Jump detection sharp point data processing method, jump detection method, storage medium and computer program product

Abstract

The invention provides a bounce detection sharp point data processing method, a bounce detection method, a storage medium and a computer program product. The bounce detection sharp point data processing method comprises the following steps: obtaining bounce data, and constructing a bounce data set; according to the bounce data set, obtaining point bounce fluctuation amplitudes corresponding to data points in the bounce data set; obtaining a point fluctuation change mean value according to the point jump fluctuation amplitude of the jump data set; according to the point bounce fluctuation amplitude and the point fluctuation change mean value, judging that the point fluctuation change mean value meets a preset sharp point condition; and determining the data point corresponding to the point fluctuation change mean value meeting the preset sharp point condition as a jumping sharp point. The sharp point data processing method can automatically identify the bouncing sharp point.

Description

Technical Field

[0001] This invention relates to the field of runout testing technology, and in particular to a cusp data processing method, a runout detection method, a storage medium, and a computer program product for runout detection. Background Technology

[0002] During the assembly process of aero engines, in order to determine the geometric assembly characteristics of the engine and ensure the high-precision engine assembly quality, it is necessary to perform runout testing on the aero engine to determine the qualification of parts processing and assembly.

[0003] However, some parts have localized burrs, which cause sharp points in the runout data curves obtained during runout measurement using a precision rotary table. When these sharp points are used to calculate runout and concentricity, the true assembly state of the engine cannot be reflected. Therefore, in actual use of runout data, the sharp point data must be discarded as error data.

[0004] However, currently common data removal methods include manual removal and large-scale data removal. Manual removal involves selecting a point near the left of the cusp of the fluctuating data curve and ending at a point near the right of the cusp, deleting the selected data, and then interpolating using the endpoints of the remaining data to obtain the final fluctuating data. This method is inefficient, requiring manual identification and selection of cusp data to be removed, and the deletion of valid data during the removal process affects the validity of the fluctuating data used in the calculation. While large-scale data removal methods do not require manual removal, they only target extreme points, i.e., the highest point or the highest value, resulting in lower accuracy in identifying and removing cusp data, which is insufficient for the fluctuating data required for aero-engine fluctuation detection. Summary of the Invention

[0005] The purpose of this invention is to provide a method for processing tip data for tip detection, a tip detection method, a storage medium, and a computer program product that can automatically identify tipping.

[0006] One aspect of the present invention provides a method for processing cusp data for jitter detection, comprising: acquiring jitter data and constructing a jitter dataset; obtaining the point jitter fluctuation amplitude corresponding to a data point in the jitter dataset based on the jitter dataset; obtaining the average fluctuation change of a point based on the point jitter fluctuation amplitude in the jitter dataset; determining that the average fluctuation change of a point satisfies a preset cusp condition based on the point jitter fluctuation amplitude and the average fluctuation change of a point; and identifying the data point corresponding to the average fluctuation change of the point that satisfies the preset cusp condition as a jitter cusp.

[0007] In one embodiment, acquiring the jitter data and constructing the jitter dataset includes: acquiring a phase angle and a jitter value corresponding to the phase angle as a data point; and constructing a jitter dataset based on multiple data points.

[0008] In one embodiment, the step of obtaining the point fluctuation amplitude corresponding to the data point in the fluctuation dataset is based on the fluctuation dataset, wherein the point fluctuation amplitude corresponding to the data point is obtained according to the following relationship:

[0009]

[0010] Among them, b i Let t be the amplitude of the point jump corresponding to the i-th data point in the jump dataset. i Let n be the fluctuation value of the i-th data point, and n be the number of data points.

[0011] In one embodiment, the step of obtaining the point fluctuation amplitude corresponding to the data point in the fluctuation dataset is based on the fluctuation dataset, wherein the point fluctuation amplitude corresponding to the data point is obtained according to the following relationship:

[0012]

[0013] Among them, b i Let t be the amplitude of the point jump corresponding to the i-th data point in the jump dataset. i Let n be the fluctuation value of the i-th data point, and n be the number of data points.

[0014] In one embodiment, obtaining the average value of point fluctuation change based on the point fluctuation amplitude of the fluctuation dataset includes: obtaining the corresponding average value of point fluctuation based on the point fluctuation amplitude of the fluctuation dataset; and obtaining the average value of point fluctuation change based on the average value of point fluctuation.

[0015] In one embodiment, the average point fluctuation is obtained based on the point fluctuation amplitude of the fluctuation dataset, wherein the average point fluctuation is obtained according to the following formula:

[0016]

[0017] Where μ is the mean fluctuation of the point, b i Let n be the amplitude of the point jump corresponding to the i-th data point in the jump dataset, and n be the number of data points.

