A vibration signal coordinate offset correction method, system, device and medium

Abstract

The application discloses a vibration signal coordinate offset correction method, system, device and medium, and relates to the field of vibration signal collection.The method comprises the following steps: acquiring an original vibration signal by using a sensor; when the length of the original vibration signal is less than or equal to a set threshold, determining a local mean point corresponding to each extreme point in the original vibration signal, and obtaining a final local mean curve based on the local mean points; when the length of the original vibration signal is greater than the set threshold, dividing the original vibration signal to obtain a plurality of original vibration sub-signals, determining a local mean point corresponding to each extreme point in any original vibration sub-signal, and obtaining an initial local mean curve of the original vibration sub-signal based on the local mean points; obtaining a final local mean curve based on the initial local mean curve of each original vibration sub-signal; and correcting the coordinate offset of the original vibration signal according to the final local mean curve of the original vibration signal.The application improves the accuracy of vibration signal collection.

Description

Technical Field

[0001] This application relates to the field of vibration signal acquisition, and in particular to a method, system, device and medium for correcting vibration signal coordinate offset. Background Technology

[0002] The phenomenon of coordinate shift during vibration signal acquisition poses a significant challenge to the accuracy and reliability of the data. Therefore, in-depth research on this phenomenon is crucial before data processing and analysis. Coordinate shift, also known as zero drift, refers to the slow change in the output value of a measuring instrument or sensor relative to its coordinates when there is no input signal or the input signal is zero. During vibration signal acquisition, this shift may alter the statistical characteristics of the data, such as changes in the root mean square value, and even distort spectral analysis. The causes of coordinate shift are complex and varied, including device aging, temperature changes, power supply voltage fluctuations, electromagnetic interference, and environmental factors such as changes in humidity and vibration. The negative impact of coordinate shift is particularly significant in applications requiring long-term stable measurements, such as precision weighing and sensor monitoring. Large coordinate shifts can mask weak signals, leading to distorted measurement results or even rendering effective measurements impossible.

[0003] To address the coordinate offset problem in vibration signal acquisition, a series of effective suppression measures are needed. These measures may include selecting highly stable components, performing temperature compensation, using high-performance power supply regulators, implementing effective shielding and grounding measures to reduce electromagnetic interference, and adopting reasonable mechanical structure design to improve the instrument's vibration and shock resistance. Furthermore, regularly performing zero-point calibration, employing appropriate digital filtering algorithms, and establishing and compensating for coordinate offset mathematical models are also important means to improve measurement accuracy and reliability.

[0004] Therefore, in-depth analysis of coordinate offset phenomena and the implementation of effective suppression measures during vibration signal acquisition are of great significance for improving the accuracy and reliability of measurement data. This not only helps ensure the accuracy of data acquisition but also provides a reliable foundation for subsequent data analysis and processing. With continuous technological advancements, new methods and techniques for suppressing coordinate offset will continue to emerge, providing more precise and reliable solutions for vibration signal acquisition. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, device, and medium for correcting the coordinate offset of a vibration signal. By selecting an appropriate method based on the length of the original vibration signal, the final local mean curve of the original vibration signal is obtained. The coordinate offset of the original vibration signal is corrected using the final local mean curve, thereby improving the accuracy of vibration signal acquisition.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] In a first aspect, this application provides a method for correcting the coordinate offset of a vibration signal, including:

[0008] The original vibration signal is acquired using a sensor;

[0009] When the length of the original vibration signal is less than or equal to a set threshold, the local mean point corresponding to each extreme point in the original vibration signal is determined, and the final local mean curve of the original vibration signal is obtained based on the local mean point.

[0010] When the length of the original vibration signal is greater than a set threshold, the original vibration signal is divided into multiple original vibration sub-signals. The local mean point corresponding to each extreme point in any original vibration sub-signal is determined. Based on the local mean point, the initial local mean curve of the original vibration sub-signal is obtained. Based on the initial local mean curve of each original vibration sub-signal, the final local mean curve of the original vibration signal is obtained.

[0011] The coordinate offset of the original vibration signal is corrected based on the final local mean curve of the original vibration signal.

[0012] Secondly, this application provides a vibration signal coordinate offset correction system, comprising:

[0013] The raw vibration signal acquisition module is used to acquire raw vibration signals using sensors.

[0014] The final local mean curve acquisition module is used to: determine the local mean point corresponding to each extreme point in the original vibration signal when the length of the original vibration signal is less than or equal to a set threshold, and obtain the final local mean curve of the original vibration signal based on the local mean points; when the length of the original vibration signal is greater than the set threshold, divide the original vibration signal into multiple original vibration sub-signals, determine the local mean point corresponding to each extreme point in any original vibration sub-signal, obtain the initial local mean curve of the original vibration sub-signal based on the local mean points; and obtain the final local mean curve of the original vibration signal based on the initial local mean curve of each original vibration sub-signal.

[0015] The correction module is used to correct the coordinate offset of the original vibration signal based on the final local mean curve of the original vibration signal.

