Alerting method

Abstract

The application provides an alarm method, comprising: filtering a vibration signal to obtain a first noise signal; judging whether the maximum amplitude of the first noise signal is greater than a preset amplitude threshold, if not, starting to execute step a again, if yes, recording the first noise signal and obtaining a vibration signal near the first noise signal as a second noise signal; dividing the second noise signal into multiple frames of noise signals, performing Fourier transform on each frame of the multiple frames of noise signals to obtain multiple energy spectra, and performing frequency segment correction on the multiple energy spectra to obtain multiple corrected energy spectra; calculating the proportion of the energy of multiple frequency components in each corrected energy spectrum to the entire spectrum to obtain multiple probability values of the multiple frequency components, and calculating the corresponding spectral entropy of each frame through the multiple probability values; obtaining a suspected spectral entropy curve, calculating the average Euclidean distance between the suspected spectral entropy curve and multiple standard spectral entropy curves, and alarming if the maximum value of the average Euclidean distance is less than a preset distance threshold.

Description

Technical Field

[0001] This invention relates primarily to a loose component monitoring system, and more particularly to an alarm method. Background Technology

[0002] Nuclear power plant systems are highly complex structures with critical safety requirements, and ensuring the safe operation of nuclear power plants is one of the primary research issues in the field of nuclear engineering. Loose components are frequently found in the coolant loop system of nuclear power plants. These originate from loosening or detachment of parts during operation, or from parts, tools, and other objects left behind in the system during construction, refueling, or maintenance. Numerous components within the nuclear power plant's loop system can pose potential malfunctions, some of which can become loose or detached. If this occurs, it can lead to serious malfunctions and accidents in the nuclear power plant's operation, potentially causing reactor shutdown. Therefore, monitoring loose components in nuclear power plants is of paramount importance. The Loose Parts Monitoring System (LPMS) is one of the fundamental diagnostic tools for fault detection in nuclear power plants. It is primarily used to monitor loose components in the coolant loop system. Its purpose is to detect and locate potentially loose or detached components in the loop, issue timely alarms, and provide information for possible subsequent operations, thereby ensuring the safety and reliability of nuclear power plant operation.

[0003] The most important function of a Loose Part Monitoring System (LPMS) is to quickly and accurately detect the fall of loose parts and issue an alarm. The alarm algorithm should be designed to minimize missed alarms while also reducing the probability of false alarms. There are generally two reasons for missed and false alarms. One is that the energy of the impact signal is too low and is drowned out by background noise. Extensive experiments show that the spectrum of loose part impact signals is mainly distributed between 1kHz and 25kHz. Therefore, bandpass filtering (1kHz to 25kHz) of the acquired impact signal can effectively eliminate most of the background noise. However, the frequency of background noise in nuclear power plants generally does not exceed 6kHz, so there is a significant overlap between the spectrum of background noise and the impact signal, especially between 3 and 5kHz. Currently used spectrum filtering methods are insufficient to separate background noise and impact signals. False alarms occur when the noise amplitude exceeds a preset value, or the effective loose part monitoring signal is drowned out by the noise, resulting in a missed alarm. The second reason is interference signals generated by non-dropped parts, such as mechanical and electrical noise, large-amplitude electrical transient signals, thermal shock vibration signals, and friction vibration signals. These interference signals have the characteristics of large amplitude and short duration, similar to the impact signals of loose parts. In particular, the acceleration time history curves of thermal shock vibration signals and friction vibration signals are very similar to those of loose part impact signals. Therefore, these interference signals often trigger false alarms. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an alarm method to reduce the number of missed alarms and false alarms in the monitoring system for loose components in nuclear power plants.

