WO2019201176A1 - 列车部件裂纹损伤预测方法和装置 - Google Patents

列车部件裂纹损伤预测方法和装置 Download PDF

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WO2019201176A1
WO2019201176A1 PCT/CN2019/082491 CN2019082491W WO2019201176A1 WO 2019201176 A1 WO2019201176 A1 WO 2019201176A1 CN 2019082491 W CN2019082491 W CN 2019082491W WO 2019201176 A1 WO2019201176 A1 WO 2019201176A1
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damage
crack
detection structure
distribution
data
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PCT/CN2019/082491
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English (en)
French (fr)
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蔡国强
王坚群
何明
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江苏必得科技股份有限公司
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Publication of WO2019201176A1 publication Critical patent/WO2019201176A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

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  • the invention relates to the technical field of damage analysis, in particular to a method and a device for predicting crack damage of train components.
  • the load or stress acting on the component tends to alternate with time.
  • the expansion of fatigue under such alternating stress is called the expansion of the fatigue crack, and the resulting damage is called fatigue failure.
  • a large number of practical data show that the stress cracks of the members with initial cracks even if they are subjected to alternating damage below static load will expand, and even cause damage in severe cases.
  • Fatigue and fracture are the more common causes of component failure in engineering. Structural fatigue originally originated from the problem of metal fatigue. In the structural fatigue problem, cracks on the metal surface are more common. The crack shape and distribution position are different and can be roughly divided into three categories: longitudinal crack, transverse crack and turtle crack.
  • Pairs formula Pairs found that the stress intensity factor amplitude ⁇ K is the most critical factor controlling the crack growth rate, and the famous pairs formula is proposed accordingly:
  • a - crack depth or width N - number of stress cycles; C, m - and material-related parameters; ⁇ K - range of stress intensity factors.
  • Kc is fracture toughness
  • embodiments of the present invention provide a method and apparatus for predicting crack damage of train components.
  • a method for predicting a crack damage of a train component includes: performing damage detection on a detection structure of a train component; acquiring historical damage data of the detection structure; wherein the damage data includes: the detecting a crack length data of the structure; obtaining a life distribution characteristic and a verification index parameter for the damage of the detection structure according to the historical damage data, and establishing a Bayesian probability prediction model corresponding to the damage of the detection structure;
  • the historical damage data analysis leads to the prior distribution of the verification index parameters; the Markov chain Monte Carlo method is used to optimize the model parameters of the Bayesian probability prediction model, and the growth rate of the damage of the detection structure is predicted.
  • the obtaining a life distribution characteristic and a verification index parameter for the damage of the detection structure comprises: determining a lifetime distribution of damage to the detection structure as a lognormal distribution:
  • ⁇ is the average of the damage dimensions and ⁇ is the standard deviation of the damage dimensions.
  • determining a prior distribution of the verification indicator parameter as a combination of a lognormal distribution f( ⁇ ) of the crack growth rate of the detection structure and a maximum likelihood estimate f(x
  • the Yesi probability prediction model combines the crack data x of the detected structure in the historical damage data to obtain the posterior distribution f( ⁇
  • Bayesian probability prediction model is described by the following formula:
  • ⁇ ldi represents the standard crack length damage
  • z 1 is the initial length of the crack
  • z 0 is the growth rate of the crack in the unit mileage
  • T is the accumulated operational kilometers since the last time the historical damage data was collected
  • the mean, ⁇ 2 is z 0 standard deviation.
  • a cellular sensor network device is disposed on the detection structure of the train component to be detected; wherein the cellular sensor network device includes a plurality of piezoelectric sensors, each of which serves as an excitation signal loading point and/or response signal Collecting points; exciting the excitation signal on the healthy detection structure at the first time interval of the excitation signal loading point, generating Lamb waves in the detection structure; collecting the first Lamb wave response signal for the Lamb wave at each response signal collection point; The first Lamb wave responds to the signal and establishes the dispersion relationship of the Lamb wave in the anisotropic composite laminate of the detection structure as a function of the propagation angle, and obtains the theoretical velocity distribution of the Lamb wave as the reference information; The second time interval excites the excitation signal on the detection structure to be detected, and generates Lamb waves in the detection structure; each response signal acquisition point collects a second Lamb wave response signal for the Lamb wave; and the second Lamb wave response signal is in the time domain And analyzing in the frequency domain to extract feature information; using the second Lamb wave response signal as
  • a train component crack damage prediction apparatus includes: a component damage detection module for performing damage detection on a detection structure of a train component; and a historical data acquisition module for acquiring the detection structure Historical damage data; wherein the damage data comprises: crack length data of the detection structure; a prediction model establishing module, configured to obtain a life distribution characteristic and a verification index parameter of the damage to the detection structure according to the historical damage data, and Establishing a Bayesian probability prediction model corresponding to the damage of the detection structure; a damage growth prediction module, configured to obtain a prior distribution of the verification indicator parameter according to the historical damage data analysis; using a Markov chain The Carlo method optimizes the model parameters of the Bayesian probability prediction model and predicts the growth rate of the damage of the detection structure.
  • the prediction model establishing module is configured to determine a lifetime distribution of damage to the detection structure as a lognormal distribution.
  • the damage growth prediction module is configured to determine a prior distribution of the verification indicator parameter as a lognormal distribution f( ⁇ ) and a maximum likelihood estimate f of the crack growth rate of the detection structure ( The combination of x
  • the method and device for predicting crack damage of a train component acquires historical damage data for a detection structure of a train component, obtains a life distribution characteristic and a verification index parameter for damage of the detection structure based on the historical damage data, and establishes a Bayesian probability prediction
  • the model is analyzed to obtain the prior distribution of the parameters of the verification index.
  • the Markov chain Monte Carlo method is used to optimize the model parameters of the Bayesian probability prediction model, and the growth rate of the damage of the detection structure is predicted.
  • the Bayesian-MCMC method is applied.
  • FIG. 1 is a flow chart showing an embodiment of a method for predicting crack damage in a train component according to the present invention
  • FIG. 2 is a schematic diagram showing a probability density of a priori distribution of train component damage growth in an embodiment of a method for predicting crack damage of a train component according to the present invention
  • FIG. 3 is a schematic view showing the arrangement of a sensor network device in one embodiment of a method for predicting crack damage of a train component according to the present invention
  • Figure 4 is a block diagram showing an embodiment of a train component crack damage prediction apparatus according to the present invention.
  • FIG. 5 is a schematic diagram of a module in an embodiment of a cellular sensor network device.
  • Embodiments of the invention may be applied to computer systems/servers that operate with numerous other general purpose or special purpose computing system environments or configurations.
  • Examples of well-known computing systems, environments, and/or configurations suitable for use with computer systems/servers include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, based on Microprocessor systems, set-top boxes, programmable consumer electronics, networked personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above, and the like.
  • the computer system/server can be described in the general context of computer system executable instructions (such as program modules) being executed by a computer system.
  • program modules may include routines, programs, target programs, components, logic, data structures, and the like that perform particular tasks or implement particular abstract data types.
  • the computer system/server can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices that are linked through a communication network.
  • program modules may be located on a local or remote computing system storage medium including storage devices.
  • FIG. 1 is a flow chart of one embodiment of a method for predicting crack damage in a train component according to the present invention, as shown in FIG.
  • step 101 damage detection is performed on the detection structure of the train component.
  • the train components can be key components such as high-speed trains and subways.
  • a piezoelectric sensor with an embedded microcontroller can be used as the excitation signal and the signal receiver respectively, and the multi-channel detection is performed according to a certain timing, and the collected lamb wave signal is stored in the sensor.
  • Step 102 Acquire historical damage data of the detection structure, and the damage data includes: detecting crack length data of the structure.
  • the lambda wave signal data stored in the sensor is transmitted to the multi-channel data converter, and the multi-channel data converter transmits the lamb wave signal data to the vehicle damage diagnosis center for pre-processing to confirm the crack position and damage. Length, get historical damage data.