[0016] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described vibration signal coordinate offset correction method.

[0017] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described vibration signal coordinate offset correction method.

[0018] According to the specific embodiments provided in this application, this application has the following technical effects:

[0019] This application provides a method, system, device, and medium for correcting vibration signal coordinate offset. By selecting an appropriate processing method based on the length of the original vibration signal, the accuracy of vibration signal processing is improved. For original vibration signals with a length less than a set threshold, the offset trend of the original vibration signal is reflected by the final local mean curve. Based on the final local mean curve in the original vibration signal, low-frequency drift of the original vibration signal is removed, making the acquired vibration signal more accurate. For original vibration signals with a length greater than or equal to the set threshold, the original vibration signal is segmented, which not only reduces the difficulty of vibration signal processing and improves the efficiency of vibration signal processing, but also improves the accuracy of vibration signal processing, making the acquired vibration signal more precise. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the construction of the local mean curve in the local feature scale decomposition algorithm in one embodiment of this application;

[0022] Figure 2 This is an application environment diagram of a vibration signal coordinate offset correction method according to an embodiment of this application;

[0023] Figure 3 A flowchart illustrating a vibration signal coordinate offset correction method according to an embodiment of this application;

[0024] Figure 4 A schematic diagram of the construction of the local mean curve of the m-th vibration signal to be processed in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0025] Figure 5 A functional module diagram of a vibration signal coordinate offset correction system provided in another embodiment of this application;

[0026] Figure 6A schematic diagram of the original vibration signals from the first to the third channels acquired by the LC0161 sensor in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0027] Figure 7 This is a schematic diagram showing that the signal segment within three adjacent extreme points in the local mean curve is approximately considered as a triangle in a vibration signal coordinate offset correction method provided in an embodiment of this application.

[0028] Figure 8 A schematic diagram of the local mean points obtained by a vibration signal coordinate offset correction method and an LCD algorithm, respectively, in an embodiment of this application;

[0029] Figure 9 This is a schematic diagram of the decomposition result of the original vibration signal of the first channel of the LCD algorithm provided in an embodiment of this application;

[0030] Figure 10 This is a schematic diagram of the correction result of the original vibration signal of the first channel of the LCD algorithm provided in an embodiment of this application;

[0031] Figure 11 A schematic diagram of the secondary correction result of the original vibration signal of the first channel of the LCD algorithm provided in an embodiment of this application;

[0032] Figure 12 This is a schematic diagram of the original vibration signal acquired by the first channel in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0033] Figure 13 A schematic diagram of the extreme points of the original vibration signal acquired by the first channel in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0034] Figure 14 A schematic diagram of the local mean point of the original vibration signal acquired by the first channel in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0035] Figure 15 A schematic diagram of the local mean curve of the original vibration signal acquired by the first channel in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0036] Figure 16 A schematic diagram of the coordinate offset trend of the original vibration signal acquired by the first channel in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0037] Figure 17A schematic diagram comparing the correction result of the original vibration signal of the first channel in this application with the correction result of the LCD algorithm of the original vibration signal of the first channel in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0038] Figure 18 A schematic diagram comparing the correction result of the original vibration signal of the second channel in this application with the correction result of the LCD algorithm for the original vibration signal of the second channel in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0039] Figure 19 A schematic diagram comparing the correction result of the original vibration signal of the third channel in this application with the correction result of the LCD algorithm for the original vibration signal of the third channel in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0040] Figure 20 A schematic diagram showing the stitching of different continuous local mean curves in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0041] Figure 21 This is a schematic diagram of a partial local mean curve in a vibration signal coordinate offset correction method provided in an embodiment of this application;

[0042] Figure 22 This is a schematic diagram of a vibration signal after correction using a misaligned weighted average method in a vibration signal coordinate offset correction method provided in an embodiment of this application. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] The Local Characteristic-scale Decomposition (LCD) algorithm is based on the conclusion that single-component signals are "locally symmetric about the zero mean," and the construction method of the local mean curve is a major breakthrough. After clarifying the construction method of the local mean curve, the remaining steps are similar to methods such as Empirical Mode Decomposition (EMD), that is, by iteratively separating the local mean curve from the original signal until the signal contains intrinsic scale components. Figure 1 As shown, A1 represents B1 represents C1 represents k is the index of the extreme point, τ k τ is the time point of the kth extreme point. k+1 For the (k+1)th extreme point, τ k+2 The time point of the (k+2)th extreme point, Let K be the local mean point at the k-th extreme point. It is the local mean point at the (k+1)th extreme point. It is the local mean point at the (k+2)th extreme point. For the intermediate variable at the time point of the k-th extreme point, For the intermediate variable at the (k+1)th extreme point, The intermediate variable at the (k+2)th extreme point. Let k be the signal value at the kth extreme point. The signal value at the (k+1)th extreme point. Let be the signal value at the (k+2)th extreme point. The method for constructing the local mean curve of the LCD algorithm is as follows:

[0046] Step 101, select any two extreme points of the same type. Connect them into line segments and find τ. k+1 The time point corresponding to

[0047] Step 102, by and Local mean point obtained: Here, a∈(0,1) is a constant, typically taking the value of 0.5.

