum β (i.e., the initial set of variables β) 0 = (1, ei0), which consists of the order of the first order eigenfrequency and bending stiffness estimate for the wire rope) is iterated continuously, the equation for the iteration is as follows:
where T is the transpose of the matrix, J r A jacobian matrix that is a residual function. When beta is s+1 And beta s When the difference value of (B) is less than a certain threshold value, the iteration is ended, and then the value can be determined according to the beta value at the end of the iteration s+1 Corresponding k and EI obtain the optimal frequency order ko and bending rigidity EI of the steel cable o 。
(3) Substituting the optimal eigenfrequency order and the optimal bending stiffness of the steel cable into the tension string model to generate the optimized tension string model, namely the tension force applied to the steel cable can be expressed as:wherein,for the optimum eigenfrequency order k o Corresponding eigenfrequency, EI o For optimal cable bending stiffness, m is the unit mass of the cable and L is the fixed length of the cable. The specification of the steel cable is consistent with that of the bridge cable, so the optimized tensioning string model obtained in the step is the optimized tensioning string model of the bridge cable.
(4) And solving the theoretical eigenfrequency of the bridge cable according to the theoretical tension value of the bridge cable and the optimized tension string model.
And (II) acquiring the measured eigenfrequency.
The main process for obtaining the measured eigenfrequency is as follows: adding a Hamming window to the acquired signal in the sensor node; then, fast Fourier transform is carried out to obtain a vibration frequency spectrum of the bridge cable; and finally, extracting the frequency which accords with the optimal eigenfrequency order in the optimized tension string model from the vibration frequency spectrum, and taking the frequency as the actually measured eigenfrequency.
And (III) selecting sensor node events.
Whether the sensor sends the acquired data or not and the adjustment setting of the sampling frequency take the ratio of the difference value between the actual measurement eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency as the standard of judgment. The invention determines three types of events according to the range of the ratio, and appoints the information sent by the sensor node:
event one: and only sending the actually measured eigenfrequency to an upper computer monitoring center. In this case, the sampling frequency of the acceleration sensor can be set according to actual needs (e.g. set to 200 Hz).
And event II: and sending the data acquired by the acceleration sensor to an upper computer monitoring center without changing the sampling frequency of the sensor, and then carrying out further analysis.
Event three: and (3) increasing the sampling frequency of the acceleration sensor (for example, changing the sampling frequency of the sensor to 1000 Hz), and sending the data acquired by the acceleration sensor to an upper computer monitoring center for further analysis.
The ratio ranges of the three types of events determined by different bridge cables are different. For example, the ratio of the event one can be set to be less than 15%, the ratio of the event two can be set to be in the range of 15% to 30%, the ratio of the event three is set to be greater than 30%, the bridge cable state of the event one is good, the bridge cable state of the event two is normal, and the bridge cable state of the event one is abnormal, so that the bridge cable abnormal condition analysis method can analyze the abnormal condition of the bridge cable according to the ratio value ranges of the three events and the ratio calculated in real time.
Compared with the prior art, the invention has the following advantages:
first, compared with the traditional tensioning string model, the tensioning string model optimized by the gauss-newton method can increase the reliability of cable force identification, and has smaller error.
Secondly, whether the acceleration sensor sends the acquired information or not and the sampling frequency can be adjusted according to the relation between actual measurement and theoretical eigen frequency, so that the situation of continuously sending the sampling signal is avoided, the power consumption of the sensor node is effectively reduced while the state of the bridge cable is continuously monitored, and meanwhile, the efficiency and the analysis accuracy are considered.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A bridge cable monitoring method based on an optimized tensioning string model is characterized by comprising the following steps: the method comprises the following steps:
selecting a steel cable with the same specification as the actually measured bridge cable to construct a residual function of the Newton-Gaussian method, then adopting a Jacobi matrix method to carry out iterative solution on the residual function of the Newton-Gaussian method to obtain the optimal eigen frequency order and the optimal bending rigidity of the steel cable of the residual function of the Newton-Gaussian method, and finally optimizing a tension string model according to the result of the iterative solution;
obtaining theoretical eigenfrequency according to the theoretical tension value of the bridge cable and the optimized tension string model;
obtaining a vibration frequency spectrum of the bridge cable according to signals acquired by the nodes of the acceleration sensor, and then screening out frequencies which accord with the optimal eigenfrequency order from the obtained vibration frequency spectrum to be used as actual measurement eigenfrequencies;
and the acceleration sensor node determines whether to send acquired data to an upper computer monitoring center or not and adjusts the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actual measurement eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency so as to balance the power consumption and the analysis accuracy.