  • Step 103 Obtain a life distribution feature and a verification index parameter for the damage of the detection structure according to the historical damage data, and establish a Bayesian probability prediction model corresponding to the damage of the detection structure.
  • step 104 the prior distribution of the verification indicator parameters is obtained according to the historical damage data analysis.
  • step 105 the model parameter of the Bayesian probability prediction model is optimized by the Markov chain Monte Carlo method, and the growth rate of the damage of the detection structure is predicted.
  • the basic theory of the Markov Chain Monte Carlo (MCMC) algorithm is the Markov process.
  • MCMC Markov Chain Monte Carlo
  • the fault feature information extracted by the onboard damage diagnosis center is input as an input value, and is transmitted to a central server including the intelligent identification diagnosis software, and the damage analysis data obtained under the real operating conditions is fitted and analyzed to obtain the most Excellent fitting function.
  • the life of a train component is subject to a statistically regular random variable, which is generally described by a distribution function of life (also called a cumulative distribution function).
  • the life span of train components is mostly subject to the probability distribution of continuous random variables, including logarithmic distribution, exponential distribution, normal distribution, and Weibull distribution.
  • the lognormal distribution is a relatively perfect distribution with non-negative properties. It is a probability distribution that can accurately describe the life of train components. It is suitable for the damage characteristics of train components accumulated with running mileage in the present invention.
  • the lifetime distribution of the damage to the detection structure is determined to be a lognormal distribution, wherein the density function of the lognormal distribution is:
  • ⁇ is the average value (mm) of the damage size
  • ⁇ is the standard deviation (mm) of the damage size.
  • the prior distribution of the verification index parameters is determined as the combination of the lognormal distribution f( ⁇ ) of the crack growth rate of the detection structure and the maximum likelihood estimate f(x
  • the crack data x of the detected structure in the damage data is obtained by the posterior distribution f( ⁇
  • Bayesian Decision Theory is based on incomplete intelligence, the subjective probability is estimated for the partially unknown state, then the Bayesian formula is used to correct the probability of occurrence, and finally the expected value and the modified probability are used to make the optimal.
  • the method of decision making can obtain a prior distribution.
  • the prior distribution is a combination of the lognormal distribution f( ⁇ ) of the crack growth rate of the observed variable train component and the maximum likelihood estimate f(x
  • x obtain the posterior distribution f( ⁇
  • the Bayesian probability prediction model is described by the following formula:
  • ⁇ ldi represents the standard crack length damage (mm)
  • the normal distribution is used to improve the accuracy of the formula, that is, the accuracy of the prediction. At this point, all parameters are no longer fixed values and will be subject to their respective distributions.
  • Equation (6) is that the probability distribution obeyed by z 0 and z 1 in equation (5) can be disassembled into the product of two probability distributions. It should be noted that this formula is implicit in the calculation process and cannot be specifically expressed, but Does not affect the calculation.
  • Equation (7) indicates that z 0 obeys the log-positive distribution, ⁇ is the mean of z 0 , obtained from historical data, and ⁇ 2 is z 0 standard deviation, obeying the inverse gamma distribution.
  • ⁇ ) maximum likelihood estimation is first obtained by the historical damage data in reverse order, and then ⁇ -G(a,b) is determined through practical experience.
  • the Bayesian formula is used to obtain the verification index parameters.
  • the embodiment uses the winbugs software for the MCMC iteration, that is, the above process is completed, and the specific calculation of the above formula and the MCMC iteration can be performed by using various existing methods, since the maximum likelihood estimation is subject to many factors.
  • the interference cannot be expressed by a specific formula.
  • the purpose of using the MCMC method in this embodiment is to generate a large amount of data obeying the distribution law of historical data, which is used to represent the maximum likelihood estimate f(x
  • the method for predicting crack damage of train components collects historical damage data of key parts of high-speed trains (accumulated length of cracks under equal operating mileage); determines life distribution and verification index parameters of key parts of high-speed trains, and establishes a Bayesian statistical distribution model; Based on the historical data, the prior distribution of the parameters of the verification index is determined. Based on the MCMC method, the damage growth rate of the key parts of the high-speed train is predicted by WinBUGS.
  • the data stored in the sensor is preprocessed, and the preprocessing process includes: 1. signal filtering: filtering out noise and false information, and compensating for temperature characteristics of the piezoelectric sensor; 2. using wavelet analysis, HHT Analysis, empirical mode decomposition and other methods to extract fault feature information, determine the damage degree of the key parts of the train; 3, through the piezoelectric element to receive the arrival signal and the delay time between them, use the elliptical positioning method to determine the damage location, near the damage near the sensor The positioning error will be eliminated by a regular hexagonal honeycomb arrangement.
  • a cellular sensor network device is provided on the detection structure of the train component to be detected.
  • the cellular sensor network device includes a plurality of piezoelectric sensors, each of which serves as an excitation signal loading point and a response signal acquisition point.
  • the probe is used to excite the excitation signal at a first time interval on a healthy detection structure to generate a Lamb wave in the detection structure.
  • Each of the response signal acquisition points acquires a first Lamb wave response signal for the Lamb wave.
  • the Lamb wave is an elastic guided wave propagating in a solid structure under free boundary conditions. It has a slow attenuation and a long propagation distance, and is very sensitive to small damage in the structure.
  • the charge amplifier can be used to amplify the excitation signal and then load it into the piezoelectric sensor to excite the Lamb wave in the detection structure.
  • the Lamb wave response signal of all excitation/sensing channels when the structure is healthy is collected as a reference signal for detecting the structure.
  • the first Lamb wave response signal is obtained and the dispersion relation of the Lamb wave with the propagation angle in the anisotropic composite laminate of the detection structure is established by Mindlin plate theory, and the theoretical velocity distribution of the Lamb wave is obtained as the reference information.
  • the Mindlin plate theory is often referred to as the first-order shear deformation theory of the plate.
  • the Mindlin plate theory assumes that the plate displacement changes linearly in the plate thickness direction, but the plate thickness does not change, and it is assumed that the normal stress in the plate thickness direction is ignored, that is, the plane stress assumption.
  • the dispersion relation of Lamb wave in the anisotropic composite laminate with the propagation angle can be established by Mindlin plate theory, and the theoretical velocity distribution of Lamb wave is obtained, which provides the reference information for damage imaging.
  • the probe is used to excite the excitation signal on the detection structure to be detected at a second time interval, and a Lamb wave is generated in the detection structure.
  • Each of the response signal acquisition points acquires a second Lamb wave response signal for the Lamb wave.
  • the second Lamb wave response signal is analyzed in the time domain and the frequency domain to extract feature information.
  • the second Lamb wave response signal is used as a damage signal
  • the first Lamb wave response signal is used as a reference signal
  • the signal difference coefficient value SDC value corresponding to each response signal collection point is calculated based on the damage signal, the reference signal, the reference information, and the feature information.
  • the acquired second Lamb wave response signal is used as a damage signal, and then the first Lamb wave response signal is collected as a reference signal according to a healthy plate structure, and then the SDC values of all the excitation/sensing channels are calculated.
  • the wavelet transform can be used to analyze the Lamb wave signal excited and received by the piezoelectric sensing element in the time-frequency domain, extract the feature information, and measure the flight time and group velocity of the actual propagation of the Lamb wave in the monitored portion, and the reference Information is compared.
  • the energy of the damage scattering signal is superimposed and amplified by the focusing method, thereby improving the signal-to-noise ratio of the signal.
  • the time reversal method is used to adaptively focus the wave source, reconstruct the signal propagation fluctuation map, and display the damage location and region through signal focusing.
  • the region where crack damage may exist in the structure is reconstructed.
  • the obtained SDC value according to the reflection and scattering of the Lamb wave monitoring signal by the crack damage, the reconstructed image information in the crack direction is strengthened by correcting the signal difference coefficient value SDC in the crack direction in the damaged region, thereby realizing the crack damage.
  • Image reconstruction using the principle of probability imaging to reconstruct the crack damage image.
