CN-122016037-A - Bridge safety monitoring system, node and method based on differential symmetry self-diagnosis

CN122016037ACN 122016037 ACN122016037 ACN 122016037ACN-122016037-A

Abstract

The invention discloses a bridge safety monitoring system, node and method based on differential symmetry self-diagnosis, the system comprises node, the node comprises a mounting base, a micro-actuator, first and second symmetrically arranged reference resonators, a bridge response sensor and a processing module. The method comprises the steps of applying homologous excitation through a micro-exciter, synchronously collecting response signals of the double-reference resonator and the bridge, calculating an installation health index representing symmetry of an installation state based on the response signals of the double-reference resonator, and decoupling the response signals of the bridge by utilizing the index to separate structural damage characteristics. According to the invention, by constructing the node internal symmetric differential measurement system, the installation coupling state which is difficult to directly observe is converted into the calculable electric signal symmetry index, so that the problem of false alarm caused by installation drift and structural damage confusion in traditional monitoring is fundamentally solved, and the reliability and operation and maintenance accuracy of the monitoring system are remarkably improved.

Inventors

LIU JINPENG
XU RUI
LIU YUHANG
LIU XIAOFEI
MA WENBO
SHEN JIAYI
XIONG JIANRONG
TIAN YUDE
YANG WEIWEI
CHEN DONGHUI
MIAO YANLI
ZHANG XIAOZHENG
LI PENGFEI
WANG YONGCHANG
WEI ZHENQIANG

Assignees

浙江交工国际工程有限公司
浙江交工集团股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260409

Claims (10)

1. Bridge safety monitoring system based on differential symmetry self-diagnosis, characterized by comprising: One or more monitoring nodes, a convergence unit and a remote management platform; The monitoring nodes are used for being installed at all to-be-detected parts of a bridge, and each monitoring node comprises an installation base, a micro-exciter, a first reference resonator, a second reference resonator, a first sensor and a second sensor, wherein the installation base is used for being fixedly connected with a bridge member; the bridge response sensor is arranged on the mounting base; The system comprises a processing module, a bridge response sensor, a first reference resonator, a second reference resonator, a first sensor and a second sensor, wherein the processing module is configured to control the micro-actuator to apply excitation, determine an installation health index representing symmetry of an installation state based on response signals of the first reference resonator and the second reference resonator, and process output signals of the bridge response sensor based on the installation health index so as to separate structural characteristics reflecting the structural state of a bridge; The aggregation unit is in communication connection with one or more monitoring nodes and is used for aggregating data of each node; the remote management platform is in communication connection with the aggregation unit, and is used for receiving data, presenting the installation health index and the structural characteristics, and generating maintenance decisions according to preset rules.
2. The bridge safety monitoring system of claim 1 wherein said installation health index is determined based on the coherence function value of the response signals of said first reference resonator and said second reference resonator over a predetermined frequency band.
3. The bridge safety monitoring system of claim 1, wherein the aggregation unit is further configured to perform a spatiotemporal correlation analysis based on the installed health index and structural characteristics of the plurality of monitoring nodes to locate abnormal areas of the bridge or identify damage patterns.
4. The node for bridge safety monitoring is characterized by comprising a mounting base and a connecting piece, wherein the mounting base is fixedly connected with a bridge member; the bridge response sensor comprises a mounting base, a micro-exciter, a first reference resonator, a second reference resonator, a first sensing unit, a second sensing unit, a bridge response sensor, a processing module and a bridge response sensor, wherein the first reference resonator and the second reference resonator are arranged on the mounting base, have consistent structural parameters, are symmetrically arranged on the mounting base and are decoupled from the bridge member; The processing module is configured to control the micro-actuator to apply excitation, determine an installation health index representing symmetry of an installation state of the installation base based on response signals acquired by the first sensing unit and the second sensing unit, and process response signals acquired by the bridge response sensor based on the installation health index to extract structural features related to damage of the bridge member.
5. The node of claim 4, wherein the first reference resonator and the second reference resonator are mounted on the mounting base by a vibration reduction element.
6. The node of claim 4 or 5, wherein the processing module is further configured to synchronously acquire signal energy of the bridge response sensor during a calibration period, and determine and flag an installation health index during the period as affected by environmental disturbances when the signal energy exceeds an energy threshold determined based on historical data.
7. A bridge safety monitoring method based on differential symmetry self-diagnosis, applied to the system according to any one of claims 1-3, the method comprising: applying excitation through micro-exciters of one or more monitoring nodes deployed at each part of the bridge; At each monitoring node, synchronously acquiring response signals of a first reference resonator, a second reference resonator and a bridge response sensor of the monitoring node; calculating an installation health index of each node based on the response signals of the first reference resonator and the second reference resonator; based on the installation health index of each node, decoupling the bridge response signals of the corresponding nodes, and extracting the structural characteristics of each part; assembling the installation health index and the structural characteristics of each node, and uploading the installation health index and the structural characteristics to a remote management platform; And carrying out fusion analysis on the remote management platform based on the installation health index and the structural characteristics, and respectively generating maintenance suggestions for the installation state of the sensor and evaluation results for the safety of the bridge structure.
8. The method of claim 7, wherein the calculating the installation health index for each node comprises: Converting response signals of the first reference resonator and the second reference resonator of each node to a frequency domain; Calculating a coherent function of the two in a preset analysis frequency band; and taking the average value of the coherence function in the preset analysis frequency band as the installation health index of the node.
9. The method of claim 7, wherein after the first installation of the node, performing an initialization step comprising applying excitation and collecting an initial response signal without significant environmental interference, calculating and storing an initial installation health index based on the initial response signals of the first and second reference resonators, wherein the installation health index calculated later is compared with the initial installation health index to determine an installation state change trend.
10. The method of claim 7, wherein the decoupling the bridge response signals of the corresponding nodes based on the installation health index of each node comprises determining that the installation state of the node is reliable when the installation health index of the node is higher than a first preset threshold value, participating in bridge safety assessment directly based on the structural features extracted from the bridge response signals of the node, determining that the installation state of the node is abnormal when the installation health index of the node is lower than a second preset threshold value, triggering a maintenance alarm for the node, and performing weight reduction processing or required review on the structural features extracted from the node.