[0005] To address the aforementioned technical problems, this invention provides an alarm method suitable for monitoring the loosening of internal components of equipment. The equipment is equipped with multiple accelerometers to acquire vibration signals from corresponding locations in real time. The alarm method includes the following steps: a. Filtering the vibration signal to obtain a first noise signal; b. Determining whether the maximum amplitude of the first noise signal is greater than a preset amplitude threshold. If not, restarting step a; if yes, recording the first noise signal and acquiring vibration signals near the first noise signal as a second noise signal; c. Dividing the second noise signal into multiple frames, performing a Fourier transform on each frame to obtain multiple energy spectra, and performing frequency band correction on the multiple energy spectra to obtain multiple corrected energy spectra; d. Calculating the proportion of energy of multiple frequency components in each corrected energy spectrum to the entire spectrum, obtaining multiple probability values ​​for multiple frequency components, and calculating the spectral entropy corresponding to each frame using these probability values; e. Normalizing the multiple spectral entropies to obtain a suspected spectral entropy curve, calculating the average Euclidean distance between the suspected spectral entropy curve and multiple standard spectral entropy curves, and triggering an alarm if the maximum value of the average Euclidean distance is less than a preset distance threshold.

[0006] In one embodiment of the present invention, the formula for obtaining multiple corrected energy spectra by frequency band correction of multiple energy spectra is as follows: Among them, Y′ m (f i Y represents the corrected energy spectrum. m (f i (This represents the uncorrected energy spectrum.) For Y m (f i The average value of ).

[0007] In one embodiment of the present invention, the alarm method further includes optimizing the suspected spectral entropy curve and the multiple standard spectral entropy curves using a dynamic time warping method before calculating the average Euclidean distance between the suspected spectral entropy curve and the multiple standard spectral entropy curves.

[0008] In one embodiment of the present invention, obtaining the vibration signal near the first noise signal as the second noise signal includes the following steps: recording the moment when the maximum amplitude of the first noise signal is greater than a preset amplitude threshold as the center moment; and extracting the signal data within a first time period before the center moment and within a second time period after the center moment as the second noise signal.

[0009] In one embodiment of the present invention, the duration of the second time period is greater than or equal to 50ms.

[0010] In one embodiment of the present invention, the alarm method further includes: selecting different positions on the inner wall of the equipment to strike the loose component under different operating conditions of the equipment, and obtaining multiple standard signals; and obtaining a preset amplitude threshold, a standard spectral entropy curve and a preset distance threshold through the multiple standard signals.

[0011] In one embodiment of the present invention, obtaining a preset amplitude threshold from standard signals includes the following steps: filtering multiple standard signals to obtain multiple first analog signals, and extracting the maximum amplitude of the multiple first analog signals. Multiple background noise signals were recorded by the device at different power levels using an accelerometer. These signals were then filtered to obtain multiple second analog signals. The maximum amplitude of each second analog signal was then extracted. And a preset amplitude threshold is obtained by using the maximum amplitude of multiple first analog signals and the maximum amplitude of multiple second analog signals.

[0012] In one embodiment of the present invention, obtaining a standard spectral entropy curve from a standard signal includes the following steps: f. Filtering the standard signal to obtain a first standard signal; g. Determining whether the maximum amplitude of the first standard signal is greater than a preset amplitude threshold. If not, restarting step a. If yes, recording the first standard signal and obtaining the vibration signal near the first standard signal as a second standard signal; h. Dividing the second standard signal into multiple frames of standard signals, performing Fourier transform on each frame of the multiple frames of standard signals to obtain multiple energy spectra, and performing frequency band correction on the multiple energy spectra to obtain multiple corrected energy spectra; i. Calculating the proportion of the energy of multiple frequency components in each corrected energy spectrum to the entire spectrum, obtaining multiple probability values ​​of multiple frequency components, and calculating the spectral entropy corresponding to each frame through multiple probability values; j. Normalizing the multiple spectral entropies to obtain a standard spectral entropy curve.

