Information processing device, information processing method and computer readable storage medium

Abstract

The present application discloses an information processing device, an information processing method and a computer-readable storage medium. The information processing device includes: a signal acquisition unit configured to acquire a test signal of an object device when the object device is in a stable operation state; and an abnormal burr determination unit configured to determine whether there are abnormal burrs that appear repeatedly and are not locally aggregated in the test signal. The abnormal burr indicates that the object device is abnormal.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing, and in particular to an information processing device, an information processing method, and a computer-readable storage medium. Background Art

[0002] The normal operation of target equipment such as mechanical equipment and electrical equipment is very important. Therefore, it is desirable to provide a technology that can effectively detect abnormalities in the target equipment. Summary of the invention

[0003] A brief overview of the disclosure is given below in order to provide a basic understanding of certain aspects of the disclosure. However, it should be understood that this overview is not an exhaustive overview of the disclosure. It is not intended to identify the key or important parts of the disclosure, nor is it intended to limit the scope of the disclosure. Its purpose is simply to give certain concepts of the disclosure in a simplified form as a prelude to a more detailed description given later.

[0004] In view of the above problems, an object of the present disclosure is to provide an information processing apparatus, an information processing method, and a computer-readable storage medium that make it possible to effectively detect abnormalities in a target device.

[0005] According to one aspect of the present disclosure, an information processing device is provided, comprising: a signal acquisition unit configured to acquire a test signal of an object device when the object device is in a stable operation state; and an abnormal glitch determination unit configured to determine whether there are abnormal glitches that occur repeatedly and are not locally aggregated in the test signal, wherein the abnormal glitch indicates that the object device is abnormal.

[0006] According to another aspect of the present disclosure, an information processing method is provided, comprising: obtaining a test signal of an object device when the object device is in a stable operation state; and determining whether there are abnormal glitches in the test signal that are repeated and not locally aggregated, wherein the abnormal glitches indicate that the object device is abnormal.

[0007] According to other aspects of the present disclosure, a computer program code and a computer program product for implementing the method according to the present disclosure are also provided, as well as a computer-readable storage medium having the computer program code for implementing the method according to the present disclosure recorded thereon.

[0008] Other aspects of the embodiments of the present disclosure are given in the following description, wherein the detailed description is used to fully disclose the preferred embodiments of the embodiments of the present disclosure without imposing limitations thereon. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The present disclosure may be better understood by referring to the detailed description given below in conjunction with the accompanying drawings, wherein the same or similar reference numerals are used throughout the drawings to represent the same or similar components. The accompanying drawings, together with the following detailed description, are included in and form a part of the present specification to further illustrate the preferred embodiments of the present disclosure and to explain the principles and advantages of the present disclosure. Among them:

[0010] Figure 1 is a block diagram showing a functional configuration example of an information processing device according to an embodiment of the present disclosure;

[0011] Figure 2 is a diagram showing an example of a normal test signal;

[0012] Figure 3A is a diagram showing an example of window division for a test signal;

[0013] Figure 3B FIG. 1 shows a case where a global candidate threshold for abnormal glitch candidates is determined for all windows. Figure 3A The test signal shown has selected abnormal glitch candidates;

[0014] Figure 3C Shows the Figure 3B a histogram of glitch values ​​of the abnormal glitch candidates shown;

[0015] Figure 3D Shows the Figure 3B A histogram plotting the temporal distances between the abnormal glitch candidates shown;

[0016] Figure 4 is a diagram showing a comparison between the information processing apparatus 100 according to an embodiment of the present disclosure and the prior art;

[0017] Figure 5 is a flowchart showing an example of the flow of an information processing method according to an embodiment of the present disclosure; and

[0018] Figure 6 : is a block diagram showing an example structure of a personal computer that can be employed in the embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual implementation are described in the specification. However, it should be understood that many implementation-specific decisions must be made in the process of developing any such actual implementation in order to achieve the developer's specific goals, such as meeting those constraints related to the system and business, and these constraints may vary from implementation to implementation. In addition, it should be understood that although the development work may be very complex and time-consuming, it is only a routine task for those skilled in the art who benefit from the content of this disclosure.

[0020] It is also necessary to explain here that in order to avoid obscuring the present disclosure due to unnecessary details, only the device structure and / or processing steps closely related to the scheme according to the present disclosure are shown in the accompanying drawings, while other details that are not closely related to the present disclosure are omitted.

[0021] The embodiments according to the present disclosure are described in detail below with reference to the accompanying drawings.

[0022] First, refer to Figure 1 An implementation example of an information processing device according to an embodiment of the present disclosure is described. Figure 1 : is a block diagram showing a functional configuration example of the information processing device 100 according to an embodiment of the present disclosure.

[0023] like Figure 1 As shown, the information processing device 100 according to an embodiment of the present disclosure may include a signal acquisition unit 110 and an abnormal burr determination unit 120 .

[0024] The signal acquisition unit 110 may be configured to acquire a test signal of the target device when the target device is operating stably.

[0025] For example, the target device may be a mechanical device, an electrical device, etc. Furthermore, for example, the test signal may be a test voltage, a test current, a vibration signal, etc.

[0026] For example, the mechanical device may be a gearbox, such as a planetary gearbox.

[0027] For example, “the target device operates stably” may mean that the target device operates while a constant driving signal is applied thereto.

[0028] The abnormal glitch determination unit 120 may be configured to determine whether there are abnormal glitches that occur repeatedly and are not locally aggregated in the test signal, wherein the abnormal glitch indicates that an abnormality occurs in the target device.

[0029] In this article, "burr" can refer to an event signal whose amplitude is lower than the amplitude of a normal signal or higher than the amplitude of a normal signal. For example, "abnormal burr" can be called "abnormal event signal".

[0030] Note that, as will be appreciated by those skilled in the art, Figure 2 The normal fluctuations of a normal test signal shown are not "glitches".

