Vibration monitoring system channel synchronization error estimation method and device and storage medium

Abstract

Embodiments of the present application disclose a vibration monitoring system channel synchronization error estimation method and device and a storage medium, wherein the method comprises: acquiring phase angles of cross-spectrum ratios in different time periods; and calculating synchronization errors between channels according to the phase angles, wherein a channel synchronization error formula mainly comprises: wherein δt ji represents the synchronization error δt ji between channels j and i, f represents a frequency, and γ(f) represents a phase angle of a cross-spectrum ratio in different time periods; the accurate calibration of the channel synchronization error can be realized, the overall working noise level can be revealed in combination with single-channel hardware noise calibration, the single-channel hardware noise and the multi-channel synchronization error estimation of different types of vibration pickups (such as accelerometers, dynamic displacement meters, etc.) with different sampling frequencies are applicable, and the method is helpful for optimizing the design of a monitoring system.

Description

Technical Field

[0001] This invention relates to the field of noise measurement technology for vibration monitoring systems, and in particular to a method, device, and storage medium for estimating channel synchronization error in vibration monitoring systems. Background Technology

[0002] Vibration monitoring is an important component of health monitoring of civil engineering structures. The measured dynamic response of the structure can be used to identify modal parameters and can also be used directly to verify structural design, construct damage indicators, and correct finite element models, playing a key role in the safe operation of civil engineering structures.

[0003] Under natural excitation, the vibration of civil engineering structures is mainly (ultra)low frequency vibration. The fundamental frequency of long-span bridges and tall buildings can be as low as 0.05Hz to 0.1Hz. However, commonly used vibration pickups (voltage type, capacitive type, etc.) in engineering are often insensitive to vibrations below 1Hz, exhibiting high low-frequency noise. On the other hand, the amplitude of structural vibration is relatively low, generally not exceeding 0.1g (g is the acceleration due to gravity), which poses a challenge to the sensitivity of the vibration pickup and data acquisition. In structural vibration monitoring, the acquired structural dynamic response is usually stored in a computer in digital form. This data contains structural vibration information, but due to various errors inevitably occurring during the measurement process, it is not equivalent to the true dynamic response of the structure. The deviation between the actual acquired data and the true structural response is called measurement noise. In addition, the complex on-site environment of the structure, with significant interference from environmental factors such as temperature, humidity, and electromagnetic fields, may cause the measurement noise of the entire vibration monitoring system to be much higher than the noise level of the vibration pickup itself measured in the laboratory. Therefore, it is necessary to conduct a more accurate overall evaluation of the operating noise of the vibration monitoring system. Summary of the Invention

[0004] This application provides a method, device, and storage medium for estimating channel synchronization error in a vibration monitoring system.

[0005] Firstly, a method for estimating channel synchronization error in a vibration monitoring system is provided, the method comprising:

[0006] Obtain the phase angle of the cross-spectral ratio at different time periods;

[0007] The synchronization error between channels is calculated based on the phase angle.

[0008] Secondly, a channel synchronization error estimation device is provided, comprising:

[0009] The first calculation module is used to obtain the phase angle of the cross-spectral ratio at different time periods;

[0010] The second calculation module is used to calculate the synchronization error between channels based on the phase angle.

[0011] Thirdly, a computer storage medium is provided, which stores one or more instructions adapted for loading by a processor and executing the steps of the first aspect and any possible implementation thereof.

[0012] This application provides a method for estimating channel synchronization error in a vibration monitoring system. By obtaining the phase angle of the cross-spectral ratio at different time periods, and calculating the synchronization error between channels based on the phase angle, the method can accurately calibrate the channel synchronization error. Combined with single-channel hardware noise calibration, it eliminates the need for manual excitation and reveals the overall operating noise level. This method is applicable to single-channel hardware noise and multi-channel synchronization error estimation for different types of vibration pickups (such as accelerometers and dynamic displacement gauges) with different sampling frequencies, which helps to optimize the design of the monitoring system. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0014] Figure 1 This is a schematic diagram of a typical data acquisition process provided in an embodiment of this application;

[0015] Figure 2 A typical accelerometer noise comparison diagram provided for an embodiment of this application;

[0016] Figure 3 This is a schematic diagram of a commonly used channel synchronization method provided in an embodiment of this application;

