Deployment system for measuring multi-object vibration by using compressed sensing and signal reconstruction method

Abstract

The invention discloses a deployment system for measuring multi-object vibration by using compressed sensing and a signal reconstruction method. The invention relates to an object vibration information acquisition and frequency period calculation system and method based on an RFID technology. The system aims at efficiently collecting vibration data of a plurality of objects through the RFID reader-writer and accurately processing and analyzing the vibration data. The system comprises an RFID vibration information acquisition module which is responsible for acquiring a unique identification EPC label, phase data and a timestamp of an object and storing the data in a CSV format; the data preprocessing module is used for cleaning collected data, deleting repeated timestamps and classifying the data; the measurement matrix generation module is used for constructing a signal measurement matrix so as to facilitate subsequent processing; the compressed sensing signal reconstruction module performs sparse representation and reconstruction on the vibration signal through a La sso regression algorithm, Fourier transform and other technologies, and recovers an original vibration signal of the object; and finally, the vibration period and frequency calculation module extracts the period characteristics of the signals through self-correlation analysis, and calculates the vibration frequency of the object. Through the system, the vibration states of multiple objects can be monitored and analyzed in real time, the system is widely applied to the fields of industrial monitoring, structural health monitoring and the like, and the precision and efficiency of object vibration detection are improved.

Description

Technical Field

[0001] The present invention relates to signal processing and radio frequency identification (RFID) technology, and in particular to a method and system for collecting vibration information of multiple objects using RFID and reconstructing signals through compressed sensing technology. Background Art

[0002] In industrial and IoT applications, accurate detection and monitoring of vibrations of multiple objects is of great significance. However, traditional vibration monitoring systems usually require multiple sensors, which are costly and susceptible to noise interference when processing signals from multiple objects. Radio frequency identification (RFID) technology has been widely used in object tracking and monitoring in recent years due to its non-contact and low-cost characteristics.

[0003] Through RFID tags, especially EPC (Electronic Product Code) tags, classification and identification of multiple objects can be achieved. However, existing RFID systems are usually used for static identification of objects. How to achieve dynamic vibration monitoring of multiple objects in an RFID environment has become a challenging problem. In addition, the acquisition of vibration signals may be affected by the uneven time intervals, and traditional signal processing methods have difficulty in accurately reconstructing signals when faced with non-uniform sampling data. Therefore, developing an RFID-based vibration detection system and combining compressed sensing technology to reconstruct non-uniform sampling signals is an effective solution to the problem of multi-object vibration monitoring. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In order to solve the problems raised in the above background technology, the present invention adopts the following technical solutions.

[0006] The present invention provides a vibration detection system and signal reconstruction method based on RFID. The system can collect vibration data of multiple objects through RFID technology, and reconstruct signals of these non-uniform sampling data using a compressed sensing method. Multiple objects are classified through RFID EPC tags to achieve independent monitoring and analysis of vibration signals of multiple objects.

[0007] The purpose of the present invention is to provide a system and method for detecting and reconstructing vibration signals of multiple objects. The system collects vibration data through RFID and identifies each object through EPC tags, thereby realizing classified monitoring of multiple objects and ensuring the accuracy of signal reconstruction. The specific method is as follows:

[0008] System deployment: Attach RFID tags to key parts of the mechanical structure or equipment to be monitored. When deploying antennas, ensure that each tag is within the reading range of the RFID reader.

[0009] Data collection and preprocessing: Collect data containing phase data and timestamps from data sources (such as CSV files). Clean the collected data, remove duplicate timestamps, and ensure that each time point corresponds to a unique phase value, thus forming a processed data set.

[0010] Construct a measurement matrix: Construct a measurement matrix based on the processed time data. The measurement matrix is ​​an M×N matrix, where M is the number of processed samples and N is the integer part of the time span. The time data is sampled through a unit pulse function to generate a sparse matrix that represents the sampling of the object at different time points.

[0011] Sparse representation and reconstruction of signals: The measured data is processed using an optimization algorithm (Lasso regression), a basis function that adapts to the signal characteristics is selected, and a basis function matrix in the frequency domain is generated. By multiplying the measured value with the basis function matrix, a sparse representation of the signal is obtained, and the most important basis function coefficients are screened out. Subsequently, based on this sparse representation, an inverse transform is applied to reconstruct the signal from the frequency domain back to the time domain, restoring the original signal waveform.

[0012] Signal analysis and period extraction: Autocorrelation analysis is performed on the reconstructed signal. First, the autocorrelation function of the reconstructed signal is calculated, and then the peak in the autocorrelation function is identified to extract the main periodic features of the signal.

