Alarm method

Abstract

The invention provides an alarm method, which comprises the following steps of: a, filtering a vibration signal to obtain a first noise signal; b, judging whether the maximum amplitude of the first noise signal is greater than a preset amplitude threshold, if so, recording the first noise signal and executing the step c; c, acquiring a vibration signal before the first noise signal as a background noise signal, establishing an AR model of the background noise signal, and calculating a residual error between the first noise signal and a prediction signal of the AR model; d, calculating a sequential probability ratio test parameter of the residual error, judging whether the sequential probability ratio is greater than a preset probability ratio threshold for multiple times, and if so, recording the moment when the sequential probability ratio is greater than the probability ratio threshold for the first time as a suspected moment; e, calculating an amplitude probability density function of the residual error, and calculating a correlation coefficient of the amplitude probability density function of the residual error and a probability density function of the standard signal; and f, obtaining a frequency ratio according to the self-power density spectrum of the first noise signal at the suspected moment.

Description

Technical Field

[0001] The invention mainly relates to a loose component monitoring system, and in particular to an alarm method. Background Art

[0002] The nuclear power plant system is a highly complex structural system with important safety requirements. The safety of nuclear power plant operation is one of the primary issues in the field of nuclear engineering. Loose parts are often found in the loop coolant system of nuclear power plants. The source is the loosening or falling of parts caused by operation, or it may be parts, tools and other objects left in the system during construction, material replacement or maintenance. There are a large number of parts in the loop system of nuclear power plants that can cause hidden faults. Some parts can be loose or detached from the body. Once this happens, serious faults and accidents in the operation of nuclear power plants may occur, and may cause the shutdown of nuclear power plant reactors. Therefore, the monitoring of loose parts in nuclear power plants is particularly important. The loose parts monitoring system (LPMS) is one of the basic diagnostic tools for nuclear power plant fault detection. It is mainly used for monitoring loose parts in the loop coolant system of nuclear power plants. Its purpose is to detect and locate loose parts or falling parts that may exist in the loop, and issue alarms in time to provide information for possible subsequent operations, thereby ensuring the safety and reliability of nuclear power plant operation.

[0003] The most important function of the loose part monitoring system (LPMS) is to quickly and accurately detect the falling of loose parts and issue an alarm. The design of the alarm algorithm should minimize missed alarms and also reduce the probability of false alarms. There are generally two reasons for missed alarms and false alarms. One is that the energy of the impact signal is too low and is submerged in the background noise. According to a large number of experiments, the spectrum of the loose part impact signal is mainly distributed in the range of 1kHz to 25kHz. Therefore, bandpass filtering (1kHz~25kHz) of the collected impact signal can effectively eliminate most of the background noise. The frequency of the background noise in a nuclear power plant generally does not exceed 6kHz, so the spectrum of the background noise and the collision signal has a large overlap, especially between 3 and 5KHz. The currently commonly used spectrum filtering method is difficult to separate the background noise and the collision signal. When the amplitude of the noise exceeds the preset value, a false alarm is generated, or the effective loose part monitoring signal is submerged in the noise, resulting in missed alarms. The second reason is the interference signals generated by non-falling 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 like 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 parts 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 for reducing missed alarms and false alarms in a loose component monitoring system of a nuclear power plant.

[0005] In order to solve the above technical problems, the present invention provides an alarm method, which is suitable for monitoring the looseness of internal components of a device. A plurality of acceleration sensors are installed inside the device to obtain vibration signals of corresponding positions in real time. The alarm method comprises the following steps: a. filtering the vibration signal to obtain a first noise signal; b. judging whether the maximum amplitude of the first noise signal is greater than a preset amplitude threshold, if not, restarting step a, if so, recording the first noise signal and executing step c; c. obtaining the vibration signal before the first noise signal as a background noise signal, establishing an AR model of the background noise signal, and calculating the residual of the first noise signal and the prediction signal of the AR model; d. calculating e. Calculate the amplitude probability density function of the residual, and calculate the correlation coefficient between the amplitude probability density function of the residual and the probability density function of the standard signal; f. Obtain the frequency ratio according to the self-power density spectrum of the first noise signal at the suspected moment; g. Compare the correlation coefficient and the frequency ratio with the preset correlation coefficient threshold and the preset frequency ratio threshold respectively, and alarm if there is a signal that makes the correlation coefficient greater than the preset correlation coefficient threshold and the frequency ratio greater than the preset frequency ratio threshold.

