385 results about "Structure health monitoring" patented technology

A structure health monitoring (“SHM”) system according to the invention can be deployed in an onboard environment, such as an aircraft, to provide an ongoing damage assessment of structural components on the aircraft. The SHM system reduces or eliminates time consuming and costly manual inspections of aircraft. The SHM system can leverage known sensor technologies to collect sensor data indicative of the structural health of the monitored components. The sensor data is processed and baselined with sensor feature baselines and damage estimate baselines to provide an accurate final damage estimate for the monitored component. The final damage estimate can be further processed or formatted for compatibility with aircraft maintenance systems.

The invention discloses a skin for morphing wings, belonging to the technical field of design of morphing aircrafts. The skin for the morphing wings is made from fibre reinforced composites, and the cross section of the skin is in an isosceles trapezoid ripple structure. The invention also carries out analysis aiming at the chordwise and spanwise equivalent elastic modulus and flexural modulus ofthe skin structure with isosceles trapezoid ripples, provides a corresponding mathematical model and can further optimize the skin structure according to the mathematical model. The invention meets the requirements of the skin of the morphing wings of the morphing aircrafts for large deformation aerodynamic load bearing, can realize the design optimization of the skin structure under different load operating conditions according to the mathematical analysis model of the equivalent elastic modulus and the flexural modulus in two different directions of the skin structure, also has structure health monitoring and self-adapting deformation capability and can realize optimal aerodynamic characteristics by controlling the shape and the vibration of the skin.

The invention provides a preparation method of a cellulose-based flexible stress-strain sensitive material. The preparation method comprises the steps of: 1) adding cellulose into water and mixing uniformly to form a 0.2-5wt% aqueous solution, and freeze-drying the aqueous solution to prepare a cellulose aerogel; 2) subjecting the cellulose aerogel prepared in the step 1) to pyrolysis processing at high temperature of 600-1000 DEG C for 0.5 to 5 hours under the protection of vacuum or inert atmosphere to prepare a carbon gel; 3) and pouring PDMS resin in the carbon gel, and carrying out vacuum defoamation, thereby the flexible stress-strain sensitive material with high sensitivity is obtained after resin is solidified. The preparation method has the advantages of rich, chip and renewable raw materials, simple and controllable preparation process, and wide strain range and high sensitivity of the prepared stress-strain sensitive material; and the preparation method has good application prospect in aspects of structural health monitoring, electronic skin, biological medicine, wearable electronic product and the like.

The invention relates to a real-time monitoring system and method for assessing damage of a truss structure, and belongs to the technical field of structure health monitoring. The monitoring system is composed of bidirectional accelerometers, a data collection module, a calculation analysis module, a real-time damage location result display module and a decision module, wherein the data collection module collects and stores signals, sent by the bidirectional accelerometers, at each measuring point before damage and in the to-be-measured state of the truss structure; the calculation analysis module calls acceleration signals of all the measuring points before damage and in the present state of the structure, and implements calculation by using the real-time truss structure damage monitoring method; real-time damage location result display module provides a damage location result, namely whether there is a damaged rod in the structure, and displays the concrete position of the damaged rod; and the decision module analyze the result and decide whether to maintain or replace the rod. The monitoring system and method are suitable for damage location and safety assessment of truss rods in different types of truss structures, and also has the advantages of being suitable for environment excitation, high in damage sensitivity, convenient to operate and laborsaving.

The invention discloses a bridge moving load and damage synergetic recognition method based on L1/2 regularization. The method includes the steps that a plurality of structural response measurement sensors such as an acceleration sensor, a displacement sensor and a strain sensor are arranged on a bridge according to the aim and accuracy requirement of bridge structure health monitoring; a finite element model of the bridge is established through beam elements; moving loads are equivalent to a linear combination of rectangular loads, and bridge damage is equivalent to element stiffness reduction; the moving loads are recognized through a Tikhonov regularization method under the condition that an element damage factor is given, a reconstructed response is calculated with the moving loads, the range of the residual L2 norm of a measured response and the reconstructed response is obtained through comparison, and an L1/2 penalty term is introduced to establish an objective function with the element damage factor as an optimized parameter; a constraint optimization problem is solved with a Matlab optimization toolbox, the corresponding damage factor is the structural damage recognition result when the objective function is the minimum, and correspondingly the moving loads worked out through Tikhonov regularization solving are the moving load recognition result.

