Signal processing method and equipment state monitoring system based on acoustic emission sensor

Abstract

The invention discloses a signal processing method and an equipment state monitoring system based on an acoustic emission sensor. The method comprises the steps that a plurality of continuous target sampling signals in a target time period are acquired, and the plurality of target sampling signals are acoustic wave signals acquired by an acoustic emission sensor arranged on target equipment; extracting an average bispectrum signal from the plurality of target sampling signals; matching the average bispectrum signal with a predetermined fault bispectrum signal; under the condition that the average bispectrum signal is matched with the fault bispectrum signal, it is determined that the multiple target sampling signals are abnormal, and the multiple target sampling signal anomalies represent state anomalies of the target equipment. The technical problem that the state monitoring result of the pressure-bearing equipment is not accurate is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, and in particular to a signal processing method and an equipment status monitoring system based on an acoustic emission sensor. Background Art

[0002] With the continuous development of the national chemical industry, the application of pressure equipment in the field of chemical industry has been greatly accelerated. During its long-term service, pressure equipment will be affected by internal and external environment, load and other factors, which will cause corrosion, fatigue, creep and other damages, and in severe cases, structural failure and damage. The medium in pressure equipment is generally flammable, explosive, toxic, corrosive and environmentally harmful. Once failure and damage occur, resulting in medium leakage, it is easy to cause serious property losses, casualties and environmental pollution.

[0003] Compared with developed countries in the chemical industry, my country has a certain gap in the safe operation of pressure equipment, among which there are many human damages. Therefore, the study of a suitable real-time monitoring system to monitor the operating status of pressure equipment in real time can reduce accident losses, even prevent accidents, and effectively improve the stability of pressure equipment operation, which is of great significance to ensure the safety of life and property.

[0004] The monitoring method for pressure-bearing equipment in the prior art cannot guarantee the stability of the installation of acoustic emission sensors, and the failure conditions of the pressure-bearing equipment targeted are relatively limited. The pressure-bearing equipment may have multiple forms of damage and destruction, which results in inaccurate status monitoring results of the pressure-bearing equipment.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present invention provide a signal processing method and an equipment status monitoring system based on an acoustic emission sensor, so as to at least solve the technical problem of inaccurate results of status monitoring of pressure-bearing equipment.

[0007] According to one aspect of an embodiment of the present invention, a signal processing method is provided, comprising: acquiring a plurality of target sampling signals continuously within a target time period, wherein the plurality of target sampling signals are acoustic wave signals collected by an acoustic emission sensor disposed on a target device; extracting an average bispectrum signal from the plurality of target sampling signals; matching the average bispectrum signal with a predetermined fault bispectrum signal; and determining that the plurality of target sampling signals are abnormal when the average bispectrum signal matches the fault bispectrum signal, wherein the abnormality of the plurality of target sampling signals indicates that the state of the target device is abnormal.

[0008] Optionally, extracting an average bispectrum signal from multiple target sampling signals includes: combining the multiple target sampling signals into target signal segments; determining a k-order cumulant spectrum of the target signal segment; and determining an average bispectrum signal of the target signal segment based on the k-order cumulant spectrum.

[0009] Optionally, according to the k-order cumulant spectrum, an average bispectral signal of the target signal segment is determined, including: setting k equal to 3 in the k-order cumulant spectrum to obtain a bispectral signal; obtaining the sampling frequency of multiple target sampling signals, determining the length of the Fourier transform used for the k-order cumulant spectrum and the upper limit of the acquisition frequency of the multiple target sampling signals; determining the average bispectral signal according to the bispectral signal, the sampling frequency, the length of the Fourier transform and the upper limit of the acquisition frequency of the multiple target sampling signals.

[0010] Optionally, before matching the average bi-spectrum signal with the fault bi-spectrum signal, it also includes: acquiring multiple continuous experimental sampling signals within the experimental time period, wherein the multiple experimental sampling signals are sound wave signals collected by experimental acoustic emission sensors arranged on the experimental equipment, and the multiple experimental equipment are abnormal status equipment whose models match the target equipment, and the models of the experimental acoustic emission sensors match the models of the acoustic emission sensors on the target equipment; extracting the average fault bi-spectrum signal from the multiple experimental sampling signals.