[0048] Step 103: Divide any real signal x(t) into several intervals according to the time point of each extreme point, and perform a linear transformation on x(t) in each interval (between any two adjacent extreme points).

[0049]

[0050] Where x(t) is an arbitrary real signal, t is a time point in the arbitrary real signal, and H k (c) represents the local mean curve of the interval containing the k-th extreme point of the signal, where c is any time point among adjacent extreme points, and x c The signal value at any given time point.

[0051] Step 104, H k (c) The local mean curves are obtained by sequentially connecting them. For any real signal x(t), the local mean curves are continuously subtracted from the signal to obtain the Intrinsic Scale Component (ISC) component.

[0052] The vibration signal coordinate offset correction method provided in this application embodiment can be applied to, for example... Figure 2 In the application environment shown, terminal 202 communicates with server 204 via a network. A data storage system can store the data that server 204 needs to process. The data storage system can be set up independently, integrated into server 204, or placed in the cloud or on another server. Terminal 202 can send the raw vibration signal to server 204, and server 204 obtains the final local mean curve of the raw vibration signal based on the raw vibration signal; it then corrects the coordinate offset of the raw vibration signal according to the final local mean curve. Server 204 can then feed back the corrected vibration signal to terminal 202.

[0053] The terminal 202 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. The server 204 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0054] In one exemplary embodiment, such as Figure 3 As shown, a method for correcting the coordinate offset of a vibration signal is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 2 Taking server 204 as an example, the explanation includes the following steps 301 to 303. Wherein:

[0055] Step 301: Acquire the original vibration signal using a sensor. The original vibration signal includes multiple time points and the signal value corresponding to each time point. The original vibration signal Sig is acquired using a sensor, in the form of (t, X), where 1 ≤ t ≤ num represents a time point in the original vibration signal, and X is the signal value corresponding to each time point. The method for acquiring the original vibration signal using a sensor is to use the LC0161 built-in IC piezoelectric vibration sensor to collect the vibration signal. When the sampling frequency is 500Hz, the time at t=1 is 1 / 500s.

[0056] Step 302: When the length of the original vibration signal is less than or equal to a set threshold, determine the local mean point corresponding to each extreme point in the original vibration signal, and obtain the final local mean curve of the original vibration signal based on the local mean point.

[0057] Step 303: When the length of the original vibration signal is greater than a set threshold, the original vibration signal is divided to obtain multiple original vibration sub-signals. The local mean point corresponding to each extreme point in any original vibration sub-signal is determined. Based on the local mean point, the initial local mean curve of the original vibration sub-signal is obtained. Based on the initial local mean curve of each original vibration sub-signal, the final local mean curve of the original vibration signal is obtained.

[0058] Step 304: Correct the coordinate offset of the original vibration signal based on the final local mean curve of the original vibration signal.

[0059] In another exemplary embodiment of this application, step 302 involves determining the local mean point corresponding to each extreme point in the original vibration signal, and obtaining the final local mean curve of the original vibration signal based on the local mean point, specifically including steps 401 to 404.

[0060] Step 401: For the m-th iteration, determine the extreme points in the vibration signal to be processed in the m-th iteration; m≥1; the vibration signal to be processed in the first iteration is the original vibration signal.

[0061] Step 402: Based on each extreme point in the m-th vibration signal to be processed and the m-th vibration signal to be processed, determine the local mean point corresponding to each extreme point in the m-th vibration signal to be processed.

[0062] Step 403: Based on the local mean points corresponding to each extreme point in the m-th vibration signal to be processed, determine the local mean curve of the m-th vibration signal to be processed.

[0063] Step 404: Determine whether the difference between the signal value of the local mean curve of the m-th vibration signal to be processed at any time point and the signal value of the local mean curve of the (m-1)-th vibration signal to be processed at the same time point is less than a set threshold. If not, use the local mean curve of the m-th vibration signal to be processed as the (m+1)-th vibration signal to be processed and perform the (m+1)-th iteration. If yes, use the local mean curve of the m-th vibration signal to be processed as the final local mean curve of the original vibration signal. The 0th vibration signal to be processed is 0.

[0064] Alternatively, in step 404, determine whether the number of extreme points in the m-th vibration signal to be processed is greater than 3; if not, take the local mean curve of the m-th vibration signal to be processed as the (m+1)-th vibration signal to be processed and perform the (m+1)-th iteration; if yes, take the local mean curve of the m-th vibration signal to be processed as the final local mean curve of the original vibration sub-signal; the 0th vibration signal to be processed is 0.