[0031] In order to ensure the normal operation of target equipment such as mechanical equipment, electrical equipment, etc., for example, the target equipment can be detected for abnormality during the actual operation of the target equipment before and / or after the target equipment leaves the factory. For some newly produced target equipment, especially newly produced new types of target equipment, the physical information of the target equipment is relatively small, the environmental noise is complex, and there is a lack of abnormal samples. Since time domain statistical methods (such as methods based on kurtosis, methods based on crest factor (CF), methods based on pulse factor (PF), and methods based on margin factor (MF)) can only reflect part of the signal characteristics and are easily affected by noise, it is difficult to effectively detect the abnormality of the target equipment. In addition, in the frequency domain, the abnormal frequency characteristics are unstable to the environmental noise and the inherent characteristics of the target equipment, resulting in the difficulty of frequency domain characteristics-based methods such as discrete Fourier transform and fast Fourier transform to effectively detect the abnormality of the target equipment. In addition, the abnormal frequency characteristics are easily mixed with various basebands of different types of target equipment, thus limiting the widespread use of methods based on frequency domain characteristics. In addition, the frequency-time domain analysis method is mainly used in scenarios where the operating state changes (for example, the operating speed changes), and the frequency-time domain analysis method is difficult to effectively detect abnormalities of the target device when the target device is running stably.

[0032] As described above, the information processing device 100 according to an embodiment of the present disclosure can detect whether there are abnormal glitches that occur repeatedly and are not locally aggregated in the test signal, thereby avoiding the noise glitches caused by noise (also referred to as "noise event signals") from being mistakenly determined as abnormal event signals, thereby effectively detecting abnormalities in the target device.

[0033] In addition, the information processing apparatus 100 according to the embodiment of the present disclosure detects whether an abnormality occurs in the target device by detecting an abnormal burr as described above, and this detection method is not affected by the type of the target device, and can therefore be applied to target devices of various types. In addition, the detection method of the information processing apparatus 100 according to the embodiment of the present disclosure makes it possible to detect whether an abnormality occurs in the target device without disassembling the target device.

[0034] For example, according to an embodiment of the present disclosure, the abnormal burr determination unit 120 may be configured to determine whether an abnormal burr exists in the following manner: selecting an abnormal burr candidate from a test signal; and determining whether the selected abnormal burr candidate satisfies the following three conditions: the abnormal burr candidate appears repeatedly; the abnormal burr candidate does not locally aggregate; and the amplitude of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates is greater than a first predetermined threshold. In the case of determining that the abnormal burr candidate satisfies the above three conditions, the abnormal burr determination unit 120 may determine that an abnormal burr exists in the test signal. For example, the first predetermined proportion and the first predetermined threshold may be determined according to actual needs and / or practice. For example, the first predetermined proportion may be 90%.

[0035] For example, according to an embodiment of the present disclosure, the abnormal burr determination unit 120 may divide the test signal into a plurality of (e.g., m) continuous and non-overlapping windows window[0], window[1] ... window[i] ... window[m-1], such as Figure 3A As shown in the figure. Where i and m are natural numbers, i ≥ 1 and m ≥ 2. In addition, Figure 3A In the example, the horizontal axis represents time (in ms) and the vertical axis represents signal strength (in m / s 2 ).in addition, Figure 3A The signal shown in is a vibration signal obtained from a planetary gearbox when the planetary gearbox as an example of the target device is stably operated.

[0036] For example, Figure 3A As shown, each window can have the same width w, such as window[0] = {0, 1, ..., w-1}, window[i] = {w*i, w*i+1, ..., w*(i+1)-1}, window[m-1] = {lw, l-w+1, ..., l-1}. Wherein, the numerical values ​​0, 1, ..., w-1 on the right side of the equal sign in window[0] = {0, 1, ..., w-1} represent the indexes of the various signals included in window[0]. Similarly, the numerical values ​​on the right side of the equal sign in window[i] = {w*i, w*i+1, ..., w*(i+1)-1} and window[m-1] = {lw, l-w+1, ..., l-1} represent the indexes of the various signals included in window[i] and window[m-1] respectively. Wherein, l represents the length of the test signal, that is, the number of signals included in the test signal.

[0037] Note that although Figure 3A Each window is shown as having the same width, but not all windows necessarily have the same width.

[0038] By dividing the test signal into a plurality of continuous windows that do not overlap each other as described above, and further analyzing the test signal based on the divided windows, local characteristics of the test signal can be considered.

[0039] For example, the width of each window may be associated with the duty cycle of the test signal. For example, the larger the duty cycle of the test signal, the larger the width of the window may be.

[0040] In this document, the term "signal" used alone refers to a signal that constitutes a test signal. For example, when the length of the test signal is l, the test signal includes l signals. Each signal can be represented by a signal index and a signal value.

[0041] Note that for ease of viewing, Figure 3A Only part of the window is shown in FIG. 1 , but in reality, the window is continuous.

[0042] For example, the abnormal glitch determination unit 120 may select an abnormal glitch candidate for each window. For example, the abnormal glitch determination unit 120 may perform the following operations for each window window[i]: determine a local amplitude boundary bnd[i] of window window[i], where the local amplitude boundary bnd[i] refers to the boundary of the amplitude of the test signal in window window[i]; determine a local candidate threshold threshold_local[i] for the abnormal glitch candidate of window window[i] based on the amplitude of the signal in window window[i] that is greater than the local amplitude boundary of window window[i]; and select a signal in window window[i] that is greater than the local candidate threshold threshold_local[i] of window window[i] as an abnormal glitch candidate. In this case, the abnormal burr determination unit 120 may be further configured to calculate, for each abnormal burr candidate, the difference between the amplitude of the abnormal burr candidate and the corresponding local candidate threshold as the burr value of the abnormal burr candidate, and determine whether the proportion of abnormal burr candidates whose burr values ​​are greater than the predetermined burr threshold threshold_burr in the abnormal burr candidates is greater than a third predetermined proportion. In the case of determining that the proportion of abnormal burr candidates whose burr values ​​are greater than the predetermined burr threshold in the abnormal burr candidates is greater than the third predetermined proportion, the abnormal burr determination unit 120 may determine that the amplitude of a first predetermined proportion of abnormal burr candidates in the abnormal burr candidates is greater than the first predetermined threshold. For example, the predetermined burr threshold and the third predetermined proportion may be determined according to actual needs and / or practice. For example, the third predetermined proportion may be equal to the first predetermined proportion.

[0043] As described above, by determining the local candidate threshold for each window and selecting the abnormal glitch candidate based on the determined local candidate threshold, it is possible to select the abnormal glitch candidate in consideration of the local characteristics of the signal.

