CN109649432B - System and method for monitoring integrity of steel rail of cloud platform based on guided wave technology - Google Patents
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Abstract
The invention discloses a guided wave technology-based cloud platform steel rail integrity monitoring system and method. The monitoring system comprises a front-end monitoring module, a cloud monitoring server and a browsing terminal, wherein the front-end monitoring module comprises a guided wave transducer, a temperature sensor and a control cabinet, and a solar panel module, a charge-discharge control circuit, an energy storage module, a transmission interface circuit module, a lower computer control circuit module, a communication interface circuit module, a guided wave transceiving module and a signal conditioning module are arranged in the control cabinet; firstly, extracting characteristic signals and coefficient matrixes of acoustic emission sources at different structures of the characterization steel rail in a strong correlation manner by noise reduction, and then performing denoising and denoising reconstruction to obtain guided wave signals representing the damage of the steel rail, so as to obtain a damage position and realize the integrity monitoring of the steel rail. The invention can effectively realize the reliable monitoring of the integrity of the steel rail, greatly improves the cross-regional property and the real-time property of the monitoring, and has important practical significance and engineering value.
Description
Technical Field
The invention relates to a steel rail integrity monitoring system and method, in particular to a cloud platform steel rail integrity monitoring system and method based on a guided wave technology.
Background
In recent years, on one hand, with the rapid development of national economy, the railway traffic concerning the life of national economy is rapidly developed unprecedentedly; on the other hand, with the rapid development of railway traffic, the running density, the running speed, the load capacity and the like of the railway are greatly improved, the load, the impact and the like which are bound to be born by the steel rail as an important component of the track are also increased, the probability of inevitable damage of the steel rail is improved, and stricter requirements are provided for the reliable and safe running of the track.
The ultrasonic guided wave technology is widely applied to nondestructive testing and on-line monitoring of various industries by the characteristics of long distance, large range, full section detection and single-end receiving and transmitting. The railway steel rail is very suitable for monitoring and evaluating the working state by applying the guided wave technology no matter the material or the component type. However, considering that the structural characteristics of the steel rail are complex, the lapping condition of the fasteners is more, and the traditional point-to-point nondestructive testing means such as ultrasonic testing, magnetic leakage testing, machine vision and penetration testing hardly meet the strict requirements of real monitoring on timeliness, cloud end online, reliability and cross-region large range. Meanwhile, considering that the railway line runs busy, the offline manual detection and monitoring technology for the track at the skylight period is difficult to meet the increasingly developed actual detection and monitoring requirements of the railway. Related early studies on the defects are provided, and patents such as a rail damage detection method disclosed in publication No. CN104535652A, a rail defect detection method and device adopting electromagnetic ultrasonic technology disclosed in publication No. CN101398410A, a rail flaw detection method adopting image fusion of laser ultrasonic and high-speed camera disclosed in publication No. CN104237381A, and a method and device for on-site ultrasonic inspection of a railway rail disclosed in publication No. CN102084245A are disclosed. With the rapid development of information technology, especially the great development of internet, cloud computing, fifth generation mobile communication technology and the like, the on-line, cross-region and real-time monitoring technology based on the cloud platform is becoming the focus and leading-edge hot spot of various industrial applications. The popularization and application of the steel rail integrity monitoring method and system based on the cloud platform are developed, and the automation and the intelligent degree of the railway track online detection and monitoring technology in China can be promoted.
Disclosure of Invention
The invention provides a cloud platform steel rail integrity monitoring system and method based on a guided wave technology aiming at the problems and defects in the background technology, and can realize real-time online cross-regional monitoring on the service state of a railway line network track.
As shown in fig. 2, the invention is realized by the following technical scheme:
the utility model provides a high in clouds platform rail integrality monitoring system based on guided wave technique:
the monitoring system comprises a front-end monitoring module, a cloud monitoring server and a browsing terminal, wherein the front-end monitoring module comprises a guided wave transducer, a temperature sensor and a control cabinet, and a solar panel module, a charging and discharging control circuit, an energy storage module, a transmission interface circuit module, a lower computer control circuit module, a communication interface circuit module, a guided wave transceiving module and a signal conditioning module are arranged in the control cabinet; the system comprises a transmission interface circuit module, a temperature sensor, a lower computer control circuit module, a solar panel module, an energy storage module, a transmission interface circuit module, a temperature sensor, a charge-discharge control circuit, a guided wave transceiver module, a signal conditioning module, a solar panel module, a cloud monitoring server and a communication interface circuit module.
The guided wave transducer sends ultrasonic guided waves to the steel rail, the ultrasonic guided waves are transmitted along the steel rail and then encounter defects to be reflected to generate echo signals, and the echo signals are received by the guided wave transducer to serve as guided wave signals for monitoring the steel rail and sent to the lower computer control circuit module; the temperature sensor detects the real-time temperature of the steel rail near the guided wave transducer and sends the real-time temperature to the lower computer control circuit module.