[0065] In another exemplary embodiment of this application, in step 401, after determining the extreme point in the m-th iteration of the original vibration signal, the method further includes: performing a mirror continuation on the m-th vibration signal to be processed. The mirror continuation in this application is a symmetrical continuation, but it can also be a periodic mirror continuation. Performing a mirror continuation on the vibration signal maintains the continuity and smoothness of the signal and reduces boundary effects.

[0066] In another exemplary embodiment of this application, step 402 includes steps 501 to 503.

[0067] Step 501: For the k-th extreme point in the m-th vibration signal to be processed, determine a first time point based on the time point of the k-th extreme point and the time point of the (k-1)-th extreme point, and determine a first signal value based on the first time point and the m-th vibration signal to be processed; 1≤k≤K; K is the number of extreme points in the m-th vibration signal to be processed; the 0th extreme point is determined by the initial time point of the m-th vibration signal to be processed and the signal value at the initial time point.

[0068] Step 502: Based on the time points of the kth extreme point and the (k+1)th extreme point, determine the second time point, and based on the second time point and the mth vibration signal to be processed, determine the second signal value; the (k+1)th extreme point is determined by the end time point of the mth vibration signal to be processed and the signal value at the end time point.

[0069] Step 503: Based on the first time point, the first signal value, the second time point, the second signal value, and the time point of the kth extreme point, determine the local mean point corresponding to the kth extreme point in the mth vibration signal to be processed.

[0070] In another exemplary embodiment of this application, such as Figure 4 As shown, for the k-th extreme point in the m-th vibration signal to be processed. Based on the time point τ of the k-th extreme point k The time point τ of the (k-1)th extreme point k-1 Determine the first time point t D Among them, t D =(τ k-1 +τ k ) / 2. If t D If the signal value is not an integer, then the signal value of the m-th vibration signal to be processed is obtained by interpolation. Where (t) a ,X a ) and (t b ,X b ) represents the m-th vibration signal to be processed, and (t) D ,X D Two adjacent known points, D(t) D X D () is the first signal point.

[0071] Based on the time point τ of the k-th extreme point k The time point τ of the (k+1)th extreme point k+1 Determine the second time point t E Among them, t E =(τ k +τ k+1 ) / 2. If t E The signal value X is not an integer. The interpolation method is used to obtain the signal value X of the m-th vibration signal to be processed. E , E(t E ,X E ( ) represents the second signal point. Compared to the LCD algorithm, which seeks solutions from outside the three extreme points, this application focuses on the signal variation trend within the three extreme points. Compared to the LCD algorithm, this application adjusts the construction method of the local mean curve, using the median-perpendicular intersection point estimation method to obtain the local mean point, which better reflects the local characteristics of the signal and makes the obtained local mean curve more accurate.

[0072] Step 503 uses the following formula to calculate the local mean point corresponding to the k-th extreme point in the m-th vibration signal to be processed: Where k is the index of the extreme point, τ k The time point of the kth extreme point, Let X be the local mean point corresponding to the k-th extreme point. D X is the first signal value. E The second signal value, t DAs the first time point, t E This is the second time point.

[0073] For each extreme point, the local mean point is interpolated using cubic spline interpolation to obtain the local mean curve of the m-th vibration signal to be processed. Specifically, interpolation is performed on the local mean point corresponding to each extreme point within the range of 1 to λ, that is, to fill in the values ​​at all time points other than the time point corresponding to the extreme point.

[0074] In another exemplary embodiment of this application, step 404 uses the following formula to determine whether the difference between the signal value of the local mean curve of the m-th vibration signal to be processed at any time point and the signal value of the local mean curve of the (m-1)-th vibration signal to be processed at that time point is less than a set threshold:

[0075]

[0076] Where t is the time point, end is the end time point of the m-th vibration signal to be processed, and h m (t) represents the signal value at time point t of the local mean curve of the m-th vibration signal to be processed, h. m-1 (t) represents the signal value at time point t of the local mean curve of the vibration signal to be processed in the (m-1)th time, and SD is the set threshold.

[0077] Wherein, the difference between the signal value of the local mean curve of the m-th vibration signal to be processed at any time point and the signal value of the local mean curve of the (m-1)-th vibration signal to be processed at the same time point is the normalized mean square error. The set threshold is 0.1. And the vibration signals to be processed that are less than the set threshold are used as the coordinate offset trend term of the original vibration signal.

[0078] In another exemplary embodiment of this application, step 303 specifically includes: dividing the original vibration signal to obtain multiple original vibration sub-signals, determining the local mean point corresponding to each extreme point in any original vibration sub-signal, obtaining the initial local mean curve of the original vibration sub-signal based on the local mean point; and obtaining the final local mean curve of the original vibration signal based on the initial local mean curve of each original vibration sub-signal, specifically including:

[0079] The original vibration signal is divided into multiple original vibration sub-signals using a sliding window with a set step size.

[0080] For any given original vibration sub-signal, determine the extreme points within the original vibration sub-signal.

[0081] Based on each extreme point in the original vibration signal, determine the local mean point corresponding to each extreme point.