[0006] In one embodiment of the present invention, obtaining a vibration signal before a first noise signal as a background 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 intercepting signal data in a first time period before the center moment and in a second time period after the center moment, and selecting part of the signal data in the first time period away from the center moment as the background noise signal.

[0007] In one embodiment of the present invention, the alarm method also includes: selecting loose parts to knock on different positions of 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 multiple standard signals.

[0008] In one embodiment of the present invention, obtaining a preset amplitude threshold through a standard signal includes the following steps: filtering a plurality of standard signals to obtain a plurality of first analog signals, extracting the maximum amplitude of the plurality of first analog signals Record multiple background noise signals of the device at different power levels through an acceleration sensor, filter the multiple background noise signals to obtain multiple second analog signals, and extract the maximum amplitude of the multiple second analog signals And a preset amplitude threshold is obtained through the maximum amplitudes of the plurality of first analog signals and the maximum amplitudes of the plurality of second analog signals.

[0009] In one embodiment of the present invention, the formula for obtaining the preset amplitude threshold value by using the maximum amplitude of the plurality of first analog signals and the maximum amplitude of the plurality of second analog signals is: Preset amplitude threshold value in, is the maximum amplitude of the first plurality of analog signals The lower limit value at the 95% confidence level, is the maximum amplitude of multiple second analog signals Upper limit value at 95% confidence level.

[0010] In one embodiment of the present invention, obtaining a preset probability ratio threshold through a standard signal includes the following steps: gradually increasing the temperature inside the device, recording the friction vibration signal and the thermal shock vibration signal that appear during the heating process, and filtering the friction vibration signal and the thermal shock vibration signal; and calculating the frequency ratio of multiple standard signals, friction vibration signals, and thermal shock vibration signals; and taking the average value of the minimum value of the frequency ratio of multiple standard signals and the maximum value of the frequency ratio of the friction vibration signal and the thermal shock vibration signal as the preset frequency ratio threshold.

[0011] In one embodiment of the present invention, obtaining a preset correlation coefficient threshold value through a standard signal includes the following steps: calculating multiple amplitude density functions of multiple standard signals, arbitrarily selecting two from the multiple amplitude density functions, and calculating the correlation coefficient of the two selected amplitude density functions, so that a total of correlation coefficients; and A probability distribution diagram is drawn based on the correlation coefficients, and the lower limit value of the correlation coefficient corresponding to the 95% confidence level is found from the probability distribution diagram as the preset correlation coefficient threshold; wherein N is the number of the multiple standard signals.

[0012] In one embodiment of the present invention, the formula for calculating the frequency ratio is: Wherein, APSD is the auto power density spectrum, low_ub and low_lb represent the upper and lower limits of the integrated frequency in the low frequency band, and high_ub and high_lb represent the upper and lower limits of the integrated frequency in the high frequency band.

[0013] In one embodiment of the present invention, the formula for establishing the AR model of the background noise signal is: Among them, w(n) is the AR model, p is the system order, a kis the coefficient of the AR model, and x(n) is the first noise signal.

[0014] In one embodiment of the present invention, the formula for calculating the residual between the first noise signal and the prediction signal of the AR model is: Among them, e(n) is the residual, p is the system order, a k is the coefficient of the AR model, and x(n) is the first noise signal.

[0015] In one embodiment of the present invention, the formula for calculating the correlation coefficient between the probability density function of the amplitude of the residual and the probability density function of the standard signal is: Among them, ρ is the correlation coefficient, x is the amplitude probability density function of the residual, y is the amplitude probability density function of the standard signal, Cov represents the covariance, and σ represents the standard deviation.