[0013] Visualization of multi-object vibration monitoring system: By plotting the reconstructed vibration signal curve of each object and comparing it with the original measurement value, the reconstruction effect is demonstrated; the distribution diagram of the sparse representation coefficient shows the sparse characteristics of the signal, and the autocorrelation function diagram shows the periodic characteristics of the signal.

[0014] Vibration pattern recognition and fault diagnosis: Based on the analysis results, the vibration pattern of the equipment is identified and compared with a known fault pattern database. Machine learning algorithms are implemented to improve the accuracy and automation of fault diagnosis.

[0015] Abnormal notification: Output vibration analysis results and fault diagnosis information to the user interface. Trigger an alarm when abnormal vibration or potential fault is detected. Set reasonable thresholds and alarm parameters to ensure timely and accurate alarms.

[0016] Compared with the prior art, the mechanical vibration detection method and system based on the commercial RFID system of the present invention have the following significant technical effects:

[0017] (1) Cost-effectiveness: By using low-cost commercial RFID tags and readers, the present invention reduces the total cost of the vibration monitoring system by about 50%, making high-precision vibration monitoring solutions affordable for more small and medium-sized enterprises.

[0018] (2) Convenience: The present invention does not require a complicated installation procedure, and the deployment time of RFID tags and readers is reduced by about 75% compared to traditional sensors, thereby speeding up the installation and startup process of the monitoring system. In addition, the present method is applicable to various types of signal processing scenarios, has good versatility and scalability, and can meet different application requirements.

[0019] (3) High efficiency: The method of the present invention can effectively restore the signal under non-uniform sampling conditions by constructing a measurement matrix that adapts to the signal characteristics, significantly reducing the complexity of data processing, and can restore more data using less data.

[0020] (4) Efficient data collection and classification: RFID technology is used to simultaneously collect vibration data of multiple objects, and EPC tags are used to classify and independently analyze the objects, avoiding the high cost and complexity of traditional multi-sensor systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the communication structure of the tag, antenna and reader of the embodiment.

[0022] Figure 2 It is a schematic diagram of the deployment structure of the system of the embodiment.

[0023] Figure 3 Schematic diagram of actual recovery of object vibration signal during implementation. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0026] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments. The present invention provides the following embodiments.

[0027] Example 1: Use the RFID system to collect vibration information of multiple objects and generate a CSV file containing EPC tags, phase data, and timestamps. Perform data preprocessing on the samples, and generate a measurement matrix after removing duplicate timestamps. Use compressed sensing technology to perform sparse representation and signal reconstruction on the measurement data of each object to restore the vibration signal of the object. Use autocorrelation analysis to extract the vibration period characteristics of each object. The experimental results show that the signal reconstruction effect is good and the vibration period of each object is accurately extracted.

[0028] Example 2: Classify multiple objects through EPC tags to ensure that the vibration signal of each object is processed independently. The system collects non-uniformly sampled vibration data of different objects through RFID, reconstructs the signal using compressed sensing method, and extracts the periodic characteristics of the signal. The experimental results show that the system can effectively distinguish and monitor the vibration patterns of multiple objects.

[0029] like Figure 1 As shown in the figure, the reader and the tag form a complete transceiver communication system. In the figure, the power P1 is emitted from the reader antenna, and only a part of it (due to free space attenuation) reaches the tag antenna. The power P1' that reaches the tag provides voltage for the tag antenna, and after rectification, it powers the tag chip. Part of the power P1' that reaches the antenna is reflected by the antenna, and its reflected power is P2. The reflected power P2 reaches the reader after passing through the free space and is received by the reader antenna. The signal received by the reader is transmitted to the transceiver through the transceiver coupler circuit, and after amplification, it is processed by the circuit to obtain useful information.

[0030] like Figure 2 As shown in the figure, the antenna and the reader are connected through a computer and the tag is attached to the object. In order to intuitively understand the vibration, we Figure 2 The geometric model of rotation is given in . Any other form of vibration is a special case of the rotation model. The vibration causes the tag attached to the vibrating surface to displace in a small range, resulting in a regular change in the backscattered signal. Therefore, the collected data is analyzed to obtain the vibration information of the object.

[0031] like Figure 3 As shown in the figure, the vibration signal graph of the object is reconstructed using compressed sensing and optimization algorithms based on the timestamp and phase value returned by the label.