[0044] For example, the set of abnormal burr candidates burrs[i] in the i-th window can be expressed as burrs[i]={burrs indexes [i],burrs values [i]}. Among them, burrs indexes [i] represents the set of signal indices corresponding to the abnormal burr candidates in the i-th window, burrs values [i] represents the set of burr values ​​of abnormal burr candidates in the i-th window. indexes [i] and burrs values [i] can be expressed as the following formulas (1) and (2), respectively.

[0045] burrs indexes [i]={j|series[j]>threshold_local[i]&j∈window[i]} Formula (1)

[0046] burrs values [i]={series[j]-threshold_local[i]|series[j]>threshold_local[i]&j∈window[i]} Formula (2)

[0047] In the above equations (1) and (2), series[j] represents the amplitude of the jth signal.

[0048] The obtained set of abnormal burr candidates burrs can be expressed as burrs = {burrs indexes ,burrs values}. Among them, burrs indexes represents the set of signal indices corresponding to the abnormal burr candidates, which can be expressed by the following formula (3). values The set of glitch values ​​representing abnormal glitch candidates can be expressed by the following equation (4).

[0049] burrs indexes = {burrs indexes [0]∪burrs indexes [1]∪…burrs indexes [i]∪…burrs indexes [m-1]} Formula (3) burrs values = {burrsvalues [0]∪burrs values [1]∪…burrs values [i]∪…burrs values [m-1]} Formula (4)

[0050] For example, the abnormal burr determination unit 120 can determine the local amplitude boundary bnd[i] of each window based on the first n (n is a natural number greater than 1 and n<w) signals with the highest amplitude in the window. For example, the average of the first n signals with the highest amplitude in the window can be used as the local amplitude boundary bnd[i] of the window. Of course, other methods can also be used to determine the local amplitude boundary bnd[i] of each window. For example, for each window, the average of the amplitudes of the signals in the window except the first x (x is a natural number greater than 0 and x<w) signals with the highest amplitude and the last y (y is a natural number greater than 0 and y<w) signals with the lowest amplitude can be used as the local amplitude boundary bnd[i] of the window. The specific values ​​of n, x and y can be set according to actual needs and / or practice.

[0051] For example, the abnormal burr determination unit 120 may calculate, for each window, the average of the amplitudes of the signals in the window whose amplitudes are greater than the local amplitude boundary bnd[i] of the window as the local candidate threshold threshold_local[i] of the window.

[0052] It is described above that the abnormal burr determination unit 120 determines the local candidate threshold threshold_local[i] for the abnormal burr candidate of the window for each window, however, the abnormal burr determination unit 120 may also determine the global candidate threshold threshold_global for the abnormal burr candidate for all windows. For example, according to an embodiment of the present disclosure, the abnormal burr determination unit 120 may determine the global amplitude boundary based on the local amplitude boundaries of the above-mentioned multiple windows; determine the global candidate threshold threshold threshold_global for the abnormal burr candidate based on the amplitude of the signal whose amplitude is greater than the global amplitude boundary; and select the signal whose amplitude is greater than the global candidate threshold threshold threshold_global as the abnormal burr candidate. In this case, the abnormal burr determination unit 120 may be further configured to calculate the difference between the amplitude of the abnormal burr candidate and the global candidate threshold threshold threshold_global as the burr value of the abnormal burr candidate for each abnormal burr candidate, and determine whether the proportion of abnormal burr candidates whose burr value is greater than the predetermined burr threshold threshold threshold_burr in the abnormal burr candidates is greater than the fourth predetermined proportion. In the case where it is determined that the proportion of abnormal burr candidates whose burr values ​​are greater than the predetermined burr threshold threshold_burr in the abnormal burr candidates is greater than the fourth predetermined proportion, the abnormal burr determination unit 120 may determine that the magnitude of the abnormal burr candidates of the first predetermined proportion in the abnormal burr candidates is greater than the first predetermined threshold. For example, the fourth predetermined proportion may be determined according to actual needs and / or practice. For example, the fourth predetermined proportion may be equal to the first predetermined proportion. In addition, for example, the fourth predetermined proportion may be equal to the third predetermined proportion.

[0053] By determining the global candidate threshold for all windows and selecting abnormal glitch candidates based on the determined global candidate threshold as described above, the influence of local noise on glitch selection can be avoided. In addition, it is found through testing that the abnormal glitch determined based on the global candidate threshold is more consistent with the glitch pattern subjectively observed from the acquired signal diagram.

[0054] For example, when the global candidate threshold threshold_global for abnormal burr candidates is determined for all windows as described above, the set of signal indexes corresponding to the abnormal burr candidates in the i-th window is burrs indexes [i] can be expressed as the following formula (5), the set of burr values ​​of abnormal burr candidates in the i-th window is burrs values [i] can be expressed as the following formula (6).

[0055] burrs indexes[i]={j|series[j]>threshold_global&j∈window[i]} Formula (5)

[0056] burrs values [i]={series[j]-threshold_global|series[j]>threshold_global&j∈window[i]} Formula (6)

[0057] For example, Figure 3B In the case where a global candidate threshold threshold_global for abnormal glitch candidates is determined for all windows, Figure 3A The test signal shown is the selected abnormal glitch candidate. Figure 3B In the figure, the horizontal axis represents the signal index corresponding to each abnormal glitch candidate, and the vertical axis represents the glitch value (in m / s) of each abnormal glitch candidate. 2 ).

[0058] In addition, for example, the abnormal burr determination unit 120 can count the number of burr values ​​falling into each burr value interval, and draw a bar graph based on the counted number, so that the distribution of the burr values ​​can be intuitively seen. Figure 3C Shows the Figure 3B The glitch values ​​of the abnormal glitch candidates are plotted in a histogram. Figure 3C It can be seen intuitively that the burr values ​​of most abnormal burr candidates are greater than 0.5m / s 2 .

[0059] For example, the width of the burr value interval may be determined based on the calculated maximum and minimum burr values, such as by dividing the difference between the calculated maximum and minimum burr values ​​by the number of burr value intervals to be divided.

[0060] For example, the predetermined burr threshold threshold_burr may be equal to the difference between the first predetermined threshold and the global candidate threshold threshold_global.

[0061] For example, according to an embodiment of the present disclosure, the abnormal burr determination unit 120 may calculate the mean of the local amplitude boundaries of the above-mentioned multiple windows as the global amplitude boundary. Of course, other methods may also be used to determine the global amplitude boundary based on the local amplitude boundaries of multiple windows. For example, the mean of the first s (s is a natural number greater than 1) signals with the largest values ​​in the local amplitude boundaries of multiple windows may be used as the global amplitude boundary.