[0016] Compared with the prior art, the alarm method provided by the present invention first determines whether the amplitude of the vibration signal exceeds the amplitude threshold, so as to realize real-time and efficient capture of abnormal signals and avoid missed alarms; the AR model can effectively remove the interference of background noise, and the AR model is adaptively updated in real time according to the changes in background noise, which improves the denoising effect, and identifies whether there are abnormal components in the signal by judging whether the sequential probability check parameters of the residual signal after stripping the background noise exceed the probability ratio threshold; finally, the two characteristic parameters of the amplitude probability density function and the frequency ratio are combined to determine whether the signal is a loose component signal. The three judgments effectively reduce the probability of false alarms and missed alarms, and still have good stability when the difference between the amplitude of the noise and the impact signal is not obvious, and there are complex background noises such as pulse noise, friction, and thermal shock signals with similar shapes to the impact signal. In addition, the various threshold parameters used in the three judgments are determined based on the standard signals of representative loose components under simulation experiments, which improves the accuracy of alarm judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are included to provide a further understanding of the present application, and they are included and constitute a part of the present application. The accompanying drawings illustrate embodiments of the present application and together with the present specification serve to explain the principles of the present invention. In the accompanying drawings:

[0018] Figure 1 It is a flow chart of an alarm method of the present application.

[0019] Figure 2 It is a time course graph of the standard signal of this application.

[0020] Figure 3-6 It is the sequential probability ratio obtained by different preset standard deviation σ values ​​of this application. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0022] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0023] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be considered as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so that once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0024] In the description of the present application, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "lateral, vertical, perpendicular, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction, and therefore cannot be understood as limiting the scope of protection of the present application; the directional words "inside and outside" refer to the inside and outside relative to the contours of each component itself.

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

[0026] In addition, it should be noted that the use of words such as "first" and "second" to define components is only for the convenience of distinguishing the corresponding components. If not otherwise stated, the above words have no special meaning and cannot be understood as limiting the scope of protection of this application. In addition, although the terms used in this application are selected from well-known and commonly used terms, some terms mentioned in the specification of this application may be selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant parts of the description of this article. In addition, it is required to understand this application not only by the actual terms used, but also by the meaning implied by each term.

[0027] It should be understood that when a component is referred to as being “on another component,” “connected to another component,” “coupled to another component,” or “contacting another component,” it can be directly on, connected to, coupled to, or contacting the other component, or intervening components may be present. In contrast, when a component is referred to as being “directly on another component,” “directly connected to,” “directly coupled to,” or “directly contacting” another component, there are no intervening components present.

[0028] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously. At the same time, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0029] Figure 1 This is a flowchart of an alarm method of the present application. Figure 1As shown, the present invention provides an alarm method suitable for monitoring the looseness of internal components of a device. A plurality of acceleration sensors are installed inside the device to obtain vibration signals of corresponding positions in real time. The alarm method 10 includes the following steps:

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

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

[0032] S13: Record the first noise signal.

[0033] S14: Acquire a vibration signal before the first noise signal as a background noise signal, establish an AR model of the background noise signal, and calculate a residual between the first noise signal and a prediction signal of the AR model.

[0034] S15: Calculate the sequential probability ratio test parameters of the residuals.

[0035] S16: Determine whether the sequential probability ratio is greater than the preset probability ratio threshold for multiple times. If not, restart step S11. If yes, execute step S17.

[0036] S17: Record the moment when the sequential probability ratio is greater than the probability ratio threshold for the first time as the suspected moment.

[0037] S18: Calculate the amplitude probability density function of the residual, and calculate the correlation coefficient between the amplitude probability density function of the residual and the probability density function of the standard signal.

[0038] S19: Obtain a frequency ratio according to the self-power density spectrum of the first noise signal at the suspected moment.

