CN-122001386-A - Track irregularity signal compression and reconstruction method and system based on compressed sensing theory

CN122001386ACN 122001386 ACN122001386 ACN 122001386ACN-122001386-A

Abstract

The invention provides a track irregularity signal compression and reconstruction method and system based on a compressed sensing theory, and mainly relates to the technical field of railway infrastructure health monitoring and big data processing; the invention mainly improves the sampling accuracy in the engineering field, reduces redundant information, improves the system bottleneck of storage and transmission, introduces a measurement matrix irrelevant to a sparse transformation matrix at a signal acquisition end, and directly acquires a compression observation value far lower than the Nyquist rate. Subsequently, at the data processing end, by solving And (3) the problem of norm minimization, and reconstructing an original track irregularity signal from a small quantity of observed values with high precision. The invention greatly reduces the front-end data sampling rate, hardware load and data transmission and storage requirements, ensures that the reconstructed signal meets the engineering analysis precision, and provides a core technical scheme for the dynamic analysis of a new-generation high-efficiency and low-cost track detection system, a real-time train-structure system and the like.

Inventors

XIN LIFENG
WAN ZHIQIANG
CHE XINLEI
WANG JIAHAO
XU LEI
MAO JIANFENG

Assignees

西北工业大学
四川天府新区西工大先进动力研究院

Dates

Publication Date: 20260508
Application Date: 20260116

Claims (9)

1. The track irregularity signal compression and reconstruction method based on the compressed sensing theory is characterized by comprising the following steps of: S1, acquiring an original track irregularity signal according to a preset sampling frequency; s2, preprocessing the original track irregularity signal to obtain a discrete signal vector set And the time-frequency analysis result of the original track irregularity signal; step S3, according to the discrete signal vector set And time-frequency analysis result, selecting sparse transformation matrix Enabling each signal in the discrete signal vector set to be expressed in a sparse form Wherein Sparse coefficient vectors for the transform domain; step S4, according to the measurement matrix Obtaining measurement vectors ; Measuring vector The method comprises the following steps: , , Is that Is a real number matrix of (a); length of the compressed signal block; for elements in a set of discrete signal vectors Length of (2) compression ratio of , ; Step S5, for the measurement vector Lossless or lossy compression is carried out on the compressed sensing transmission core parameters to obtain a compression vector; transmitting the compressed vector at a transmitting end, reconstructing at a receiving end, and further completing the transmission of the track signal acquisition information, wherein the compressed sensing transmission core parameters comprise elements in a discrete signal vector set Length of (2) Sparse transform basis matrix Type of (d) measurement matrix Type and sampling frequency of (a); step S6, the receiving end carries out lossless decoding or lossy decoding according to the received compressed vector to obtain a measurement vector And compressed sensing transmission core parameters; step S7, the receiving end is used for measuring vectors according to the measurement vectors And compressing the perception transmission core parameters, solving the sparse reconstruction optimization problem, and obtaining the estimated value of the transform domain sparse coefficient ; The sparse reconstruction optimization problem is solved as follows: I.e. under constraint Next, make A kind of electronic device The norm is minimum, so that an estimated value of the sparse coefficient of the transformation domain is obtained Wherein In order to perceive the matrix of the device, ; S8, the receiving end estimates the sparse coefficient of the transform domain Substitution of sparse representation forms Transform domain sparse coefficient vector of (a) Transforming the basis matrix according to sparsity Calculating to obtain estimated value of discrete signal vector Then estimating the discrete signal vector And performing overlapped splicing or non-overlapped splicing to finally obtain the reconstructed track irregularity signal.
2. The method for compressing and reconstructing a track irregularity signal based on a compressed sensing theory as set forth in claim 1, wherein the preprocessing comprises sequentially processing an original track irregularity signal by a noise reduction method, a signal blocking method and a time-frequency analysis method to obtain a discrete signal vector set And the time-frequency analysis result of the original track irregularity signal; as a vector of the discrete signals, Length of (2) 。
3. The method for compressing and reconstructing a track irregularity signal based on compressed sensing theory of claim 1, wherein the measuring matrix Including but not limited to partial fourier matrices, partial hadamard matrices, random gaussian matrices, and bernoulli matrices.
4. The method for compressing and reconstructing a track irregularity signal based on the compressed sensing theory of claim 2, wherein the signal blocking method comprises non-overlapped division or overlapped division.
5. The method for compressing and reconstructing the track irregularity signal based on the compressed sensing theory of claim 1, wherein the sparse transform basis matrix is characterized by Including a single sparse transform basis or a combination of multiple single sparse transform bases including, but not limited to, discrete wavelet transform bases, discrete cosine transform bases, curvelet transform bases, discrete fourier bases, and deep learning based dictionaries.
6. The method for compressing and reconstructing the track irregularity signal based on the compressed sensing theory according to claim 1, wherein the method for solving the sparse reconstruction optimization problem comprises a base tracking algorithm, an orthogonal matching tracking algorithm, a compressed sampling matching tracking algorithm, a tree-type orthogonal matching algorithm, a sparsity self-adaptive matching tracking algorithm, an iterative shrinkage threshold algorithm and a rapid iterative shrinkage threshold algorithm.
7. The method for compressing and reconstructing a track irregularity signal based on a compressed sensing theory according to claim 1, wherein the method for transmitting the compressed vector by the transmitting end includes simplex transmission, half duplex transmission, full duplex transmission, parallel transmission, serial transmission, synchronous transmission and asynchronous transmission.
8. A system applying a track irregularity signal compression and reconstruction method based on a compressed sensing theory is characterized by comprising a data acquisition module, a transmitting end processing module and a receiving end processing module, wherein the acquisition module executes a method for acquiring an original track irregularity signal and preprocessing to obtain a discrete signal vector set And the time-frequency analysis result of the original track irregularity signal; the transmitting end processing module executes the methods from step S3 to step S5 and transmits the compressed vector to the opposite end, and the receiving end processing module executes the methods from step S6 to step S8 to obtain the reconstructed track irregularity signal.
9. The system of claim 8, wherein the normalized root mean square estimation error NRMSE and the decision coefficient are calculated based on the original track irregularity signal and the reconstructed track irregularity signal, respectively And obtaining a reconstruction effect verification report by the peak absolute error PAE and the spectrum peak deviation SPD.