[0018] In one embodiment, the average point fluctuation is obtained based on the amplitude of the point fluctuation, wherein the average point fluctuation is obtained according to the following formula:

[0019]

[0020] Where σ is the average value of the fluctuation change at the point, and b i Let n be the amplitude of the point jump corresponding to the i-th data point in the jump dataset, and n be the number of data points.

[0021] In one embodiment, determining that the average value of point fluctuation meets a preset cusp condition based on the amplitude of the point fluctuation and the average value of the point fluctuation includes: if the amplitude of the point fluctuation corresponding to the data point is greater than a preset multiple of the average value of the point fluctuation, then the data point is determined to meet the preset cusp condition.

[0022] In one embodiment, the preset multiple is 3.

[0023] In one embodiment, the cusp data processing method further includes: determining a replacement cusp value based on the cusp value and the cusp values ​​of adjacent data points; replacing the cusp value in the cusp dataset with the replacement cusp value to obtain a reconstructed cusp dataset; and updating the cusp dataset if the mean value of the point fluctuation change of the data points does not meet the preset cusp condition, based on the reconstructed cusp dataset, to obtain an updated cusp dataset.

[0024] In one embodiment, a replacement jump value is determined based on the jump value of the jump apex and the jump values ​​of the adjacent data points of the jump apex, wherein the replacement jump value is obtained according to the following relationship:

[0025]

[0026] Among them, t i The bounce value of the i-th data point that is identified as the bounce cusp.

[0027] Another aspect of the present invention provides a jitter detection method, including the steps of the cusp data processing method for jitter detection described in any of the above embodiments.

[0028] Another aspect of the present invention provides a storage medium for storing non-transitory computer instructions, which, when executed, perform the spike data processing method for bounce detection as described in any of the above embodiments.

[0029] Another aspect of the present invention is a computer program product comprising a computer program that, when executed by a processor, implements the steps of the cusp data processing method for jitter detection as described in any of the above embodiments.

[0030] The jitter detection cusp data processing method of the present invention judges whether each data point in the jitter dataset is a jitter cusp based on the average value of the point fluctuation change. It can automatically identify jitter cusps, reduce manual removal of cusp data, reduce labor costs and human error, make the jitter detection results more accurate, improve processing precision, and facilitate further data processing. Attached Figure Description

[0031] The above and other features, properties and advantages of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings and embodiments, wherein:

[0032] Figure 1 This is a flowchart illustrating an embodiment of the cusp data processing method for jerk detection according to the present invention;

[0033] Figure 2 This is a flowchart illustrating another embodiment of the cusp data processing method for judder detection according to the present invention;

[0034] Figure 3 It is the phase angle-runout value distribution curve of the runout dataset for aero-engine component runout detection;

[0035] Figure 4 yes Figure 3 The jitter dataset shown is used for jitter cusp identification according to the cusp data processing method of the present invention, and the phase angle-jitter value distribution curve of the replaced jitter dataset is also shown. Detailed Implementation

[0036] Reference will now be made in detail to embodiments of the invention, one or more examples of which are illustrated in the accompanying drawings. Each example is provided to explain the invention and not to limit it. In fact, it will be apparent to those skilled in the art that various modifications and variations may be made to the invention without departing from the scope or spirit thereof. For example, a feature shown or described as part of one embodiment may be used with another embodiment to produce yet another embodiment. Therefore, the invention is intended to cover these modifications and variations that fall within the scope of the appended claims and their equivalents.

[0037] Figure 1 An embodiment of the cusp data processing method for spur detection of the present invention is shown. For example... Figure 1 As shown, the cusp data processing method for spur detection of the present invention includes steps S100 to S500:

[0038] In step S100, the bouncing data is obtained and a bouncing dataset is constructed.

[0039] In step S200, the amplitude of point fluctuations corresponding to the data points in the fluctuation dataset is obtained based on the fluctuation dataset.

[0040] In step S300, the average value of point fluctuation change is obtained based on the point fluctuation amplitude of the fluctuation dataset.

[0041] In step S400, based on the amplitude of the point jump fluctuation and the average value of the point fluctuation change, it is determined that the average value of the point fluctuation change meets the preset cusp condition.

[0042] In step S500, the data point corresponding to the average value of the fluctuation change of the point that meets the preset cusp condition is determined as the jumping cusp.

[0043] The jitter detection cusp data processing method of the present invention judges whether each data point in the jitter dataset is a jitter cusp based on the average value of the point fluctuation change. It can automatically identify jitter cusps, reduce manual removal of cusp data, reduce labor costs, reduce human error, make the jitter detection results more accurate, improve processing precision, and facilitate further data processing.