The invention discloses an aircraft structure checking task planning method based on risk and cost analysis. The checking mode comprises traditional structure non-destructive detection and structure health monitoring. The method comprises the steps that based on the principle of probability damage tolerance analysis, the calculation model of single flight failure probability of an aircraft structure element is established; based on the model, a cumulative risk calculation model of a fleet in a certain period of time is established; in the existing aircraft structure plan maintenance mode, the maintenance cost calculation model of the aircraft structure element in the service life cycle is established, wherein the calculation model comprises structure checking cost, repair cost and false alarm cost; the single flight failure risk and fleet cumulative risk are used as the risk assessment indicators of a structure maintenance policy; under the premise that a safety risk requirement is satisfied, the optimal structure checking policy can be determined according to the analysis of the structure whole service life cycle maintenance cost. The optimal structure checking interval and repair threshold can be determined based on the tradeoff between the structure failure risk and the service life cycle maintenance cost.

The invention discloses a phase sensitive optical time-domain reflectometer (phi-OTDR) sensing signal noise separation and signal extraction method, a longitudinal time sequence signal of each point of a phi-OTDR space is used as a processing object, multi-scale decomposition is performed for the time sequence signal by utilization of wavelet transforming, component signals in each scale are analyzed and reorganized in a selective mode, undulate background noise, acoustic waves and other time-varying interference signals are separated, real disturbance invasion signals are extracted, not only can undulate background noise be restrained, but also influences from other complicated time-varying interference noise are avoided, an invasion detection accuracy rate can be effectively improved, a system false alarm rate is reduced, detection performance of an invasion detection system based on a phi-OTDR in an actual complicated noise environment is improved, and significant application requirements in perimeter security, long-distance pipeline safety, large structure health monitoring and the like for a country can be met.

A super high-rise building integration health management system design method relates to building health management. A containment health monitoring subsystem, a building structure health monitoring subsystem, a fire protection monitoring subsystem, a multi-function sensor network, a super high-rise building safety decision and maintenance subsystem, a super high-rise building integration evaluation subsystem and a super high-rise building integration control subsystem are included, wherein the containment health monitoring subsystem is used for monitoring a super-high-rise-building external containment system (including a fence and a glass wall) in real time; the building structure health monitoring subsystem is used for monitoring an important structure unit of a super high-rise building in real time; the fire protection monitoring subsystem is used for monitoring a fire protection system of the super high-rise building in real time; the multi-function sensor network is used for collecting various monitoring parameters in real time; the super high-rise building safety decision and maintenance subsystem is used for making a decision according to an evaluation result of the integration evaluation subsystem, generating corresponding early warning or maintenance information and predicting a service life of a building; the super high-rise building integration evaluation subsystem is used for comparing and processing data from a monitoring system, and carrying out integration evaluation on the data; and the super high-rise building integration control subsystem is used for executing a decision command from a safety decision and maintenance system and managing a sensor network.

The invention discloses a method for improving the accuracy of a monitoring result of a stochastic damage locating vector (SDLV) method, relating to an improvement on the locating effect of the SDLV method, and belonging to the technical field of structure health monitoring. The method is characterized in that a step of selecting an observation matrix C is added in the SDLV method, so that a more accurate locating result can be obtained. The method specifically comprises the following steps of analyzing the relevance between the selection of the matrix C and a damage locating result under a proportion of different numbers of measurement points through a core of the SDLV method; then determining a matrix C selection basis of the SDLV method under the conventional sparse measurement point arrangement scheme, namely a measurement point number percentage; finally providing two methods for determining a threshold value of the measurement point number percentage. According to the method, the accuracy of the damage monitoring result of a truss structure based on the SDLV method is improved, and the application of real-time safety monitoring of the truss structure in engineering is facilitated.

A structural health monitoring system is provided. The system includes a drive system having a first drive member a second drive member 38 operationally connected to the first drive member, and a tension member 39 operationally connecting the first drive member to the second drive member. At least one sensor 50 is configured to monitor a characteristic of at least one of the first drive member and the second drive member. A processor 54, in communication with the at least one sensor, is configured to determine if the structural health of the tension member is compromised based on the monitored characteristic. The processor is further configured to perform a safety response action when the structural health of the tension member is determined to be compromised.