[0039] S20: Compare the correlation coefficient and the frequency ratio with a preset correlation coefficient threshold and a preset frequency ratio threshold respectively, and generate an alarm if there is a signal that makes the correlation coefficient greater than the preset correlation coefficient threshold and the frequency ratio greater than the preset frequency ratio threshold.

[0040] Specifically, before executing the alarm method 10, it is necessary to install multiple acceleration sensors inside the device to obtain the vibration signals of the corresponding positions in real time. In step S11, the signals obtained by multiple acceleration sensors in real time are filtered to eliminate the noise of some frequency bands, realize the initial screening of background noise, and avoid false alarms. Specifically, according to a large number of experiments, when there are parts loose inside the device, the frequency spectrum of the loose parts impact signal (hereinafter referred to as the standard signal) is mainly distributed in the range of 1kHz to 25kHz. Therefore, in the specific implementation, the collected impact signal is band-pass filtered at 1kHz to 25kHz to effectively eliminate most of the background noise. However, the background noise will not exceed 6kHz, and the spectrum of the background noise and the standard signal has a large overlap, especially between 3 and 5KHz. It is difficult to separate the background noise and the standard signal using a general spectrum filtering method, so a subsequent algorithm is required to further extract the signal.

[0041] 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 appears at this time, that is, no parts are loose inside the equipment, and no alarm is required. 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 parts may be loose. The first noise signal is recorded for further processing.

[0042] It is understandable that the background noise of the device at different times is not consistent. When further processing the first noise signal that may be abnormal, it is also necessary to record its background noise signal. In step S14, the vibration signal before the first noise signal is obtained as the background noise signal, and the AR model of the background noise signal is established and the residual of the first noise signal and the prediction signal of the AR model is calculated. In one embodiment of the present invention, obtaining the vibration signal before the first noise signal as the background noise signal further includes the following steps: recording the moment when the maximum amplitude of the first noise signal is greater than the preset amplitude threshold as the center moment, intercepting the signal data in the first time period before the center moment and the second time period after the center moment, and selecting part of the signal data in the first time period away from the center moment as the background noise signal. It is understandable that for the standard signal, its waveform usually appears suddenly at the impact moment and lasts for a period of time, so the part of the intercepted time range before the center moment is less than the part after the center moment, and the background noise signal should be before the impact moment, that is, in the first time period, otherwise it will be interfered by the standard signal. Preferably, the selection of the background noise signal should be as far away from the center moment as possible. In a preferred embodiment, 100 ms of signal data is captured around the central moment exceeding the preset amplitude threshold, the capture time range is 30 ms before the central moment to 70 ms after the central moment, and then the background noise data of the first 20 ms is selected to establish the AR model.

[0043] In one embodiment of the present invention, the formula for establishing the AR model of the background noise signal is: w(n)= Among them, w(n) is the AR model, p is the system order, a k is the coefficient of the AR model, and x(n) is the first noise signal. Further, the formula for calculating the residual of the first noise signal and the prediction signal of the AR model is: Among them, e(n) is the residual, p is the system order used to establish the AR model of the background noise signal, and a k is the coefficient of the AR model, and x(n) is the first noise signal. If it is noise, then its prediction residual becomes white noise. If a collision signal appears, since it cannot be accurately predicted by noise, its residual appears to be relatively large. The AR model can effectively remove the interference of background noise, thereby improving the signal-to-noise ratio of the standard signal and the background noise signal. In addition, the AR model established by selecting the background noise signal adjacent to the abnormal signal can be adaptively updated in real time according to the change of background noise, thereby improving the denoising effect.

[0044] The formula used in S15 to calculate the sequential probability ratio test parameters for residuals is: By simplification we can get: Next, determine whether the sequential probability ratio obtained in step S15 exceeds the preset probability ratio threshold multiple times. If so, define the first noise signal obtained in step S13 as a suspected signal, and record the moment when the sequential probability ratio is greater than the probability ratio threshold for the first time as the suspected moment. Otherwise, return to step S11 to continue monitoring.