The solar panel module collects solar energy and converts the solar energy into electric energy, the electric energy is charged to the energy storage module through the charge-discharge control circuit, and then the energy storage module supplies power to the guided wave transducer, the temperature sensor and the whole control cabinet.
The browsing terminal comprises a computer/mobile phone terminal.
Secondly, a method for monitoring integrity of a cloud platform steel rail based on a guided wave technology, which adopts the system, and then the method specifically comprises the following steps:
s1, extracting characteristic signals and coefficient matrixes of sound emission sources with strong correlation at different structures of the characterization steel rail through noise reduction in the first part of the monitoring method, and performing the following steps:
s1.1: according to the geometric and physical properties of the steel rail to be detected, the mode and frequency of ultrasonic guided waves adopted by a guided wave transducer are preset, the acquisition monitoring parameters of the guided wave transducer are preset, and guided wave signals and temperature signals of the monitored steel rail are obtained by discontinuously and continuously acquiring the guided wave transducer and a temperature sensor;
the acquisition parameters of the guided wave transducer comprise acquisition time duration A, data storage quantity B, acquisition times C, an acquisition period Ts, monitoring temperature D, a monitoring time interval E, a distance threshold value F, total iteration times N, an amplitude threshold value Z, sampling frequency Fs and ultrasonic guided wave speed V.
The acquisition parameters of the guided wave transducer further comprise a data storage amount B, and the data storage amount B is the data capacity of the stored guided wave signals and the stored temperature signals.
The acquisition time duration a is greater than the acquisition period TS.
S1.2: the acquisition times C are carried out in the acquisition time duration A, and each acquisition period TSInterior guided wave signal X, the temperature signal that guided wave signal X and same time were gathered transmit to high in the clouds monitoring server, and data format Y according to C signal data dimension is (X ═1，X2，…，Xc)TStoring to form guided wave monitoring data, and taking the acquisition times as the dimensionality of signal data;
the time interval between adjacent acquisition time durations a is the monitoring time interval E.
The guided wave signals are substantially decomposed into linear superposition representing different sound source signals: y ═ MR + n (t), and represents noise signal data in order to determine the weight matrix M and the sound source signal data R, n (t). The invention can directly extract and obtain the sound source signal data R from any guided wave signal containing damage defects without collecting and processing the over-redundant guided wave signals in different complex environments when the steel rail structure is complete enough.
S1.3: for front and rear groups of guided wave monitoring data (cross processing) acquired under different acquisition time durations A, the signal data dimensions of the two groups of guided wave monitoring data are q and w, the two groups of guided wave monitoring data are overlapped according to the row direction to jointly form new higher-dimensional data Z 'to be analyzed, and then the data Z' to be analyzed is subjected to characteristic scaling and sparse processing standardization processing according to the following formula to obtain the scale-normalized multidimensional data Z to be analyzed with the mean value of 0 and the variance of 1:
wherein E (Z) and σ are the mean and standard deviation of the data Z' to be analyzed, ZiRepresenting the i-th set of signals in the multi-dimensional data Z to be analyzed, Zi'represents the ith set of signals in the data Z' to be analyzed;
s1.4: carrying out target optimization: blind source separation is carried out on the multidimensional data Z to be analyzed, and sound source signal data representing steel rail damage are obtained:
firstly, the following formula is adopted to iteratively solve and obtain the weight coefficient of the sound source signal in the guided wave monitoring data:
s1.4.1: initializing a weight factor W with a two-norm 10And an iteration count n ═ 1;
s1.4.2: the iterative solution is performed according to the following formula:
Wn＝E{Z(WT n-1Z)3}-3Wn-1，n＝n+1
wherein, WnRepresenting a weight coefficient vector obtained by nth iteration, Z representing multidimensional data to be analyzed, and E { } representing an expectation function; delta1n～δLnRespectively represent the nth weight coefficient vector WnL represents the total number of strongly correlated weight coefficients;
s1.4.3: after each iteration solution, weighting coefficient vector W is calculatednCarrying out normalization processing, and then judging:
if WT nWn-1If | does not converge to 1, then step S1.4.2 is re-executed;
if WT nWn-1If | converges to 1 and satisfies the condition that the iteration number N is less than the total iteration number N, outputting the weight coefficient vector W under the current iteration numbernAdding the strong correlation weight coefficients into the coefficient matrix W and continuing to carry out step S1.4.2 until the iteration number N is equal to the total iteration number N, and terminating the iteration to obtain q + W groups of strong correlation weight coefficients;
s1.4.4: finally, a coefficient matrix W, W ═ W (W) is obtained1，W2，…，Wq+w)TObtaining sound source signal data R representing different sound sources in the steel rail to be measured according to a formula R-W- × Z, wherein R-W (R-R)1,r2,...,rL)T，r1,r2,...,rLA source subsignal representing each guided wave signal corresponding to the guided wave monitoring data in the source signal data R;