[0062] For example, the abnormal burr determination unit 120 may calculate the mean of the amplitudes of the signals whose amplitudes are greater than the global amplitude boundary as the global candidate threshold threshold_global. Of course, those skilled in the art may also calculate the global candidate threshold threshold_global in other ways according to actual needs, which will not be described in detail here.

[0063] For example, according to an embodiment of the present disclosure, the abnormal glitch determination unit 120 may be further configured to calculate the time distance between adjacent abnormal glitch candidates, and determine that the abnormal glitch candidate recurs when a second predetermined proportion of the obtained time distances is less than a predetermined distance threshold.

[0064] For example, the time distance between adjacent abnormal glitch candidates can be represented by the difference between the signal indexes corresponding to the adjacent abnormal glitch candidates, as shown in the following equation (7).

[0065] burr index_gap [k] = burr index [k+1]-burr index [k] Formula (7)

[0066] In the above formula (7), burr index [k+1] and burr index [k] represents the signal indexes corresponding to the k+2th abnormal burr candidate burr[k+1] and the k+1th abnormal burr candidate burr[k], respectively. index_gap [k] represents the time distance between the k+2th abnormal glitch candidate and the k+1th abnormal glitch candidate, such as Figure 3B As shown in FIG. 1 , k is a natural number greater than or equal to 0.

[0067] For example, the second predetermined ratio may be determined according to actual needs and / or practice, such as 95%.

[0068] For example, according to an embodiment of the present disclosure, the abnormal burr determination unit 120 may be further configured to calculate the time distance between adjacent abnormal burr candidates, and when the number of abnormal burr candidates corresponding to each time distance interval is less than a predetermined number, determine that the abnormal burr candidates do not have local aggregation. For example, the specific value of the predetermined number may be determined according to actual needs and / or practice.

[0069] For example, the abnormal burr determination unit 120 may calculate the time distance between adjacent abnormal burr candidates, and determine the width of the time distance interval based on the maximum and minimum values ​​of the calculated time distances, such as by dividing the difference between the maximum and minimum values ​​of the calculated time distance by the number of time distance intervals to be divided to obtain the width of the time distance interval. Then, the abnormal burr determination unit 120 may count the number of time distances falling into the time distance interval for each time distance interval, and use the number as the number of abnormal burr candidates corresponding to the time distance interval.

[0070] For example, a bar graph can be drawn based on the counted numbers so that the distribution of time distance can be intuitively seen. Figure 3D Shows the Figure 3B A histogram of the time distances between the abnormal glitch candidates is shown. Figure 3D It can be seen intuitively that the number of abnormal burr candidates corresponding to each time distance interval is less than 50, so it can be determined that there is no local aggregation of abnormal burr candidates. Figure 3D It can also be intuitively seen that most of the time the distance is less than 5000, so it can be determined that the abnormal burr candidates appear repeatedly.

[0071] Figure 4 is a diagram showing a comparison between the information processing device 100 according to an embodiment of the present disclosure and the prior art. Figure 4 In the example shown, the object device as the test sample is a planetary gearbox, and the test signal is a vibration signal. Figure 4 In the example shown, anomaly detection is performed on a total of 1411 test samples, of which 1347 are normal test samples and 64 are abnormal test samples. Figure 4 In the example shown, the prior art as a comparative example is a time domain statistical method based on angle, peak factor, pulse factor and margin factor. Specifically, the planetary gearbox is determined to be abnormal when at least any one of the following four conditions is met: 1) the angle is greater than the angle threshold; 2) the peak factor is greater than the peak factor threshold; 3) the pulse factor is greater than the pulse factor threshold; and 4) the margin factor is greater than the margin factor threshold.

[0072] like Figure 4 As shown, the precision and recall of the information processing device 100 according to the embodiment of the present disclosure are improved compared with the prior art, and the false alarm rate and missing alarm rate are reduced compared with the prior art. This shows that the information processing device 100 according to the embodiment of the present disclosure can improve the accuracy of abnormality detection.

[0073] The information processing device according to the embodiment of the present disclosure has been described above. Corresponding to the embodiment of the above-mentioned information processing device, the present disclosure also provides the following embodiment of the information processing method.

[0074] Figure 5 is a flowchart showing an example of the process of the information processing method 500 according to an embodiment of the present disclosure. Figure 5 As shown, the information processing method according to the embodiment of the present disclosure may start at the start step S502 and end at the end step S508. The information processing method according to the embodiment of the present disclosure may include a signal acquisition step S504 and an abnormal burr determination step S506.

[0075] In the signal acquisition step S504, the test signal of the target device can be acquired when the target device is in a stable operation state. For example, the signal acquisition step S504 can be implemented by the signal acquisition unit 110 described above, so the specific details can refer to the description of the signal acquisition unit 110 above.

[0076] For example, the target device may be a mechanical device, an electrical device, etc. Furthermore, for example, the test signal may be a test voltage, a test current, a vibration signal, etc.

[0077] In the abnormal glitch determination step S506, it can be determined whether there are abnormal glitches that occur repeatedly and are not locally aggregated in the test signal. The abnormal glitch indicates that the target device is abnormal. For example, the abnormal glitch determination step S506 can be implemented by the abnormal glitch determination unit 120 described above, so the specific details can refer to the description of the abnormal glitch determination unit 120 above.

[0078] In order to ensure the normal operation of target equipment such as mechanical equipment, electrical equipment, etc., for example, the target equipment can be detected for abnormality during its actual operation before and / or after the target equipment leaves the factory. As described above, the information processing method 500 according to the embodiment of the present disclosure can detect whether there are abnormal glitches that occur repeatedly and are not locally aggregated in the test signal, thereby avoiding the error of determining the noise glitches (also referred to as "noise event signals") caused by noise as abnormal event signals, thereby effectively detecting the abnormality of the target equipment.

[0079] In addition, the information processing method 500 according to the embodiment of the present disclosure detects whether an abnormality occurs in the target device by detecting abnormal glitches as described above. This detection method is not affected by the type of the target device and can therefore be applied to various types of target devices.