[0017] Figure 4 A schematic diagram of a connecting bridge structure is provided in an embodiment of this application;

[0018] Figure 5 This is a schematic diagram of a channel noise estimation result provided in an embodiment of this application;

[0019] Figure 6 A schematic diagram of inter-channel phase angle provided in an embodiment of this application;

[0020] Figure 7 A schematic diagram of channel synchronization error provided in an embodiment of this application;

[0021] Figure 8 This is a schematic diagram of a time-domain signal provided in an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0023] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0024] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] The method for estimating channel synchronization error of a vibration monitoring system provided in this application embodiment can be applied to the evaluation scheme of measurement noise of vibration monitoring system. It can realize the calibration of single-channel noise and channel synchronization error without artificial excitation, and reveal its overall operating noise level.

[0026] To more clearly describe the channel synchronization error estimation method in the embodiments of this application, vibration monitoring is first introduced.

[0027] The vibration monitoring involved in this application embodiment is an important component of the health monitoring of civil engineering structures. The measured dynamic response of the structure can be used to identify modal parameters or directly to verify structural design, construct damage indicators, and correct finite element models, playing a key role in the safe operation of civil engineering structures.

[0028] Under natural excitation, the vibration of civil engineering structures is mainly (ultra)low frequency vibration. The fundamental frequency of long-span bridges and tall buildings can be as low as 0.05Hz to 0.1Hz. However, commonly used vibration pickups (voltage type, capacitive type, etc.) in engineering are often insensitive to vibrations below 1Hz, exhibiting high low-frequency noise. On the other hand, the amplitude of structural vibration is relatively low, generally not exceeding 0.1g (g is the acceleration due to gravity), which poses a challenge to the sensitivity of vibration pickups and data acquisition. In addition, the initial stage of structural damage often has little impact on the dynamic response. In order to accurately capture the subtle differences caused by damage as early as possible, the measurement noise of the vibration monitoring system must be kept at an extremely low level.

[0029] Vibration monitoring systems typically include vibration pickups (accelerometers, velocities, dynamic displacement gauges, etc.), communication cables, and data acquisition instruments. Noise evaluation of the pickups is generally conducted simultaneously with pickup calibration under ideal laboratory conditions (such as specific temperature and humidity), and the evaluation results are recorded as nominal noise in the relevant manual. Nominal noise is instructive for pickup selection, but communication cables and data acquisition instruments undoubtedly introduce additional measurement noise. Furthermore, the complex on-site environment of structures, with significant interference from environmental factors such as temperature, humidity, and electromagnetic fields, can cause the overall vibration monitoring system's measurement noise to be far higher than the pickup's own noise level as measured in the laboratory. Therefore, it is necessary to conduct a comprehensive evaluation of the operating noise of the vibration monitoring system in structural health monitoring applications.

[0030] In this embodiment, the sources of measurement noise will be analyzed and its statistical characteristics will be discussed starting from the vibration monitoring data acquisition process. Then, a detailed study will be conducted on single-channel hardware noise and multi-channel synchronization error, and the corresponding evaluation methods will be introduced. Finally, the practical application will be demonstrated by taking the measurement noise evaluation of a vibration monitoring system as an example.

[0031] The embodiments of this application are described below with reference to the accompanying drawings.

[0032] In structural vibration monitoring, the acquired structural dynamic response is stored digitally in a computer. This data contains information about structural vibration, but due to unavoidable errors during the measurement process, it is not equivalent to the true dynamic response of the structure. The deviation between the actual acquired data and the true structural response is called measurement noise.