[0044] Compared to related technologies that use big data to identify cusps, the cusp data processing method of this invention can achieve continuous identification of cusp data. It can identify not only the highest / lowest cusp data and the first cusp data, but also cusps in all data points, with a wider identification coverage and the ability to accurately identify all cusps.

[0045] If no data point in the bouncing dataset meets the preset cusp condition, it is determined that no bouncing cusp has been identified in the bouncing dataset, and subsequent data processing can proceed directly.

[0046] In one embodiment, step S100 further includes steps S110 to S120:

[0047] In step S110, the phase angle and the corresponding jitter value are obtained as a data point. That is, the jitter data in step S100 includes the phase angle and the corresponding jitter value, which can be expressed as (j i ,t i ), where j i Let t be the phase angle of the i-th data point. i Let be the jump value of the i-th data point.

[0048] Furthermore, in step S110, the phase angle and the corresponding runout value can be obtained through a precision turntable and its measurement system.

[0049] In step S120, a bouncing dataset is constructed based on multiple data points. Specifically, the bouncing dataset can be represented as:

[0050] {(j1,t1),(j2,t2)…(j n ,t n )}

[0051] Where n is the number of data points.

[0052] In this embodiment, the bouncing dataset is an ordered set, and the bouncing dataset must meet the following condition: j i+1 >j i .

[0053] In one embodiment, after identifying cusp data processing points, the cusp data processing method can further remove and replace the cusps. The cusp processing method also includes steps S600 to S800:

[0054] In step S600, a replacement jump value is determined based on the jump value of the jump tip and the jump values ​​of the adjacent data points of the jump tip.

[0055] In step S700, the bouncing values ​​of the bouncing cusps in the bouncing dataset are replaced with replacement bouncing values ​​to obtain the reconstructed bouncing dataset. The reconstructed bouncing dataset is the new bouncing dataset based on the original bouncing dataset, with the bouncing values ​​of the bouncing cusps replaced.

[0056] In step S800, if the mean value of the fluctuation change of the data points does not meet the preset cusp condition, the jump dataset is updated to obtain the updated jump dataset.

[0057] In this embodiment, if the mean value of the fluctuation of at least one data point in the reconstructed jitter dataset satisfies the preset cusp condition, then step S200 is repeated until there are no data points that satisfy the preset cusp condition.

[0058] The reconstructed and updated bouncing datasets mentioned above need to be reordered according to the phase angle, meaning that both remain ordered datasets.

[0059] In step S600, the replacement jump value is obtained according to relation (1):

[0060]

[0061] Among them, t i Let be the bounce value of the i-th data point that is identified as the bounce cusp.

[0062] This embodiment uses interpolation to replace the bounce values ​​of the removed cusp points. By replacing the data, the data is made to better reflect the actual data change state. After reordering, a reconstructed bounce dataset is obtained, which solves the problem of automatically replacing bounce data with valid bounce data. After cusp identification, it can automatically replace the data with new data.

[0063] In one embodiment, in step S200, the amplitude of the point jump fluctuation corresponding to the data point is obtained according to the relationship (2):

[0064]

[0065] Among them, b i Let t be the amplitude of the point fluctuation corresponding to the i-th data point in the fluctuation dataset. i Let be the jump value of the i-th data point, and n be the number of data points.

[0066] This embodiment calculates the fluctuation amplitude of a data point based on a neighboring data point, and uses the variation pattern to determine the cusp data. This method abandons the traditional approach of using big data and helps to accurately identify cusps.

[0067] In another embodiment, calculations are performed based on the two nearest neighboring data points of a given data point to further improve the accuracy of identifying the cusp of a jump. In step S200, the amplitude of the point jump corresponding to the data point is obtained according to the relationship (3):

[0068]

[0069] Among them, b i Let t be the amplitude of the point fluctuation corresponding to the i-th data point in the fluctuation dataset. i Let be the jump value of the i-th data point, and n be the number of data points.

[0070] In another embodiment, the amplitude of point fluctuation is calculated based on two or more neighboring data points of a certain data point. The calculation principle is the same as above and will not be repeated here.

[0071] In one embodiment, step S300 further includes steps S310 to S320:

[0072] In step S310, the average value of point fluctuation is obtained based on the point fluctuation amplitude of the fluctuation dataset.

[0073] In step S320, the average value of point fluctuation change is obtained based on the average value of point fluctuation.