[0032] The above content is a further detailed description of the present invention in combination with specific implementation methods. It cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope determined by the claims submitted for the present invention.

Claims

1. A deployment system and signal reconstruction method for measuring vibration of multiple objects using compressed sensing, characterized in that the system is used to collect vibration information of multiple objects through RFID technology, and process the vibration information through compressed sensing technology, thereby calculating the vibration frequency and period of the objects, and specifically includes the following modules: 1.1 RFID vibration information collection module, used to collect vibration information of multiple objects through RFID reader / writer, the vibration information includes the unique identification EPC tag, phase data and timestamp information of the object, and store the data in CSV file format; 1.2 Data preprocessing module, used to preprocess the collected vibration information, including identifying and deleting repeated timestamps to ensure that each time point corresponds to a unique phase value, thereby ensuring the continuity and accuracy of the data. The module is also responsible for classifying multiple objects according to EPC tags to ensure that the vibration data of different objects can be stored and processed independently; 1.3 Measurement matrix generation module, used to generate a measurement matrix according to the collected timestamps, the measurement matrix represents the sampling of vibration data at different time points, and the measurement matrix is ​​generated by using a unit pulse function, the matrix maps the input signal to the corresponding time domain for subsequent processing; a) Identify and delete duplicate timestamps to ensure that each time point corresponds to a unique phase value; b) Extract phase data and timestamp data to form a processed data set. 1.4 Compressed sensing signal reconstruction module, used to reconstruct the collected vibration data by using compressed sensing technology. First, the vibration signal is sparsely represented, and the sparse coefficient is estimated using the Lasso regression algorithm. The module reconstructs the signal by using Fourier basis function or other basis function, and converts the sparsely represented signal back to the time domain, thereby restoring the original vibration signal of the object; a) selecting a basis function that is adapted to the signal characteristics and generating a basis function matrix in the frequency domain; b) multiplying the measured value by the basis function matrix to obtain a sparse representation of the signal, and screening out the most important basis function coefficients to obtain a sparse representation of the signal; 1.5 Vibration period and frequency calculation module, used to perform autocorrelation function analysis on the reconstructed vibration signal, determine the period of the vibration signal by identifying the peak position and the distance between the peaks in the autocorrelation function, and then calculate the vibration frequency of the object, and conduct in-depth analysis of the vibration characteristics of the object. a) calculating the autocorrelation function of the reconstructed signal; b) Identify the peaks in the autocorrelation function to extract the main periodic features of the signal.

2. The system and method according to claim 1, characterized in that: The RFID vibration information acquisition module can collect vibration data of multiple objects at the same time. The vibration information is classified based on the EPC tag to ensure that the vibration information of each object can be processed and stored independently, and the object information and vibration data are bound together through RFID technology.

3. The system and method according to claim 1, characterized in that: The data preprocessing module cleans the collected vibration data by identifying and deleting duplicate timestamps to ensure that each time point has only one phase value, thereby improving the accuracy of data processing and the reliability of signal reconstruction.

4. The system and method according to claim 1, characterized in that: The measurement matrix generation module constructs an M×N measurement matrix, where M is the number of samples collected and N is the time span. The measurement matrix is ​​used to represent the vibration data sampling conditions at different time points and serves as the basis for signal reconstruction.

5. The system and method according to claim 1, characterized in that: The compressed sensing signal reconstruction module adopts the Lasso regression algorithm for sparse representation and reconstructs the vibration signal through Fourier transform or other basis functions, which can effectively process non-uniform sampling data and realize high-precision recovery of the original vibration signal.

6. The system and method according to claim 1, characterized in that: The vibration period and frequency calculation module extracts the periodic features in the reconstructed signal through autocorrelation analysis, specifically calculates the period of the vibration signal by identifying the peak position and the distance between the peaks in the autocorrelation function, and determines the vibration frequency based on this, which is used to evaluate and analyze the vibration state of the object.

7. The system and method according to claim 1, characterized in that: The system can monitor the vibration data of multiple objects at the same time, and combines RFID technology with compressed sensing technology to collect, classify, reconstruct and analyze the vibration data of objects, and recognize and evaluate the vibration patterns of multiple objects through period calculation and frequency analysis modules.

8. The system and method according to claim 1, characterized in that: The system has the ability to monitor and process multiple objects simultaneously. It binds the vibration data of each object to its unique identity through the EPC tag, and uses signal reconstruction technology and period calculation modules to achieve accurate reconstruction and period calculation of the vibration information of each object. It is suitable for object vibration monitoring in a variety of scenarios.

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