  • the signal difference coefficient SDC can be used to characterize the statistical difference between the damage signal and the reference signal, and the magnitude of the SDC value reflects the degree of damage and the damage distance.
  • the reconstructed image information in the crack direction is strengthened by correcting the signal difference coefficient (SDC) in the crack direction in the damaged area, thereby realizing the crack damage.
  • SDC signal difference coefficient
  • the traditional probability imaging principle can be used to reconstruct the region where the crack damage may exist in the plate; the crack direction can be determined according to the difference of the SDC value calculated by the path of the piezoelectric sensor in the damaged region.
  • the SDC value in the crack determination direction can be corrected to 1, and the crack damage image is reconstructed again using the conventional probability imaging principle.
  • the SDC values of the six excitation/sense channels are arranged into a regular hexagon, and the probability distribution maps corresponding to all the sensing paths are superimposed, thereby obtaining the damage distribution probability of any point in the detection area, and reconstructing the damage image of the crack. .
  • the crack direction is determined based on the SDC value, and the SDC value in the crack direction is corrected to enhance the reconstructed image information in the crack direction, and the crack damage image is reconstructed by the principle of probability imaging.
  • the SDC profile is plotted and the length of the crack is estimated based on the SDC profile.
  • the SDC profile can be drawn using a variety of existing methods and the length of the crack can be estimated.
  • the energy of the damage scattering signal can be superimposed and amplified by the focusing method, thereby improving the signal-to-noise ratio of the signal; using the time reversal method to adaptively focus the wave source, reconstructing the signal propagation fluctuation pattern,
  • the signal focus shows the damage position and the area; according to the imaging result, the crack damage length is calculated according to the number and spacing of image pixel points exceeding the set threshold value on the monitoring path of the determined crack direction.
  • the energy of the damage scatter signal is superimposed and amplified by the focusing method, thereby improving the signal-to-noise ratio of the signal.
  • the adaptive focusing ability of the wave source is reconstructed by the time reversal method, and the signal propagation fluctuation map is reconstructed, and the damage position and region are displayed by signal focusing.
  • the crack size evaluation process according to the reflection and scattering of the Lamb wave monitoring signal by the crack damage, the reconstructed image information in the crack direction is strengthened by correcting the signal difference coefficient SDC value in the crack direction in the damaged area to achieve crack damage. The image was reconstructed and the length of the crack was estimated from the SDC profile at the receiving end.
  • a plurality of piezoelectric sensors are arranged in a honeycomb array including at least one regular hexagonal basic detecting unit.
  • Each piezoelectric sensor is a node in the honeycomb array as an excitation signal loading point and a response signal acquisition point.
  • the basic detecting unit includes six piezoelectric sensors arranged in a regular hexagon shape; the piezoelectric sensor is embedded with a microcontroller, and the piezoelectric sensor acts as a Lamb wave excitation signal and a Lamb wave signal receiver.
  • the waveform generator is connected to the power amplifier via a wire, and the power amplifier is connected by a wire to an actuator in a monitoring path composed of a cellular sensor network device, the cellular sensor network device being disposed on the structure to be tested.
  • the sensor in the monitoring path is connected to the charge amplifier via a wire, and the charge amplifier is connected to the data acquisition and processing device via a wire.
  • a piezoelectric sensor with a certain number of embedded microcontrollers is used to form a regular hexagonal honeycomb array covering the surface of the part to be tested.
  • the number of arrangements of the cellular sensor network can be determined according to the actual situation of the structure to be monitored. Theoretically, six piezoelectric elements can form a monitoring unit.
  • the plurality of cellular sensor networks can be closely arranged according to the situation. The way of scanning is carried out.
  • the wavelet transform algorithm is used to analyze the second Lamb wave response signal in the time domain and the frequency domain, and the feature information is extracted to measure the time and speed of the actual propagation of the Lamb wave in the detection structure to be detected.
  • the Lamb wave signal excited and received by the piezoelectric sensing element is analyzed in the time-frequency domain by wavelet transform.
  • the first Lamb wave response signal is used as a base signal, and the second Lamb wave response signal is subtracted from the first Lamb wave response signal to obtain a damage scattering signal; the energy of the damage scattering signal is superimposed and amplified by a focusing method to improve damage scattering.
  • the second Lamb wave response signal including the defect information may be time-reversed, and the processed second Lamb wave response signal is loaded as a new wave source to the excitation signal loading point for transmission, and the detection structure to be detected is used.
  • the excitation signal is excited to realize the secondary focusing of the Lamb wave at the defect, and an amplitude focusing map is established to image and identify the damage location and the region.
  • a cellular sensor network device is first disposed on a portion to be tested, the monitoring region is divided into a plurality of basic monitoring units, and the nodes of the cellular sensor network device are used to excite and receive Lamb waves, and are reconstructed by focusing and time reversal.
  • the signal propagates a wave map that uses signal focus to show the location and area of the damage.
  • the signal difference coefficient value in the crack direction in the damaged region is corrected to reconstruct the reconstructed image information in the crack direction, and the length of the crack is evaluated by the SDC distribution map at the receiving end.
  • the present invention provides a train component crack damage prediction apparatus 40, including: a component damage detection module 41, a historical data acquisition module 42, a prediction model establishment module 43, and a damage growth prediction module 44. .
  • the component damage detecting module 41 performs damage detection on the detection structure of the train component.
  • the historical data acquisition module 42 acquires historical damage data of the detection structure, and the damage data includes: detection of crack length data of the structure.
  • the prediction model establishing module 43 obtains the life distribution characteristics and the verification index parameters of the damage to the detection structure based on the historical damage data, and establishes a Bayesian probability prediction model corresponding to the damage of the detection structure.
  • the damage growth prediction module 44 obtains the prior distribution of the verification index parameters according to the historical damage data analysis, and optimizes the model parameters of the Bayesian probability prediction model by using the Markov chain Monte Carlo method, and predicts the growth rate of the damage of the detection structure.
  • the predictive model building module 43 determines that the lifetime distribution of the damage to the detected structure is a lognormal distribution, and the density function of the lognormal distribution is:
  • ⁇ is the average of the damage dimensions and ⁇ is the standard deviation of the damage dimensions.
  • the damage growth prediction module 44 determines the prior distribution of the verification indicator parameters as a combination of the lognormal distribution f( ⁇ ) of the crack growth rate of the detection structure and the maximum likelihood estimate f(x
  • the prediction model is combined with the crack data x of the detected structure in the historical damage data to obtain the posterior distribution f( ⁇
  • the component damage detecting module 41 includes a first signal firing module 411, a reference information acquiring module 412, a second signal firing module 413, a damage information acquiring module 414, and an imaging analyzing module 415.
  • the cellular sensor network device 46 includes a plurality of piezoelectric sensors, each acting as an excitation signal loading point, a response signal acquisition point.
  • a cellular sensor network device 46 is provided on the detection structure of the train component to be detected.
  • a plurality of piezoelectric sensors are arranged in a honeycomb array comprising at least one regular hexagonal basic detection unit. Each piezoelectric sensor is a node in the honeycomb array as an excitation signal loading point and a response signal acquisition point.
  • the basic detecting unit includes six piezoelectric sensors arranged in a regular hexagon.
  • the piezoelectric sensor has a built-in microcontroller, and the piezoelectric sensor acts as a Lamb wave excitation signal and a Lamb wave signal receiver.
  • the first signal excitation module 411 uses the probe to excite the excitation signal on the healthy detection structure at the first time interval at the excitation signal loading point, generates a Lamb wave in the detection structure, and collects the first Lamb for the Lamb wave for each response signal collection point. Wave response signal.
  • the reference information acquisition module 412 obtains the first Lamb wave response signal and establishes the dispersion relationship of the Lamb wave in the anisotropic composite material layer of the detection structure by the Mindlin plate theory, and obtains the theoretical velocity distribution of the Lamb wave. Benchmark information.
  • the second signal excitation module 413 uses the probe to excite the excitation signal on the detection structure to be detected at a second time interval at the excitation signal loading point, generates a Lamb wave in the detection structure, and collects a second Lamb wave for each Lamb wave in each of the response signal acquisition points. Lamb wave response signal.