[0074] In step S310, the average point fluctuation is obtained according to equation (4):

[0075]

[0076] Where μ is the mean of point fluctuation, b i Let n be the amplitude of the jump fluctuation corresponding to the i-th data point in the jump dataset, where n is the number of data points.

[0077] Furthermore, in step S320, the mean value of the point fluctuation change is obtained according to relation (5):

[0078]

[0079] Where σ is the mean value of the point fluctuation, and b i Let n be the amplitude of the jump fluctuation corresponding to the i-th data point in the jump dataset, where n is the number of data points.

[0080] In one embodiment, step S400 further includes step S410:

[0081] In step S410, if the amplitude of the point fluctuation at a data point is greater than the average value of the point fluctuation by a preset multiple, the data point is determined to meet the preset cusp condition. That is, the preset cusp condition is expressed by equation (6):

[0082] b i >mσ (6)

[0083] Where m is a preset multiplier. The preset multiplier m may be adjusted based on parameters such as engine size data and processing status, and can be corrected based on the data of the units to be evaluated, i.e., based on the parameters of multiple engines to be evaluated. Based on testing experience, the preset multiplier m is preferably 3, which is sufficient to identify the cusp of vibration in the vibration dataset.

[0084] The cusp data processing method of the present invention solves the problem of avoiding the removal of valid data when removing cusp data. It can determine the optimal preset multiple m to avoid misidentifying valid parameters (i.e., non-jumping cusp data points).

[0085] Using the mean of point fluctuations instead of the data mean to identify cusps can improve the accuracy of cusp identification.

[0086] In conclusion, Figure 2 Another embodiment of the cusp data processing method of the present invention is shown. For example... Figure 2 As shown, this embodiment includes steps S110 to S800:

[0087] In step S110, the phase angle and the corresponding jump value of the phase angle are obtained as a data point.

[0088] In step S120, a bouncing dataset is constructed based on multiple data points.

[0089] In step S200, the amplitude of point fluctuations corresponding to the data points in the fluctuation dataset is obtained based on the fluctuation dataset.

[0090] In step S310, the average value of point fluctuation is obtained based on the point fluctuation amplitude of the fluctuation dataset.

[0091] In step S320, the average value of point fluctuation change is obtained based on the average value of point fluctuation.

[0092] In step S410, it is determined whether the amplitude of the point fluctuation at a data point is greater than the average value of the point fluctuation change by a preset multiple m. If yes, then step S500 is executed; otherwise, step S800 is executed.

[0093] In step S500, the data point corresponding to the mean value of the point fluctuation change is determined as the jumping peak.

[0094] In step S600, a replacement jump value is determined based on the jump value of the jump tip and the jump values ​​of the adjacent data points of the jump tip.

[0095] In step S700, the fluctuation values ​​of the fluctuation cusps in the fluctuation dataset are replaced with replacement fluctuation values ​​to obtain the reconstructed fluctuation dataset. After completing step S700, step S200 is repeated. That is, new fluctuations b, fluctuation mean μ, and change values ​​σ are obtained until no data exceeds 3 times σ.

[0096] In step S800, the bouncing dataset is updated to obtain the updated bouncing dataset. At this point, all bouncing cusps in the bouncing dataset have been identified and replaced.

[0097] By implementing the cusp data processing method of the present invention, Figure 3 The phase angle-jitter value distribution curve of the jitter dataset shown ( Figure 3 The horizontal axis represents the phase angle, and the vertical axis represents the runout value. Figure 4 (Same as above, will not be repeated) Perform cusp identification, and the identified jumping cusps are as follows: Figure 3 The data point 10 shown has coordinates (111.8°, 1.0351).

[0098] After performing cusp removal and cusp replacement using the cusp data processing method of this invention, the final obtained curve phase angle-runaway value distribution curve is as follows: Figure 4 As shown.

[0099] This invention can automatically remove burrs that traditional algorithms cannot identify, avoiding the need for manual burr removal by visual inspection, as traditional algorithms require. This method greatly improves the performance of turntable data processing, enabling the data to more accurately reflect the state and performance of the surface under test, significantly enhancing turntable performance and providing more possibilities.

[0100] It should be noted that the cusp data processing method of the present invention can not only process the runout data of aero-engine parts, but also process other types of data curves containing cusps.

[0101] The spur detection method of the present invention includes the steps of the cusp data processing method for spur detection described in the above embodiments.

[0102] The storage medium of the present invention is used to store non-transitory computer instructions, which, when executed, perform the spike data processing method for bounce detection as described in the above embodiments.

[0103] The computer program product of the present invention includes a computer program that, when executed by a processor, implements the steps of the spike data processing method for jitter detection described in the above embodiments.

[0104] Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus.