[0011] According to another aspect of an embodiment of the present invention, a signal processing device is also provided, including: an acquisition module, used to acquire multiple target sampling signals continuously within a target time period, wherein the multiple target sampling signals are sound wave signals collected by an acoustic emission sensor arranged on a target device; an extraction module, used to extract an average bi-spectrum signal from the multiple target sampling signals; a matching module, used to match the average bi-spectrum signal with a predetermined fault bi-spectrum signal; and a determination module, used to determine that the multiple target sampling signals are abnormal when the average bi-spectrum signal matches the fault bi-spectrum signal, wherein the abnormality of the multiple target sampling signals represents an abnormal state of the target device.

[0012] According to another aspect of an embodiment of the present invention, there is also provided an equipment status monitoring system based on an acoustic emission sensor, comprising: a target equipment, an acoustic emission sensor, a signal amplifier, a signal acquisition card, a metal mounting device and a signal processing device, wherein the acoustic emission sensor is installed in the metal mounting device; the metal mounting device is arranged on the surface of the target equipment; the acoustic emission sensor is communicatively connected to the signal amplifier, the signal amplifier is communicatively connected to the signal acquisition card, the signal acquisition card is communicatively connected to the signal processing device, and the signal processing device executes any one of the above-mentioned signal processing methods.

[0013] Optionally, the metal mounting device comprises: a metal mounting ring, a hinge, a throat clamp and an acoustic sensor mounting groove, wherein the metal mounting ring comprises a first semicircular ring and a second semicircular ring, one end of the first semicircular ring is connected to one end of the second semicircular ring through a hinge, and the other end of the first semicircular ring is fixed to the other end of the second semicircular ring through a throat clamp; the acoustic sensor mounting grooves are evenly arranged on the metal mounting ring.

[0014] Optionally, the acoustic sensor mounting slot includes: an acoustic sensor mounting cover, a rubber gasket and a screw, wherein the acoustic sensor mounting cover is provided with a through hole, the through hole is used to extend the wiring of the acoustic emission sensor; the acoustic sensor mounting cover is fixed to the metal mounting ring by screws; and a rubber gasket is provided between the acoustic sensor mounting cover and the acoustic emission sensor.

[0015] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided. The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above signal processing methods.

[0016] According to yet another aspect of an embodiment of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program. When the program is run, any one of the above signal processing methods is executed.

[0017] In an embodiment of the present invention, an average bispectral signal of an extracted acoustic wave signal is used as a signal feature to determine whether a pressure-bearing equipment has a fault. The method comprises obtaining a plurality of target sampling signals continuously within a target time period, wherein the plurality of target sampling signals are acoustic wave signals collected by an acoustic emission sensor arranged on the target equipment; extracting an average bispectral signal from the plurality of target sampling signals; matching the average bispectral signal with a predetermined fault bispectral signal; and determining that the plurality of target sampling signals are abnormal when the average bispectral signal matches the fault bispectral signal, wherein the plurality of target sampling signals are abnormal indicative of an abnormal state of the target equipment, thereby achieving the purpose of extracting detailed features from the sampling signals to monitor the state of the equipment, thereby realizing the technical effect of improving the accuracy of the results of the state monitoring of the pressure-bearing equipment, and further solving the technical problem of inaccurate results of the state monitoring of the pressure-bearing equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 A hardware structure block diagram of a computer terminal for implementing a signal processing method is shown;

[0020] Figure 2 is a flow chart of a signal processing method provided according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of a signal collected by an acoustic emission sensor of a target device under abnormal conditions according to an optional embodiment of the present invention;

[0022] Figure 4 is a schematic diagram of an average bispectrum of a signal collected by an acoustic emission sensor of a target device under abnormal conditions provided by an optional embodiment of the present invention;

[0023] Figure 5 is a structural block diagram of a signal processing device provided according to an embodiment of the present invention;

[0024] Figure 6 is a schematic diagram of an equipment status monitoring system based on an acoustic emission sensor provided according to an embodiment of the present invention;

[0025] Figure 7 is a schematic diagram of a metal mounting ring provided according to an optional embodiment of the present invention;

[0026] Figure 8 It is a schematic diagram of the installation of an acoustic emission sensor provided according to an optional embodiment of the present invention.