[0080] For example, according to an embodiment of the present disclosure, in the abnormal glitch determination step S506, it can be determined whether an abnormal glitch exists in the following manner: selecting an abnormal glitch candidate from the test signal; and determining whether the selected abnormal glitch candidate satisfies the following three conditions: the abnormal glitch candidate appears repeatedly; the abnormal glitch candidate does not locally aggregate; and the amplitude of a first predetermined proportion of the abnormal glitch candidates in the abnormal glitch candidates is greater than a first predetermined threshold. When it is determined that the abnormal glitch candidate satisfies the above three conditions, it can be determined that an abnormal glitch exists in the test signal.

[0081] For example, the width of each window may be associated with the duty cycle of the test signal. For example, the larger the duty cycle of the test signal, the larger the width of the window may be.

[0082] For example, in the abnormal burr determination step S506, the selection of abnormal burr candidates can be performed for each window. For example, in the abnormal burr determination step S506, the following operations can be performed for each window: determining the local amplitude boundary bnd[i] of the window, the local amplitude boundary bnd[i] refers to the boundary of the amplitude of the test signal in the window; determining the local candidate threshold threshold_local[i] of the abnormal burr candidate for the window based on the amplitude of the signal in the window whose amplitude is greater than the local amplitude boundary of the window; and selecting the signal in the window whose amplitude is greater than the local candidate threshold of the window as the abnormal burr candidate. In this case, the abnormal burr determination step S506 can be further configured to calculate the difference between the amplitude of the abnormal burr candidate and the local candidate threshold as the burr value of the abnormal burr candidate for each abnormal burr candidate, and determine whether the proportion of abnormal burr candidates whose burr value is greater than the predetermined burr threshold threshold_burr in the abnormal burr candidates is greater than a third predetermined proportion. When it is determined that the proportion of abnormal spur candidates with spur values ​​greater than the predetermined spur threshold in the abnormal spur candidates is greater than a third predetermined proportion, it can be determined that the amplitudes of a first predetermined proportion of the abnormal spur candidates are greater than the first predetermined threshold.

[0083] For example, in the abnormal burr determination step S506, for each window, the local amplitude boundary bnd[i] of the window can be determined based on the top n (n is a natural number greater than 1 and n<w) signals with the highest amplitude in the window. For example, the average of the top n signals with the highest amplitude in the window can be used as the local amplitude boundary bnd[i] of the window.

[0084] For example, in the abnormal burr determination step S506 , for each window, the average of the amplitudes of the signals in the window whose amplitudes are greater than the local amplitude boundary bnd[i] of the window may be calculated as the local candidate threshold threshold_local[i] of the window.

[0085] It is described above that in the abnormal glitch determination step S506, a local candidate threshold threshold_local[i] for an abnormal glitch candidate of the window is determined for each window. However, in the abnormal glitch determination step S506, a global candidate threshold threshold threshold_global for abnormal glitch candidates may also be determined for all windows. For example, according to an embodiment of the present disclosure, in the abnormal glitch determination step S506, a global amplitude boundary may be determined based on the local amplitude boundaries of the above-mentioned multiple windows; a global candidate threshold threshold threshold_global for abnormal glitch candidates may be determined based on the amplitude of a signal having an amplitude greater than the global amplitude boundary; and a signal having an amplitude greater than the global candidate threshold threshold threshold_global is selected as an abnormal glitch candidate. In this case, the abnormal burr determination step S506 may be further configured to calculate, for each abnormal burr candidate, the difference between the amplitude of the abnormal burr candidate and the global candidate threshold threshold_global as the burr value of the abnormal burr candidate, and determine whether the proportion of abnormal burr candidates whose burr values ​​are greater than the predetermined burr threshold threshold_burr in the abnormal burr candidates is greater than a fourth predetermined proportion. In the case of determining that the proportion of abnormal burr candidates whose burr values ​​are greater than the predetermined burr threshold threshold_burr in the abnormal burr candidates is greater than the fourth predetermined proportion, it may be determined that the amplitude of the first predetermined proportion of abnormal burr candidates in the abnormal burr candidates is greater than the first predetermined threshold. For example, the fourth predetermined proportion may be equal to the first predetermined proportion. In addition, for example, the fourth predetermined proportion may be equal to the third predetermined proportion.

[0086] For example, according to an embodiment of the present disclosure, in the abnormal burr determination step S506, the average of the local amplitude boundaries of the above-mentioned multiple windows may be calculated as the global amplitude boundary.

[0087] For example, in the abnormal burr determination step S506 , the average of the amplitudes of the signals whose amplitudes are greater than the global amplitude boundary may be calculated as the global candidate threshold threshold_global.

[0088] For example, according to an embodiment of the present disclosure, in the abnormal glitch determination step S506, the time distance between adjacent abnormal glitch candidates may also be calculated, and when a second predetermined proportion of the obtained time distances is less than a predetermined distance threshold, it is determined that the abnormal glitch candidate is repeated.

[0089] For example, the time distance between adjacent abnormal glitch candidates can be represented by the difference between the signal indexes corresponding to the adjacent abnormal glitch candidates, as shown in the above formula (7).

[0090] For example, according to an embodiment of the present disclosure, in the abnormal glitch determination step S506, the time distance between adjacent abnormal glitch candidates may be calculated, and when the number of abnormal glitch candidates corresponding to each time distance interval is less than a predetermined number, it is determined that the abnormal glitch candidates are not locally aggregated.

[0091] It should be pointed out that although the functional configuration and operation of the information processing device and information processing method according to the embodiments of the present disclosure are described above, this is only an example and not a limitation, and those skilled in the art may modify the above embodiments according to the principles of the present disclosure, for example, the functional modules and operations in each embodiment may be added, deleted or combined, and such modifications shall fall within the scope of the present disclosure.

[0092] In addition, it should be pointed out that the method embodiment here corresponds to the above-mentioned device embodiment. Therefore, for the contents not described in detail in the method embodiment, please refer to the description of the corresponding parts in the device embodiment, and the description will not be repeated here.

[0093] In addition, the present disclosure also provides a storage medium and a program product. It should be understood that the machine-executable instructions in the storage medium and the program product according to the embodiments of the present disclosure can also be configured to execute the above-mentioned information processing method, so the contents not described in detail here can refer to the description of the previous corresponding parts, and will not be repeated here.