s1.5: solving the generalized inverse matrix of the coefficient matrix W to obtain a weight matrix M, which is expressed as:
in the formula, β11～βqLThe guided wave monitoring data respectively representing the dimension q corresponds to q rows of coefficient values of the acoustic source signal as a set of coefficient groups α11～αwLThe guided wave monitoring data with the dimension w corresponds to the w rows of coefficient values of the sound source signal as another group of coefficient groups; the q rows of coefficient values and the w rows of coefficient values respectively correspond to signal data dimensions q and w of the front and rear groups of guided wave monitoring data;
the following reference matrix K is constructed:
wherein the number of the element-1 in each column is q and the number of the element-1 in each column is w;
s1.6: performing similarity measurement, and respectively calculating a column p of the weight matrix MiOne column K corresponding to the reference matrix KiSimilarity between:
for each column vector p of the weight matrix MiAnd each column vector K of the reference matrix KiBy normalizing in the same manner as in step S1.3 to obtain normalized respective row-column vectors p* iAnd k* iThen, the similarity distance is obtained according to the following formula:
θi＝|1-p* i×k* i|
in the formula, thetaiA normalized column of vectors p representing a weight matrix M* iNormalized column of vectors K corresponding to reference matrix K* iSimilarity distance between them;
all similarity distance results are then grouped into a similarity distance set ξ ═ θ1,θ2,...,θL}；
S2, reducing the dimensions and denoising, reconstructing to obtain guided wave signals representing the damage of the steel rail, and obtaining the damage position:
s2.1: and (3) performing feature extraction and dimension reduction processing on the weight matrix M to obtain a strongly-correlated sound source signal:
extracting the similarity distance set ξ satisfying the inequality relation theta according to the distance threshold value Fi<Distance of similarity of F θiEach column vector in the corresponding weight matrix M and each sound source sub-signal in the sound source signal data R are composed of the extracted column vectors as the following damage matrix P(q+w)×HFrom the extracted source sub-signals, the following strongly correlated source matrix is formed
Wherein H represents the number of strongly correlated sound source signals as strongly correlated sound source components;
according to the method, the required number of reconstructed sound source signals can be automatically extracted through the threshold value F to obtain the damage signals, and the shorter the similarity distance is, the more the required sound source signals are related to the damage.
S2.2: damage matrix P(q+w)×HThe two coefficient groups of the matrix are abandoned, and the coefficient group with relatively larger matrix coefficient is reserved, so that the matrix P is damaged(q+w)×HExtracting to obtain a structural coefficient matrix Pw×H；
The following takes as an example a coefficient group correspondence of w coefficient values, i.e. coefficient values which are relatively large:
s2.3: reconstructing ultrasonic guided wave damage signal Y containing track damage characteristic information according to construction coefficient matrix and strongly-correlated sound source matrixdefect；
For the constructed coefficient matrix Pw×HTaking the mean value to remove the interference to the coefficient value of each column according to the following formula to obtain a new one-dimensional row vector P1×H NEWThis avoids the mixing of noise and anomalous interference of the calculation results:
further, an ultrasonic guided wave damage signal Y containing track damage characteristic information is obtained according to the following formuladefect：
S2.4: according to the damage signal YdefectThe processing obtains the envelope information and the envelope information,
if the part of the envelope information, of which the envelope amplitude is greater than the amplitude threshold value Z, determining that the steel rail to be detected is damaged;
if the part of the envelope information, of which the envelope amplitude is not greater than the amplitude threshold value Z, determining that the steel rail to be detected has no damage;
under the condition that the steel rail to be detected is damaged, the ultrasonic guided wave damage signal YdefectAnd (3) processing the acquisition time t corresponding to the damage of the steel rail to be detected by adopting the following formula to obtain the positioning of the damage of the steel rail:
s＝t×V/2
t＝1/Fs×i，i＝0,1,…,num，num＝Ts×Fs
wherein Ts represents the acquisition period of the guided wave transducer, Fs represents the sampling frequency of the guided wave transducer, V represents the ultrasonic guided wave speed sent by the guided wave transducer, i represents the ordinal number of the ith acquisition, num represents the total number of all the acquisitions, and s represents the distance from the position of the damage existing in the steel rail to the guided wave transducer;
the integrity monitoring of the steel rail takes the damage of the steel rail and the positioning position thereof as the representation of the integrity.
The discontinuous continuous acquisition is the acquisition of guided wave signals and temperatures of which the times C and the data length are Ts × Fs are continuously carried out under each acquisition interval duration A according to the monitoring requirements.