The invention discloses a cable force measurement method based on edge recognition in computer vision, and belongs to the technical field of structure health monitoring. According to the method, a structure does not need to be reached, no sensor device needs to be mounted on cables, normal operation of the structure cannot be disturbed, synchronous non-contact measurement of multi-cable force is realized, and test costs and time are saved; and an edge recognition method combining a Sobel operator and a Zernike moment is utilized, vibration time history of the structure cables can be accuratelymeasured, and the vibration characteristics and the cable force of multiple cable members in the structure can be quickly and simultaneously measured. The method of the invention is not sensitive toenvironmental conditions of illumination changes, cable background changes and the like, and can be used for long-term monitoring.

The invention relates to an intelligent bolt for an optical fiber grating sensor and relates to the optical fiber sensing and structure health monitoring field. According to the intelligent bolt, the optical fiber grating sensor is packaged in a bolt body and is used for monitoring, in an on-line mode, extension of the bolt body in a working state. The intelligent bolt comprises a transmission optical fiber, a protection sleeve, a rubber layer and the bolt body. The optical fiber grating sensor is arranged in the middle of the transmission optical fiber. A nut is arranged at the top end of the bolt body. The bolt body is provided with a countersink defined to be a sensor heeling-in hole. The bottom end of the bolt body is not through, and the top end of the bolt body is provided with a through hole. The intelligent bolt is simple in working principle, simple and convenient to manufacture, exquisite in structure, low in cost, and capable of achieving distribution type measurement easily, and effectively prevents large-scale structural pieces from causing accidents due to coming-off or breakage of bolts or other reasons.

The present invention relates to a flexible sheet structure impact load identification method based on combination of a distributed SNS (Single-mode-No core-Single mode) multiple-mode interference fiber optic sensor and an automatic encoder deep learning algorithm, belonging to the technical field of structure health monitoring impact monitoring. The method comprises the following steps of: the step 1: layout of an SNS fiber optic sensor; the step 2: construction of a distributed SNS fiber optic sensor sheet structure impact load monitoring system; the step 3: real-time monitoring and collection of impact response dynamic signals, and recording of an impact test data to generate a sample bank through impact test for different positions and different energy; the step 4: preprocessing for the sheet structure SNS fiber optic sensor impact sample bank data; the step 5: selection of an automatic encoder as a deep learning model, construction of a network structure, and training of a deep learning neural network; and the step 6: through adoption of a trained model obtained in the step 5, processing of the SNS fiber optic sensor impact response data to achieve identification for the impact load position and energy.

The invention relates to a 1-1 type piezoelectric ceramic fibrous composite material. The composite material is formed by cutting an alternative lamination of a piezoelectric ceramic sheet and a high molecular material polymer. The invention also provides a method for preparing the 1-1 type piezoelectric ceramic fibrous composite material, comprising the following steps: sintering by using a ceramic powder tape-casting method to obtain piezoelectric ceramic sheets; vertically arranging a certain amount of the piezoelectric ceramic sheets in parallel, and inserting PE (Polyethylene) thin film between every two piezoelectric ceramic sheets to form a piezoelectric ceramic stack; injecting epoxy resin casting liquid to a gap of the piezoelectric ceramic stack, finally, pressurizing, raising the temperature and demoulding to obtain 2-2 type piezoelectric ceramic composite lamination, and cropping and preparing the 1-1 type piezoelectric ceramic fibrous composite material with required size. The 1-1 type piezoelectric ceramic fibrous composite material obtained by using the method can be used as a driver applied to the fields such as structure control, vibration abatement, structure health monitoring and the like after being polarized by an interdigital electrode and has a wide application prospect.

The invention proposes a Lamb wave group velocity and frequency domain measurement method for a complex structure, and belongs to the field of Lamb wave structure health monitoring. The method comprises the following steps: (1), arranging a piezoelectric patch in the structure, and collecting a structure Lamb wave reference signal; (2), introducing a scatterer to the structure, and collecting a Lamb wave sensing signal in a current structure state; (3), solving a Lamb wave scattering signal according to the Lamb wave sensing signal and the reference signal thereof; (4), extracting a scatteredwave packet in a selected mode from the Lamb wave scattering signal; (5), calculating the frequency spectrum of an Lamb wave excitation signal and the frequency spectrum of the scattered wave packet;(6), solving the phase difference spectrum of the Lamb wave excitation signal and the scattered wave packet; (7), calculating and obtaining the group velocity of the selected mode of the Lamb wave through the phase difference spectrum. The method avoids the impact of Lamb wave scattering in a conventional time domain measurement method, and improves the measurement accuracy.