  • the damage information acquisition module 414 analyzes the second Lamb wave response signal in the time domain and the frequency domain to extract feature information.
  • the damage information acquiring module 414 uses the second Lamb wave response signal as a damage signal, uses the first Lamb wave response signal as a reference signal, and calculates a signal corresponding to each response signal collection point based on the damage signal, the reference signal, the reference information, and the feature information. Difference coefficient value SDC value.
  • the imaging analysis module 415 reconstructs the regions in the detection structure where crack damage may exist based on the obtained SDC values and using the principle of probabilistic imaging.
  • the imaging analysis module 415 determines the crack direction based on the SDC value, corrects the SDC value in the crack direction, and is used to enhance the reconstructed image information in the crack direction, and reconstructs the crack damage image by using the probability imaging principle.
  • the imaging analysis module 415 plots the SDC profile and estimates the length of the crack based on the SDC profile.
  • the damage information obtaining module 414 analyzes the second Lamb wave response signal in the time domain and the frequency domain by using a wavelet transform algorithm, and extracts feature information for measuring the time and speed of the actual propagation of the Lamb wave in the detection structure to be detected. .
  • the damage information acquisition module 414 subtracts the second Lamb wave response signal from the first Lamb wave response signal by using the first Lamb wave response signal as a base signal to obtain a damage scatter signal.
  • the damage information acquisition module 414 superimposes the damage scattering signal energy by focusing to improve the signal-to-noise ratio of the damage scattering signal, and performs time reversal processing on the damage scattering signal.
  • the second signal excitation module 413 performs time reversal processing on the second Lamb wave response signal including the defect information, and loads the processed second Lamb wave response signal as a new wave source to the excitation signal loading point for transmission.
  • the excitation signal is excited on the detection structure to be detected, the secondary focus of the Lamb wave at the defect is realized, and the amplitude focus map is established to image and identify the damage location and the region.
  • the method and device for predicting crack damage of train components obtain historical damage data for the detection structure of the train components, obtain life distribution characteristics and verification index parameters for damage of the detection structure according to historical damage data, and establish Bayesian
  • the probabilistic prediction model is used to analyze the prior distribution of the parameters of the verification index.
  • the Markov chain Monte Carlo method is used to optimize the model parameters of the Bayesian probability prediction model, and the growth rate of the damage of the detection structure is predicted.
  • Bayesian-MCMCMC The method predicts the damage growth of train components based on a large amount of historical data. Subjective probability estimation can be applied to the damage of some unknown train components in the case of incomplete data, and then the probability of occurrence is corrected by Bayesian formula, and finally the expected value is used. And make corrections to make optimal maintenance or update decisions, provide a more accurate and intuitive basis for train maintenance and replacement, and have high calculation accuracy.
  • the method and apparatus, apparatus of the present invention may be implemented in a number of ways.