[0045] In step S18, the amplitude probability density function of the residual obtained in step S14 is extracted, and the correlation coefficient between the amplitude probability density function of the residual and the probability density function of the standard signal is calculated. In one embodiment of the present invention, the formula for calculating the correlation coefficient between the amplitude probability density function of the residual and the probability density function of the standard signal is: Among them, ρ is the correlation coefficient, x is the amplitude probability density function of the residual, y is the amplitude probability density function of the standard signal, Cov represents the covariance, and σ represents the standard deviation.

[0046] In step S19, the self-power density spectrum of the suspected signal defined in step S13 is extracted, and the ratio of the self-power spectrum after integration in the low frequency band and the high frequency band is calculated, which is defined as the frequency ratio. In one embodiment of the present invention, the frequency ratio is the ratio of the integral of the self-power density spectrum in the low frequency band to the integral of the self-power density spectrum in the high frequency band. Specifically, it can be calculated using the following formula: Wherein, APSD is the auto-power density spectrum of the signal (in this case, the suspected signal) whose power ratio needs to be calculated, low_ub and low_lb represent the upper and lower limits of the integral frequency of the low frequency band, and high_ub and high_lb represent the upper and lower limits of the integral frequency of the high frequency band.

[0047] In specific implementation, the initial values ​​of low_ub, low_lb, high_ub and high_lb are set to 15kHz, 4kHz, 45kHz and 17kHz respectively. Based on this, it is calculated through a large amount of test data that the frequency ratio range of the standard signal is 2 to 40, the frequency ratio range of the thermal shock vibration signal caused by thermal expansion is 0.01 to 0.02, and the frequency ratio range of the friction vibration signal is 0.03 to 0.08. Therefore, the preset frequency ratio threshold in step S20 is taken as 1.

[0048] In step S20, the correlation coefficient (time domain index) obtained in step S18 and the frequency ratio (frequency domain index) obtained in step S19 are compared with the preset correlation coefficient threshold and the preset frequency ratio threshold respectively. If there is a signal that makes the correlation coefficient greater than the preset correlation coefficient threshold and the frequency ratio greater than the preset frequency ratio threshold, an alarm is issued, otherwise return to step S11 to continue monitoring.

[0049] Further, the method for determining the continuous probability ratio test parameter in step S15 and the preset probability ratio threshold in step S20 includes the following steps:

[0050] (1) The residual obtained in step S14 is normalized.

[0051]

[0052] Sequence x ′ (k) will conform to the (0, 1) distribution, for x ′ (k) Perform the sequential probability ratio test as above.

[0053] (2) The standard deviation of the noise signal is assumed to be σ 0 =1, the standard deviation of the preset vibration signal σ=1.1, slightly larger than the standard deviation of the noise signal σ 0 , the preset false alarm rate is 10%, the preset missed alarm rate is 10%

[0054] (3) Based on the standard deviation, false alarm rate, and missed alarm rate preset in step (2), the upper and lower limits of the sequential probability ratio are calculated as follows:

[0055]

[0056] (4) Given the initial value of the sequential probability ratio as λ 0 =0.

[0057] (5) Based on the sequential probability ratio λ at the previous time point n Calculate the sequential probability ratio λ at the next moment n+1 (n=1,2,3……), If n+1 >b and n+1>100, then λ n+1 =0.

[0058] Figure 2 is a time course graph of the standard signal of this application, Figure 3-6 is the sequential probability ratio obtained for different preset standard deviation values ​​of this application. n+1 >b and n+1>100, then λ n+1 = 0" makes the execution of this logical judgment make the sequential probability ratio λ n When the upper limit is reached, it returns to zero directly. Therefore, when an impact signal appears in the background, the test parameter will jump between 0 and the upper limit and reach the upper limit value many times, such as Figure 2 Therefore, step S16 determines whether the signal has abnormal components by judging whether the continuous probability ratio exceeds the preset probability ratio threshold value for multiple times.