[0105] Computer storage media can be machine-readable storage devices, machine-readable storage substrates, random or serial access memory devices, or combinations thereof.

[0106] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0107] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0108] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for processing cusp data in squirm detection, characterized in that, The cusp data processing method includes: Obtain bouncing data and construct a bouncing dataset; Based on the jitter dataset, the point jitter fluctuation amplitude corresponding to the data points in the jitter dataset is obtained; Based on the amplitude of the point jump fluctuation in the jump dataset, the mean value of the point fluctuation change is obtained; Based on the amplitude of the point jump fluctuation and the average value of the point fluctuation change, it is determined that the average value of the point fluctuation change meets the preset cusp condition; The data point corresponding to the average fluctuation change of the point that meets the preset cusp condition is determined as the jumping cusp.

2. The cusp data processing method as described in claim 1, characterized in that, The process of acquiring pulsation data and constructing a pulsation dataset includes: Obtain the phase angle and the corresponding jump value of the phase angle as a data point; A bouncing dataset is constructed based on multiple data points.

3. The cusp data processing method as described in claim 2, characterized in that, The step involves obtaining the point fluctuation amplitude corresponding to each data point in the fluctuation dataset, wherein the point fluctuation amplitude corresponding to each data point is obtained according to the following formula: Among them, b i Let t be the amplitude of the point jump corresponding to the i-th data point in the jump dataset. i Let n be the fluctuation value of the i-th data point, and n be the number of data points.

4. The cusp data processing method as described in claim 2, characterized in that, The step involves obtaining the point fluctuation amplitude corresponding to each data point in the fluctuation dataset, wherein the point fluctuation amplitude corresponding to each data point is obtained according to the following formula: Among them, b i Let t be the amplitude of the point jump corresponding to the i-th data point in the jump dataset. i Let n be the fluctuation value of the i-th data point, and n be the number of data points.

5. The cusp data processing method according to any one of claims 2 to 4, characterized in that, The step of obtaining the average value of point fluctuation change based on the point fluctuation amplitude of the fluctuation dataset includes: Based on the point fluctuation amplitude of the fluctuation dataset, the corresponding point fluctuation mean is obtained; Based on the average point fluctuation, the average point fluctuation change is obtained.

6. The cusp data processing method as described in claim 5, characterized in that, The step involves obtaining the corresponding average point fluctuation value based on the point fluctuation amplitude of the fluctuation dataset, wherein the average point fluctuation value is obtained according to the following formula: Where μ is the mean fluctuation of the point, b i Let n be the amplitude of the point jump corresponding to the i-th data point in the jump dataset, and n be the number of data points.

7. The cusp data processing method as described in claim 6, characterized in that, The average value of point fluctuation is obtained based on the amplitude of the point fluctuation, wherein the average value of point fluctuation is obtained according to the following formula: Where σ is the average value of the fluctuation change at the point, and b i Let n be the amplitude of the point jump corresponding to the i-th data point in the jump dataset, and n be the number of data points.

8. The cusp data processing method as described in claim 7, characterized in that, The step of determining whether the average value of the point fluctuation meets the preset cusp condition based on the point fluctuation amplitude and the average value of the point fluctuation includes: If the amplitude of the point fluctuation corresponding to the data point is greater than the average value of the point fluctuation by a preset multiple, the data point is determined to meet the preset cusp condition.

9. The cusp data processing method as described in claim 8, characterized in that, The preset multiple is 3.

10. The cusp data processing method according to any one of claims 2 to 4, characterized in that, The cusp data processing method further includes: The replacement jump value is determined based on the jump value of the jump tip and the jump values ​​of the adjacent data points of the jump tip; The bouncing value of the bouncing cusp in the bouncing dataset is replaced with the replacement bouncing value to obtain the reconstructed bouncing dataset; If the mean value of the fluctuation of the data points does not meet the preset cusp condition, the jump dataset is updated based on the reconstructed jump dataset to obtain the updated jump dataset.

11. The cusp data processing method as described in claim 10, characterized in that, The replacement jump value is determined based on the jump value of the jump tip and the jump values ​​of the adjacent data points of the jump tip, wherein the replacement jump value is obtained according to the following relationship: Among them, t i The bounce value of the i-th data point that is identified as the bounce cusp.

12. A method for detecting jitter, characterized in that, The method includes the steps of the cusp data processing method for spur detection as described in any one of claims 1-11.

13. A storage medium, characterized in that, Used to store non-transitory computer instructions, which, when executed, perform the cusp data processing method for jitter detection as described in any one of claims 1-11.

14. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the cusp data processing method for jitter detection as described in any one of claims 1-11.

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