  • the methods, apparatus, and apparatus of the present invention can be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware.
  • the above-described sequence of steps for the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless otherwise specifically stated.
  • the invention may also be embodied as a program recorded in a recording medium, the program comprising machine readable instructions for implementing the method according to the invention.
  • the invention also covers a recording medium storing a program for performing the method according to the invention.

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Abstract

一种列车部件裂纹损伤预测方法和装置，其中的方法包括：在待检测列车部件的检测结构上设置蜂窝传感器网络装置，利用lamb采集历史损伤数据（102），利用根据历史损伤数据获得对于检测结构的损伤的寿命分布特征和验证指标参数，并建立贝叶斯概率预测模型（103），分析得出验证指标参数的先验分布（104），采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测检测结构的损伤的增长速率（105）。该方案通过应用Bayesian-MCMC的方法并基于大量历史数据预测列车部件损伤增长，用贝叶斯公式对发生概率进行修正，再利用期望值和修正概率做出最优维修或更新决策，为列车的维修、更换提供更为准确的、直观化的依据。

Description

列车部件裂纹损伤预测方法和装置 技术领域
本发明涉及损伤分析技术领域，尤其涉及一种列车部件裂纹损伤预测方法和装置。
背景技术
作用在构件上的载荷或应力往往随时间呈交替变化，疲劳在这种交变应力下的扩展称为疲劳裂纹的扩展，由此产生的破坏称为疲劳破坏。大量实践数据表明，具有初始裂纹的构件，即使受到交变低于静载荷破坏时的应力裂纹也会扩展，严重时甚至导致破坏。疲劳和断裂是工程中较为常见的构件失效原因。结构疲劳最初源于金属疲劳问题，在结构疲劳问题中，金属表面出现裂纹更为普遍，这种裂纹形态，分布位置各异，大致可分为三类：纵裂纹、横裂纹和龟裂纹。
目前，常用的裂纹扩展模型如下：(1)Pairs公式：Pairs发现应力强度因子幅度△K是控制裂纹扩展速率的最关键因素,据此提出了著名的pairs公式：
Figure PCTCN2019082491-appb-000001
其中,a——裂纹深度或宽度；N——应力循环次数；C、m——和材料有关的参数；ΔK——应力强度因子变化范围。
(2)Forman公式：在裂纹扩展分析中,中速率区决定作动筒的剩余寿命,不同应力比下的dA/dN-△K曲线几乎是平行的。关于在应力比和断裂韧性的影响下dA/dN-△K曲线的修正模型,是Forman在Pairs公式基础上提出的:
Figure PCTCN2019082491-appb-000002
其中，Kc为断裂韧度。
考虑到实际应用,对此公式的修正角度有很多,例如加入应力比和门槛应力强度因子幅的影响,将其进一步修正得到：
Figure PCTCN2019082491-appb-000003
(3)裂纹全程扩展公式：
Figure PCTCN2019082491-appb-000004
上述公式虽然综合考虑了材料自身参数和外载荷对裂纹扩展速率的影响,但应力强度因子的幅值依然是影响裂纹扩展速率的最显著影响因素。目前的结构损伤和裂纹增长预测技术大多基于上述方法，不能克服数据不充足、经验不足等客观因素，尤其是无法解决裂纹增长速率预测问题。
发明内容
有鉴于此，本发明实施例提供一种列车部件裂纹损伤预测方法和装置。
根据本发明实施例的一个方面，提供一种列车部件裂纹损伤预测方法，包括：对列车部件的检测结构进行损伤检测；获取所述检测结构的历史损伤数据；其中，损伤数据包括：所述检测结构的裂纹长度数据；根据所述历史损伤数据获得对于所述检测结构的损伤的寿命分布特征和验证指标参数，并建立与所述检测结构的损伤相对应的贝叶斯概率预测模型；根据所述历史损伤数据分析得出所述验证指标参数的先验分布；采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测所述检测结构的损伤的增长速率。
可选地，所述获得对于所述检测结构的损伤的寿命分布特征和验证指标 参数包括：确定对于所述检测结构的损伤的寿命分布为对数正态分布：
其中，所述对数正态分布的密度函数为：
Figure PCTCN2019082491-appb-000005
其中，μ是损伤尺寸的平均值，σ是损伤尺寸的标准差。
可选地，将所述验证指标参数的先验分布确定为所述检测结构的裂纹增长速率的对数正态分布f(θ)与最大似然估计f(x|θ)的结合；运用贝叶斯概率预测模型并结合所述历史损伤数据中的检测结构的裂纹数据x，获得后验分布f(θ|x)，并进行迭代计算获得裂纹在单位里程中的增长速率。
可选地，所述贝叶斯概率预测模型用以下公式描述：
σ 1di＝z 1+z 0T i，i＝1，...，n～N(z 1+z 0T i；σ 2)；
f(z 0，z 1，σ 2)＝f(z 0，z 1)*f(σ 2)；
log(z 0)～N 2(μ，σ 2)；
σ 2～IG(a，b)；
其中，δ ldi表示标准裂纹长度损伤量，δ ldi服从均值为σ 1di＝z 1+z 0T i的正态分布，z 1是裂纹初始的长度，z 0是裂纹在单位里程中的增长速率，T是自上一次采集历史损伤数据后的积累运营公里数,τ＝σ -2，则τ～G(a,b)，其中a＝b＝0.01，f为概率分布函数，μ为z 0的均值，σ 2为z 0标准差。
可选地，在待检测列车部件的检测结构上设置蜂窝传感器网络装置；其中，所述蜂窝传感器网络装置包括多个压电传感器，每个压电传感器都作为激励信号加载点和/或响应信号采集点；在激励信号加载点以第一时间间隔在健康的检测结构上激发激励信号，在检测结构中产生Lamb波；各个响应信号采集点采集对于此Lamb波的第一Lamb波响应信号；获取第一Lamb波响应信号并建立Lamb波在检测结构的各向异性复合材料层板中随传播角度变化的 频散关系，获得Lamb波的理论速度分布，作为基准信息；在激励信号加载点以第二时间间隔在待检测的检测结构上激发激励信号，在检测结构中产生Lamb波；各个响应信号采集点采集对于此Lamb波的第二Lamb波响应信号；对第二Lamb波响应信号在时域和频域上进行分析，提取特征信息；将第二Lamb波响应信号作为损伤信号，将第一Lamb波响应信号作为参考信号，基于损伤信号、参考信号以及基准信息、特征信息计算与各个所述响应信号采集点相对应的信号差异系数值SDC值；根据获得的SDC值并采用概率成像原理，重构出检测结构中裂纹损伤可能存在的区域；基于SDC值判定裂纹方向，校正裂纹方向上的SDC值，用于强化裂纹方向上的重构图像信息，采用概率成像原理重构裂纹损伤图像；绘制SDC分布图，基于SDC分布图评估出裂纹的长度，生成所述损伤数据。
根据本发明的另一方面，提供一种列车部件裂纹损伤预测装置，包括：部件损伤检测模块，用于对列车部件的检测结构进行损伤检测；历史数据获取模块，用于获取所述检测结构的历史损伤数据；其中，损伤数据包括：所述检测结构的裂纹长度数据；预测模型建立模块，用于根据所述历史损伤数据获得对于所述检测结构的损伤的寿命分布特征和验证指标参数，并建立与所述检测结构的损伤相对应的贝叶斯概率预测模型；损伤增长预测模块，用于根据所述历史损伤数据分析得出所述验证指标参数的先验分布；采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测所述检测结构的损伤的增长速率。
可选地，所述预测模型建立模块，用于确定对于所述检测结构的损伤的寿命分布为对数正态分布。
可选地，所述损伤增长预测模块，用于将所述验证指标参数的先验分布确定为所述检测结构的裂纹增长速率的对数正态分布f(θ)与最大似然估计f(x|θ)的结合；运用贝叶斯概率预测模型并结合所述历史损伤数据中的检测结 构的裂纹数据x，获得后验分布f(θ|x)，并进行迭代计算获得裂纹在单位里程中的增长速率。