The invention relates to the field of civil engineering structure damage identification, in particular to a civil engineering structure damage pre-warning method in consideration of a temperature influence. An AR model in time series analysis and principal component analysis in multivariate statistical analysis are utilized, and a standard deviation control chart is combined for structure pre-warning research. Firstly, accelerated speed response data before and after structure damage are fit by the AR model, and a model coefficient is extracted; secondarily, the influence of temperature on the AR model coefficient is removed through principal component analysis; and finally, damage pre-warning is performed by using a standard deviation control chart. The pre-warning method is rigorous in theory, novel, reasonable in scheme and high in operability. The civil engineering structure damage pre-warning method has the advantages that accelerated speed response is directly used, a finite element model and modal parameters are not required, and the method belongs to a data driving method and is suitable for performing structure health monitoring in real time; and simultaneously, according to the method, under the condition of temperature change, the damage pre-warning is successfully performed through the anti-noise capacity.

A method and system of compensating for environmental effect when detecting signals using a structural health monitoring system includes collecting baseline data signals for one or more values of the environmental effect variable from signals transmitted along selected paths between transducers in an array attached to the structure. A threshold is selected based on the baseline data for determining if the signal is detected. Current data signals are collected and matched to the best fit baseline data. The value of the environmental effect variable is determined on the basis of the matching. A signal is detected according to the selected threshold.

The invention relates to the equipment structure health monitoring field and discloses a metal material crack quantitative monitoring method based on a ultrasonic guided wave. The method comprises thefollowing steps of using a sensor network with a specific shape to carry out an excitation test on a metal material so as to obtain monitoring signals under different conditions; obtaining a damage index value through a damaged monitoring signal and a reference signal without damage; constructing a crack quantitative evaluation model of the metal material through a damage path probability imagingmethod; from monitoring imaging of a damage area of the crack quantitative evaluation model of the metal material, acquiring a slope of a straight line where a crack is located; setting a specific threshold, averaging coordinate values of discrete points which are greater than the specific threshold, and obtaining the coordinate values of the points through which the crack passes; acquiring an equation of the straight line where the crack is located through the slope of the straight line and the coordinate values of the points through which the crack passes; and through an intersection pointof the straight line and a damaged boundary line in a damaged imaging graph, acquiring end points of the crack. The method has advantages of being sensitive to small defects, having a large monitoringrange and the like.

The invention discloses a low speed impact position identification method based on approximate entropy calculation and belongs to the impact monitoring technology field of structure health monitoring. The method comprises the following steps of step1, carrying out distributed optical fiber bragg grating sensor layout; step2, adopting a calculating method of optical fiber bragg grating sensor impulse response signal approximate entropy; step3, collecting an optical fiber bragg grating sensor response signal corresponding to an impulse point to be measured and carrying out approximate entropy calculation; step4, determining an area where the point to be measured is located and calculating an approximation entropy of a sample impulse point response signal in the area; step5, constructing a relation model of a distance of the impulse point to a sensor and a corresponding approximation entropy difference value; and step6, based on a three-line crossing method, determining a position of the impulse point to be measured. A positioning algorithm in the invention possesses characteristics that a lot of prior knowledge is not needed; and practicality is high and so on.

The invention discloses a monitoring data exception diagnosis method based on a generative adversarial network, which is suitable for exception diagnosis of long-term measured data of a structural health monitoring system; the method includes dividing the recorded data into a plurality of sub-segments by selecting a proper basic time interval; converting the monitoring time sequence data of each sub-segment into a grayscale image by adopting a Gram angle field, training two unsupervised deep artificial neural networks including a generative adversarial network (GANs) and an auto-encoder (AE) according to the grayscale image, and verifying a training effect of the obtained network according to a prediction error of a test data set. Secondly, selecting an optimal index suitable for data anomaly diagnosis according to samples of the training set and the test set, and judging the state of the measured data in combination with a cumulative summation function. According to the method, rapidand accurate diagnosis of abnormal monitoring data can be realized, and effective data support can be provided for structural state diagnosis and abnormal early warning.