Description

Bridge safety monitoring system, node and method based on differential symmetry self-diagnosis Technical Field The invention belongs to the technical field of bridge structure health monitoring, and particularly relates to a long-term and online safety monitoring system and method for large civil engineering structures such as urban bridges. Background In the long-term service process of the urban bridge, the parameters such as stress, vibration, displacement and the like of key components are continuously monitored, so that the method is a conventional technical target for evaluating the safety state of the structure and realizing predictive maintenance. In order to achieve the aim, sensors such as an accelerometer and a strain gauge are fixedly arranged at key parts of a bridge in the prior art, and the health state of the structure is judged by means of long-term data acquisition, threshold value setting or modal analysis. However, those skilled in the art find in practice that the prior art solutions have the following specific drawbacks and bottlenecks which have long plagued the industry. For example, false damage false alarm problem is prominent, because the sensor node is fixed on the bridge member for a long time through bolts or adhesive, under the effect of factors such as long-term environmental temperature circulation, salt spray corrosion, traffic vibration and the like, the problems of bolt pretightening force relaxation, adhesive aging, base local corrosion and the like of the sensor mounting base are unavoidable, and the coupling state of the sensor and the structure slowly drifts. The signal characteristics generated by such installation drift in the monitored data are highly similar to the characteristic changes caused by early structural damage (e.g., microcracks, loose joints). The existing diagnosis method based on the threshold value or the statistical model can not effectively distinguish the threshold value from the statistical model, so that a large number of false alarms are generated by the system, operation and maintenance resources are seriously consumed, and the reliability of the system is reduced. The in-situ and on-line diagnosis capability of the installation state is lacking, and when the monitoring system gives an alarm, operation and maintenance personnel cannot judge whether the alarm root is the real damage of the bridge structure or the installation fault of the sensor from the data layer. Currently, the only method for distinguishing the two is to send personnel to the site to perform physical inspection on the sensor nodes, which is not only high in operation and maintenance cost and lagged in response, but also extremely difficult to perform inspection on large bridge groups or sensors installed in places which are difficult to access, such as high altitude, underwater and the like. Although there have been attempts to compensate for installation drift by adding temperature sensors or employing complex algorithms, it is essentially a post-interpretation of the mixed signal that fails to provide an independent observation of the installation status from the underlying layers of the measurement system. The long-term stability of the sensor itself is too high, and some of them attempt to introduce schemes for compensating for the reference sensor, the effectiveness of which is severely dependent on the long-term absolute accuracy and stability of the reference sensor itself. In the severe outdoor environment of the bridge, the performance of a single sensor is ensured not to drift for years or even tens of years, and the technology is extremely difficult and high in cost in engineering practice, so that the large-scale application of the technology is restricted. In addition ,"A Sensor Fault Detection Algorithm for Structural Health Monitoring Using Adaptive Differential Evolution"A.Rama Mohan Rao, et al propose that in a large sensor network, the calculation time required for isolating a faulty sensor by using the existing algorithm is not bearable for fault isolation of an online sensor, and the algorithm based on Principal Component Analysis (PCA) is combined with an adaptive differential evolution algorithm to improve the performance of fault isolation of the sensor. The above-mentioned prior art is commonly directed to a more fundamental and more concealed technical problem, namely, in a long-term and unattended monitoring scenario, a mechanism which is built in a monitoring node itself, can directly quantify the sensor-structure installation coupling state online, robustly and at low cost, and physically strips the sensor-structure installation coupling state from a mixed signal is lacking. The structural health monitoring system always faces the dilemma of high false alarm rate and unexplained operation and maintenance, and the development of the structural health monitoring system in the intelligent and accurate direction is severely restricted. Therefore, ai