[0059] The key to achieving accurate alarm using the sequential probability ratio method is to set appropriate standard deviations σ for noise and vibration signals respectively. 0and σ, but the mean and variance of the data collected in real time by the loose parts monitoring system are constantly changing and cannot be set in advance. Therefore, converting the sensor signal sequence into a fixed (0, 1) normal distribution can not only simplify the calculation, but more importantly, a unified sequential probability ratio calculation formula can be obtained, which is conducive to the realization of real-time automatic monitoring. Therefore, using the normalized signal sequence as the input sequence of the sequential probability ratio test will have many advantages.

[0060] When the normalized signal sequence is used as a sequential probability ratio test, the standard deviation σ of the noise and vibration signals used in the above formula is 0 and σ have a great influence on the test results. After a large amount of data calculation, the standard deviation of the preset noise signal is σ 0 =1, the standard deviation σ of the preset vibration signal is in the range of 1.1 to 1.9, and σ is slightly larger than the standard deviation σ of the noise signal 0 , setting the parameters in this way will achieve good monitoring results, such as Figure 2-6 shown. Figure 2 Given the original signal time history, Figure 3-6 The sequential probability ratios obtained by different preset values ​​of the standard deviation σ of the vibration signal are given in . It can be seen that when σ is greater than σ 0 When the value is between 1.1 and 1.9, the sequential probability ratio method can better detect abnormal components in the signal. However, this method alone cannot completely determine whether it is a loose component signal, because pulse noise, friction vibration signals, thermal shock vibration signals, etc. will be detected as suspected signals by this method, and subsequent methods are still needed to further analyze whether it is a real loose component alarm.

[0061] In one embodiment of the present invention, the alarm method further includes: selecting loose parts to knock different positions of 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 multiple standard signals. Specifically, obtaining multiple standard signals is achieved through simulation experiments: deploying a loose parts monitoring system on a simulated loop test bench or an actual reactor loop, selecting a number of steel balls, bolts, pins, etc. within a certain mass range to represent loose parts in a loop, and knocking different positions of the container wall of the main equipment of the loop at a speed of not less than 0.68J of impact energy by free fall, simple pendulum, etc. under different operating conditions of the loop test bench, recording the impact signal of the loose parts as a standard signal, and further processing based on the standard signal to obtain preset data such as a preset amplitude threshold, a preset probability ratio threshold and a preset correlation coefficient threshold.

[0062] In one embodiment of the present invention, obtaining a preset amplitude threshold through a standard signal includes the following steps: filtering a plurality of standard signals to obtain a plurality of first analog signals, extracting the maximum amplitude of the plurality of first analog signals Record multiple background noise signals of the device at different power levels through an acceleration sensor, filter the multiple background noise signals to obtain multiple second analog signals, and extract the maximum amplitude of the multiple second analog signals And a preset amplitude threshold is obtained through the maximum amplitudes of the plurality of first analog signals and the maximum amplitudes of the plurality of second analog signals.

[0063] In one embodiment of the present invention, the formula for obtaining the preset amplitude threshold value through the maximum amplitude of the plurality of first analog signals and the maximum amplitude of the plurality of second analog signals is: in, is the maximum amplitude of the first plurality of analog signals The lower limit value at the 95% confidence level, is the maximum amplitude of multiple second analog signals Upper limit value at 95% confidence level.

[0064] In one embodiment of the present invention, obtaining a preset probability ratio threshold value through a standard signal includes the following steps: gradually increasing the temperature inside the device, recording the friction vibration signal and the thermal shock vibration signal that appear during the heating process, and filtering the friction vibration signal and the thermal shock vibration signal; and calculating the frequency ratio of multiple standard signals, friction vibration signals, and thermal shock vibration signals; and taking the minimum value of the frequency ratio of multiple standard signals and the average value of the maximum value of the frequency ratio of the friction vibration signal and the thermal shock vibration signal as the preset frequency ratio threshold value. It can be understood that the formula is also used when calculating the preset probability ratio threshold value of the standard signal: Wherein, APSD is the auto-power density spectrum of the signal (in this case, the standard signal) whose power ratio needs to be calculated, low_ub and low_lb represent the upper and lower limits of the integral frequency of the low frequency band, and high_ub and high_lb represent the upper and lower limits of the integral frequency of the high frequency band.