[0033] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating a typical data acquisition process provided in an embodiment of this application. Figure 1As shown, taking commonly used acceleration measurement as an example, the entire process of data acquisition for a bridge vibration monitoring system is illustrated. First, under the action of an external random load (typical power spectral density (a)), the structure generates low-frequency random vibrations. Its corresponding power spectral density (b) can be considered as the product of the excitation power spectral density (a) and the structure's own frequency response function, showing a significant peak near the structure's natural frequency. The structural vibration response is converted into voltage or current signals by multiple accelerometers installed on the structure, and after amplification by circuitry, transmitted to the data acquisition instrument in analog signal form via a communication cable. Due to noise from the vibration pickups, electronic components in the data acquisition instrument, and the communication cable, the measured analog signal power spectral density (c) is superimposed with noise power spectral density information, exhibiting high noise in the (ultra) low frequency band. Finally, the data acquisition instrument performs time-domain sampling and amplitude-domain discretization of the analog data to obtain digital signals, which are stored on a computer hard drive or other storage media. Time-domain sampling generally uses a fixed sampling frequency f. s The amplitude domain discrete accuracy depends on the number of bits in the acquisition instrument. Additionally, anti-aliasing filtering must be performed in hardware before sampling to eliminate the Nyquist frequency (f0). s / 2) Interference from the above components. The final digital signal power spectral density is shown in (d). It can be seen that only modes below the Nyquist frequency are preserved, and the Nyquist frequency is significantly interfered with by anti-aliasing filtering. When multiple channels exist, it is also necessary to ensure the synchronization of sampling of each channel in order to accurately identify the mode shape information of the structure.

[0034] Analysis of the entire vibration monitoring data acquisition process reveals that the measurement noise of the vibration sensing system mainly includes hardware noise, sampling error, discrete error, and channel synchronization error. The amplitudes of these various noises differ, and their impact on vibration measurements varies.

[0035] The hardware noise mentioned in this application refers to the noise present in the analog signal caused by the vibration pickup, amplifier, communication cable, and acquisition instrument circuit. Generally speaking, in structural vibration monitoring, hardware noise accounts for the majority of the measurement noise. Therefore, in this application, a vibration pickup with low noise level and high sensitivity is selected, while the length of the communication cable is reduced and a shielded cable is selected.

[0036] Figure 2 A typical accelerometer noise comparison diagram is provided for an embodiment of this application, as shown below. Figure 2 As shown, the noise levels of three commonly used accelerometers in the 0.05Hz–40Hz frequency band are presented in the form of (one-sided) root power spectral density squared density (Root PSD). Servo accelerometers exhibit excellent low-frequency measurement performance; for example, the hardware noise is only about [value missing] at 0.1Hz. It is far superior to piezoelectric accelerometers and MEMS accelerometers used in mobile phones.

[0037] The sampling error involved in this embodiment is the discrete error in the time domain caused by the analog-to-digital conversion performed by the data acquisition instrument. The sampling error is determined by Shannon's sampling theorem, which states that to recover the analog signal without distortion, the sampling frequency f of the discrete signal... s It should be greater than twice the highest frequency in the analog signal spectrum. If the above sampling condition cannot be met, it should be higher than the Nyquist frequency (f...). s The frequency components of / 2) will be reconstructed to be lower than f. s Signals with a frequency of / 2 exhibit spectral aliasing. To reduce aliasing, the sampling frequency is typically increased or a low-pass filter (commonly known as an anti-aliasing filter) is introduced. Anti-aliasing filters limit the signal bandwidth to satisfy the sampling theorem. However, perfect filters exist only theoretically; in practice, frequency components above the Nyquist frequency cannot be completely filtered out. However, by properly configuring the filter, the aliasing error can be made significantly less than hardware noise. Furthermore, anti-aliasing filters can interfere with components near the Nyquist frequency; in actual testing, a sampling frequency no lower than 2.4 times the highest frequency of the vibration being measured can be selected.

[0038] For discrete errors, the data acquisition instrument samples the analog signal in the time domain and discretizes it in the amplitude domain, converting it into binary data for storage. Therefore, the discrete accuracy largely depends on the number of bits in the acquisition instrument. For example, for an accelerometer with a measurement range of ±2g, assuming a 24-bit acquisition instrument is used, its discrete accuracy Δg is 2g×2 / (2...). 24 -1)≈0.24μg, corresponding to a discrete error amplitude of less than 0.24μg / 2=0.12μg. Although the amplitude of random structural vibrations is generally very low, this accuracy is sufficient to meet the requirements of vibration monitoring.

[0039] To compare the discrete error with the hardware noise level, we assume that the discrete error approximately satisfies a uniformly distributed white noise within [-Δg / 2, Δg / 2], and is statistically independent of the input signal. Then its one-sided power spectral density satisfies S... Q =Δg 2 / 6f s

[18] . When the sampling frequency f is selected... s At 50Hz, the square root of the discrete error power spectral density in the above example is approximately Much lower than the hardware noise level. Based on the one-sided power spectral density S Q The expression can be obtained by increasing the sampling frequency f in this embodiment. s Alternatively, a higher-resolution data acquisition instrument can be used to reduce discrete errors.