[0082] Based on the local mean point corresponding to each extreme point, the initial local mean curve of the original vibration sub-signal is determined.

[0083] Based on the initial local mean curve of each original vibration sub-signal, the final local mean curve of the original vibration signal is obtained.

[0084] In another exemplary embodiment of this application, the original vibration signal Sig is divided into multiple original vibration sub-signals using a sliding window with a set step size. The multiple original vibration sub-signals include a first original vibration sub-signal to an Nth original vibration sub-signal; N≥1. The set step size is less than the length of the sliding window. The length of the sliding window is set to λ, and the set step size is... Each processed signal is represented by sn. (i) This indicates that the superscript i ≤ num - λ is the starting point flag, representing that processing begins from the original vibration signal Sig at t = i. Let i = 1 first.

[0085] Based on the initial local mean curve of each original vibration sub-signal, the final local mean curve of the original vibration signal is obtained, specifically including:

[0086] For the z-th iteration, the signal value in the z-th reference curve is extracted as the z-th reference array; z≥1; the first reference curve is the initial local mean curve of the first original vibration sub-signal.

[0087] The signal values ​​in the initial local mean curves of the z-th reference array and the (z+1)-th original vibration signal are weighted and staggered to obtain the signal value corresponding to each time point in the (z+1)-th initial local mean curves.

[0088] Based on the signal value corresponding to each time point in the first z+1 initial local mean curves, the z+1th reference curve is obtained.

[0089] Determine whether z+1 is greater than or equal to N. If not, proceed to the z+1th iteration. If yes, use the z+1th reference curve as the final local mean curve of the original vibration signal.

[0090] When the set step size is equal to the length of the sliding window, the original vibration signal is divided in this application to obtain the initial local mean curve of each original vibration sub-signal. When the initial local mean curves of each original vibration sub-signal are spliced ​​together to obtain the final local mean curve of the original vibration signal, a large number of endpoint effects will occur at the splicing points, i.e., at both ends of the initial local mean curves of the original vibration sub-signals, making the final local mean curve smooth. Therefore, the set step size in this application is smaller than the length of the sliding window, and a staggered weighted averaging method is used to eliminate the endpoint effect and make the final local mean curve smooth. The staggered weighted averaging method is as follows:

[0091] The last λ signal values ​​are extracted from the reference curve and used as a reference array. Where end is the end time of the original vibration signal, and λ is the length of the sliding window. For reference array, This is a reference curve.

[0092] Use reference array The initial local mean curve h of the original vibration signal starting from the i-th time point. (i) Perform a staggered weighted average. The specific method for staggered weighted average is as follows: ... The signal value corresponding to the center time point and h (i) The signal values ​​corresponding to the first time point are weighted and averaged. The signal value corresponding to the center time point +1 and h (i) The signal values ​​at the second time point are weighted and averaged... and so on, until... At this point, i > 1.

[0093]

[0094] Where avg[s] is h (i) The signal value at the s-th time point after the misaligned weighted average, where mid is... The central time point, s is h (i) The sequence number of the time point in the time frame is also the offset of the center time point mid. for The weight of the signal value corresponding to the (mid+s-1)th time point, w δ,k for h (i) The weight of the signal value corresponding to the s-th time point. The closer the time point is to the center, the higher the weight:

[0095] Reference curve The signal value after the misaligned weighted average is:

[0096]

[0097] At the same time, h (i) The signal values ​​in the middle and latter half that were not subjected to misalignment weighted averaging were also added to the reference curve. The end:

[0098]

[0099] Thus, after each misalignment weighted average processing, the reference curve The length will increase Until the signal values ​​in the original vibration signal are processed or or The remaining trend term h (last) Added as residual amount Finally, the original vibration signal is corrected. in, This represents the floor function. For example, if the sliding window λ is 1000 and the set step size of the sliding window is 500, then when i = 1, the initial local mean curve of the first original vibration signal corresponds to the time points from 1 to 1000. When i = 501, the initial local mean curve of the second original vibration signal is h. (501) This corresponds to time points 501 to 1500. When i = 1001, the final initial mean curve of the third original vibration signal is h. (1001) This corresponds to the time points from 1001 to 2000.

[0100] For the first iteration, the last λ signal values ​​are extracted from the first reference curve as the first reference array. Let... Among them, h (1) This is the initial local mean curve of the first original vibration signal. This is the first reference curve. Where end is the end time of the first original vibration sub-signal. This is the first reference array.

[0101] The first reference array The initial local mean curve h of the second original vibration signal (501) Perform a staggered weighted average. with h (501) For overlapping time points 501–1000, the signal values ​​corresponding to these overlapping time points are subjected to a staggered weighted average. Specifically… The signal value corresponding to the 501st time point and h (501) The signal value corresponding to the 501st time point is weighted and averaged. The 502nd corresponding signal value in h (501) The signal values ​​corresponding to the 502nd time point are weighted and averaged until... After taking a weighted average of all the signal values ​​corresponding to the 501st to the 1000th time points, we obtain the weighted average signal value of the 501st to the 1000th time points.

