Rock soil acoustic emission signal monitoring system and method

Abstract

The invention provides a rock-soil acoustic emission signal monitoring system and method, and belongs to the technical field of rock-soil monitoring, in the scheme, an acoustic emission probe is in contact with rock-soil to monitor fracture sound waves in real time and transmit the fracture sound waves to a signal processing module, and the signal processing module filters acoustic emission signals and then transmits the acoustic emission signals to an upper computer; the upper computer extracts rock-soil fracture acoustic emission signal features and displays the rock-soil fracture acoustic emission signal features; the upper computer circularly receives the data uploaded by the FPGA acquisition card and directly accesses the uploaded data through memory mapping; setting multiple threads for concurrently processing acoustic emission signal data, adopting a lock-free queue for communication among the threads, and triggering the queue by using semaphore; and after the thread queue reaches a triggering condition, storing the acoustic emission signal and displaying a pulse waveform. The method has the beneficial effects that the problems of poor real-time performance and high false alarm rate in the rock-soil fracture acoustic emission monitoring process are solved, and the situation of missed alarm due to hardware communication delay is avoided.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of geotechnical monitoring, and particularly relates to a geotechnical acoustic emission signal monitoring system and method. BACKGROUND

[0002] Geotechnical rupture monitoring is a key technology for preventing geological disasters such as landslides and collapses, and is mainly achieved by capturing acoustic emission signals generated by internal rupture of rock mass. Existing means mainly rely on a distributed acoustic emission sensor array and a data acquisition system based on LabVIEW, and a fixed threshold trigger is combined with a traditional filter (such as IIR / Butterworth) for signal processing. However, such a method has significant defects: at the hardware level, long cable transmission leads to signal delay (typical value > 1 ms) and electromagnetic interference coupling, causing loss of key transient signals; at the software level, a sequential single-thread processing architecture cannot meet the microsecond-level real-time requirement, and the total delay of the monitoring system reaches 1-5 ms, resulting in that the signal has ended when triggering, and the traditional "first acquisition-then processing" mode cannot meet the real-time requirement. Moreover, the conventional filtering algorithm has limited signal-to-noise ratio improvement in a complex noise environment (such as mechanical vibration and power frequency interference), and the false triggering rate is as high as 15%-20%. In addition, the existing system lacks dynamic parameter adjustment capability and cannot adapt to the differences in acoustic wave propagation characteristics of different geotechnical media (such as granite and shale), resulting in a positioning accuracy fluctuation of more than 3 meters. SUMMARY

[0003] Therefore, the application aims to provide a geotechnical acoustic emission signal monitoring system and method to solve the problems of poor real-time performance and high false alarm rate in acoustic emission monitoring of the geotechnical rupture process, and the missing report due to hardware communication delay.

[0004] To achieve the above-mentioned purpose, the technical scheme of the application is as follows:

[0005] A geotechnical acoustic emission signal monitoring method.

[0006] Further, the host computer monitoring method comprises:

[0007] T1, the host computer cyclically receives data uploaded by the FPGA acquisition card and directly accesses the uploaded data through memory mapping;

[0008] T2, a multi-threaded concurrent processing of acoustic emission signal data is set, and a lock-free queue is used for inter-thread communication and a semaphore is used to trigger the queue;

[0009] T3, when the thread queue reaches the triggering condition, the acoustic emission signal is stored and the pulse waveform is displayed.

[0010] Furthermore, the multi-threaded execution order in T2 is as follows: acquisition loop thread, filtering loop thread, noise reduction loop thread, and trigger thread. The acquisition loop thread dynamically filters the uploaded data through the FPGA to transmit only valid signal segments and is given the first priority.

[0011] The filtering loop thread uses IIR bandpass filtering to eliminate out-of-band noise and is the second priority thread.

[0012] The noise reduction loop thread reconstructs the signal through wavelet decomposition to highlight the key features of the acoustic emission signal from the rock and soil, and is the fourth priority.

[0013] The triggering thread determines the validity of the acoustic emission signal of rock and soil fracture based on hardware triggering, energy ratio triggering, and characteristic triggering. After the trigger is determined, it switches to T3, which is the third priority.