When the data of the guided wave signals and the temperature signals stored in the lower computer control circuit module reach the data storage amount B, mining the big ultrasonic guided wave data obtained by monitoring, and reducing the missing report rate by adopting the following set cross processing analysis method to find out the damage:
a: processing and analyzing the guided wave signals at the same monitoring temperature D but different time according to the steps to obtain the location of the steel rail damage;
b: under a fixed monitoring time interval E, processing and analyzing two groups of guided wave monitoring data with the time difference of the monitoring time interval E according to the steps to obtain the location of the damage of the steel rail;
c: and randomly extracting two groups of guided wave monitoring data in the stored data, and processing and analyzing the two groups of guided wave monitoring data according to the steps to obtain the positioning of the steel rail damage.
The invention has the beneficial effects that:
the method has the characteristics of online real-time performance, region-crossing performance, low cost and high robustness, not only greatly improves the defects and problems in the traditional detection and monitoring means and technology, but also improves the automation and intelligentization level of the evaluation and monitoring of the service state of the railway line network; the inherent advantages of the ultrasonic guided wave technology can greatly improve the detection monitoring efficiency, range and system reliability of the railway track; development and application based on a cloud monitoring algorithm can greatly improve the cross-platform performance and remote convenience of steel rail online monitoring.
The invention can realize the monitoring treatment of the defects of fracture and broken lines, and can realize the monitoring trend evaluation of the corrosion defect information in the slow conversion, particularly, the adopted cross treatment method greatly improves the discrimination and tracking of corrosion damage, improves the monitoring precision and avoids the problems of insufficient monitoring precision and difficult discovery in the corrosion growth process in the traditional monitoring method.
The monitoring system has high intelligent degree, can realize the tracking and monitoring of corrosion growth information and real-time perception through a cross processing analysis method, and is favorable for carrying out real-time long-term monitoring on the whole life cycle of the steel rail.
According to the invention, through algorithm optimization, on one hand, redundancy-removed mutually independent sound source signals are obtained through iteration, noise reduction is realized, and the signal-to-noise ratio of the signals is greatly improved, so that the precision of a monitoring algorithm is improved; on the other hand, the method carries out triple dimension reduction processing aiming at damage reconstruction, greatly reduces the calculation complexity, reduces the monitoring time, improves the efficiency and the reliability of the monitoring algorithm, and has important significance for the real-time requirement of finding the damage of the steel rail in service. Firstly, carrying out iterative extraction on a sound source signal algorithm by using an original guided wave signal to realize dimension reduction; secondly, the obtained large number of sound source signals are subjected to double noise reduction according to the similarity distance relationship provided by the invention, so that signals with strong correlation damage are further obtained, and the redundancy of the traditional monitoring method is reduced; and thirdly, in the step of reconstructing the damage signal, further removing the coefficient values with small contribution according to the relative magnitude relation of the two groups of coefficient values in the damage matrix, and extracting to obtain a lower-dimensional structural coefficient matrix to realize triple noise reduction. In the embodiment of the present invention, a fourth dimension reduction is further mentioned, that is, a certain row of vectors is extracted from a finally obtained structural coefficient matrix, so as to simplify the operation of mean value interference removal. Therefore, the calculation complexity is reduced, the efficiency and the reliability of the monitoring algorithm are greatly improved, and the method has important significance for analyzing the big data obtained by steel rail guided wave monitoring.
The invention considers and combines the excavation problem of the big data of the steel rail guided wave obtained by long-term monitoring, reduces the false alarm rate of the damage through cross processing analysis and improves the monitoring precision. Meanwhile, innovation is made on the algorithm, and compared with the traditional method that only damage comparison can be carried out on the two groups of monitoring data with a reference library, namely a guided wave database under the complete and healthy working state of the steel rail, the method can realize processing and analysis of guided wave data at different monitoring moments, skillfully construct a reference matrix, realize measurement and extraction of the sound source signal obtained by iteration, process the coefficient value of the constructed coefficient matrix by a set averaging method so as to remove interference, reduce false report of the damage of the steel rail caused by accidental factors and improve the precision.
Meanwhile, the invention does not need to establish an over-redundant reference library at the early stage of the service of the steel rail, and carries out multi-dimensional monitoring analysis from the current moment of monitoring along with monitoring, so that the evaluation judgment and damage positioning of a plurality of defects can be realized.
Therefore, the method can effectively realize reliable monitoring of the integrity of the steel rail, greatly improves the cross-regional property and the real-time property of monitoring, and has important practical significance and engineering value.
Drawings
FIG. 1 is a system block diagram of the system of the present invention.
FIG. 2 is a flow chart of the method of the present invention.