[0027] Among them, the above-mentioned drawings include the following figure marks: 1. target device; 2. acoustic emission sensor; 3. signal amplifier; 4. signal acquisition card; 5. metal mounting device; 6. signal processing device; 7. metal mounting ring; 8. hinge; 9. throat hoop; 10. acoustic sensor mounting groove; 11. acoustic sensor mounting cover; 12. rubber gasket; 13. screw; 14. through hole. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] According to an embodiment of the present invention, a method embodiment of signal processing is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a signal processing method. Figure 1 As shown, the computer terminal 100 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0032] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 100. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0033] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the signal processing method in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the signal processing method of the above-mentioned application is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 100 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 100 .

[0035] Figure 2 is a flow chart of a signal processing method according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0036] Step S202: acquiring a plurality of target sampling signals continuously within a target time period, wherein the plurality of target sampling signals are sound wave signals collected by an acoustic emission sensor disposed on a target device.

[0037] In this step, an acoustic emission sensor is provided on the target device. The acoustic emission sensor can be provided on the target device by the metal mounting device provided by the present invention. Before the acoustic emission sensor is provided on the target device, an acoustic emission coupling agent can be applied on the target device so that the acoustic emission sensor can better collect the sound wave signal transmitted through the target device. The acoustic emission sensor can send the collected sound wave signal to the signal processing device of the execution body of the present method. The signal processing device can perform data processing on the sound wave signal, analyze whether the signal is abnormal, and determine that the state of the target device is abnormal when the signal is abnormal. Among them, the target device can be a pressure-bearing device.

[0038] Step S204: extracting an average bispectral signal from the multiple target sampling signals.

[0039] In this step, when data processing is performed on multiple target sampling signals, the average bispectral signal is determined as the signal characteristic quantity, that is, by analyzing the average bispectral signal of the multiple target sampling signals, it is determined whether the multiple target sampling signals are abnormal. The average bispectral signal can extract detailed features from the multiple target sampling signals, so using the average bispectral signal as the signal characteristic quantity can improve the accuracy of signal analysis, thereby improving the accuracy of the results of the condition monitoring of the pressure-bearing equipment.

[0040] Step S206: Match the average bispectrum signal with a predetermined fault bispectrum signal.

[0041] Step S208 , when the average bispectral signal matches the fault bispectral signal, determining that the multiple target sampling signals are abnormal, wherein the multiple target sampling signals are abnormal indicating that the state of the target device is abnormal.

[0042] In this step, if the average bispectrum signal can match the predetermined fault bispectrum signal, it means that the average bispectrum signal is also the fault bispectrum signal, and then multiple target sampling signals are determined to be abnormal, and then the state of the target device is determined to be abnormal.

[0043] Through the above steps, the purpose of extracting detailed features in the sampling signal to monitor the equipment status is achieved, thereby achieving the technical effect of improving the accuracy of the results of the status monitoring of the pressure-bearing equipment, and further solving the technical problem of inaccurate results of the status monitoring of the pressure-bearing equipment.

[0044] As an optional embodiment, extracting an average bispectral signal from multiple target sampling signals includes: combining multiple target sampling signals into target signal segments; determining the k-order cumulative spectrum of the target signal segments; and determining the average bispectral signal of the target signal segments based on the k-order cumulative spectrum.

[0045] As an optional embodiment, the average bispectral signal of the target signal segment is determined according to the k-order cumulant spectrum, including: setting k equal to 3 in the k-order cumulant spectrum to obtain the bispectral signal; obtaining the sampling frequency of multiple target sampling signals, determining the length of the Fourier transform used for the k-order cumulant spectrum and the upper limit of the acquisition frequency of the multiple target sampling signals; determining the average bispectral signal according to the bispectral signal, the sampling frequency, the length of the Fourier transform and the upper limit of the acquisition frequency of the multiple target sampling signals.