[0102] h (501) The signal values ​​corresponding to time points 1001 to 1500 in the middle and latter half are added to Then, the signal values ​​corresponding to the first to the 1500th time points in the first two initial local mean curves are obtained, and based on the signal values ​​corresponding to the first to the 1500th time points, the second reference curve is obtained. based on The signal value corresponding to each time point is used to obtain the second reference array. Will The signal values ​​corresponding to time points 1001 to 1500 and the initial local mean curve h of the third original vibration sub-signal. (1001) The signal values ​​corresponding to the 1001st to 1500th time points in the first half of the process are subjected to staggered weighted averaging, and this process is repeated until all signal values ​​in the original vibration signal have been processed.

[0103] Based on the same inventive concept, such as Figure 5 As shown in the figure, this application embodiment also provides a vibration signal coordinate offset correction system. The vibration signal coordinate offset correction system includes:

[0104] The original vibration signal acquisition module 501 is used to acquire the original vibration signal using a sensor.

[0105] The final local mean curve acquisition module 502 is used to determine the local mean point corresponding to each extreme point in the original vibration signal when the length of the original vibration signal is less than or equal to a set threshold, and obtain the final local mean curve of the original vibration signal based on the local mean points. When the length of the original vibration signal is greater than the set threshold, the original vibration signal is divided into multiple original vibration sub-signals, and the local mean point corresponding to each extreme point in any original vibration sub-signal is determined. The initial local mean curve of the original vibration sub-signal is obtained based on the local mean points; and the final local mean curve of the original vibration signal is obtained based on the initial local mean curve of each original vibration sub-signal.

[0106] The correction module 503 is used to correct the coordinate offset of the original vibration signal based on the final local mean curve of the original vibration signal.

[0107] In one exemplary embodiment, the method of this application is verified experimentally.

[0108] 1. Signal Source: The LC0161's built-in piezoelectric vibration sensor collects vibration signals. This type of sensor operates based on the piezoelectric effect, converting mechanical vibration into an electrical signal. Specifically, when the vibration sensor senses external mechanical vibration, its internal piezoelectric element generates a corresponding potential difference, which is related to the amplitude of the vibration. The sensor converts this potential difference into an electrical signal output, which can then be amplified and filtered by a signal conditioning circuit to ultimately obtain a vibration signal that can be used for analysis and processing.

[0109] like Figure 6 The graph shows the raw vibration signals acquired from the first to third channels. In this graph, the horizontal axis represents time, indicating how the raw vibration signal changes over time, i.e., how the signal value changes over time. The scale is determined by the sampling frequency, typically 500Hz. The vertical axis represents the amplitude or intensity of the vibration, reflecting the strength and frequency characteristics of the vibration.

[0110] The LC0161 sensor boasts advantages such as low impedance output, strong anti-interference capability, and low noise, making it particularly suitable for long cable transmission and multi-point measurement. However, after being fabricated into a benchtop device, its output results exhibit significant coordinate shifts due to temperature variations, power supply voltage fluctuations, electromagnetic interference, and changes in environmental factors. This significantly impacts further data processing.

[0111] 2. Differences between the LCD algorithm and the method of this application for finding local mean points.

[0112] 2.1 Theoretical Explanation: The LCD algorithm seeks local mean points outside the three extreme points; conversely, this application focuses on the signal variation trend inside the three extreme points. This can be... Figure 4 The signal segments within the three adjacent extreme points shown are approximately considered as follows: Figure 7 The triangle shown is illustrated. Here, D is the first signal point, E is the second signal point, and O is the local mean point. However, when the local characteristics of the vibration signal are as follows... Figure 8 As shown, the LCD algorithm's method of finding the local mean point O' using a linear approach does not consider the inflection point characteristics to the right of the extreme value. Compared with this application, this application adjusts the construction method of the local mean curve, using the median-perpendicular intersection point estimation method to obtain the local mean point, which better reflects the local characteristics of the signal.

[0113] 2.2 Example: To verify the effectiveness of the method in this application for finding local mean points, the method in this application for finding local mean points is compared with the LCD algorithm.

[0114] 2.2.1 Coordinate Correction of Vibration Signals Using LCD Algorithm: The original vibration signals from channels 1 to 9 are selected for LCD decomposition (the number of channels is generally a multiple of 3). The decomposition result of the original vibration signal in channel 1 is as follows: Figure 9 As shown. To suppress coordinate shift, the residual components were removed from the original vibration signal for the first correction, and the correction result is shown below. Figure 10 As shown, since the last ISC component also contains some low-frequency components, the result of the first correction is further decomposed and residual components are removed to obtain the second correction result of the original vibration signal of the first channel, as shown. Figure 11 As shown, the LCD algorithm also has the ability to correct coordinate offsets, but the number of corrections is uncertain.