[0094] Accordingly, the storage medium for carrying the program product including the machine executable instructions is also included in the disclosure of the present invention, including but not limited to a floppy disk, an optical disk, a magneto-optical disk, a memory card, a memory stick, and the like.

[0095] In addition, it should be noted that the above series of processes and devices can also be implemented by software and / or firmware. In the case of being implemented by software and / or firmware, from a storage medium or a network to a computer with a dedicated hardware structure, such as Figure 6 The general-purpose personal computer 800 shown installs the programs constituting the software, and when the various programs are installed, the computer can execute various functions and the like.

[0096] exist Figure 6 In the embodiment, a central processing unit (CPU) 801 executes various processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage section 808 to a random access memory (RAM) 803. In the RAM 803, data required when the CPU 801 executes various processes and the like is also stored as needed.

[0097] The CPU 801, the ROM 802, and the RAM 803 are connected to one another via a bus 804. An input / output interface 805 is also connected to the bus 804.

[0098] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet.

[0099] A drive 810 is also connected to the input / output interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0100] In the case where the above-described series of processing is realized by software, a program constituting the software is installed from a network such as the Internet or a storage medium such as the removable medium 811 .

[0101] It should be understood by those skilled in the art that such storage media is not limited to Figure 6 The removable medium 811 shown has a program stored therein and is distributed separately from the device to provide the program to the user. Examples of the removable medium 811 include magnetic disks (including floppy disks (registered trademark)), optical disks (including compact disk read-only memory (CD-ROM) and digital versatile disks (DVD)), magneto-optical disks (including minidiscs (MD) (registered trademark)), and semiconductor memories. Alternatively, the storage medium may be a ROM 802, a hard disk included in the storage portion 808, or the like, in which the program is stored and distributed to the user together with the device containing them.

[0102] The preferred embodiments of the present disclosure are described above with reference to the accompanying drawings, but the present disclosure is certainly not limited to the above examples. Those skilled in the art may obtain various changes and modifications within the scope of the appended claims, and it should be understood that these changes and modifications will naturally fall within the technical scope of the present disclosure.

[0103] For example, a plurality of functions included in one unit in the above embodiments may be implemented by separate devices. Alternatively, a plurality of functions implemented by a plurality of units in the above embodiments may be implemented by separate devices, respectively. In addition, one of the above functions may be implemented by a plurality of units. Needless to say, such a configuration is included in the technical scope of the present disclosure.

[0104] In this specification, the steps described in the flowchart include not only the processing performed in time series in the order described, but also the processing performed in parallel or individually rather than necessarily in time series. In addition, even in the steps processed in time series, it goes without saying that the order can be appropriately changed.

[0105] Additionally, the technology according to the present disclosure may also be configured as follows.

[0106] Note 1. An information processing device, comprising:

[0107] a signal acquisition unit configured to acquire a test signal of the target device when the target device is in a stable operation state; and

[0108] an abnormal burr determination unit, configured to determine whether there are abnormal burrs that appear repeatedly and are not locally aggregated in the test signal,

[0109] The abnormal burr indicates that an abnormality occurs in the target device.

[0110] Supplement 2. The information processing device according to Supplement 1, wherein the abnormal burr determination unit is configured to determine whether there is an abnormal burr in the following manner:

[0111] selecting abnormal glitch candidates from the test signal; and

[0112] Determine whether the abnormal burr candidates meet the following three conditions: the abnormal burr candidates appear repeatedly; the abnormal burr candidates are not locally aggregated; and the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold,

[0113] Wherein, when it is determined that the abnormal glitch candidate satisfies the above three conditions, the abnormal glitch determination unit determines that an abnormal glitch exists in the test signal.

[0114] Supplement 3. The information processing device according to Supplement 2, wherein:

[0115] Selecting abnormal glitch candidates from the test signal includes:

[0116] Dividing the test signal into a plurality of continuous and non-overlapping windows; and

[0117] The following operations are performed for each window: determining a local amplitude boundary of the window, wherein the local amplitude boundary refers to a boundary of the amplitude of the test signal in the window; determining a local candidate threshold for abnormal glitch candidates in the window based on the amplitude of the signal in the window whose amplitude is greater than the local amplitude boundary of the window; and selecting the signal in the window whose amplitude is greater than the local candidate threshold of the window as the abnormal glitch candidate;

[0118] The abnormal burr determination unit is further configured to calculate, for each abnormal burr candidate, a difference between the amplitude of the abnormal burr candidate and the corresponding local candidate threshold as the burr value of the abnormal burr candidate; and

[0119] Wherein, determining that the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold includes: determining that the burr values ​​of a third predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a predetermined burr threshold.

[0120] Note 4. An information processing device according to Note 3, wherein determining the local candidate threshold comprises: for each window, calculating the average of the amplitudes of signals in the window whose amplitudes are greater than the local amplitude boundary of the window as the local candidate threshold of the window.

[0121] Supplement 5. The information processing device according to Supplement 2, wherein:

[0122] Selecting abnormal glitch candidates from the test signal includes:

[0123] Dividing the test signal into a plurality of continuous and non-overlapping windows;

[0124] For each window, determining a local amplitude boundary of the window, wherein the local amplitude boundary refers to a boundary of the amplitude of the test signal in the window;

[0125] determining a global amplitude boundary based on the local amplitude boundaries of the plurality of windows;

[0126] determining a global candidate threshold for abnormal glitch candidates based on the amplitude of the signal having an amplitude greater than the global amplitude boundary; and

[0127] Selecting a signal with an amplitude greater than the global candidate threshold as an abnormal glitch candidate;

[0128] The abnormal burr determination unit is further configured to calculate, for each abnormal burr candidate, a difference between the amplitude of the abnormal burr candidate and the global candidate threshold as the burr value of the abnormal burr candidate; and

[0129] Wherein, determining that the magnitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold includes: determining that the burr values ​​of a fourth predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a predetermined burr threshold.

[0130] Supplement 6. The information processing device according to Supplement 5, wherein:

[0131] Determining the global amplitude boundary includes calculating an average of the local amplitude boundaries of the plurality of windows as the global amplitude boundary; and

[0132] Determining the global candidate threshold includes calculating an average of the amplitudes of signals whose amplitudes are greater than the global amplitude boundary as the global candidate threshold.