[0013] In one embodiment of the present invention, obtaining a preset distance threshold using a standard signal includes: calculating the average Euclidean distance d between the spectral entropy curves of any two frames of standard signals. ij If there exists 0 < α ≤ 0.20 such that P{d ij >d α If (n)}=α holds true, then d is determined. α (n) is the preset amplitude threshold; where d α (n) satisfies

[0014] In one embodiment of the present invention, the formula for calculating the average Euclidean distance is: Where, x′ i1 , x′ i2 ,…,x′ inThese are multiple spectral entropy curves optimized using the dynamic time warping method.

[0015] Compared with existing technologies, the alarm method provided by this invention first determines whether the amplitude of the vibration signal exceeds the amplitude threshold, thereby achieving real-time and efficient capture of abnormal signals and avoiding missed alarms. Then, a weighted method is used to obtain a corrected energy spectrum for the 6kHz-15kHz frequency band, which can eliminate interference from information outside this frequency band and enhance the ability of the spectral entropy curve to distinguish vibration signals from background noise signals. Furthermore, the signal processing method based on signal spectral entropy can detect collision signals with low signal-to-noise ratios and eliminate interference from pulse signals such as electrical interference. The method for determining the preset distance threshold considers the influence of circuit environment, type of impacting object, impact speed, and operating conditions on the spectral entropy curve, which helps improve the accuracy of identifying loose component impact signals through the signal spectral entropy curve. Euclidean distance can quantitatively compare the similarity of two time series. Combined with a classification threshold at a certain confidence level determined based on the average Euclidean distance probability density function, the spectral entropy curve corresponding to the loose component impact signal can be accurately identified, improving the accuracy of alarm judgment. Attached Figure Description

[0016] The accompanying drawings are included to provide a further understanding of this application; they are incorporated into and constitute a part of this application. The drawings illustrate embodiments of this application and, together with this specification, serve to explain the principles of the invention. In the drawings:

[0017] Figure 1 This is a flowchart of one alarm method according to this application.

[0018] Figure 2 This is a waveform diagram of a vibration signal according to this application.

[0019] Figure 3 This application is as follows Figure 2 The uncorrected energy spectrum entropy curve of the vibration signal is shown.

[0020] Figure 4-6 This application is as follows Figure 2 The image shows the spectral entropy curves of the corrected energy spectrum of the vibration signal under different correction parameters. Detailed Implementation

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0022] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0023] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0024] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0025] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0026] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. In addition, although the terminology used in this application is selected from commonly known and used terms, some terms mentioned in this application's specification may have been chosen by the applicant according to his or her judgment, and their detailed meanings are explained in the relevant sections of this description. Moreover, this application should be understood not only through the actual terms used, but also through the meaning implied by each term.

[0027] It should be understood that when a component is referred to as "on another component," "connected to another component," "coupled to another component," or "in contact with another component," it can be directly on, connected to, coupled to, or in contact with that other component, or there may be an inserting component. In contrast, when a component is referred to as "directly on another component," "directly connected to," "directly coupled to," or "directly in contact with" another component, there is no inserting component.

[0028] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0029] Figure 1 This is a flowchart of one alarm method according to this application. (Reference) Figure 1As shown, the present invention provides an alarm method suitable for monitoring the loosening of internal components of equipment. Multiple acceleration sensors are installed inside the equipment to acquire vibration signals at corresponding locations in real time. The alarm method 10 includes the following steps:

[0030] S11: Filter the vibration signal to obtain the first noise signal.

[0031] S12: Determine whether the maximum amplitude of the first noise signal is greater than the preset amplitude threshold. If not, restart step S11. If yes, proceed to step S13.

[0032] S13: Record the first noise signal and obtain the vibration signal near the first noise signal as the second noise signal.

[0033] S14: Divide the second noise signal into multiple frames of noise signals, perform Fourier transform on each frame of the multiple frames of noise signals to obtain multiple energy spectra, and perform frequency band correction on the multiple energy spectra to obtain multiple corrected energy spectra.

[0034] S15: Calculate the proportion of the energy of multiple frequency components in each corrected energy spectrum to the entire spectrum, obtain multiple probability values ​​of multiple frequency components, and calculate the spectral entropy corresponding to each frame through multiple probability values.