[0014] Furthermore, the noise reduction loop thread sets several candidate wavelet bases and calculates the correlation coefficients between the current acoustic emission signal and all candidate wavelet base functions:

[0015] ;

[0016] In the formula, The correlation coefficient between the signal and the wavelet basis function. For signal functions, These are wavelet basis functions;

[0017] The wavelet basis with the largest correlation coefficient is selected to decompose the acoustic emission signal, and the high-frequency subbands in the acoustic emission signal are hard-thresholded to reconstruct the acoustic emission signal and retain the effective frequency band.

[0018] Furthermore, in the triggering thread, the energy ratio triggering condition and the feature triggering condition are calculated in real time. The energy ratio triggering condition factors include short-term energy and long-term energy with a fixed window length.

[0019] ;

[0020] In the formula, Energy ratio For short-term energy, For long-term energy;

[0021] Characteristic triggering factors include signal rise time, kurtosis, and waveform factor;

[0022] Determining whether a thread queue has been triggered includes the following steps:

[0023] S1. Loop through and determine the FPGA trigger signal. If triggered, buffer the acoustic emission signal data packet after filtering and denoising by the filtering loop thread and the denoising loop thread, and then proceed to S2.

[0024] S2. Determine the energy ratio If the preset threshold is reached, proceed to S3; otherwise, if the judgment time is exceeded, return to S1.

[0025] S3. Calculate the rise time, kurtosis, and waveform factor of the acoustic emission signal data packet based on the buffer. If the rise time is lower than the threshold, the kurtosis is higher than the threshold, and the waveform factor is higher than the threshold, then the acoustic emission signal data packet is deemed valid.

[0026] Furthermore, the calculation of the signal rise time, kurtosis, and waveform factor includes:

[0027] ;

[0028] ;

[0029] ;

[0030] In the formula, The point in time when the signal amplitude first reaches 10% of its peak amplitude. The point at which the signal amplitude first reaches 90% of its peak amplitude. The steepness of the acoustic emission signal. Kurtosis coefficient The fourth-order center distance of the signal. The standard deviation of the signal. The peak voltage of the signal. The root mean square of the signal, Waveform factor;

[0031] The short-time energy and long-time energy include:

[0032] ;

[0033] ;

[0034] In the formula, Let n be the nth discrete value of the signal. The window length for short-time energy. Indexed by the current time. The average number of windows for long-term energy. This is a cumulative index.

[0035] Furthermore, the host computer uses LabVIEW as the monitoring platform and the backplane clock of the PXI system as a reference to calibrate the delay values ​​of the acquisition loop thread, filtering loop thread, noise reduction loop thread, and trigger thread to correct the trigger time of the acoustic emission signal event of rock and soil fracture:

[0036] ;

[0037] In the formula, This is the original timestamp of the signal recorded by the system. To filter the processing delay time of the loop thread, This refers to the processing time of the noise reduction loop thread. To determine the computation time of the trigger thread, This represents the corrected time of occurrence of soil and rock fracturing.

[0038] Furthermore, a rock and soil acoustic emission signal monitoring system, based on the aforementioned rock and soil acoustic emission signal monitoring method, includes an acoustic emission probe, a signal processing module, and a host computer. The acoustic emission probe is in real-time contact with the rock and soil to listen to the fracture acoustic waves and transmit them to the signal processing module. The signal processing module filters the acoustic emission signal and transmits it to the host computer. The host computer extracts and displays the characteristics of the rock and soil fracture acoustic emission signal.

[0039] Furthermore, the acoustic emission probe has a built-in pre-trigger buffer. When a threshold trigger is detected, the buffered data is automatically packaged with subsequent signals and uploaded to the signal processing module. The buffered data range is adjusted according to the characteristics of the acoustic emission signals of soil and rock.

[0040] The signal processing module is a high-speed digitizer that uses a pre-amplifier to filter the acoustic emission signal and transmits it to the host computer after synchronizing multiple channels.