本发明的列车部件裂纹损伤预测方法和装置，获取对于列车部件的检测结构的历史损伤数据，根据历史损伤数据获得对于检测结构的损伤的寿命分布特征和验证指标参数，并建立贝叶斯概率预测模型，分析得出验证指标参数的先验分布，采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测检测结构的损伤的增长速率；通过应用Bayesian-MCMC的方法并基于大量历史数据预测列车部件损伤增长，可以在数据不完全的情况下，对部分未知的列车部件损伤采用主观概率估计，然后用贝叶斯公式对发生概率进行修正，最后再利用期望值和修正概率做出最优维修或更新决策，为列车的维修、更换提供更为准确的、直观化的依据，并且计算准确度高。
本发明实施例附加的方面和优点将在下面的描述中部分给出，这些将从下面的描述中变得明显，或通过本发明的实践了解到。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其它的附图：
图1为根据本发明的列车部件裂纹损伤预测方法的一个实施例的流程图；
图2为根据本发明的列车部件裂纹损伤预测方法的一个实施例中的列车部件损伤增长先验分布概率密度示意图；
图3为根据本发明的列车部件裂纹损伤预测方法的一个实施例中的传感器网络装置的布置示意图；
图4为根据本发明的列车部件裂纹损伤预测装置的一个实施例中的模块示意图；
图5为蜂窝传感器网络装置的一个实施例中的模块示意图
具体实施方式
现在将参照附图来详细描述本发明的各种示例性实施例。应注意到：除非另外具体说明，否则在这些实施例中阐述的部件和步骤的相对布置、数字表达式和数值不限制本发明的范围。
同时，应当明白，为了便于描述，附图中所示出的各个部分的尺寸并不是按照实际的比例关系绘制的。
以下对至少一个示例性实施例的描述实际上仅仅是说明性的，决不作为对本发明及其应用或使用的任何限制。
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论，但在适当情况下，所述技术、方法和设备应当被视为说明书的一部分。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步讨论。
本发明实施例可以应用于计算机系统/服务器，其可与众多其它通用或专用计算系统环境或配置一起操作。适于与计算机系统/服务器一起使用的众所周知的计算系统、环境和/或配置的例子包括但不限于：个人计算机系统、服务器计算机系统、瘦客户机、厚客户机、手持或膝上设备、基于微处理器的系统、机顶盒、可编程消费电子产品、网络个人电脑、小型计算机系统﹑大型计算机系统和包括上述任何系统的分布式云计算技术环境，等等。
计算机系统/服务器可以在由计算机系统执行的计算机系统可执行指令(诸如程序模块)的一般语境下描述。通常，程序模块可以包括例程、程序、目标程序、组件、逻辑、数据结构等等，它们执行特定的任务或者实现特定 的抽象数据类型。计算机系统/服务器可以在分布式云计算环境中实施，分布式云计算环境中，任务是由通过通信网络链接的远程处理设备执行的。在分布式云计算环境中，程序模块可以位于包括存储设备的本地或远程计算系统存储介质上。
下文中的“第一”、“第二”仅用于描述上相区别，并没有其它特殊的含义。
图1为根据本发明的列车部件裂纹损伤预测方法的一个实施例的流程图，如图1所示：
步骤101，对列车部件的检测结构进行损伤检测。
列车部件可以为高速列车、地铁等的关键部件等。可以采用内嵌微控制器的压电传感器分别作为激发信号器及信号接收器，按照一定时序进行多路检测，并将收集的lamb波信号储存在传感器。
步骤102，获取检测结构的历史损伤数据，损伤数据包括：检测结构的裂纹长度数据。
在高速列车行至等公里时，将储存在传感器的lamb波信号数据传输至多通道数据转换器，多通道数据转换器将lamb波信号数据传输到车载损伤诊断中心进行预处理，确认裂纹位置及损伤长度，得到历史损伤数据。
步骤103，根据历史损伤数据获得对于检测结构的损伤的寿命分布特征和验证指标参数，并建立与检测结构的损伤相对应的贝叶斯概率预测模型。
步骤104，根据历史损伤数据分析得出验证指标参数的先验分布。
步骤105，采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测检测结构的损伤的增长速率。
马尔可夫链蒙特卡罗(Markov Chain Monte Carlo,MCMC)算法的基础理论为马尔可夫过程，在MCMC算法中，为了在一个指定的分布上采样，根据马尔可夫过程，首先从任一状态出发，模拟马尔可夫过程，不断进行状态转移， 最终收敛到平稳分布。
在一个实施例中，将车载损伤诊断中心提取的故障特征信息作为输入值，传入含智能辨识诊断软件的中心服务器，通过对获取的真实运营条件下的损伤增长数据进行拟合分析，得到最优的拟合函数。
列车部件的寿命服从一定统计规律的随机变量,一般用寿命的分布函数(也称累积分布函数)来描述。列车部件的寿命大多服从连续型随机变量的概率分布，包括对数分布、指数分布、正态分布、威布尔分布等。对数正态分布是一种比较完善的分布，具有非负性，是一种可以准确描述列车部件寿命的概率分布，适用于本发明中列车部件随运行里程累积产生的损伤特征。
确定对于检测结构的损伤的寿命分布为对数正态分布，其中，对数正态分布的密度函数为：
Figure PCTCN2019082491-appb-000006
其中，μ是损伤大小的平均值(mm)；σ是损伤大小的标准差(mm)。
将验证指标参数的先验分布确定为检测结构的裂纹增长速率的对数正态分布f(θ)与最大似然估计f(x|θ)的结合，运用贝叶斯概率预测模型并结合历史损伤数据中的检测结构的裂纹数据x，获得后验分布f(θ|x)，并进行迭代计算获得裂纹在单位里程中的增长速率。
由于贝叶斯决策(Bayesian Decision Theory)是在不完全情报下，对部分未知的状态用主观概率估计，然后用贝叶斯公式对发生概率进行修正，最后再利用期望值和修正概率做出最优决策的方法，根据如下贝叶斯公式，可以得到先验分布。
Figure PCTCN2019082491-appb-000007
在本实施例中先验分布为观察变量列车部件的裂纹增长速率的对数正态分布f(θ)与最大似然估计f(x|θ)的结合，运用贝叶斯决策模型结合实际数据x，获得后验分布f(θ|x)，通过大量迭代，最终得到裂纹增长速率均值(期望值)，这个均值也就是模型中高速列车关键部位裂纹增长趋于稳定后的单位里程增长速率。
例如，某城市发生了一起汽车撞人逃跑事件，该城市只有两种颜色的车，蓝色15％，绿色85％，事发时有一个人A在现场看见了，A指证是蓝车。但是根据专家在现场分析,当时那种条件能看正确的可能性是80％，那么,肇事的车是蓝车的概率采用下述的方法进行计算：
A：目击证人看到的车辆颜色为蓝色；B 1：肇事车辆是蓝色；B 2肇事车辆是绿色
P(A|B 1)＝80％；P(A)＝80％P(B 1)+20％P(B 2)
Figure PCTCN2019082491-appb-000008
贝叶斯概率预测模型用以下公式描述：
σ 1di＝z 1+z 0T i，i＝1，...，n～N(z 1+z 0T i；σ 2)   (5)；
f(z 0，z 1，σ 2)＝f(z 0，z 1)*f(σ 2)   (6)；
log(z 0)～N 2(μ，σ 2)   (7)；
σ 2～IG(a，b)   (8)；
其中，δ ldi表示标准裂纹长度损伤量(mm)，z 1是裂纹初始的长度，如果列车部件不存在初始裂纹，z 0是裂纹在单位里程中的增长速率(mm/3000km、mm/5000km或mm/10000km，根据实际不同测量数据值确定)，T是自上一次采集历史损伤数据后的积累运营公里数,τ＝σ -2，则τ－G(a,b)，其中a＝b＝0.01。
式(5)为裂纹长度与积累运营公里数的线性关系，其中，δ ldi服从均值为σ 1di＝z 1+z 0T i的正态分布，该公式用于计算累计裂纹长度，但考虑δ ldi受多方因素影响，引入贝叶斯概率公式，使用正态分布提高公式准确性，即预测的准确性，至此，所有参数都不再是定值，将服从各自分布。
式(6)为式(5)中z 0、z 1所服从的概率分布可拆解成两种概率分布的乘积，需要说明的是，此公式隐含于计算过程中，不能具体表达，但并不影响计算。
式(7)表示z 0服从对数正太分布，μ为z 0的均值，由历史数据获得，σ 2为z 0标准差，服从逆gamma分布。以式(5)-(8)为顺序，在实际计算过程中是逆序通过历史损伤数据首先得到f(x|θ)最大似然估计，之后通过实际经验确定τ－G(a,b)，其中a＝b＝0.01，如式(7)先验分布已确定为对数正态分布f(θ)，最终，结合式(6)及通过历史损伤数据得到的f(x|θ)最大似然估计使用贝叶斯公式，从而得到验证指标参数。建立预测模型后，本实施例使用winbugs软件进行MCMC迭代，即完成上述过程，上述公式的具体计算以及进行MCMC迭代可以采用现有的多种方法具体进行执行，由于最大似然估计受太多因素干扰，不能用具体公式表示，本实施例使用MCMC方法的目的旨在产生大量服从历史数据分布规律的数据，该数据用于表示最大似然估计f(x|θ)，最终获得z 0的均值，即损伤增长速率。
上述实施例中的列车部件裂纹损伤预测方法，收集高速列车关键部位历史损伤数据(等运行里程下裂纹累计长度)；确定高速列车关键部位寿命分布和验证指标参数，建立贝叶斯统计分布模型；基于历史数据确定验证指标参数的先验分布，基于MCMC方法，利用WinBUGS预测高速列车关键部位损伤增长速率。