[0133] Note 7. An information processing device according to any one of Notes 3 to 6, wherein determining the local amplitude boundary comprises: for each window, calculating the average of the amplitudes of n test signals with the highest amplitudes in the window as the local amplitude boundary, wherein n is a natural number greater than 1.

[0134] Note 8. An information processing device according to any one of Notes 2 to 6, wherein the abnormal glitch determination unit is further configured to calculate the time distance between adjacent abnormal glitch candidates, and determine that the abnormal glitch candidate is repeated when a second predetermined proportion of the time distances in the obtained time distances is less than a predetermined distance threshold.

[0135] Note 9. An information processing device according to any one of Notes 2 to 6, wherein the abnormal glitch determination unit is further configured to calculate the time distance between adjacent abnormal glitch candidates, and when the number of abnormal glitch candidates corresponding to each time distance interval is less than a predetermined number, determine that the abnormal glitch candidates have not undergone local aggregation.

[0136] Note 10. The information processing device according to any one of Notes 3 to 6, wherein the multiple windows have the same width.

[0137] Note 11. The information processing device according to any one of Notes 3 to 6, wherein the width of each of the plurality of windows is associated with a duty cycle of the test signal.

[0138] Supplement 12. The information processing apparatus according to any one of Supplements 1 to 6, wherein the target device is a gear box, and the test signal is a vibration signal.

[0139] Supplement 13. The information processing device according to Supplement 11, wherein the gear box is a planetary gear box.

[0140] Note 14. An information processing method comprising:

[0141] Acquiring a test signal of the target device when the target device is in a stable operating state; and

[0142] determining whether there are abnormal glitches in the test signal that occur repeatedly and are not locally concentrated,

[0143] The abnormal burr indicates that an abnormality occurs in the target device.

[0144] Note 15. The information processing method according to Note 14, wherein determining whether there is an abnormal burr in the test signal comprises:

[0145] selecting abnormal glitch candidates from the test signal; and

[0146] Determine whether the abnormal burr candidates meet the following three conditions: the abnormal burr candidates appear repeatedly; the abnormal burr candidates are not locally aggregated; and the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold,

[0147] Wherein, when it is determined that the abnormal glitch candidate satisfies the above three conditions, it is determined that the test signal has an abnormal glitch.

[0148] Note 16. The information processing method according to Note 15, wherein:

[0149] Selecting abnormal glitch candidates from the test signal includes:

[0150] Dividing the test signal into a plurality of continuous and non-overlapping windows; and

[0151] The following operations are performed for each window: determining a local amplitude boundary of the window, wherein the local amplitude boundary refers to a boundary of the amplitude of the test signal in the window; determining a local candidate threshold for abnormal glitch candidates in the window based on the amplitude of the signal in the window whose amplitude is greater than the local amplitude boundary of the window; and selecting the signal in the window whose amplitude is greater than the local candidate threshold of the window as the abnormal glitch candidate;

[0152] Wherein, for each abnormal burr candidate, the difference between the amplitude of the abnormal burr candidate and the corresponding local candidate threshold is calculated as the burr value of the abnormal burr candidate; and

[0153] Wherein, determining that the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold includes: determining that the burr values ​​of a third predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a predetermined burr threshold.

[0154] Note 17. The information processing method according to Note 16, wherein determining the local candidate threshold comprises: for each window, calculating the average of the amplitudes of the signals in the window whose amplitudes are greater than the local amplitude boundary of the window as the local candidate threshold of the window.

[0155] Note 18. The information processing method according to Note 15, wherein:

[0156] Selecting abnormal glitch candidates from the test signal includes:

[0157] Dividing the test signal into a plurality of continuous and non-overlapping windows;

[0158] For each window, determining a local amplitude boundary of the window, wherein the local amplitude boundary refers to a boundary of the amplitude of the test signal in the window;

[0159] determining a global amplitude boundary based on the local amplitude boundaries of the plurality of windows;

[0160] determining a global candidate threshold for abnormal glitch candidates based on the amplitude of the signal having an amplitude greater than the global amplitude boundary; and

[0161] Selecting a signal with an amplitude greater than the global candidate threshold as an abnormal glitch candidate;

[0162] Wherein, for each abnormal burr candidate, the difference between the amplitude of the abnormal burr candidate and the global candidate threshold is calculated as the burr value of the abnormal burr candidate; and

[0163] Wherein, determining that the magnitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold includes: determining that the burr values ​​of a fourth predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a predetermined burr threshold.

[0164] Note 19. The information processing method according to Note 18, wherein:

[0165] Determining the global amplitude boundary includes calculating an average of the local amplitude boundaries of the plurality of windows as the global amplitude boundary; and

[0166] Determining the global candidate threshold includes calculating an average of the amplitudes of signals whose amplitudes are greater than the global amplitude boundary as the global candidate threshold.

[0167] Note 20. A computer-readable storage medium storing program instructions, which is used to execute the information processing method according to any one of Notes 14 to 19 when the program instructions are executed by a computer.

Claims

1. An information processing device, comprising: A signal acquisition unit, configured to acquire a test signal of the target device when the target device is in a stable operation state; as well as an abnormal burr determination unit, configured to determine whether there are abnormal burrs that appear repeatedly and are not locally aggregated in the test signal, The abnormal burr indicates that the target device is abnormal. The abnormal burr determination unit is configured to determine whether there is an abnormal burr in the following manner: selecting abnormal glitch candidates from the test signal; and Determine whether the abnormal burr candidates meet the following three conditions: the abnormal burr candidates appear repeatedly; the abnormal burr candidates are not locally aggregated; and the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold, Wherein, when it is determined that the abnormal glitch candidate satisfies the above three conditions, the abnormal glitch determination unit determines that an abnormal glitch exists in the test signal, The step of selecting abnormal burr candidates from the test signal includes: Dividing the test signal into a plurality of continuous and non-overlapping windows; and The following operations are performed for each window: determining a local amplitude boundary of the window, wherein the local amplitude boundary refers to a boundary of the amplitude of the test signal in the window; determining a local candidate threshold for abnormal glitch candidates in the window based on the amplitude of the signal in the window whose amplitude is greater than the local amplitude boundary of the window; and selecting the signal in the window whose amplitude is greater than the local candidate threshold of the window as the abnormal glitch candidate; The abnormal burr determination unit is further configured to calculate, for each abnormal burr candidate, a difference between the amplitude of the abnormal burr candidate and the corresponding local candidate threshold as the burr value of the abnormal burr candidate; and Wherein, determining that the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold includes: determining that the burr values ​​of a third predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a predetermined burr threshold.