Fig. 3 is a waveform diagram of correlation according to an embodiment of the present invention.
Fig. 4 is a waveform diagram of a damage signal according to an embodiment of the present invention.
Fig. 5 is a graph of an impairment signal versus an envelope according to an embodiment of the present invention.
Fig. 6 is an envelope diagram of an embodiment of the invention.
FIG. 7 is a map of lesion localization in accordance with an embodiment of the present invention.
Fig. 8 is a damage signal of an embodiment of the present invention.
Figure 9 is a map of lesion localization for an embodiment of the present invention.
FIG. 10 is a graph of a damage signal for an embodiment of the present invention.
Fig. 11 is a diagram of an actual rail monitoring signal according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
As shown in fig. 1, the monitoring system includes a front-end monitoring module, a cloud monitoring server and a browsing terminal, the front-end monitoring module includes a guided wave transducer, a temperature sensor and a control cabinet, and a solar panel module, a charge-discharge control circuit, an energy storage module, a transmission interface circuit module, a lower computer control circuit module, a communication interface circuit module, a guided wave transceiver module and a signal conditioning module are arranged in the control cabinet; the system comprises a transmission interface circuit module, a temperature sensor, a lower computer control circuit module, a solar panel module, an energy storage module, a transmission interface circuit module, a temperature sensor, a charge-discharge control circuit, a guided wave transceiver module, a signal conditioning module, a solar panel module, a cloud monitoring server and a communication interface circuit module.
The solar panel module collects solar energy and converts the solar energy into electric energy, the electric energy is charged to the energy storage module through the charge-discharge control circuit, and then the energy storage module supplies power to the guided wave transducer, the temperature sensor and the whole control cabinet. The guided wave transducer sends ultrasonic guided waves to the steel rail, the ultrasonic guided waves encounter defects after propagating along the steel rail and are reflected to generate echo signals, the echo signals are received by the guided wave transducer to serve as guided wave signals for monitoring the steel rail, the guided wave signals are conditioned by the signal conditioning module and received by the guided wave receiving and sending module in sequence and then are sent to the lower computer control circuit module, and the lower computer control circuit module transmits received data to the cloud monitoring server for storage; the temperature sensor detects the real-time temperature of the steel rail near the guided wave transducer and sends the real-time temperature to the lower computer control circuit module. The browsing terminal comprises a computer/mobile phone terminal, and the computer/mobile phone terminal is connected with the access cloud monitoring server and receives alarm and message information.
As shown in fig. 2, an embodiment of the complete method according to the invention is as follows:
example 1:
as shown in fig. 3 to 7, the cloud platform performs monitoring processing analysis according to the guided wave data obtained by monitoring the front end.
Further mining a large amount of guided wave monitoring data, namely guided wave big data, and analyzing the data through cross processing, wherein two groups of rail web monitoring data Y with the interval E are takeniAnd YjPerforming monitoring analysis, wherein i is 22, j is 22 monitoring data, performing standardization processing according to step S1.3, processing according to steps S1.4.1 to S1.4.3 in claim 5 to obtain a coefficient matrix W with dimension 44 × 23, obtaining 23 characteristic sound source information for the guided wave signals with 44 dimensions after processing according to step S1.4.4 in claim 5, and visualizing each group of coefficient data of the characteristic signals after programming in MATLAB to obtain 23 groups.
To obtain thetai＝|1-p* i×k* iHere, the data needs to be normalized to avoid inaccurate result caused by non-normalized size of the data itself, and here, the covariance calculation is taken as an example to illustrate that Y is made* i＝k×Yi,Y* j＝k×YjObviously, cov (Y)* i，Y* j)＝k2cov(Yi，Yj) It can be seen that the data bookAre not uniform enough, resulting in data of the same nature with different results. I.e. to avoid pi,kiThe influence of the magnitude of the value itself on the similarity, where p is to be measuredi,kiProcessing according to the standardized formula in the step S1.3 to obtain processed data p* iAnd k* i. The new data p are obtained by processing according to the standardized formula in step S1.3 of claim 5* iAnd k* i。
Further according to step S1.6, a similarity distance set ξ is obtained { θ ═ θ1,θ2,...,θLAnd obtaining a trend curve of similarity, and displaying the trend curve from low to high according to the correlation distance, as shown in figure 3. The dashed black line in fig. 3 is the distance threshold F set in step S2.1, while the parameter θ is plotted in fig. 3iIt can be seen visually that the predetermined relationship theta is satisfiedi<The smaller the partial coefficient of F, the better the coefficient and sound source component in the corresponding weight matrix M characterize the damage. The damage signal Y obtained in accordance with steps S2.1 to 2.3defectAs shown in fig. 4.