[0035] S16: Normalize multiple spectral entropies to obtain suspected spectral entropy curves, and calculate the average Euclidean distance between the suspected spectral entropy curves and multiple standard spectral entropy curves.

[0036] S17: Determine whether the maximum value of the average Euclidean distance is less than the preset distance threshold. If not, restart step S11. If yes, issue an alarm.

[0037] Specifically, before executing alarm method 10, multiple accelerometers need to be installed inside the equipment to acquire vibration signals at the corresponding locations in real time. In step S11, the signals acquired in real time by the multiple accelerometers are filtered to eliminate noise in some frequency bands, achieving initial screening of background noise and avoiding false alarms. Specifically, according to numerous experiments, when a part inside the equipment becomes loose, the spectrum of the impact signal (hereinafter referred to as the standard signal) is mainly distributed between 1kHz and 25kHz. Therefore, in specific implementation, bandpass filtering of the acquired impact signal at 1kHz to 25kHz can effectively eliminate most of the background noise. However, the background noise will not exceed 6kHz, and the spectra of the background noise and the standard signal overlap significantly, especially between 3 and 5kHz. It is difficult to separate the background noise and the standard signal using general spectrum filtering methods, so subsequent algorithms are needed to further extract the signal.

[0038] In step S12, it is necessary to determine whether the maximum amplitude of the first noise signal after filtering is greater than the preset amplitude threshold. If the maximum amplitude does not exceed the preset amplitude threshold, it means that no standard signal has appeared at this time, that is, no part has become loose inside the device, and no alarm is needed. Then, return to step S11 to continue monitoring. If the maximum amplitude of the first noise signal is greater than the preset amplitude threshold, it is determined that the first noise signal is abnormal and a part may have become loose. Record the first noise signal for further processing.

[0039] In one embodiment of the present invention, step S13, obtaining the vibration signal near the first noise signal as the second noise signal, includes: recording the moment when the maximum amplitude of the first noise signal is greater than a preset amplitude threshold as the center moment, and extracting signal data within a first time period before the center moment and a second time period after the center moment as the second noise signal. It is understood that when a component inside the device becomes loose and causes an impact, the waveform of a standard signal typically appears suddenly at the moment of impact and remains for a period of time. Therefore, the portion of the extracted time range before the center moment is less than the portion after the center moment. In other words, the first time period before the center moment is essentially background noise, and the waveform of the impact signal is generally located within the second time period after the center moment. Therefore, in some common embodiments of the present invention, the duration of the first time period is usually shorter than the duration of the second time period. This signal extraction method can completely preserve the waveform curve of the impact signal and ensure that the overall scale and recording time of all extracted signal segments are consistent, facilitating subsequent calculation of Euclidean distance based on the dynamic time warping method. It also facilitates the calculation of the arrival time difference of signals recorded from different channels when performing a loose component positioning algorithm. To further ensure the complete preservation of the waveform curve of the impact signal, in a preferred embodiment, the duration of the second time period is greater than or equal to 50ms.

[0040] In a preferred embodiment, 100ms of signal data is extracted around the center time of the amplitude exceeding a preset threshold. The extraction time range is from 30ms before the center time to 70ms after the center time, that is, the duration of the first time period is 30ms, the duration of the second time period is 70ms, and the duration of the second noise signal is 100ms.

[0041] In step S14, the captured second noise signal is divided into frames. A Fourier transform is performed on each frame to obtain multiple energy spectra, and these energy spectra are then frequency-band corrected to obtain multiple corrected energy spectra. In a specific embodiment, the total number of frames is M, and a Fourier transform is performed on each frame to obtain an energy spectrum Y. m (f i), where m = 1, 2, 3…M and i = 1, 2, 3…N, and N is the total number of frequency band components. That is, in a preferred embodiment, the second noise signal is divided into M frames, and the M frame signals are subjected to Fourier transform to obtain M energy spectra, and then the M energy spectra are corrected into N frequency bands to obtain multiple corrected energy spectra.