[0115] 2.2.2 Example 2: Correcting the coordinate offset of vibration signals using this application

[0116] Taking the original vibration signal acquired from the first channel as an example, correction is performed where the original vibration signal is less than or equal to a set threshold. The specific steps of this application are as follows:

[0117] Step 1: Obtain the raw vibration signal sn from the first channel, in the form of (t, X). t = 1 to end represents the time index points, and X is the corresponding signal value. The raw vibration signal data is obtained using the LC0161 built-in IC piezoelectric vibration sensor. When the sampling frequency is 500Hz, the time at t = 1 is 1 / 500s, as follows: Figure 12 As shown.

[0118] Step 2: Using the original vibration signal as the signal to be processed, determine the extreme points of the original vibration signal sn, such as... Figure 13 As shown.

[0119] Step 3: Determine the local mean point corresponding to each extreme point in the original vibration signal. Some local mean points of the original vibration signal are shown below. Figure 14 As shown.

[0120] Step 4: Use cubic spline interpolation to obtain the local mean curve, such as... Figure 15 As shown.

[0121] Step 5: When the local mean curve meets the set conditions, the local mean curve at this time is used as the coordinate offset trend term of the vibration signal, such as... Figure 16 As shown, otherwise, repeat steps three through five. Correct the vibration signal based on the coordinate offset trend term of the vibration signal.

[0122] The correction results of the original vibration signals from channels one to nine in this application are compared with the correction results of the LCD algorithm. For example, Figure 17 , Figure 18 and Figure 19 As shown, the correction results of the original vibration signals from the first to the third channels in this application are compared with the correction results of the LCD algorithm. It can be seen that the correction results of this application are closer to the original vibration signals than the correction results of the LCD algorithm.

[0123] 2.2.3 Example 3: The misaligned weighted average method removes the endpoint effect of multiple sliding windows.

[0124] exist Figure 12 As can be seen, although the coordinate offset trend term generally conforms to... Figure 14 The sn signal shows a shift trend, but it still exhibits an endpoint effect.

[0125] The raw vibration signal from the first channel is acquired. This vibration signal is longer than a set threshold, making it a relatively long signal. Taking λ = 1000 and a step size of 1000 as an example, h is processed. (1) h (1001) h (2001) h (3001) h (4001) Listed in Figure 20 It is evident that direct concatenation exhibits significant endpoint effects when dealing with large datasets. When the step size is set to 500, with h... (1) h (501) For example, Figure 21 As shown in the figure, the coordinate offset trend term exhibits the most pronounced endpoint effect near the endpoints. Therefore, a misaligned weighted average is used to correct for this effect. The correction results are as follows. Figure 22 As shown. It can be seen that h (1) Right end and h (501) The left end has removed the endpoint effect and formed a new one. The elements are preserved, in The endpoint effect still exists on the right side, and will be utilized in the next iteration using h. (1001) Remove it.

[0126] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described vibration signal coordinate offset correction method.

[0127] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described vibration signal coordinate offset correction method.

[0128] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0129] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for correcting the coordinate offset of a vibration signal, characterized in that, The method includes: The raw vibration signal is acquired using a sensor; the raw vibration signal includes multiple time points and the signal value corresponding to each time point. When the length of the original vibration signal is less than or equal to a set threshold, the local mean point corresponding to each extreme point in the original vibration signal is determined, and the final local mean curve of the original vibration signal is obtained based on the local mean point. When the length of the original vibration signal is greater than a set threshold, the original vibration signal is divided into multiple original vibration sub-signals. The local mean point corresponding to each extreme point in any original vibration sub-signal is determined. Based on the local mean point, the initial local mean curve of the original vibration sub-signal is obtained. Based on the initial local mean curve of each original vibration sub-signal, the final local mean curve of the original vibration signal is obtained. Based on the final local mean curve of the original vibration signal, the coordinate offset of the original vibration signal is corrected; Specifically, determining the local mean point corresponding to each extreme point in the original vibration signal, and obtaining the final local mean curve of the original vibration signal based on the local mean points, includes: For the m-th iteration, determine the extreme points in the vibration signal to be processed in the m-th iteration; m≥1; the vibration signal to be processed in the 1st iteration is the original vibration signal; Based on each extreme point in the m-th vibration signal to be processed and the m-th vibration signal to be processed, determine the local mean point corresponding to each extreme point in the m-th vibration signal to be processed; Based on the local mean points corresponding to each extreme point in the m-th vibration signal to be processed, the local mean curve of the m-th vibration signal to be processed is determined. Determine whether the difference between the signal value of the local mean curve of the m-th vibration signal to be processed at any time point and the signal value of the local mean curve of the (m-1)-th vibration signal to be processed at that time point is less than a set threshold; if not, then use the local mean curve of the m-th vibration signal to be processed as the (m+1)-th vibration signal to be processed, and perform the (m+1)-th iteration; if yes, then use the local mean curve of the m-th vibration signal to be processed as the final local mean curve of the original vibration signal; the 0th vibration signal to be processed is 0. Specifically, based on each extreme point in the m-th vibration signal to be processed and the m-th vibration signal to be processed, the local mean point corresponding to each extreme point in the m-th vibration signal to be processed is determined, including: For the m-th vibration signal to be processed, the first... The extreme point, based on the first extreme point The time point of the extreme point and the first extreme point -1 extreme points are used to determine the first time point, and based on the first time point and the m-th vibration signal to be processed, the first signal value is determined; 1≤ ≤K; K is the number of extreme points in the m-th vibration signal to be processed; the 0th extreme point is determined by the initial time point of the m-th vibration signal to be processed and the signal value at the initial time point; Based on the first The time points of the extreme points and The time point of the +1 extreme point is used to determine the second time point, and the second signal value is determined based on the second time point and the m-th vibration signal to be processed; the K+1 extreme point is determined by the end time point of the m-th vibration signal to be processed and the signal value at the end time point. Based on the first time point, the first signal value, the second time point, the second signal value, and the first... The time point of the extreme point is used to determine the m-th vibration signal to be processed. The local mean points corresponding to the extreme points.