[0040] In structural vibration monitoring, multiple vibration pickups are typically deployed on the structure for synchronous measurements. When multiple data acquisition devices are used, differences in sampling clocks between the devices cause synchronization errors between channels, which is particularly common when using wireless sensors for vibration monitoring. Additionally, clock drift within a single channel can also lead to synchronization errors, but its impact is generally much smaller than that of inter-channel synchronization errors.

[0041] In modal identification, the identification of vibration frequency and damping ratio is less affected by synchronization errors, but the vibration mode is significantly disturbed, leading to complete misidentification. For example, in the frequency domain decomposition method, channel synchronization errors will cause phase errors in the vibration mode at the sensor measurement point. For a vibration frequency of f... i The synchronization error of δt causes a mode phase error of approximately 2πf. i δt

[20] , therefore the synchronization error has a more significant impact on the high-frequency mode shape. To ensure that the relative error of the real part of the mode shape does not exceed 5% (the phase error is approximately arccos(1-5%)≈0.1π), the synchronization error δt≤0.1π / 2πf i ≈0.05T i That is, the synchronization error should be less than the corresponding vibration period T. i 5%.

[0042] Figure 3 This is a schematic diagram of a commonly used channel synchronization method provided in an embodiment of this application, such as... Figure 3 As shown, in structural vibration monitoring, when the data acquisition instrument has a sufficient number of channels, all vibration pickups can be directly connected via cables. Figure 3 a) In this case, there is basically no synchronization error. However, when the measuring points are far apart and a longer cable is required, more hardware noise will be introduced. When there are multiple data acquisition units in the vibration measurement system ( Figure 3 (b) It can be connected via optical fiber, and a high-precision acquisition instrument can be used as the master clock to achieve synchronization of all channels, with a synchronization accuracy of 1–10 μs. To avoid the inconvenience of connecting optical fibers, structural vibration monitoring systems often use satellite time synchronization. Figure 3 c) Systems such as BeiDou and GPS have a synchronization error of no more than 10 μs. In short-time field vibration tests, Figure 3 The method shown in d is also quite common. After initial synchronization, multiple independent high-precision clocks control the acquisition unit to achieve synchronous sampling. For a temperature-controlled crystal oscillator clock with an error of 0.2 μs / s, the synchronization error caused by 8 hours is 0.2 × 8 × 3600 = 5.76 ms. According to the previous analysis, this synchronization error has little impact on mode shapes below 10 Hz.

[0043] In vibration monitoring, understanding the actual noise level of the measurement system is helpful for sensor system selection and has important guiding significance for optimizing the monitoring scheme. Through the aforementioned qualitative and simple quantitative analysis of common measurement noises, hardware noise plays a controlling role in the total measurement noise, and channel synchronization error causes mode shape phase error, which has a particularly significant impact on higher-order modes. In the embodiments of this application, a "three-channel" method suitable for field evaluation of single-channel noise will be introduced, and a new method for calculating channel synchronization error will be proposed, thereby realizing a comprehensive evaluation of the measurement noise of the vibration monitoring system.

[0044] The "three-channel" method mentioned in this application embodiment is used to evaluate the measurement noise level of an earthquake monitoring system. This method uses three closely parallel vibration pickups to measure the vibration at the same measurement point, which can effectively avoid dependence on specific signal inputs and measurement system frequency response function information, and has wide applicability. For example, it can be used to evaluate the channel noise of test systems with different sampling frequencies and different types of vibration pickups (such as accelerometers and displacement gauges).

[0045] This application provides a method for estimating channel synchronization error in a vibration monitoring system, which mainly calculates the synchronization error between channels based on the phase angle of the cross-spectral ratio at different time periods. The cross-spectral ratio at different time periods can include the cross-spectral ratio during synchronized periods and the cross-spectral ratio during asynchronous periods.

[0046] In practical applications, the aforementioned phase angle can be the phase angle between two seismographs. When calculating the phase angle, the cross-spectral ratio during the synchronous period can be estimated based on the synchronous period data of each seismograph, while the cross-spectral ratio during the asynchronous period can be estimated based on the preset time (e.g., 5 minutes) length data.