Description

Track irregularity signal compression and reconstruction method and system based on compressed sensing theory Technical Field The invention belongs to the technical field of railway infrastructure health monitoring and big data processing, and mainly relates to a track irregularity signal compression and reconstruction method based on a compressed sensing theory. Background In the field of railway engineering, track irregularity refers to the deviation between the actual profile of a rail surface and the ideal state of design, and is a core excitation source for determining the dynamic coupling characteristics of a rolling stock-track system. The presence of track irregularities can directly cause fluctuations in dynamic forces between the wheel and the track. The overrun track irregularity not only accelerates the fatigue degradation of key components such as a vehicle bogie, steel rails and fasteners, reduces the riding comfort of passengers, but also is more likely to induce the instability of wheel rails under complex working conditions, thereby forming a great threat to the driving safety. Different from the conventional engineering signals, the track irregularity signals have the characteristics of wide-band distribution and strong random evolution, wherein the frequency components of the track irregularity signals generally cover a key dynamic frequency range of 0.1-20/m, and the track irregularity signals comprise low-frequency slow-change components caused by roadbed settlement and high-frequency abrupt-change components such as rail wave grinding and the like. Meanwhile, the track irregularity state presents remarkable time-varying characteristics under the comprehensive influence of long-term train load, environmental corrosion (such as temperature change and rain wash) and material aging degradation. The dual attribute of the broadband and the time-varying randomness determines that the railway operation and maintenance department must acquire dynamic evolution data through regular high-precision detection, thereby realizing preventive maintenance based on the state and avoiding catastrophic accidents such as derailment or structural failure. However, with the iterative upgrade of track detection technology and the continued expansion of railway network scale, track irregularity data has entered an "explosive growth" phase, which presents serious challenges for full life cycle management of the data. Taking a high-speed railway as an example, if an inertial reference detection vehicle with a sampling density of 4 points/meter is adopted, original irregularity data of about tens of gigabytes can be generated per 1000 km line detection. By 2025, china has built the world's largest scale railway network (about 15.6 kilometers), and if the frequency is calculated according to the detection frequency of the whole line of 2 times per month, the amount of track irregularity data generated in the national railway network year can reach the class of beat bytes (PB). The huge storage resources are occupied by the massive data, the real-time transmission requirement of 'on-site detection-remote analysis' is more difficult to meet, and the huge storage resources become a key bottleneck for restricting the transformation from railway operation and maintenance to 'digital and intelligent'. Therefore, developing a track irregularity signal processing technology with both high-efficiency compression ratio and high-fidelity reconstruction performance is a core topic to be broken through in the current railway engineering field. The prior art generally adopts a two-step process flow of 'sampling at a high speed and then compressing data (such as ZIP lossless compression or lossy compression'). However, the method has inherent defects that firstly, sampling resources are wasted, because track irregularity signals have sparseness in a specific transformation domain (such as a wavelet domain and a curvelet domain), effective information of the track irregularity signals is far less than the number of original sampling points, the traditional high-rate sampling is redundant sampling, performances of a sensor, an analog-digital converter and a data bus are wasted, secondly, system power consumption and cost are high, a high-speed ADC (analog-digital converter), a high-capacity buffer memory and a high-speed processor increase hardware cost, volume and power consumption of a vehicle-mounted detection system, and finally, storage and transmission bottlenecks are caused by redundant sampling, and even if the subsequent compression is carried out, temporary storage and transmission of massive original data are still system bottlenecks. Disclosure of Invention In order to overcome the defects of the prior art, improve the sampling accuracy in the engineering field, reduce redundant information and improve the system bottleneck of storage and transmission, the invention provides a track irregularity signal compression and r