2. The bridge cable monitoring method based on the optimized tension string model as claimed in claim 1, wherein: the method comprises the following steps of selecting a steel cable with the same specification as an actually measured bridge cable to construct a residual function of the Newton-Gaussian method, then performing iterative solution on the residual function of the Newton-Gaussian method by adopting a Jacobi matrix method to obtain the optimal eigen frequency order and the optimal bending rigidity of the steel cable of the residual function of the Newton-Gaussian method, and finally optimizing a tension string model according to the result of the iterative solution, wherein the steps comprise:
selecting a steel cable with the same specification as the actually measured bridge cable on a tension tester to carry out tension test, constructing a residual function of the Newton-Gaussian method, and expressing the residual function r of the Newton-Gaussian methodThe formula is as follows:wherein k is an eigenfrequency order, EI is bending stiffness of the steel cable, m is unit mass of the steel cable, L is the length between two fixed test points of the steel cable during a tension test, f is the central frequency of a steel cable vibration frequency spectrum, and N is tension applied to the steel cable;
taking (m, L, f, N) as a known variable and beta = (k, EI) as a variable to be solved, solving the residual function of the Newton Gaussian method by adopting a Jacobi matrix iteration method, and obtaining the optimal eigen frequency order k of the residual function of the Newton Gaussian method o And optimal steel cable bending stiffness EI o ；
The optimal eigenfrequency order k o And optimal wire rope bending stiffness EI o Substituting the tension string model into the tension string model to generate an optimized tension string model, wherein the expression of the optimized tension string model is as follows:wherein,for the optimum eigenfrequency order k o The corresponding eigenfrequency.
3. The bridge cable monitoring method based on the optimized tensioning string model as claimed in claim 2, wherein: the method comprises the following steps of taking (m, L, f, N) as known variables and beta = (k, EI) as variables to be solved, adopting a Jacobi matrix iteration method to solve a residual function of the Newton Gaussian method, and obtaining an optimal eigenfrequency order of the residual function of the Newton Gaussian method and an optimal steel cable bending rigidity, wherein the steps comprise the following steps:
n residual functions r constructed according to n different tension tests 1 ,r 2 ,...r n Obtaining a Jacobian matrix of a residual function r, the Jacobian matrix J of the residual function r r The expression of (a) is:
wherein n is not less than 2 and n is an integer,andas a residual function r for the nth run n Partial derivatives of k and EI, respectively;
according to the obtained Jacobian matrix J r Iterating the variable beta to be solved until the variable beta is reached s+1 And beta s Until the difference is less than the set threshold, finally beta at the end of the iteration s+1 The corresponding k and EI are respectively used as the optimal eigenfrequency order k o And optimal wire rope bending stiffness EI o Wherein, the formula for iterating the variable beta to be solved is as follows:wherein s is the number of iterations, s is greater than or equal to 0 and less than or equal to n, and the initial value of the iteration is beta 0 = (1, ei0), EI0 is the minimum estimate of steel cable bending stiffness.
4. The bridge cable monitoring method based on the optimized tensioning string model as claimed in claim 1, wherein: the step of obtaining a vibration frequency spectrum of the bridge cable according to signals acquired by the nodes of the acceleration sensor, and then screening out a frequency which accords with an optimal eigen frequency order from the obtained vibration frequency spectrum as an actually measured eigen frequency comprises the following steps:
in an acceleration sensor node, adding a Hamming window to the acquired signal to obtain the windowed acquired signal;
carrying out fast Fourier transform on the windowed acquired signal to obtain a vibration frequency spectrum of the bridge cable;
and screening out the frequency which accords with the optimal eigenfrequency order from the obtained vibration frequency spectrum as the actually measured eigenfrequency.
5. A bridge cable monitoring method based on an optimized tensioning string model according to any one of claims 1-4, characterized in that: the acceleration sensor node determines whether to send collected data to an upper computer monitoring center or not and adjust the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actually-measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency so as to balance the power consumption and the analysis accuracy, and the method comprises the following steps:
calculating the ratio of the difference between the actually measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency;
and the acceleration sensor node executes the operation of the corresponding event according to the ratio range of the calculated ratio: if the calculated ratio belongs to the ratio range of the event I, only the actually measured eigenfrequency is sent to the upper computer monitoring center; if the calculated ratio belongs to the ratio range of the second event, the sampling frequency of the acceleration sensor is not changed, and the data acquired by the acceleration sensor is sent to the upper computer monitoring center; and if the calculated ratio belongs to the ratio range of the event III, improving the sampling frequency of the acceleration sensor and sending the data acquired by the acceleration sensor to an upper computer monitoring center.
6. The bridge cable monitoring method based on the optimized tensioning string model as claimed in claim 5, wherein: the ratio range of the event I is [0, 15%), and the event I sets the sampling frequency of the acceleration sensor to 200Hz; the value range of the second event is [15%,30% ]; the value range of the event III is (30%, 100%), and the sampling frequency of the acceleration sensor is increased to 1000Hz by the event III.