And further step 2.4, obtaining a clear defect position oscillogram, determining that the steel rail has damage according to a set amplitude threshold value Z as shown in fig. 5 and 6, giving out a damage position, and finishing positioning evaluation as shown in fig. 7.
In further practical detection, step S2.3 may be implemented in such a way as to improve monitoring efficiency, and the coefficient matrix P may be constructedw×HThe damage signal structure is performed for a certain row of coefficient values, and the correspondence of the coefficient groups of w coefficient values is taken as an example, and is expressed as follows:
the mean value interference removing operation can be simplified to construct the damage by extracting any row vector, and the new one-dimensional row vector P is obtained by extracting the ith row example of the construction coefficient matrix1×H NEW＝[αi1αii… αiH]According to
Can obtain an ultrasonic guided wave damage signal Y containing track damage characteristic informationdefectFurther step S2.4 is again by YdefectAnd processing to obtain envelope information, and setting a threshold value to obtain damage position information to complete monitoring. The calculation complexity of the operation can be reduced, and the calculation speed is improved.
Example 2:
fig. 11 shows an actual rail web monitor signal, which has a low signal-to-noise ratio and is difficult to determine a damage.
The invention can also monitor, evaluate and position the multiple damage conditions, wherein the detection, analysis and comparison based on the invention and the traditional reference library method are respectively carried out on the actual conditions of the steel rail with 4 damages and 5 damages, namely the damage growth process is monitored through cross processing analysis. In actual monitoring, the guided wave collection mode is set as excitation emission of the transducer at one end of the steel rail, the transducer at the other end receives guided wave signals of the steel rail, collected data are obtained, and the collected data are transmitted to the cloud monitoring server through the front-end monitoring module.
According to the method, after treatment, characteristic sound source signal and coefficient extraction is completed according to the steps from S1.3 to S1.6, reconstructed signals of a graph 8 are obtained from the steps from S2.1 to S2.3, signals of a graph 9 are obtained from the step S2.4, damage judgment and positioning are completed, newly-added signals of a fifth damage can be obtained, and therefore a better treatment result can be obtained.
By the class I, when more damages are generated, such as nine damages and ten damages, the method selects different analysis data through cross processing analysis, and can better judge. When the traditional analysis method based on the reference library is adopted, because the guided wave signal of the reference library is a complete guided wave monitoring signal without damage, when the condition that five damages are contained in the analysis, the equivalent is 0 damage and five damagesIn consideration of the fact that in actual propagation, guided wave signals (different acoustic emission sources) are reflected back and forth for multiple times, superposed, cancelled and converted, and after the five characteristics are reconstructed, the position of each damage cannot be obtained from the obtained guided wave signals containing the five characteristics, as shown in Y of fig. 10defectAll signals are mixed together and are mutually superposed and offset, and the damage position is difficult to judge.
By analogy, the traditional monitoring method based on the reference library or the sample library can complete the damage assessment aiming at less damage, and in practical application, along with the continuous monitoring, the increase of data volume and the growth and increase of damage, the reliable monitoring and analysis of the steel rail damage can be hardly completed.
In the meantime, the defined steps S1.5 to S1.6 pass through the similarity distance thetai＝|1-|p* i×k* iThe method for measuring the similarity between a biogenic source signal and a rail damage by means of | |, the two groups of guided wave data used in the step S1.3 do not have the requirements on sequence and signal source by taking an absolute value mode, the traditional method requires that the two groups of data have the requirements on sequence and source, because the traditional method is based on a reference library or a sample library, namely the first group of data needs to be data in the reference library or the sample library, and the second group of data is data from damage monitoring, namely the first group of data is defaulted to be non-damaged by the traditional method, the second group of data is a damage signal needing to be analyzed, the application range of the traditional method is limited, the method does not need to consider, and the method has no special requirements on the sequence of the two groups of data.
Monitoring data are processed in a cross mode, transverse comparison is carried out at a given monitoring temperature D according to preset data, steel rail guided wave signals in different monitoring periods are processed and analyzed according to monitoring steps, and steel rail monitoring states are obtained, so that detection and positioning of service states in different preset monitoring periods and under the same temperature condition are achieved; or two groups of monitoring data Y with the interval E under the monitoring time interval E according to the presetiAnd YjMonitoring and analyzing to obtain the service state analysis result of the steel rail; can also be used forRandomly extracting two groups of data Y in the stored data according to presetiAnd YjAnd performing cross treatment according to the monitoring treatment method to obtain the service state of the monitored steel rail.
The foregoing detailed description is intended to illustrate and not limit the invention, which is intended to be within the spirit and scope of the appended claims, and any changes and modifications that fall within the true spirit and scope of the invention are intended to be covered by the following claims.