[0065] In one embodiment of the present invention, obtaining a preset correlation coefficient threshold value through a standard signal includes the following steps: calculating multiple amplitude density functions of multiple standard signals, arbitrarily selecting two from the multiple amplitude density functions, and calculating the correlation coefficient of the two selected amplitude density functions, so that a total of correlation coefficients; and A probability distribution diagram is drawn based on the correlation coefficients, and the lower limit value of the correlation coefficient corresponding to the 95% confidence level is found from the probability distribution diagram as the preset correlation coefficient threshold; wherein N is the number of the multiple standard signals.

[0066] Compared with the prior art, the alarm method provided by the present invention first determines whether the amplitude of the vibration signal exceeds the amplitude threshold, so as to realize real-time and efficient capture of abnormal signals and avoid missed alarms; the AR model can effectively remove the interference of background noise, and the AR model is adaptively updated in real time according to the changes in background noise, which improves the denoising effect, and identifies whether there are abnormal components in the signal by judging whether the sequential probability check parameters of the residual signal after stripping the background noise exceed the probability ratio threshold; finally, the two characteristic parameters of the amplitude probability density function and the frequency ratio are combined to determine whether the signal is a loose component signal. The three judgments effectively reduce the probability of false alarms and missed alarms, and still have good stability when the difference between the amplitude of the noise and the impact signal is not obvious, and there are complex background noises such as pulse noise, friction, and thermal shock signals with similar shapes to the impact signal. In addition, the various threshold parameters used in the three judgments are determined based on the standard signals of representative loose components under simulation experiments, which improves the accuracy of alarm judgment.

[0067] The basic concepts have been described above. Obviously, for those skilled in the art, the above invention disclosure is only used as an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0068] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0069] Some aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". 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. In addition, various aspects of the present application may be expressed as computer products located in one or more computer-readable media, which include computer-readable program codes. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes ...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs ...), smart cards, and flash memory devices (e.g., cards, sticks, key drives ...).

[0070] A computer-readable medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer-readable medium may be propagated via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above mediums.

[0071] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0072] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate 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 change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0073] Although the present application has been described with reference to the current specific embodiments, ordinary technicians in this technical field should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions may be made without departing from the spirit of the present application. Therefore, as long as the changes and modifications to the above embodiments are within the essential spirit of the present application, they will fall within the scope of the claims of the present application.

Claims

1. An alarm method suitable for monitoring the looseness of internal components of equipment, It is characterized in that The device is internally installed with a plurality of acceleration sensors to obtain vibration signals at corresponding positions in real time. The alarm method comprises the following steps: a. filtering the vibration signal to obtain a first noise signal; b. Determine whether the maximum amplitude of the first noise signal is greater than a preset amplitude threshold, if not, restart step a, if yes, record the first noise signal and execute step c; c. obtaining a vibration signal before the first noise signal as a background noise signal, establishing an AR model of the background noise signal, and calculating a residual between the first noise signal and the prediction signal of the AR model; d. Calculate the sequential probability ratio test parameter of the residual, and determine whether the sequential probability ratio is greater than the preset probability ratio threshold for multiple times. If not, restart step a. If yes, record the moment when the sequential probability ratio is greater than the probability ratio threshold for the first time as the suspected moment; e. calculating the amplitude probability density function of the residual, and calculating the correlation coefficient between the amplitude probability density function of the residual and the probability density function of the standard signal; f. obtaining a frequency ratio based on the self-power density spectrum of the first noise signal at the suspected moment; g. Compare the correlation coefficient and the frequency ratio with a preset correlation coefficient threshold and a preset frequency ratio threshold respectively, and generate an alarm if there is a signal that makes the correlation coefficient greater than the preset correlation coefficient threshold and the frequency ratio greater than the preset frequency ratio threshold.