[0042] Specifically, in one embodiment, the formula for obtaining multiple corrected energy spectra by frequency band correction of multiple energy spectra is as follows: Among them, Y′ m (f i Y represents the corrected energy spectrum. m (f i (This represents the uncorrected energy spectrum.) For Y m (f i The average value of ) is used. In simple terms, this process uses a weighting method to obtain the corrected energy spectrum Y′ for the energy spectrum outside the 6kHz-15kHz frequency band. m (f i Within the specified frequency band, the original energy spectrum remains unchanged. For the energy spectrum outside the 6kHz-15kHz band, a larger number is assigned. In specific implementation, the energy spectrum outside the 6kHz-15kHz band is assigned a value of [value missing]. in For energy spectrum Y m (f i The average value of ).

[0043] Figure 2 This is a waveform diagram of a vibration signal according to this application. Figure 3 This application is as follows Figure 2 The uncorrected energy spectrum entropy curve of the vibration signal is shown. Figure 4-6 This application is as follows Figure 2 The image shows the spectral entropy curves of the corrected energy spectrum of the vibration signal under different correction parameters. (Refer to reference...) Figure 2-6 As shown, it is clear that the spectral entropy curve calculated using the corrected energy spectrum can effectively eliminate interference from information outside this frequency band and significantly enhance the ability of the spectral entropy curve to distinguish between the second noise signal (i.e., the suspected impact signal) and the background noise signal.

[0044] In step S15, for the corrected energy spectrum of each frame, the proportion of the energy of each frequency component in the entire spectrum is calculated to obtain the probability value of each frequency component. Then substitute the probability value into the formula for calculating spectral entropy. Obtain the spectral entropy for each frame. Generally, the spectrum of an impact signal is much wider than that of background noise. The spectrum of background noise is generally no higher than 6kHz, while the impact signal has a very wide bandwidth at the beginning, which gradually narrows as the signal attenuates. Therefore, even when the signal-to-noise ratio (SNR) is very low, the SNR remains high at high frequencies. Based on this principle, the presence of an impact signal can be determined by calculating the spectral entropy of the high-frequency band. Specifically, the spectral entropy of an impact signal has the following characteristics: the spectral entropy of the vibration signal differs from that of background noise. As long as the spectrum remains unchanged, the spectral entropy will not change. Generally, a flat spectrum has a relatively low spectral entropy, while the spectral entropy decreases when an impact signal occurs.

[0045] In step S16, the spectral entropy curve obtained in step S15 is normalized to obtain a suspected spectral entropy curve. Specifically, this can be achieved using the formula... Where H normal Let H be the spectral entropy (not normalized), min(H) be the minimum value of the spectral entropy curve, and max(H) be the maximum value of the spectral entropy curve.

[0046] In one embodiment of the present invention, the alarm method further includes optimizing the suspected spectral entropy curve and the multiple standard spectral entropy curves using a dynamic time warping method before calculating the average Euclidean distance between the suspected spectral entropy curve and the multiple standard spectral entropy curves. Then, the average Euclidean distance between the suspected spectral entropy curve and the multiple standard spectral entropy curves is calculated.

[0047] Specifically, the Dynamic Time Warping (VTW) algorithm is an algorithm for calculating the similarity between two sets of time series data. Its core idea is to establish a non-linear correspondence between the time axes of the two sets of data. Since the lengths of sequence segments representing the same meaning may vary each time a similar event occurs, the two sequences will not perfectly match. In this situation, directly using Euclidean distance to calculate similarity is prone to incorrect identification results. The VTW algorithm stretches or compresses the time series, using dynamic programming to make them as similar as possible, and then calculates the similarity between the two time series.

[0048] Because the impact signals from loose parts received by accelerometers at different locations have a time difference, the suspected spectral entropy curves and standard spectral entropy curves obtained by processing vibration signals intercepted from different channels through steps S11-S16 have similar overall shapes, but they are not aligned on the time axis. The dynamic time warping algorithm calculates the similarity between two time series by extending, shortening, and shifting the time series.