[0046] Optionally, every n sampled signals can be combined into a target signal segment, and then the target signal segment is defined as x(n). Then the k-order cumulant c of the target signal segment x(n) is k,x (τ1 ,…τ k-1 ) is defined as: Take x 1 =x(n),x 2 =x(n+τ 1 ),…,x k =x(n+τ k ) after the variable set {x(n),x(n+τ 1 ),…,x(n+τ k )}, the k-order cumulant, the k-order cumulant c k,x (τ 1 ,…τ k-1 ) is calculated as:

[0047]

[0048] In the formula, τ 1 ,τ 2 ,…,τ k-1 is the time lag, j is the imaginary number sign, f 1 ,f 2 ,…,f k-1 are respectively related to the variable set {x(n),x(n+τ 1 ),…,x(n+τ k )} corresponding frequency.

[0049] Moment Generating Function Among them, p(x 1 ,…,x k-1 ) is k-1 variables x 1 ,..,x k-1 The joint probability density function of .

[0050] Cumulant generating function Ψ(f 1 ,f 2 ,…,f k-1 )=lnΦ(f 1 ,f 2 ,…,f k-1 ).

[0051] Secondly, the k-th order cumulant spectrum S can be calculated k,x :

[0052]

[0053] Secondly, the k-order cumulant spectrum S k,x The k in the equation is 3, and the third-order cumulant spectrum B can be obtained. x (f 1 ,f 2 ), also called the bispectrum:

[0054]

[0055] Finally, the average bispectral Q of the selected signal can be calculated:

[0056]

[0057] Where N is the length of the Fourier transform used to estimate the signal bispectrum, and f s is the sampling frequency of the signal, and f is the upper limit of the signal acquisition frequency.

[0058] As an optional embodiment, before matching the average bi-spectrum signal with a predetermined fault bi-spectrum signal, it also includes: acquiring multiple continuous experimental sampling signals within an experimental time period, wherein the multiple experimental sampling signals are sound wave signals collected by experimental acoustic emission sensors arranged on experimental equipment, and the multiple experimental equipment are abnormal status equipment whose models match those of target equipment, and the models of the experimental acoustic emission sensors match those of the acoustic emission sensors on the target equipment; extracting the fault bi-spectrum signal from the multiple experimental sampling signals.

[0059] Optionally, before signal processing, an experiment may be conducted in a laboratory using equipment of the same type to determine that when the experimental equipment fails, an average bispectral signal extracted from the acoustic wave signal collected by the acoustic emission sensor is a fault bispectral signal.

[0060] Figure 3 is a schematic diagram of a signal collected by an acoustic emission sensor of a target device under abnormal conditions according to an optional embodiment of the present invention, Figure 4 is a schematic diagram of an average bispectrum of a signal collected by an acoustic emission sensor of a target device under abnormal conditions according to an optional embodiment of the present invention, such as Figure 3 and Figure 4 As shown, it can be clearly seen that when an abnormal situation occurs in the target equipment, the average bispectrum value increases significantly. This phenomenon can be used to determine the occurrence of abnormal conditions in the pipeline. The present invention selects the average bispectrum as a signal feature for signal processing, thereby overcoming the shortcomings of the prior art in monitoring pressure equipment, such as poor real-time performance and large errors, so that the present invention has the advantages of low computational complexity and simple technical implementation.

[0061] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0062] Through the description of the above implementation methods, those skilled in the art can clearly understand that the signal processing method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0063] According to an embodiment of the present invention, a signal processing device for implementing the above signal processing method is also provided. Figure 5 is a structural block diagram of a signal processing device provided according to an embodiment of the present invention, such as Figure 5 As shown, the signal processing device includes: an acquisition module 52, an extraction module 54, a matching module 56 and a determination module 58. The signal processing device is described below.