[0047] Specifically, the synchronization error between channels can be calculated according to a preset channel synchronization error formula, which includes:

[0048]

[0049] Where, δt ji The synchronization error δt between channels j and i is represented by... ji f represents the frequency, and γ(f) represents the phase angle of the cross-spectral ratio at different time periods.

[0050] In one alternative implementation,

[0051] Where angle(·) represents the complex phase angle, ρ ji and ρ' ji represents the cross-spectral ratio during the synchronous and asynchronous periods, respectively, where i represents channel i and j represents channel j.

[0052] In one alternative implementation:

[0053] ρ ji =S ji / S ij ;

[0054] ρ' ji =S' ij / S' ij ;

[0055] Among them, S ji and S ji S' represents the cross-power spectral density between channel j and channel i when there is no synchronization error between channels. ji and S' ij This represents the cross-power spectral density between channel j and channel i when synchronization errors exist between channels.

[0056] Optionally, the ρ can be calculated based on a three-channel method. ji ;

[0057] in, H i (f) represents the frequency response function in the frequency domain. Let X(f) be the power spectral density of the input signal, X(f) represent the input signal in the frequency domain, and E[·] represent the mathematical expectation. The superscript... It indicates complex conjugate.

[0058] The explanation of the above formulas will be based on the introduction of the "three-channel" method in the derivation process.

[0059] In the three-channel method, three pickups are arranged closely in parallel and measure a common "input vibration". It is assumed that each pickup satisfies a linear input-output relationship:

[0060] y i (t)=h i (t)*x(t)+ε i (t) (1)

[0061] Where y i (t) represents the output signal of the vibration pickup i (i = 1, 2, 3), '*' represents the convolution operation, h i x(t) is the impulse response function of the vibration pickup i, x(t) is the vibration input, and ε is the impulse response function of the vibration pickup i. i (t) is the channel noise of pickup i. Taking a Fourier transform of both sides of equation (1), we can obtain the input-output relationship in the frequency domain:

[0062] Y i (f)=H i (f)X(f)+E i (f) (2)

[0063] Where Y i (f), H i (f), X(f) and E i (f) represent the output signal, frequency response function, input signal, and channel noise of the pickup in the frequency domain, respectively.

[0064] Assuming the input signal and the pickup channel noise satisfy statistically independent stationary random processes, and there is no synchronization error between channels, then the power spectral density of the input and output signals satisfies:

[0065]

[0066]

[0067] in Represents Y i (f) and Y j The cross-power spectral density of (f), Let E[·] represent the mathematical expectation of the input signal power spectral density, and the superscript... For the sake of simplicity, the frequency f has been omitted from formulas (3) and (4) in the embodiments of this application.

[0068] During the on-site calibration of channel noise, due to the frequency response function H... i and input signal power spectral density S XX Since the channel noise S is unknown, it cannot be directly solved using formula (3). ei When both vibration pickups j and k are present, formula (4) can be used to obtain...

[0069]

[0070] Then, multiply both sides of formulas (4) and (5) together.

[0071]

[0072] Substituting into formula (3), we can finally obtain...

[0073]

[0074] This refers to the noise level of vibration pickup i. From the assumptions and specific steps of the three-channel method, it can be seen that its essence is to decompose the measured signal into completely coherent and completely incoherent signals. The completely coherent signal is considered the true vibration response, while the completely incoherent signal is the corresponding channel noise.

[0075] Based on actual measurement data, the three-channel method first estimates the self-power spectral density S. ii and cross-power spectral density S ij S jk S kiThen, substitute the values ​​into formula (7) to estimate the channel noise S. ei To reduce the impact of random noise, it is recommended to use the Welch algorithm, which involves averaging the measurement data in segments and applying an appropriate window function.

[0076]

[0077] Here, 'Re(·)' represents taking the real part of the complex number to eliminate the influence of computer truncation errors. It should be noted that although the three-channel method can theoretically completely separate channel noise and actual vibration signal, its effectiveness often decreases as the intensity of the actual vibration signal increases. Therefore, it is recommended to arrange the vibration pickups closely parallel to each other on a quiet ground during field testing to achieve accurate estimation of channel noise in the vibration testing system.