在一个实施例中，将储存在传感器的数据进行预处理，预处理过程包括：1、信号滤波：滤除噪声及虚假信息、对压电传感器的温度特性进项补偿；2、 采用小波分析、HHT分析、经验模式分解等方法提取故障特征信息，确定列车关键部位损伤程度；3、通过压电原件接收到达信号及它们之间的延迟时间，利用椭圆定位的方法确定损伤位置，其中靠近传感器附近损伤定位的误差，将通过正六边形蜂窝状布置方式来消除。
例如，在待检测列车部件的检测结构上设置蜂窝传感器网络装置。蜂窝传感器网络装置包括多个压电传感器，每个压电传感器都作为激励信号加载点、响应信号采集点。在激励信号加载点利用探头以第一时间间隔在健康的检测结构上激发激励信号，在检测结构中产生Lamb波。各个响应信号采集点采集对于此Lamb波的第一Lamb波响应信号。
Lamb波是在自由边界条件下，固体结构中传播的弹性导波，具有衰减慢传播距离远，且对结构中的微小损伤十分敏感。可以利用电荷放大器将激励信号放大后加载到压电传感器，从而在检测结构中激发出Lamb波。采集检测结构健康时的所有激励/传感通道的Lamb波响应信号，作为检测结构的基准信号。
获取第一Lamb波响应信号并通过Mindlin板理论建立Lamb波在检测结构的各向异性复合材料层板中随传播角度变化的频散关系，获得Lamb波的理论速度分布，作为基准信息。Mindlin板理论通常被称为板的一阶剪切变形理论。Mindlin板理论假设在板厚度方向板位移线性变化，但是板厚度不变，并假设忽略板厚度方向的正应力，即平面应力假设。可以通过Mindlin板理论建立Lamb波在各向异性复合材料层板中随传播角度变化的频散关系，得到Lamb波的理论速度分布，为损伤成像提供基准信息。
在激励信号加载点利用探头以第二时间间隔在待检测的检测结构上激发激励信号，在检测结构中产生Lamb波。各个响应信号采集点采集对于此Lamb波的第二Lamb波响应信号。对第二Lamb波响应信号在时域和频域上进行分析，提取特征信息。将第二Lamb波响应信号作为损伤信号，将第一Lamb波 响应信号作为参考信号，基于损伤信号、参考信号以及基准信息、特征信息计算与各个响应信号采集点相对应的信号差异系数值SDC值。将采集到的第二Lamb波响应信号作为损伤信号，再根据在健康的板结构中采集第一Lamb波响应信号作为参考信号，然后计算所有激励/传感通道的SDC值。
可以利用小波变换对由压电传感元件激励和接收的Lamb波信号在时频域进行分析，提取特征信息，测量出Lamb波在监测的部位中实际传播的飞行时间和群速度，并与基准信息相比较。根据Lamb波信号传播自身的特性，通过聚焦的方法使损伤散射信号能量叠加放大，从而提高信号的信噪比。利用时间反转法对波源进行自适应聚焦能力，重建信号传播波动图，通过信号聚焦显示损伤位置和区域。
根据获得的SDC值并采用概率成像原理，重构出检测结构中裂纹损伤可能存在的区域。根据得到的SDC值，根据裂纹损伤对Lamb波监测信号的反射及散射作用，通过校正损伤区域中裂纹方向上的信号差异系数值SDC，强化裂纹方向上的重构图像信息，实现对裂纹损伤的图像重构，采用概率成像原理重构裂纹损伤图像。
根据Lamb波传播的基本原理，当传播介质出现中断或不连续时，大多数的Lamb波信号会因受到阻碍而难以继续向前传播，即使裂纹宽度很窄，只要其长度大于Lamb波波长，就会造成在传播前进方向上的Lamb波出现十分明显的衰减。可以利用信号差异系数SDC来表征损伤信号和参考信号的统计特性差异，SDC值的大小反映了损伤程度和损伤距离。
根据裂纹损伤对Lamb波监测信号的反射及散射作用,通过校正损伤区域中裂纹方向上的信号差异系数值(Signal difference coefficient,SDC),强化裂纹方向上的重构图像信息,实现对裂纹损伤的图像重构,并由接收端SDC分布图评估出裂纹的长度。
根据得到的SDC值，可以利用传统概率成像原理，重构出板中裂纹损伤 可能存在的区域；根据通过损伤区域内的压电传感器所在的路径计算其SDC值差值，可以判定裂纹方向。可以将裂纹判定方向上的SDC值校正为1，采用传统概率成像原理再次重构裂纹损伤图像。把六个激励/传感通道的SDC值布置为一个正六边形，将所有传感路径对应的概率分布图进行叠加，从而得到检测区域内任意点的损伤分布概率，重构出裂纹的损伤图像。
基于SDC值判定裂纹方向，校正裂纹方向上的SDC值，用于强化裂纹方向上的重构图像信息，采用概率成像原理重构裂纹损伤图像。绘制SDC分布图，基于SDC分布图评估出裂纹的长度。可以采用现有的多种方法绘制SDC分布图，并评估出裂纹的长度。
可以根据Lamb波信号传播自身的特性，通过聚焦的方法使损伤散射信号能量叠加放大，从而提高信号的信噪比；利用时间反转法对波源的自适应聚焦能力，重建信号传播波动图，通过信号聚焦显示损伤位置和区域；根据成像结果，在判定的裂纹方向的监测路径上，按照超过设定阈值的图像像素点数量和间距计算裂纹损伤长度。
在基于Lamb的成像过程中，通过聚焦的方法使损伤散射信号能量叠加放大，从而提高信号的信噪比。在损伤定位过程中，利用时间反转法对波源的自适应聚焦能力，重建信号传播波动图，通过信号聚焦显示损伤位置和区域。在裂纹大小评估过程中，根据裂纹损伤对Lamb波监测信号的反射及散射作用，通过校正损伤区域中裂纹方向上的信号差异系数SDC值，强化裂纹方向上的重构图像信息，实现对裂纹损伤的图像重构，并由接收端SDC分布图评估出裂纹的长度。
如图3所示，多个压电传感器排列成包括至少一个正六边形的基本检测单元的蜂窝状阵列。每个压电传感器都为蜂窝状阵列中的一个节点，作为激励信号加载点、响应信号采集点。基本检测单元包括呈正六边形排列的六个压电传感器；压电传感器内嵌有微控制器，压电传感器作为Lamb波激发信号 器和Lamb波信号接收器。
波形发生器通过导线与功率放大器连接，功率放大器通过导线与蜂窝传感器网络装置组成的监测路径中的激励器连接，该蜂窝传感器网络装置设置在待测结构上。监测路径中的传感器通过导线与电荷放大器连接，电荷放大器通过导线与数据采集处理装置连接。
根据检测区域大小，使用一定数量的内嵌微控制器的压电传感器组成正六边形蜂窝状阵列覆在待测部位表面。作为激励，蜂窝传感器网络的布置数量可以根据待监测结构的实际情况进行确定，理论上六个压电元件就可以组成一个监测单元，结构较大时根据情况可通过紧密布置多个蜂窝传感器网络以扫查的方式进行。
采用小波变换算法对第二Lamb波响应信号在时域和频域上进行分析，提取特征信息，用于测量出Lamb波在待检测的检测结构中的实际传播的时间和速度。在提取待测构件的特征信息过程中，利用小波变换对由压电传感元件激励和接收的Lamb波信号在时频域进行分析。
以第一Lamb波响应信号作为基信号，将第二Lamb波响应信号与第一Lamb波响应信号相减，得到损伤散射信号；通过聚焦的方法使损伤散射信号能量叠加放大，用以提高损伤散射信号的信噪比，并对损伤散射信号进行时间反转处理。可以对包含缺陷信息的第二Lamb波响应信号进行时间反转处理，并将处理后的第二Lamb波响应信号作为新的波源加载到激励信号加载点进行发射，用以在待检测的检测结构上激发激励信号，实现Lamb波在缺陷处的二次聚焦，建立幅值聚焦图，用以对损伤位置和区域进行成像识别。
在一个实施例中，首先在待测部位上布置蜂窝传感器网络装置，将监测区域分割成若干个基本监测单元，利用蜂窝传感器网络装置的节点激励和接收Lamb波，通过聚焦和时间反转法重建信号传播波动图，利用信号聚焦显示损伤位置和区域。根据Lamb波监测信号的反射及散射作用校正损伤区域中裂 纹方向上的信号差异系数值强化裂纹方向上的重构图像信息，并由接收端SDC分布图评估出裂纹的长度。
在一个实施例中，如图4所示，本发明提供一种列车部件裂纹损伤预测装置40，包括：部件损伤检测模块41、历史数据获取模块42、预测模型建立模块43和损伤增长预测模块44。
部件损伤检测模块41对列车部件的检测结构进行损伤检测。历史数据获取模块42获取检测结构的历史损伤数据，损伤数据包括：检测结构的裂纹长度数据。预测模型建立模块43根据历史损伤数据获得对于检测结构的损伤的寿命分布特征和验证指标参数，并建立与检测结构的损伤相对应的贝叶斯概率预测模型。损伤增长预测模块44根据历史损伤数据分析得出验证指标参数的先验分布，采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测检测结构的损伤的增长速率。
预测模型建立模块43确定对于检测结构的损伤的寿命分布为对数正态分布，对数正态分布的密度函数为：
Figure PCTCN2019082491-appb-000009
其中，μ是损伤尺寸的平均值，σ是损伤尺寸的标准差。
损伤增长预测模块44将验证指标参数的先验分布确定为检测结构的裂纹增长速率的对数正态分布f(θ)与最大似然估计f(x|θ)的结合，运用贝叶斯概率预测模型并结合历史损伤数据中的检测结构的裂纹数据x，获得后验分布f(θ|x)，并进行迭代计算获得裂纹在单位里程中的增长速率。
如图5所示，部件损伤检测模块41包括:第一信号激发模块411、基准信息获取模块412、第二信号激发模块413、损伤信息获取模块414和成像分析模块415。