[0064] The acquisition module 52 is used to acquire multiple continuous target sampling signals within a target time period, wherein the multiple target sampling signals are acoustic wave signals collected by an acoustic emission sensor set on a target device.

[0065] The extraction module 54 is connected to the acquisition module 52 and is used to extract an average bispectral signal from a plurality of target sampling signals.

[0066] The matching module 56 is connected to the extraction module 54 and is used to match the average bispectrum signal with a predetermined fault bispectrum signal.

[0067] The determination module 58 is connected to the matching module 56 and is used to determine that the multiple target sampling signals are abnormal when the average bispectral signal matches the fault bispectral signal, wherein the multiple target sampling signals are abnormal indicating that the state of the target device is abnormal.

[0068] It should be noted that the acquisition module 52, extraction module 54, matching module 56 and determination module 58 correspond to steps S202 to S208 in the embodiment, and the examples and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the embodiment.

[0069] According to an embodiment of the present invention, there is also provided a device status monitoring system based on an acoustic emission sensor. Figure 6 is a schematic diagram of a device status monitoring system based on an acoustic emission sensor provided according to an embodiment of the present invention. Figure 6 As shown, the monitoring system includes: a target device 1, an acoustic emission sensor 2, a signal amplifier 3, a signal acquisition card 4, a metal mounting device 5 and a signal processing device 6, wherein the acoustic emission sensor 2 is installed in the metal mounting device 5; the metal mounting device 5 is arranged on the surface of the target device 1; the acoustic emission sensor 2 is communicatively connected to the signal amplifier 3, the signal amplifier 3 is communicatively connected to the signal acquisition card 4, the signal acquisition card 4 is communicatively connected to the signal processing device 6, and the signal processing device 6 executes any one of the above-mentioned signal processing methods.

[0070] The acoustic emission sensor 2 is arranged on the outer surface of the target device 1, and is used to convert the elastic stress generated by abnormal events occurring during the working process of the monitored target device 1 into an analog electrical signal; the signal collected by the acoustic emission sensor 2 is transmitted to the signal amplifier 3 for signal amplification; the amplified signal is transmitted to the signal acquisition card 4 for digital signal conversion; the digital signal output by the signal acquisition card 4 is transmitted to the signal processing device 6; the signal processing device 6 processes the signal of the signal acquisition card 4, and performs data analysis with the average bispectral value of the signal segment as the feature value. If the average bispectral value of the real-time signal data analyzed and processed by the signal processing device 6 is abnormal, an alarm signal is generated.

[0071] Optionally, the frequency spectrum characteristics of the acoustic emission signal of the acoustic emission sensor 1 are between 400kHz and 500kHz.

[0072] Optionally, the signal amplifier 3 filters out signals less than 10 kHz or greater than 1 MHz, and amplifies the received signal by 10 times before transmitting it to the signal processing device 6 .

[0073] Optionally, the signal processing device 6 uses the alarm signal generated by it to find the metal installation device 5 where the acoustic emission sensor 1 is located when the alarm signal occurs, and then determines the location where the abnormality occurs in the target device 1 according to the location of the metal installation device 5. The alarm signal generated by the signal processing device 6 and the location where the abnormality occurs will be manually sent to personnel in the monitoring area, and then further judged by on-site professionals to avoid losses caused by false alarms.

[0074] Optionally, Figure 7 is a schematic diagram of a metal mounting ring provided according to an optional embodiment of the present invention, such as Figure 7 As shown, the metal mounting device 5 includes: a metal mounting ring 7, a hinge 8, a throat clamp 9 and an acoustic sensor mounting groove 10, wherein the metal mounting ring 7 includes a first semicircular ring and a second semicircular ring, one end of the first semicircular ring is connected to one end of the second semicircular ring through the hinge 8, and the other end of the first semicircular ring is fixed to the other end of the second semicircular ring through the throat clamp 9; the acoustic sensor mounting grooves 10 are evenly arranged on the metal mounting ring.