[0049] The following is a detailed explanation using suspected spectral entropy Q and standard spectral entropy C as examples:

[0050] The lengths of the suspected spectral entropy Q and the standard spectral entropy C are n and m, respectively, where Q = [q1, q2, ..., q i ,…,q n ], C = [c1, c2, ..., c j ,…,c m To align the two sequences, an n×m matrix grid needs to be constructed, and the Euclidean distance d(q) is used. i ,c j )=(q i -c j ) 2 q i and c j The distance between two points, according to the dynamic time warping method, can be reduced to finding a path through several grid points in the grid. These grid points are the points where the two sequences are aligned for computation. The optimal path is the one that minimizes the accumulated distance along the path. This path can be obtained using a dynamic programming algorithm. The optimal aligned sequence [x′] after stretching, compressing, and translation using the dynamic time warping method described above is... i1 , x′ i2 ,…,x′ in ] and [x′ j1 , x′ j2 ,…,x′ jn The average Euclidean distance is:

[0051]

[0052] In one embodiment of the present invention, the alarm method further includes: selecting different locations on the inner wall of the equipment under different operating conditions of the equipment to obtain multiple standard signals; and obtaining a preset amplitude threshold, a preset probability ratio threshold, and a preset correlation coefficient threshold through the multiple standard signals. Specifically, the multiple standard signals are obtained through simulation experiments: a loose component monitoring system is deployed on a simulated loop test bench or an actual reactor loop, and several steel balls, bolts, pins, etc., within a certain mass range are selected to represent loose components in the primary loop. Under different operating conditions of the loop test bench, the components are struck at different locations on the main equipment container wall of the primary loop at a speed of not less than 0.68J impact energy through free fall, pendulum, etc., under different operating conditions. The impact signals of the loose components are recorded as standard signals, and the preset data such as the preset amplitude threshold, the standard spectral entropy curve, and the preset distance threshold are further processed based on the standard signals.

[0053] In one embodiment of the present invention, obtaining a preset amplitude threshold from standard signals includes the following steps: filtering multiple standard signals to obtain multiple first analog signals, and extracting the maximum amplitude of the multiple first analog signals. Multiple background noise signals were recorded by the device at different power levels using an accelerometer. These signals were then filtered to obtain multiple second analog signals. The maximum amplitude of each second analog signal was then extracted. And a preset amplitude threshold is obtained by using the maximum amplitude of multiple first analog signals and the maximum amplitude of multiple second analog signals.

[0054] The standard spectral entropy curve takes into account the influence of circuit environment, type of impacting object, impact speed, and operating conditions on the spectral entropy curve, which helps to improve the accuracy of identifying the impact signal of loose parts through the signal spectral entropy curve. In one embodiment of the present invention, the standard spectral entropy curve is obtained through a standard signal, that is, the standard signal is used as the vibration signal to perform actions such as... Figure 1 Steps S11-S16 shown specifically include the following steps:

[0055] (1) Filter the standard signal to obtain the first standard signal;

[0056] (2) Determine whether the maximum amplitude of the first standard signal is greater than the preset amplitude threshold. If not, restart step (1). If yes, record the first standard signal and obtain the vibration signal near the first standard signal as the second standard signal.

[0057] (3) Divide the second standard signal into multiple standard signals, perform Fourier transform on each frame of the multiple standard signals to obtain multiple energy spectra, and perform frequency band correction on the multiple energy spectra to obtain multiple corrected energy spectra.

[0058] (4) Calculate the proportion of the energy of multiple frequency components in each corrected energy spectrum to the whole spectrum, obtain multiple probability values ​​of multiple frequency components, and calculate the spectral entropy corresponding to each frame through multiple probability values.

[0059] (5) Normalize multiple spectral entropies to obtain the standard spectral entropy curve.