7. The utility model provides a bridge cable monitoring system based on optimize tensioning string model which characterized in that: the system comprises the following modules:
the tensioning string model optimizing module is used for selecting a steel cable with the same specification as the actually measured bridge cable to construct a residual function of the Newton-Gaussian method, then performing iterative solution on the residual function of the Newton-Gaussian method by adopting a Jacobi matrix method to obtain the optimal eigen frequency order and the optimal bending rigidity of the steel cable of the residual function of the Newton-Gaussian method, and finally optimizing a tensioning string model according to the result of the iterative solution;
the theoretical eigenfrequency calculation module is used for solving the theoretical eigenfrequency according to the theoretical tension value of the bridge cable and the optimized tensioning string model;
the actual measurement eigenfrequency acquisition module is used for obtaining a vibration frequency spectrum of the bridge cable according to signals acquired by the acceleration sensor nodes, and then screening frequencies which accord with the optimal eigenfrequency order from the obtained vibration frequency spectrum to be used as actual measurement eigenfrequencies;
and the acceleration sensor node determining and adjusting module is used for determining whether to send acquired data to the upper computer monitoring center or not and adjusting the sampling frequency of the acceleration sensor according to the ratio of the difference value of the actual measurement eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency by the acceleration sensor node so as to balance the power consumption and the analysis accuracy.
8. The bridge cable monitoring system based on the optimized tension string model as claimed in claim 7, wherein: the tensioned string model optimization module includes:
the construction unit is used for selecting a steel cable with the same specification as the actually measured bridge cable on the tensile testing machine for a tensile test, and constructing a residual function of a Newton Gaussian method, wherein the expression of the residual function r of the Newton Gaussian method is as follows:
wherein k is an eigenfrequency order, EI is bending stiffness of the steel cable, m is unit mass of the steel cable, L is the length between two fixed test points of the steel cable during a tension test, f is the central frequency of a steel cable vibration frequency spectrum, and N is tension applied to the steel cable;
the iteration unit is used for solving the residual function of the Newton Gaussian method by using the Jacobi matrix iteration method by taking (m, L, f and N) as a known variable and beta = (k, EI) as a variable to be solved so as to obtain the optimal eigenfrequency order of the residual function of the Newton Gaussian method and the optimal bending stiffness of the steel cable;
substitution unit for substituting the optimal eigenfrequency order k o And optimal wire rope bending stiffness EI o Substituting the tension string model into an optimized tension string model, wherein the expression of the optimized tension string model is as follows:
wherein,for the optimum eigenfrequency order k o The corresponding eigenfrequency.
9. The bridge cable monitoring system based on the optimized tension string model as claimed in claim 8, wherein: the iteration unit comprises:
a Jacobian matrix acquisition subunit for acquiring n residual functions r constructed according to n different tensile tests 1 ,r 2 ,...r n Obtaining a Jacobian matrix of a residual function r, the Jacobian matrix J of the residual function r r The expression of (a) is:
wherein n is not less than 2 and n is an integer,andas a residual function r for the nth run n Partial derivatives of k and EI, respectively;
an iteration subunit for obtaining a Jacobian matrix J according to the obtained Jacobian matrix r Iterating the variable beta to be solved until the variable beta is reached s+1 And beta s Difference of (2)Until the value is less than the set threshold, finally beta at the end of the iteration s+1 Respectively taking the corresponding k and EI as the optimal eigenfrequency order k o And optimal steel cable bending stiffness EI o Wherein, the formula for iterating the variable beta to be solved is as follows:
wherein s is iteration number, s is more than or equal to 0 and less than or equal to n, and the initial value of iteration is beta 0 = (1, EI0), EI0 is the minimum estimate of the bending stiffness of the steel cable.
10. A bridge cable monitoring system based on an optimized tension string model according to claim 7, 8 or 9, characterized in that: the acceleration sensor node determination and adjustment module comprises:
the calculation unit is used for calculating the ratio of the difference value between the actually measured eigenfrequency and the theoretical eigenfrequency to the theoretical eigenfrequency;
and the execution event determining unit is used for executing the operation of the corresponding event according to the ratio range of the calculated ratio by the acceleration sensor node: if the calculated ratio belongs to the ratio range of the event I, only the actually measured eigenfrequency is sent to the upper computer monitoring center; if the calculated ratio belongs to the ratio range of the second event, the sampling frequency of the acceleration sensor is not changed, and the data acquired by the acceleration sensor is sent to the upper computer monitoring center; and if the calculated ratio belongs to the ratio range of the event III, increasing the sampling frequency of the acceleration sensor and sending the data acquired by the acceleration sensor to the upper computer monitoring center.
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