[0075] Optionally, Figure 8 is a schematic diagram of the installation of an acoustic emission sensor according to an optional embodiment of the present invention, such as Figure 8 As shown, the acoustic sensor mounting slot 10 includes: an acoustic sensor mounting cover 11, a rubber gasket 12 and a screw 13, wherein the acoustic sensor mounting cover 11 is provided with a through hole 14, and the through hole 14 is used to extend the wiring of the acoustic emission sensor 2; the acoustic sensor mounting cover 11 is fixed to the metal mounting ring 7 by the screw 13; and a rubber gasket 12 is provided between the acoustic sensor mounting cover 11 and the acoustic emission sensor 2.

[0076] Optionally, the metal mounting ring 7 can be replaced according to the diameter d of the target device 1. Optionally, the metal mounting ring 7 is made of a material similar to that of the target device 1, and 304 austenitic stainless steel is generally used as the material of the metal mounting ring 7.

[0077] Optional, such as Figure 7 As shown, four acoustic emission sensors 2 are dispersedly installed in a metal mounting ring 7, the inner diameter of the metal mounting ring 7 is D, and the diameter of the target device 1 is d.

[0078] Optionally, the number of metal mounting rings 7 is determined according to the length of the target device 1, and they are equidistantly arranged on the outer surface of the monitored target device 1. The interval between the metal mounting rings 7 does not exceed 2m. Each acoustic emission sensor uses a separate signal amplifier 3, and the amplified signals on all metal mounting rings 7 are aggregated into the signal acquisition card 4.

[0079] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one network device among multiple network devices of a computer network. The computer device includes a memory and a processor.

[0080] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the signal processing method and device in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned signal processing method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0081] The processor can call the information and application program stored in the memory through the transmission device to perform the following steps: obtain multiple target sampling signals continuously within a target time period, wherein the multiple target sampling signals are acoustic wave signals collected by an acoustic emission sensor set on a target device; extract an average bispectral signal from the multiple target sampling signals; match the average bispectral signal with a predetermined fault bispectral signal; when the average bispectral signal matches the fault bispectral signal, determine that the multiple target sampling signals are abnormal, wherein the abnormality of the multiple target sampling signals indicates that the state of the target device is abnormal

[0082] By adopting the embodiment of the present invention, a signal processing scheme is provided. The average bispectral signal of the extracted acoustic wave signal is used as a signal feature to determine whether the pressure-bearing equipment has a fault, by obtaining multiple continuous target sampling signals within a target time period, wherein the multiple target sampling signals are acoustic wave signals collected by an acoustic emission sensor set on the target equipment; extracting the average bispectral signal from the multiple target sampling signals; matching the average bispectral signal with a predetermined fault bispectral signal; determining that the multiple target sampling signals are abnormal when the average bispectral signal matches the fault bispectral signal, wherein the abnormality of the multiple target sampling signals indicates that the state of the target equipment is abnormal, achieving the purpose of extracting the detailed features in the sampling signal to monitor the state of the equipment, thereby achieving the technical effect of improving the accuracy of the results of the state monitoring of the pressure-bearing equipment, and further solving the technical problem of inaccurate results of the state monitoring of the pressure-bearing equipment.

[0083] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0084] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the signal processing method provided by the above embodiment.

[0085] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0086] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing the following steps: acquiring a plurality of target sampling signals continuously within a target time period, wherein the plurality of target sampling signals are acoustic wave signals collected by an acoustic emission sensor disposed on a target device; extracting an average bispectral signal from the plurality of target sampling signals; matching the average bispectral signal with a predetermined fault bispectral signal; and determining that the plurality of target sampling signals are abnormal when the average bispectral signal matches the fault bispectral signal, wherein the abnormality of the plurality of target sampling signals indicates that the state of the target device is abnormal.