[0060] Furthermore, in this embodiment, obtaining the preset distance threshold through the standard signal is also based on the standard spectral entropy curve of the standard signal. Specifically, a total of m sets of signals [x] are obtained according to the above steps (1)-(5). i1 ,x i2 ,…,x in Let i = 1, 2, ..., m. Based on the dynamic time warping method, calculate the spectral entropy curves of any two sets of signals, i.e., the average Euclidean distance d between the i-th and j-th data sets. ij The preferred formula is: Where, x′ i1 , x′ i2 ,…,x′ in These are multiple spectral entropy curves optimized using the dynamic time warping method.

[0061] Furthermore, the calculation of the preset distance threshold requires a given α, 0 < α ≤ 0.20, if P{d ij >d α If (n)}=α holds true, then d is called d α (n) represents the upper bound of the threshold when using the longest distance method and the shortest distance method for classification at the α level, which is the preset distance threshold. The above d α The value of (n) can be determined based on the average Euclidean distance d. ij Determined by the distribution function:

[0062] Finally, in step S17, it is determined whether the maximum value of the average Euclidean distance is less than the preset distance threshold. If not, it is considered that no collision has occurred, and step S11 is restarted. If so, an alarm is triggered.

[0063] The method for determining the preset distance threshold also considers the influence of circuit environment, type of impacting object, impact speed, and operating conditions on the spectral entropy curve, which helps to improve the accuracy of identifying the impact signal of loose parts through the signal spectral entropy curve. In addition, based on the average Euclidean distance probability density distribution function, it can scientifically and reasonably give the Euclidean distance threshold at different confidence levels, thereby improving the accuracy of identifying the impact signal of loose parts.

[0064] Compared with existing technologies, the alarm method provided by this invention first determines whether the amplitude of the vibration signal exceeds the amplitude threshold, thereby achieving real-time and efficient capture of abnormal signals and avoiding missed alarms. Then, a weighted method is used to obtain a corrected energy spectrum for the 6kHz-15kHz frequency band, which can eliminate interference from information outside this frequency band and enhance the ability of the spectral entropy curve to distinguish vibration signals from background noise signals. Furthermore, the signal processing method based on signal spectral entropy can detect collision signals with low signal-to-noise ratios and eliminate interference from pulse signals such as electrical interference. The method for determining the preset distance threshold considers the influence of circuit environment, type of impacting object, impact speed, and operating conditions on the spectral entropy curve, which helps improve the accuracy of identifying loose component impact signals through the signal spectral entropy curve. Euclidean distance can quantitatively compare the similarity of two time series. Combined with a classification threshold at a certain confidence level determined based on the average Euclidean distance probability density function, the spectral entropy curve corresponding to the loose component impact signal can be accurately identified, improving the accuracy of alarm judgment.

[0065] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0066] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0067] Some aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The aforementioned hardware or software may be referred to as a "data block," "module," "engine," "unit," "component," or "system." The processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, aspects of this application may manifest as computer products residing in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical discs (e.g., compressed CDs, digital multifunction DVDs, etc.), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0068] A computer-readable medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or similar media, or any combination of the above media.

[0069] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0070] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0071] Although this application has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate this application, and various equivalent changes or substitutions can be made without departing from the spirit of this application. Therefore, any changes or modifications to the above embodiments within the essential spirit of this application will fall within the scope of the claims of this application.