[0078] Based on the above description, the derivation process of the channel synchronization error calculation formula in the embodiments of this application will be further explained:

[0079] The three-channel method described above assumes no synchronization error between channels, resulting in completely coherent vibration responses. When synchronization errors exist between channels, the vibration response becomes partially coherent. For example, channel j may have a δt relationship with channel i. ji The synchronization error, the time-domain response of channel j can be expressed as:

[0080] y j (t)=h j (t)*x(t+δt ji )+ε j (t) (9)

[0081] In the frequency domain

[0082]

[0083] in The imaginary unit is used. Assuming that the channel noise and the input signal remain statistically independent, the cross-power spectral density between channel j and channel i becomes...

[0084]

[0085]

[0086] Dividing the two equations above, we can obtain the following:

[0087]

[0088] Due to the frequency response function H i and H jTo test the system's inherent characteristics, which do not change over time, this embodiment of the application can use data from the three-channel method (or data from the synchronization period of the two pickups) to estimate... Right now

[0089]

[0090] Substituting formula (14) into formula (13) yields...

[0091]

[0092] Where ρ ji =S ji / S ij and ρ' ji =S' ji / S' ij These represent the cross-spectral ratios during the synchronous and asynchronous periods, respectively. From formula (15), it can be seen that the synchronization error δt... ji The phase angle γ(f) is proportional to the cross-spectral ratio at different time periods, i.e.

[0093]

[0094] Where 'angle(·)' represents the phase angle taken as a complex number.

[0095] The above analysis assumes a synchronization error δt between channels j and i. ji The process remains unchanged for a certain period of time, which essentially treats the non-stationary process caused by "clock jitter" as a short-term stationary process, and then uses the cross power spectrum between the two channels to calculate the synchronization error. Since the influence of the frequency response function has been removed, formula (16) is theoretically applicable to the synchronization error correction of different sampling frequencies and different types of pickups, and its applicable scope is similar to that of the three-channel method.

[0096] Optionally, considering that formula (16) requires the cross-spectral ratio of the vibration signal, the Welch algorithm can be used when calculating the synchronization error to reduce the influence of random noise. Meanwhile, considering the synchronization error δt... ji It does not change with the frequency f. In the embodiments of this application, the phase angle can be estimated first by the least squares method. The slope is then divided by 4π to obtain the synchronization error.

[0097] The vibration monitoring system channel synchronization error estimation method in this embodiment is designed for on-site calibration of channel hardware noise. It does not rely on specific signal inputs or measurement system frequency response function information and is applicable to the calibration of various types of vibration pickups with different sampling frequencies. Based on cross-spectrum estimation, it can accurately calculate the millisecond-level synchronization error between two channels. The aforementioned channel hardware noise and synchronization error estimation can constitute the main framework for evaluating the measurement noise of vibration monitoring systems. It can be used to calibrate the operating noise levels of various types of vibration testing systems with different sampling frequencies, providing guidance for the standardization and optimization of structural vibration monitoring.

[0098] To more clearly illustrate the channel synchronization error estimation method of the vibration monitoring system in the embodiments of this application, an example analysis is provided.

[0099] Figure 4 This is a schematic diagram of a connecting bridge structure provided in an embodiment of this application, showing the monitoring structure and measuring point arrangement of the connecting bridge. The following will use... Figure 4 Taking the short-term vibration monitoring system of the connecting bridge shown as an example, this application demonstrates the application of single-channel hardware noise and channel synchronization error calibration involved in the embodiments of this application.

[0100] Specifically, the vibration monitoring system employs eight Kinemetrics Etna 2 seismographs, including triaxial servo accelerometers, a 24-bit data acquisition unit (integrated with the accelerometers), a GPS receiver for synchronization, and a high-capacity battery (capable of more than two days of uninterrupted sampling). During noise calibration, the accelerometer measurement range was set to ±2g to negligible discrete errors. Based on the structure's inherent frequency characteristics (the fifth-order frequency is 42Hz), the sampling frequency f was selected. s =100Hz. Since the discrete error and sampling error are very small, the channel noise calculated using the three-channel method can be understood as the hardware noise of this vibration monitoring system. Because a GPS receiver is installed, the seismograph can achieve microsecond-level synchronization via GPS time synchronization.