2. The alarm method according to claim 1, It is characterized in that Acquiring the vibration signal before the first noise signal as a background 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 the center time; and The signal data in a first time period before the central moment and in a second time period after the central moment are intercepted, and part of the signal data in the first time period far from the central moment is selected as the background noise signal.

3. The alarm method according to claim 1, It is characterized in that Also includes: Selecting loose parts to knock different positions of the inner wall of the device under different operating conditions of the device to obtain a plurality of standard signals; as well as The preset amplitude threshold, the preset probability ratio threshold and the preset correlation coefficient threshold are obtained through the multiple standard signals.

4. The alarm method according to claim 3, It is characterized in that Obtaining the preset amplitude threshold through the standard signal comprises the following steps: Filter the plurality of standard signals to obtain a plurality of first analog signals, and extract the maximum amplitude of the plurality of first analog signals. Record multiple background noise signals of the device at different power levels by the acceleration sensor, filter the multiple background noise signals to obtain multiple second analog signals, and extract the maximum amplitude of the multiple second analog signals as well as The preset amplitude threshold is obtained according to the maximum amplitude of the plurality of first analog signals and the maximum amplitude of the plurality of second analog signals.

5. The alarm method according to claim 4, It is characterized in that The formula for obtaining the preset amplitude threshold value by the maximum amplitude of the plurality of first analog signals and the maximum amplitude of the plurality of second analog signals is: in, is the maximum amplitude of the plurality of first analog signals The lower limit value at the 95% confidence level, is the maximum amplitude of the plurality of second analog signals Upper limit value at 95% confidence level.

6. The alarm method according to claim 3, It is characterized in that Obtaining the preset probability ratio threshold through the standard signal comprises the following steps: gradually increasing the temperature inside the device, recording the friction vibration signal and the thermal shock vibration signal occurring during the temperature increase process, and filtering the friction vibration signal and the thermal shock vibration signal; as well as calculating a frequency ratio of the plurality of standard signals, the friction vibration signal, and the thermal shock vibration signal; as well as An average value of a minimum value of the frequency ratios of the plurality of standard signals and a maximum value of the frequency ratios of the friction vibration signal and the thermal shock vibration signal is used as a preset frequency ratio threshold.

7. The alarm method according to claim 3, It is characterized in that Obtaining the preset correlation coefficient threshold through the standard signal comprises the following steps: Calculate multiple amplitude density functions of the multiple standard signals, select any two of the multiple amplitude density functions, calculate the correlation coefficient of the two selected amplitude density functions, and obtain a total of correlation coefficients; as well as according to A probability distribution diagram is drawn based on the correlation coefficients, and the lower limit of the correlation coefficient corresponding to the 95% confidence level is found from the probability distribution diagram as the preset correlation coefficient threshold; wherein, N is the number of the plurality of standard signals.

8. The alarm method according to any one of claims 1 to 7, It is characterized in that The formula for calculating the frequency ratio is: Wherein, APSD is the auto power density spectrum, low_ub and low_lb represent the upper and lower limits of the integrated frequency in the low frequency band, and high_ub and high_lb represent the upper and lower limits of the integrated frequency in the high frequency band.

9. The alarm method according to claim 8, It is characterized in that The formula for establishing the AR model of the background noise signal is: Wherein, w(n) is the AR model, p is the system order, a k is the coefficient of the AR model, and x(n) is the first noise signal.

10. The alarm method according to claim 9, It is characterized in that The formula for calculating the residual between the first noise signal and the prediction signal of the AR model is: Where, e(n) is the residual, p is the system order, a k is the coefficient of the AR model, and x(n) is the first noise signal.

11. The alarm method according to claim 8, It is characterized in that The formula for calculating the correlation coefficient between the probability density function of the amplitude of the residual and the probability density function of the standard signal is: Wherein, ρ is the correlation coefficient, x is the amplitude probability density function of the residual, y is the amplitude probability density function of the standard signal, Cov represents the covariance, and σ represents the standard deviation.

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