Claims

1. An alarm method adapted to monitor the loosening of internal components of a device, characterized in that, The device is internally installed with multiple acceleration sensors, which acquire vibration signals of corresponding positions in real time, and the alarm method comprises the following steps: a. filtering the vibration signals to obtain a first noise signal; b. determining whether the maximum amplitude of the first noise signal is greater than a preset amplitude threshold, if not, re-executing step a, if yes, recording the first noise signal and acquiring vibration signals near the first noise signal as a second noise signal; c. dividing the second noise signal into multiple frames of noise signals, performing Fourier transform on each frame of the multiple frames of noise signals to obtain multiple energy spectra, and performing frequency segment correction on the multiple energy spectra to obtain multiple corrected energy spectra, wherein the formula for performing frequency segment correction on the multiple energy spectra to obtain multiple corrected energy spectra is: wherein, is the modified energy spectrum, is the unmodified energy spectrum, is the average of the average of d. calculating the proportion of the energy of multiple frequency components in each corrected energy spectrum in the entire frequency spectrum to obtain multiple probability values of the multiple frequency components, and calculating the spectral entropy corresponding to each frame through the multiple probability values; e. normalizing multiple spectral entropies to obtain a suspected spectral entropy curve, calculating the average Euclidean distance between the suspected spectral entropy curve and multiple standard spectral entropy curves, and alarming if the maximum value of the average Euclidean distance is less than a preset distance threshold.

2. The alerting method of claim 1, wherein, Further comprising, before calculating the average Euclidean distance between the suspected spectral entropy curve and the multiple standard spectral entropy curves, optimizing the suspected spectral entropy curve and the multiple standard spectral entropy curves using dynamic time warping method.

3. The alerting method of claim 2, wherein, Acquiring vibration signals near the first noise signal as a second noise signal comprises the following steps: recording the time when the maximum amplitude of the first noise signal is greater than the preset amplitude threshold as a center time; and intercepting signal data within a first time period before the center time and a second time period after the center time as the second noise signal.

4. The alerting method of claim 3, wherein, The duration of the second time period is greater than or equal to 50 ms.

5. The alerting method of claim 3, wherein, Further comprising: selecting different positions of the inner wall of the device struck by loose components under different operating conditions of the device to obtain multiple standard signals; and obtaining the preset amplitude threshold, the standard spectral entropy curve and the preset distance threshold through the multiple standard signals.

6. The alerting method of claim 5, wherein, Obtaining the preset amplitude threshold through the standard signals comprises the following steps: Filtering the plurality of standard signals to obtain a plurality of first analog signals, and extracting maximum amplitudes of the plurality of first analog signals ; A plurality of background noise signals of the device at different power levels are recorded by the acceleration sensor, the plurality of background noise signals are filtered to obtain a plurality of second analog signals, and the maximum amplitude of the plurality of second analog signals is extracted (i=1,2,3…n); and obtaining the preset amplitude threshold through the maximum amplitudes of the multiple first analog signals and the maximum amplitudes of the multiple second analog signals.

7. The alerting method of claim 5, wherein, Obtaining the standard spectral entropy curve through the standard signals comprises the following steps: f. filtering the standard signals to obtain a first standard signal; g. determining whether the maximum amplitude of the first standard signal is greater than a preset amplitude threshold, if not, re-executing step a, if yes, recording the first standard signal and acquiring vibration signals near the first standard signal as a second standard signal; h. dividing the second standard signal into multiple frames of standard signals, performing Fourier transform on each frame of the multiple frames of standard signals to obtain multiple energy spectra, and performing frequency segment correction on the multiple energy spectra to obtain multiple corrected energy spectra; i. calculating a proportion of energy of each of the plurality of frequency components in the modified energy spectrum to the entire frequency spectrum, to obtain a plurality of probability values of the plurality of frequency components, and calculating a spectrum entropy corresponding to each frame through the plurality of probability values; j. performing normalization processing on the plurality of spectrum entropies to obtain a standard spectrum entropy curve.

8. The alerting method of claim 7, wherein, The preset distance threshold is obtained through the standard signal, and the preset distance threshold includes: calculating the average Euclidean distance between the spectral entropy curves of any two frames of standard signals if there is such that P{ }= is established, it is determined that the preset amplitude threshold value is the amplitude threshold value; wherein, satisfy .

9. The alerting method of claim 8, wherein, A formula for calculating the average Euclidean distance is: wherein, , , …, are a plurality of spectral entropy curves optimized by dynamic time warping.

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