[0101] To achieve simultaneous measurement of hardware noise across multiple channels, eight seismometers were placed side-by-side on the bridge deck, with the measurement directions of each accelerometer kept as parallel as possible to minimize estimation errors when using the three-channel method. The test duration was set at 15 minutes, and a relatively smooth noise spectrum estimate was obtained based on the Welch algorithm.

[0102] See also Figure 5 , Figure 5 This diagram illustrates a channel noise estimation result provided in an embodiment of this application. To estimate the channel noise of seismograph No. 2, based on the principle of close parallel arrangement, vibration measurement data from seismographs No. 1-3 are simultaneously selected. The X, Y, and Z channel noises are estimated using a three-channel method, and the calculation results are shown below. Figure 5As shown in (a), the noise levels of the three channels are basically the same; for a single channel, its noise spectrum exhibits certain pink noise characteristics, that is, the noise level decreases with increasing frequency and gradually approaches the normal level. At 0.05Hz, the channel noise of this seismograph is only... It exhibits excellent low-frequency measurement performance. To observe the differences in channel noise among different seismographs, the channel noise of seismograph No. 3 can be estimated based on data from seismographs No. 2-4, such as... Figure 5 As shown in (b), the channel noise of the two seismographs is roughly the same, but the Z-direction channel noise of seismograph 3 is slightly higher.

[0103] To understand the accuracy of the embedded clock in the seismograph, after completing the channel noise estimation, the GPS receiver of the even-numbered seismographs was removed, allowing it to sample entirely based on its own clock. The entire test lasted 24 hours.

[0104] Figure 6 This is a schematic diagram of inter-channel phase angles provided in an embodiment of this application. Taking seismograph No. 2 as an example, its phase angles relative to seismograph No. 1 at the 10th and 20th hours are as follows: Figure 6 As shown in (a), it can be seen that the phase angle between the two seismographs is close to zero at the 10th hour, thus there is no synchronization error. However, a significant phase difference appears between the two seismographs at the 20th hour. By estimating the slope of the phase difference, the synchronization error can be calculated to be approximately 129.5 milliseconds. When calculating the phase angle, the cross-spectral ratio ρ... ji Based on the data from the synchronization period (15 minutes) of each seismograph, the cross-spectral ratio ρ is estimated. j ' i This is estimated based on 5-minute data. For comparison, this paper also calculates the phase angle of seismograph 3 relative to seismograph 1, such as... Figure 6 As shown in (b), since the two seismographs are synchronized via GPS, the phase difference between them at different times is close to zero, which proves the superiority of GPS time synchronization.

[0105] Figure 7 This diagram illustrates a channel synchronization error provided in an embodiment of this application. To gain a deeper understanding of the synchronization status between seismographs during a 24-hour test, the synchronization errors of seismographs 2 and 3 relative to seismograph 1 can be calculated every hour. The results are as follows: Figure 7 As shown. From Figure 7 As can be seen, the synchronization error between seismographs synchronized by GPS time is always zero; the embedded clock of the seismograph can guarantee synchronization for 11 hours, but then the synchronization error first increases sharply (111.4 milliseconds), and then begins to increase uniformly at a rate of about 0.6 μs / s, reaching 138.3 milliseconds after 24 hours.

[0106] To understand Figure 7 The abrupt change in the synchronization error of the seismograph is in Figure 8 The time history curves of each unconnected GPS seismograph were plotted. It can be seen that after 11.9 hours, all four seismographs experienced missed sampling, resulting in signal loss and subsequently causing a sudden change in synchronization error. After the missed sampling occurred, the crystal oscillator frequency of the seismograph's embedded clock also changed, causing the synchronization error to begin to increase uniformly.

[0107] This application proposes a method for estimating channel synchronization error in a vibration monitoring system. Specifically, it analyzes the main sources and statistical characteristics of measurement noise throughout the entire data acquisition process, employs a "three-channel" method to calibrate single-channel hardware noise, and proposes an optimized calculation scheme to reduce estimation errors. For multi-channel synchronization error calibration, it proposes a synchronization error calculation method based on the frequency domain characteristics of time-delay test signals, and applies the least squares principle to achieve millisecond-level synchronization error estimation. Finally, the application of the proposed method is demonstrated using the on-site calibration of measurement noise in a bridge vibration monitoring system as an example. The proposed on-site calibration method for measurement noise in vibration monitoring systems is applicable to single-channel hardware noise and multi-channel synchronization error estimation for different types of vibration pickups (such as accelerometers and dynamic displacement gauges) with different sampling frequencies, and helps optimize the design of monitoring systems.