[0087] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0088] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0091] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0092] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0093] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A signal processing method, characterized in that: include: Acquire a plurality of target sampling signals continuously within a target time period, wherein the plurality of target sampling signals are acoustic wave signals collected by an acoustic emission sensor disposed on a target device; extracting an average bispectral signal from the plurality of target sampled signals; matching the average bispectral signal with a predetermined fault bispectral signal; In a case where the average bispectrum signal matches the fault bispectrum signal, it is determined that the multiple target sampling signals are abnormal, wherein the abnormality of the multiple target sampling signals indicates that the state of the target device is abnormal.

2. The method according to claim 1, characterized in that: The step of extracting an average bispectral signal from the plurality of target sampling signals comprises: Combining the plurality of target sampled signals into target signal segments; Determining a k-order cumulant spectrum of the target signal segment; The average bispectral signal of the target signal segment is determined according to the k-th order cumulant spectrum.

3. The method according to claim 2, characterized in that The step of determining the average bispectral signal of the target signal segment according to the k-order cumulant spectrum comprises: In the k-order cumulant spectrum, let k be equal to 3 to obtain a bispectral signal; Acquiring the sampling frequencies of the plurality of target sampling signals, determining the length of the Fourier transform used for the k-order cumulant spectrum and the upper limit of the acquisition frequency of the plurality of target sampling signals; The average bispectrum signal is determined according to the bispectrum signal, the sampling frequency, the length of the Fourier transform and the upper limit of the acquisition frequency of the multiple target sampling signals.

4. The method according to claim 1, characterized in that: Before matching the average bispectral signal with a predetermined fault bispectral signal, the method further includes: Acquire multiple continuous experimental sampling signals within an experimental time period, wherein the multiple experimental sampling signals are sound wave signals collected by an experimental acoustic emission sensor set on an experimental device, the multiple experimental devices are devices with abnormal status whose models match the target devices, and the model of the experimental acoustic emission sensor matches the model of the acoustic emission sensor on the target device; A fault bispectrum signal is extracted from the plurality of experimental sampling signals.

5. A signal processing device, characterized in that: include: An acquisition module, used to acquire a plurality of target sampling signals continuously within a target time period, wherein the plurality of target sampling signals are sound wave signals collected by an acoustic emission sensor arranged on a target device; An extraction module, used for extracting an average bispectral signal from the multiple target sampling signals; A matching module, used for matching the average bispectrum signal with a predetermined fault bispectrum signal; The determination module is used to determine that the multiple target sampling signals are abnormal when the average bispectrum signal matches the fault bispectrum signal, wherein the abnormality of the multiple target sampling signals indicates that the state of the target device is abnormal.

6. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the signal processing method according to any one of claims 1 to 4.

7. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is run, the processor executes the signal processing method according to any one of claims 1 to 4.

8. An equipment status monitoring system based on acoustic emission sensors, characterized in that: include: The target device, the acoustic emission sensor, the signal amplifier, the signal acquisition card, the metal mounting device and the signal processing device, wherein the acoustic emission sensor is installed in the metal mounting device; the metal mounting device is arranged on the surface of the target device; the acoustic emission sensor is communicatively connected to the signal amplifier, the signal amplifier is communicatively connected to the signal acquisition card, the signal acquisition card is communicatively connected to the signal processing device, and the signal processing device executes the signal processing method described in any one of claims 1 to 4.

9. The system according to claim 8, characterized in that The metal installation device comprises: a metal installation ring, a hinge, a throat hoop and an acoustic sensor installation groove, wherein: The metal mounting ring includes a first semicircular ring and a second semicircular ring, one end of the first semicircular ring is connected to one end of the second semicircular ring through the hinge, and the other end of the first semicircular ring is fixed to the other end of the second semicircular ring through the throat clamp; the acoustic sensor mounting grooves are evenly arranged on the metal mounting ring.

10. The system according to claim 9, characterized in that The acoustic sensor installation slot includes: an acoustic sensor installation cover, a rubber gasket and screws, wherein: The acoustic sensor mounting cover is provided with a through hole, and the through hole is used to extend the wiring of the acoustic emission sensor; the acoustic sensor mounting cover is fixed to the metal mounting ring by the screw; and the rubber gasket is provided between the acoustic sensor mounting cover and the acoustic emission sensor.

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