Claims (3)
1. A cloud platform steel rail integrity monitoring method based on a guided wave technology adopts a cloud platform steel rail integrity monitoring system based on the guided wave technology, the monitoring system comprises a front end monitoring module, a cloud monitoring server and a browsing terminal, the front end monitoring module comprises a guided wave transducer, a temperature sensor and a control cabinet, and a solar panel module, a charging and discharging control circuit, an energy storage module, a transmission interface circuit module, a lower computer control circuit module, a communication interface circuit module, a guided wave receiving and sending module and a signal conditioning module are arranged in the control cabinet; the system comprises a power supply, a transmission interface circuit module, a lower computer control circuit module, a temperature sensor, a charge-discharge control circuit, a guided wave transceiving module, a solar panel module, an energy storage module, a transmission interface circuit module, a solar panel module, a power supply module and a cloud monitoring server, wherein the guided wave transducer and the temperature sensor are both arranged on a steel rail to be detected;
the guided wave transducer sends ultrasonic guided waves to the steel rail, the ultrasonic guided waves are transmitted along the steel rail and then encounter defects to be reflected to generate echo signals, and the echo signals are received by the guided wave transducer to serve as guided wave signals for monitoring the steel rail and sent to the lower computer control circuit module; the temperature sensor detects the real-time temperature of the steel rail near the guided wave transducer and sends the real-time temperature to the lower computer control circuit module;
the method is characterized in that:
s1, the steps are as follows:
s1.1: according to the geometric and physical properties of the steel rail to be detected, the mode and frequency of ultrasonic guided waves adopted by a guided wave transducer are preset, the acquisition monitoring parameters of the guided wave transducer are preset, and guided wave signals and temperature signals of the monitored steel rail are obtained by discontinuously and continuously acquiring the guided wave transducer and a temperature sensor;
s1.2: the acquisition times C are carried out in the acquisition time duration A, and each acquisition period TSInterior guided wave signal X, the temperature signal that guided wave signal X and same time were gathered transmit to high in the clouds monitoring server, and data format Y according to C signal data dimension is (X ═1，X2，…，Xc)TStoring to form guided wave monitoring data;
s1.3: for front and rear groups of guided wave monitoring data acquired under different acquisition time durations A, the signal data dimensions of the two groups of guided wave monitoring data are q and w, the two groups of guided wave monitoring data are overlapped together according to the row direction to form new higher-dimensional data Z 'to be analyzed, and then the data Z' to be analyzed is subjected to standardized processing of feature scaling and sparse processing according to the following formula to obtain scale-normalized multidimensional data Z to be analyzed, wherein the mean value of the data Z to be analyzed is 0, and the variance of the data Z to be analyzed is 1:
wherein E (Z) and σ are the mean and standard deviation of the data Z' to be analyzed, ZiRepresenting the i-th set of signals in the multi-dimensional data Z to be analyzed, Zi'represents the ith set of signals in the data Z' to be analyzed;
s1.4: carrying out target optimization: blind source separation is carried out on the multidimensional data Z to be analyzed, and sound source signal data representing steel rail damage are obtained:
firstly, the following formula is adopted to iteratively solve and obtain the weight coefficient of the sound source signal in the guided wave monitoring data:
s1.4.1: initializing a weight factor W with a two-norm 10And an iteration count n ═ 1;
s1.4.2: the iterative solution is performed according to the following formula:
wherein, WnRepresenting a weight coefficient vector obtained by nth iteration, Z representing multidimensional data to be analyzed, and E { } representing an expectation function; delta1n～δLnRespectively represent the nth weight coefficient vector WnL represents the total number of strongly correlated weight coefficients;
s1.4.3: after each iteration solution, weighting coefficient vector W is calculatednCarrying out normalization processing, and then judging:
if WT nWn-1If | does not converge to 1, then step S1.4.2 is re-executed;
if WT nWn-1If | converges to 1 and satisfies the condition that the iteration number N is less than the total iteration number N, outputting the weight coefficient vector W under the current iteration numbernAdding the strong correlation weight coefficients into the coefficient matrix W and continuing to carry out step S1.4.2 until the iteration number N is equal to the total iteration number N, and terminating the iteration to obtain q + W groups of strong correlation weight coefficients;
s1.4.4: finally, a coefficient matrix W, W ═ W (W) is obtained1，W2，…，Wq+w)TObtaining sound source signal data R representing different sound sources in the steel rail to be measured according to a formula R-W- × Z, wherein R-W (R-R)1,r2,...,rL)T，r1,r2,...,rLA source subsignal representing each guided wave signal corresponding to the guided wave monitoring data in the source signal data R;
s1.5: solving the generalized inverse matrix of the coefficient matrix W to obtain a weight matrix M, which is expressed as:
in the formula, β11～βqLThe guided wave monitoring data respectively representing the dimension q corresponds to q rows of coefficient values of the acoustic source signal as a set of coefficient groups α11～αwLThe guided wave monitoring data with the dimension w corresponds to the w rows of coefficient values of the sound source signal as another group of coefficient groups; the q rows of coefficient values and the w rows of coefficient values respectively correspond to signal data dimensions q and w of the front and rear groups of guided wave monitoring data;
the following reference matrix K is constructed:
wherein the number of the element-1 in each column is q and the number of the element-1 in each column is w;
s1.6: performing similarity measurement, and respectively calculating a column p of the weight matrix MiOne column K corresponding to the reference matrix KiSimilarity between:
for each column vector p of the weight matrix MiAnd each column vector K of the reference matrix KiBy normalizing to obtain normalized respective row-column vectors p* iAnd k* iThen, the similarity distance is obtained according to the following formula:
θi＝|1-p* i×k* i|
in the formula, thetaiA normalized column of vectors p representing a weight matrix M* iNormalized column of vectors K corresponding to reference matrix K* iSimilarity distance between them;
all similarity distance results are then grouped into a similarity distance set ξ ═ θ1,θ2,...,θL}；
S2, reducing the dimensions and denoising, reconstructing to obtain guided wave signals representing the damage of the steel rail, and obtaining the damage position:
s2.1: and (3) performing feature extraction and dimension reduction processing on the weight matrix M to obtain a strongly-correlated sound source signal:
extracting the similarity distance set ξ satisfying the inequality relation theta according to the distance threshold value Fi<Distance of similarity of F θiThe column vectors in the corresponding weight matrix M and the sound source sub-signals in the sound source signal data R form the following impairment matrix P from the extracted column vectors(q+w)×HFrom the extracted source sub-signals, the following strongly correlated source matrix is formed
Wherein H represents the number of strongly correlated sound source signals as strongly correlated sound source components;
s2.2: damage matrix P(q+w)×HThe two coefficient groups of the matrix are abandoned, and the coefficient group with relatively larger matrix coefficient is reserved, so that the matrix P is damaged(q+w)×HExtracting to obtain a structural coefficient matrix Pw×H；
The following takes as an example a coefficient group correspondence of w coefficient values, i.e. coefficient values which are relatively large:
s2.3: reconstructing ultrasonic guided wave damage signal Y containing track damage characteristic information according to construction coefficient matrix and strongly-correlated sound source matrixdefect；
For the constructed coefficient matrix Pw×HTaking the mean value to remove the interference to the coefficient value of each column according to the following formula to obtain a new one-dimensional row vector P1×H NEW：
Further, an ultrasonic guided wave damage signal Y containing track damage characteristic information is obtained according to the following formuladefect：
S2.4: according to the damage signal YdefectThe processing obtains the envelope information and the envelope information,
if the part of the envelope information, of which the envelope amplitude is greater than the amplitude threshold value Z, determining that the steel rail to be detected is damaged;
if the part of the envelope information, of which the envelope amplitude is not greater than the amplitude threshold value Z, determining that the steel rail to be detected has no damage;
under the condition that the steel rail to be detected is damaged, the ultrasonic guided wave damage signal YdefectAnd (3) processing the acquisition time t corresponding to the damage of the steel rail to be detected by adopting the following formula to obtain the positioning of the damage of the steel rail:
s＝t×V/2
t＝1/Fs×i，i＝0,1,…,num，num＝Ts×Fs
wherein Ts represents the acquisition period of the guided wave transducer, Fs represents the sampling frequency of the guided wave transducer, V represents the ultrasonic guided wave speed sent by the guided wave transducer, i represents the ordinal number of the ith acquisition, num represents the total number of all the acquisitions, and s represents the distance from the position of the damage existing in the steel rail to the guided wave transducer.
2. The method and the system for monitoring the integrity of the cloud platform steel rail based on the guided wave technology are characterized in that the discontinuous continuous acquisition is the acquisition of guided wave signals and temperature of which the times C of continuous development and the data length are Ts × Fs at each acquisition interval time A according to monitoring requirements.
3. The method and the system for monitoring integrity of the steel rail of the cloud platform based on the guided wave technology are characterized in that: when the data of the guided wave signals and the temperature signals stored by the lower computer control circuit module reach a data storage amount B, the following set cross processing analysis method is adopted to reduce the missing report rate and find out the damage:
a: processing and analyzing the guided wave signals at the same monitoring temperature D but different time according to the steps to obtain the location of the steel rail damage;
b: under a fixed monitoring time interval E, processing and analyzing two groups of guided wave monitoring data with the time difference of the monitoring time interval E according to the steps to obtain the location of the damage of the steel rail;
c: and randomly extracting two groups of guided wave monitoring data in the stored data, and processing and analyzing the two groups of guided wave monitoring data according to the steps to obtain the positioning of the steel rail damage.
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