2. The vibration signal coordinate offset correction method according to claim 1, characterized in that, The m-th vibration signal to be processed is calculated using the following formula. Local mean points corresponding to each extreme point: ； in, The index of the extreme point. For the first The time points of each extreme point For the first The local mean points corresponding to the extreme points The first signal value, The second signal value, As the first point in time, This is the second time point.

3. The vibration signal coordinate offset correction method according to claim 1, characterized in that, The following formula is used to determine whether the difference between the signal value of the local mean curve of the m-th vibration signal to be processed at any time point and the signal value of the local mean curve of the (m-1)-th vibration signal to be processed at that time point is less than a set threshold: ； in, For a point in time, Let m be the end time of the vibration signal to be processed. Let be the signal value at time point t of the local mean curve of the m-th vibration signal to be processed. Let be the signal value at time point t of the local mean curve of the (m-1)th vibration signal to be processed. To set a threshold.

4. The vibration signal coordinate offset correction method according to claim 1, characterized in that, The original vibration signal is divided into multiple original vibration sub-signals. The local mean point corresponding to each extreme point in any original vibration sub-signal is determined. Based on the local mean points, an initial local mean curve of the original vibration sub-signal is obtained. Based on the initial local mean curve of each original vibration sub-signal, a final local mean curve of the original vibration signal is obtained, specifically including: The original vibration signal is divided into multiple original vibration sub-signals using a sliding window with a set step size; For any given original vibration sub-signal, determine the extreme points within the original vibration sub-signal; Based on each extreme point in the original vibration sub-signal, determine the local mean point corresponding to each extreme point; based on the local mean point corresponding to each extreme point, determine the initial local mean point of the original vibration sub-signal. Mean curve; Based on the initial local mean curve of each original vibration sub-signal, the final local mean curve of the original vibration signal is obtained.

5. The vibration signal coordinate offset correction method according to claim 4, characterized in that, The original vibration signal includes multiple time points and the signal value corresponding to each time point; the multiple original vibration sub-signals include the first original vibration sub-signal to the Nth original vibration sub-signal; N≥1; the set step size is less than the length of the sliding window; Based on the initial local mean curve of each original vibration sub-signal, the final local mean curve of the original vibration signal is obtained, specifically including: For the z-th iteration, the signal value in the z-th reference curve is extracted as the z-th reference array; z≥1; the first reference curve is the initial local mean curve of the first original vibration sub-signal; The signal values ​​in the initial local mean curves of the z-th reference array and the z+1-th original vibration signal are weighted and staggered to obtain the signal value corresponding to each time point in the first z+1 initial local mean curves. Based on the signal value corresponding to each time point in the first z+1 initial local mean curves, the z+1th reference curve is obtained; Determine whether z+1 is greater than or equal to N. If not, proceed to the z+1th iteration. If yes, use the z+1th reference curve as the final local mean curve of the original vibration signal.

6. A vibration signal coordinate offset correction system, characterized in that, The system employing the vibration signal coordinate offset correction method according to any one of claims 1-5 comprises: The raw vibration signal acquisition module is used to acquire raw vibration signals using sensors. The final local mean curve acquisition module is used to: determine the local mean point corresponding to each extreme point in the original vibration signal when the length of the original vibration signal is less than or equal to a set threshold, and obtain the final local mean curve of the original vibration signal based on the local mean points; when the length of the original vibration signal is greater than the set threshold, divide the original vibration signal into multiple original vibration sub-signals, determine the local mean point corresponding to each extreme point in any original vibration sub-signal, obtain the initial local mean curve of the original vibration sub-signal based on the local mean points; and obtain the final local mean curve of the original vibration signal based on the initial local mean curve of each original vibration sub-signal. The correction module is used to correct the coordinate offset of the original vibration signal based on the final local mean curve of the original vibration signal.

7. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the vibration signal coordinate offset correction method according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the vibration signal coordinate offset correction method according to any one of claims 1-5.

Images