[0108] Based on the description of the foregoing method embodiments, this application also provides a channel synchronization error estimation apparatus. The channel synchronization error estimation apparatus may include:

[0109] The first calculation module is used to obtain the phase angle of the cross-spectral ratio at different time periods;

[0110] The second calculation module is used to calculate the synchronization error between channels based on the phase angle.

[0111] Optionally, the second calculation module described above is specifically used to calculate the synchronization error between the channels using the phase angle based on a preset channel synchronization error formula. This preset channel synchronization error formula may include:

[0112]

[0113] Where, δt ji The synchronization error δt between channels j and i is represented by... ji f represents the frequency, and γ(f) represents the phase angle of the cross-spectral ratio at different time periods.

[0114] In one embodiment, the channel synchronization error estimation device described above can be used to perform any step of the channel synchronization error estimation method as described in the foregoing embodiments, which will not be repeated here.

[0115] This application also provides a computer storage medium (memory), which is a memory device in an electronic device used to store programs and data. It is understood that the computer storage medium here can include both built-in storage media in the electronic device and extended storage media supported by the electronic device. The computer storage medium provides storage space that stores the operating system of the electronic device. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer storage medium located remotely from the aforementioned processor.

[0116] In one embodiment, a processor may load and execute one or more instructions stored in a computer storage medium to implement the corresponding steps in the above embodiments; in a specific implementation, one or more instructions in the computer storage medium may be loaded and executed by the processor to execute any step of the channel synchronization error estimation method in the embodiments of this application, which will not be elaborated here.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0118] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the division of modules is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling, direct coupling, or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0119] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0120] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid state disks (SSDs).

Claims

1. A method for estimating channel synchronization error in a vibration monitoring system, characterized in that, The method includes: Obtain the phase angle of the cross-spectral ratio at different time periods; Calculate the synchronization error between channels based on the phase angle; The calculation of the synchronization error between channels based on the phase angle includes: Based on a preset channel synchronization error formula, the synchronization error between the channels is calculated using the phase angle; the preset channel synchronization error formula includes: ； Indicates channel and Synchronization error between , Indicates frequency, The phase angle representing the cross-spectral ratio at different time periods; ； in, This represents the phase angle when taken as a complex number. and These represent the cross-spectral ratios during the synchronous and asynchronous periods, respectively. i Indicates channel i , j Indicates channel j ; ； ； in, and Indicates the channel when there is no synchronization error between channels j and channel i Cross-power spectral density between and This indicates that there is a synchronization error between the channels. j and channel i Cross-power spectral density between them; The method includes: The calculation based on the three-channel method yielded the following. , , and H j f Representing channels respectively i and channel j The frequency response function, The power spectral density of the input signal. Represents the input signal in the frequency domain. Represents mathematical expectation, superscript ' ' indicates complex conjugate; The following formula is used to obtain the... : Official 1: ; Official 2: ; Substituting Formula 2 into Formula 1 yields: = ; in It is the imaginary unit.

2. The method for estimating channel synchronization error in a vibration monitoring system according to claim 1, characterized in that, Before calculating the synchronization error between channels based on the phase angle, the method further includes: The slope of the phase angle is obtained by estimating using the least squares method; The calculation of the synchronization error between channels based on the phase angle includes: Divide the slope of the phase angle by The synchronization error is obtained.

3. The method for estimating channel synchronization error in a vibration monitoring system according to claim 1, characterized in that, The method further includes: The channel noise is calculated using a three-channel method to obtain the hardware noise of the vibration monitoring system.

4. A channel synchronization error estimation device, characterized in that, For performing the method as described in any one of claims 1-3; the apparatus includes: The first calculation module is used to obtain the phase angle of the cross-spectral ratio at different time periods; The second calculation module is used to calculate the synchronization error between channels based on the phase angle.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the processor performs the steps of the method as described in any one of claims 1-3.

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