2. The information processing device according to claim 1, wherein: Determining the local candidate threshold comprises: for each window, calculating the average of the amplitudes of the signals in the window whose amplitudes are greater than the local amplitude boundary of the window as the local candidate threshold of the window.

3. An information processing device, comprising: A signal acquisition unit, configured to acquire a test signal of the target device when the target device is in a stable operation state; as well as an abnormal burr determination unit, configured to determine whether there are abnormal burrs that appear repeatedly and are not locally aggregated in the test signal, The abnormal burr indicates that the target device is abnormal. The abnormal burr determination unit is configured to determine whether there is an abnormal burr in the following manner: selecting abnormal glitch candidates from the test signal; and determining whether the abnormal glitch candidate satisfies the following three conditions: the abnormal glitch candidate appears repeatedly; the abnormal glitch candidate does not locally aggregate; and the amplitudes of a first predetermined proportion of the abnormal glitch candidates among the abnormal glitch candidates are greater than a first predetermined threshold, wherein, in the case of determining that the abnormal glitch candidate satisfies the above three conditions, the abnormal glitch determination unit determines that an abnormal glitch exists in the test signal, The step of selecting abnormal burr candidates from the test signal includes: Dividing the test signal into a plurality of continuous and non-overlapping windows; For each window, determining a local amplitude boundary of the window, wherein the local amplitude boundary refers to a boundary of the amplitude of the test signal in the window; determining a global amplitude boundary based on the local amplitude boundaries of the plurality of windows; determining a global candidate threshold for abnormal glitch candidates based on the amplitude of the signal having an amplitude greater than the global amplitude boundary; and Selecting a signal with an amplitude greater than the global candidate threshold as an abnormal glitch candidate; The abnormal burr determination unit is further configured to calculate, for each abnormal burr candidate, a difference between the amplitude of the abnormal burr candidate and the global candidate threshold as the burr value of the abnormal burr candidate; and Wherein, determining that the magnitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold includes: determining that the burr values ​​of a fourth predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a predetermined burr threshold.

4. The information processing device according to claim 3, wherein: Determining the global amplitude boundary includes calculating an average of the local amplitude boundaries of the plurality of windows as the global amplitude boundary; as well as Determining the global candidate threshold includes calculating an average of the amplitudes of signals whose amplitudes are greater than the global amplitude boundary as the global candidate threshold.

5. The information processing device according to any one of claims 1 to 4, wherein: The abnormal glitch determination unit is further configured to calculate time distances between adjacent abnormal glitch candidates, and determine that the abnormal glitch candidates appear repeatedly when a second predetermined proportion of the obtained time distances is less than a predetermined distance threshold.

6. The information processing device according to any one of claims 1 to 4, wherein: The abnormal burr determination unit is further configured to calculate the time distance between adjacent abnormal burr candidates, and determine that the abnormal burr candidates are not locally aggregated when the number of abnormal burr candidates corresponding to each time distance interval is less than a predetermined number.

7. An information processing method, comprising: Acquiring a test signal of the target device when the target device is in a stable operating state; as well as determining whether there are abnormal glitches in the test signal that occur repeatedly and are not locally concentrated, The abnormal burr indicates that the target device is abnormal. Wherein, determining whether there is an abnormal burr in the test signal includes: selecting abnormal glitch candidates from the test signal; and Determine whether the abnormal burr candidate satisfies the following three conditions: the abnormal burr candidate appears repeatedly; the abnormal burr candidate does not locally aggregate; and the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold, wherein, if it is determined that the abnormal burr candidate satisfies the above three conditions, it is determined that abnormal burrs exist in the test signal, The step of selecting abnormal burr candidates from the test signal includes: Dividing the test signal into a plurality of continuous and non-overlapping windows; and The following operations are performed for each window: determining a local amplitude boundary of the window, wherein the local amplitude boundary refers to a boundary of the amplitude of the test signal in the window; determining a local candidate threshold for abnormal glitch candidates in the window based on the amplitude of the signal in the window whose amplitude is greater than the local amplitude boundary of the window; and selecting the signal in the window whose amplitude is greater than the local candidate threshold of the window as the abnormal glitch candidate; The method further comprises: for each abnormal burr candidate, calculating the difference between the amplitude of the abnormal burr candidate and the corresponding local candidate threshold as the burr value of the abnormal burr candidate; and Wherein, determining that the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold includes: determining that the burr values ​​of a third predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a predetermined burr threshold.

8. An information processing method, comprising: Acquiring a test signal of the target device when the target device is in a stable operating state; as well as determining whether there are abnormal glitches in the test signal that occur repeatedly and are not locally concentrated, The abnormal burr indicates that the target device is abnormal. Wherein, determining whether there is an abnormal burr in the test signal includes: selecting abnormal glitch candidates from the test signal; and Determine whether the abnormal burr candidate satisfies the following three conditions: the abnormal burr candidate appears repeatedly; the abnormal burr candidate does not locally aggregate; and the amplitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold, wherein, if it is determined that the abnormal burr candidate satisfies the above three conditions, it is determined that abnormal burrs exist in the test signal, The step of selecting abnormal burr candidates from the test signal includes: Dividing the test signal into a plurality of continuous and non-overlapping windows; For each window, determining a local amplitude boundary of the window, wherein the local amplitude boundary refers to a boundary of the amplitude of the test signal in the window; determining a global amplitude boundary based on the local amplitude boundaries of the plurality of windows; determining a global candidate threshold for abnormal glitch candidates based on the amplitude of the signal having an amplitude greater than the global amplitude boundary; and Selecting a signal with an amplitude greater than the global candidate threshold as an abnormal glitch candidate; The method further comprises: for each abnormal burr candidate, calculating the difference between the amplitude of the abnormal burr candidate and the global candidate threshold as the burr value of the abnormal burr candidate; and Wherein, determining that the magnitudes of a first predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a first predetermined threshold includes: determining that the burr values ​​of a fourth predetermined proportion of the abnormal burr candidates among the abnormal burr candidates are greater than a predetermined burr threshold.

9. A computer-readable storage medium storing program instructions, which is used to execute the information processing method according to claim 7 or 8 when the program instructions are executed by a computer.

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