蜂窝传感器网络装置46包括多个压电传感器，每个压电传感器都作为激 励信号加载点、响应信号采集点。在待检测列车部件的检测结构上设置蜂窝传感器网络装置46。多个压电传感器排列成包括至少一个正六边形的基本检测单元的蜂窝状阵列。每个压电传感器都为蜂窝状阵列中的一个节点，作为激励信号加载点、响应信号采集点。基本检测单元包括呈正六边形排列的六个压电传感器。压电传感器内嵌有微控制器，压电传感器作为Lamb波激发信号器和Lamb波信号接收器。
第一信号激发模块411在激励信号加载点利用探头以第一时间间隔在健康的检测结构上激发激励信号，在检测结构中产生Lamb波，各个响应信号采集点采集对于此Lamb波的第一Lamb波响应信号。基准信息获取模块412获取第一Lamb波响应信号并通过Mindlin板理论建立Lamb波在检测结构的各向异性复合材料层板中随传播角度变化的频散关系，获得Lamb波的理论速度分布，作为基准信息。
第二信号激发模块413在激励信号加载点利用探头以第二时间间隔在待检测的检测结构上激发激励信号，在检测结构中产生Lamb波，各个响应信号采集点采集对于此Lamb波的第二Lamb波响应信号。损伤信息获取模块414对第二Lamb波响应信号在时域和频域上进行分析，提取特征信息。损伤信息获取模块414将第二Lamb波响应信号作为损伤信号，将第一Lamb波响应信号作为参考信号，基于损伤信号、参考信号以及基准信息、特征信息计算与各个响应信号采集点相对应的信号差异系数值SDC值。
成像分析模块415根据获得的SDC值并采用概率成像原理，重构出检测结构中裂纹损伤可能存在的区域。成像分析模块415基于SDC值判定裂纹方向，校正裂纹方向上的SDC值，用于强化裂纹方向上的重构图像信息，采用概率成像原理重构裂纹损伤图像。成像分析模块415绘制SDC分布图，基于SDC分布图评估出裂纹的长度。
损伤信息获取模块414采用小波变换算法对第二Lamb波响应信号在时域 和频域上进行分析，提取特征信息,用于测量出Lamb波在待检测的检测结构中的实际传播的时间和速度。损伤信息获取模块414以第一Lamb波响应信号作为基信号，将第二Lamb波响应信号与第一Lamb波响应信号相减，得到损伤散射信号。损伤信息获取模块414通过聚焦的方法使损伤散射信号能量叠加放大，用以提高损伤散射信号的信噪比，并对损伤散射信号进行时间反转处理。
第二信号激发模块413对包含缺陷信息的第二Lamb波响应信号进行时间反转处理，并将处理后的第二Lamb波响应信号作为新的波源加载到激励信号加载点进行发射，用以在待检测的检测结构上激发激励信号，实现Lamb波在缺陷处的二次聚焦，建立幅值聚焦图，用以对损伤位置和区域进行成像识别。
上述实施例提供的列车部件裂纹损伤预测方法和装置，获取对于列车部件的检测结构的历史损伤数据，根据历史损伤数据获得对于检测结构的损伤的寿命分布特征和验证指标参数，并建立贝叶斯概率预测模型，分析得出验证指标参数的先验分布，采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测检测结构的损伤的增长速率；通过应用Bayesian-MCMC的方法并基于大量历史数据预测列车部件损伤增长，可以在数据不完全的情况下，对部分未知的列车部件损伤采用主观概率估计，然后用贝叶斯公式对发生概率进行修正，最后再利用期望值和修正概率做出最优维修或更新决策，为列车的维修、更换提供更为准确的、直观化的依据，并且计算准确度高。
可能以许多方式来实现本发明的方法和装置、设备。例如，可通过软件、硬件、固件或者软件、硬件、固件的任何组合来实现本发明的方法和装置、设备。用于方法的步骤的上述顺序仅是为了进行说明，本发明的方法的步骤不限于以上具体描述的顺序，除非以其它方式特别说明。此外，在一些实施例中，还可将本发明实施为记录在记录介质中的程序，这些程序包括用于实 现根据本发明的方法的机器可读指令。因而，本发明还覆盖存储用于执行根据本发明的方法的程序的记录介质。
本发明的描述是为了示例和描述起见而给出的，而并不是无遗漏的或者将本发明限于所公开的形式。很多修改和变化对于本领域的普通技术人员而言是显然的。选择和描述实施例是为了更好说明本发明的原理和实际应用，并且使本领域的普通技术人员能够理解本发明从而设计适于特定用途的带有各种修改的各种实施例。

Claims (6)

  1. 一种列车部件裂纹损伤预测方法，其特征在于，包括：
    对列车部件的检测结构进行损伤检测；
    获取所述检测结构的历史损伤数据；其中，损伤数据包括：所述检测结构的裂纹长度数据；
    根据所述历史损伤数据获得对于所述检测结构的损伤的寿命分布特征和验证指标参数，并建立与所述检测结构的损伤相对应的贝叶斯概率预测模型；
    根据所述历史损伤数据分析得出所述验证指标参数的先验分布；
    采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测所述检测结构的损伤的增长速率。
  2. 如权利要求1所述的方法，其特征在于，所述获得对于所述检测结构的损伤的寿命分布特征和验证指标参数包括：
    确定对于所述检测结构的损伤的寿命分布为对数正态分布：
    其中，所述对数正态分布的密度函数为：
    Figure PCTCN2019082491-appb-100001
    其中，μ是损伤尺寸的平均值，σ是损伤尺寸的标准差。
  3. 如权利要求2所述的方法，其特征在于，还包括：
    将所述验证指标参数的先验分布确定为所述检测结构的裂纹增长速率的对数正态分布f(θ)与最大似然估计f(x|θ)的结合；
    运用贝叶斯概率预测模型并结合所述历史损伤数据中的检测结构的裂纹数据x，获得后验分布f(θ|x)，并进行迭代计算获得裂纹在单位里程中的增长速率。
  4. 如权利要求3所述的方法，其特征在于，还包括：
    所述贝叶斯概率预测模型用以下公式描述：
    σ 1di＝z 1+z 0T 1，i＝1，...，n～N(z 1+z 0T i；σ 2)；
    f(z 0，z 1，σ 2)＝f(z 0，z 1)*f(σ 2)；
    log(z 0)～N 2(μ，σ 2)；
    σ 2～IG(a，b)；
    其中，
    Figure PCTCN2019082491-appb-100002
    表示标准裂纹长度损伤量，
    Figure PCTCN2019082491-appb-100003
    服从均值为σ 1di＝z 1+z 0T 1的正态分布，z 1是裂纹初始的长度，z 0是裂纹在单位里程中的增长速率，T是自上一次采集历史损伤数据后的积累运营公里数,τ＝σ -2，则τ～G(a,b)，其中a＝b＝0.01，f为概率分布函数，μ为z 0的均值，σ 2为z 0标准差。
  5. 如权利要求1所述的方法，其特征在于，还包括：
    在待检测列车部件的检测结构上设置蜂窝传感器网络装置；其中，所述蜂窝传感器网络装置包括多个压电传感器，每个压电传感器都作为激励信号加载点和/或响应信号采集点；
    在激励信号加载点以第一时间间隔在健康的检测结构上激发激励信号，在检测结构中产生Lamb波；各个响应信号采集点采集对于此Lamb波的第一Lamb波响应信号；
    获取第一Lamb波响应信号并建立Lamb波在检测结构的各向异性复合材料层板中随传播角度变化的频散关系，获得Lamb波的理论速度分布，作为基准信息；
    在激励信号加载点以第二时间间隔在待检测的检测结构上激发激励信号，在检测结构中产生Lamb波；各个响应信号采集点采集对于此Lamb波的第二Lamb波响应信号；
    对第二Lamb波响应信号在时域和频域上进行分析，提取特征信息；将第 二Lamb波响应信号作为损伤信号，将第一Lamb波响应信号作为参考信号，基于损伤信号、参考信号以及基准信息、特征信息计算与各个所述响应信号采集点相对应的信号差异系数值SDC值；
    根据获得的SDC值并采用概率成像原理，重构出检测结构中裂纹损伤可能存在的区域；
    基于SDC值判定裂纹方向，校正裂纹方向上的SDC值，用于强化裂纹方向上的重构图像信息，采用概率成像原理重构裂纹损伤图像；绘制SDC分布图，基于SDC分布图评估出裂纹的长度，生成所述损伤数据。
  6. 一种列车部件裂纹损伤预测装置，其特征在于，包括：
    部件损伤检测模块，用于对列车部件的检测结构进行损伤检测；
    历史数据获取模块，用于获取所述检测结构的历史损伤数据；其中，损伤数据包括：所述检测结构的裂纹长度数据；
    预测模型建立模块，用于根据所述历史损伤数据获得对于所述检测结构的损伤的寿命分布特征和验证指标参数，并建立与所述检测结构的损伤相对应的贝叶斯概率预测模型；
    损伤增长预测模块，用于根据所述历史损伤数据分析得出所述验证指标参数的先验分布；采用马尔科夫链蒙特卡洛方法优化贝叶斯概率预测模型的模型参数，并预测所述检测结构的损伤的增长速率。
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