[0041] Furthermore, the host computer uses an FPGA acquisition card to process acoustic emission signal data packets. The FPGA acquisition card triggers data upload based on the signal rise time and kurtosis. The signal rise time threshold and kurtosis threshold are adjusted in real time based on historical noise statistics.

[0042] Compared with the prior art, the rock and soil acoustic emission signal monitoring system and method of the present invention have the following beneficial effects:

[0043] (1) The rock and soil acoustic emission signal monitoring system and method of the present invention has a moving standard deviation calculation module embedded in FPGA to generate dynamic thresholds in real time, synchronously detect signal rise time, upload only valid signal segments, reduce the amount of invalid data transmission by hardware-level screening, and reduce latency;

[0044] (2) The rock and soil acoustic emission signal monitoring system and method described in this invention adopts memory mapping to directly access the acquisition card DMA buffer, and combines lock-free queues to realize four-level parallel threads (acquisition → filtering → noise reduction → triggering), and zero-copy data stream transmission to greatly improve throughput and reduce the probability of missed detection;

[0045] (3) The rock and soil acoustic emission signal monitoring system and method described in this invention dynamically selects the optimal wavelet basis for three-layer wavelet packet decomposition and reconstruction of the effective frequency band; triggers decision to fuse amplitude, energy ratio and pulse characteristics, thereby improving the recovery rate of the effective signal under complex noise environment. Attached Figure Description

[0046] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0047] Figure 1 This is a schematic diagram of the monitoring system structure according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram illustrating the principle of the monitoring method described in an embodiment of the present invention;

[0049] Figure 3 This is a LabVIEW code diagram illustrating the monitoring method described in an embodiment of the present invention. Detailed Implementation

[0050] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0051] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0052] A rock and soil acoustic emission signal monitoring system, the hardware components of the monitoring system are as follows: Figure 1 As shown, it includes an acoustic emission probe, a signal processing module, and a host computer. The acoustic emission probe is in real-time contact with the rock and soil to listen to the fracture sound waves and transmit them to the signal processing module. The signal processing module filters the acoustic emission signal and then transmits it to the host computer. The host computer extracts and displays the characteristics of the acoustic emission signal of the rock and soil fracture.

[0053] In one specific embodiment, an acoustic emission probe equipped with a ring buffer is preferred. The acoustic emission probe has a built-in pre-trigger buffer. When a threshold trigger is detected, the buffered data is automatically packaged with subsequent signals and uploaded to the signal processing module. The buffered data range is adjusted according to the characteristics of the acoustic emission signal of the soil and rock. The advantage is that the first 50ms of signal (calculated at a 1MHz sampling rate) is buffered at the sensor end. When a threshold trigger is detected, the buffered data is automatically packaged with subsequent signals and uploaded, solving the problem of mechanical wave propagation delay. The signal processing module is a high-speed digitizer, which uses a pre-amplifier to filter the acoustic emission signal. A PXIe series high-speed digitizer is preferred. Strict synchronization of multiple channels is achieved through a PXIe backplane star trigger bus. After synchronizing multiple channels, the signal is transmitted to the host computer.

[0054] In one specific embodiment, the host computer preferably uses an FPGA acquisition card to process acoustic emission signal data packets. The FPGA acquisition card has embedded dynamic threshold triggering and pulse feature prediction processing logic. Dynamic threshold triggering: The trigger threshold is adjusted in real time based on historical noise statistics (moving window standard deviation σ). Pulse characteristic prediction: For signals exceeding the threshold, the rise time and kurtosis factor are calculated within the FPGA. Specifically, the FPGA acquisition card triggers data upload based on the signal rise time and kurtosis, and the signal rise time threshold and kurtosis threshold are adjusted in real time based on historical noise statistics.

[0055] In another specific embodiment, an FPGA array is embedded in the high-speed digitizer, and dynamic threshold triggering and pulse feature prediction processing logic is solidified. Valid signals are uploaded and cached to the host computer through dynamic threshold and pulse feature triggering.

[0056] A method for monitoring acoustic emission signals in rock and soil, based on the aforementioned acoustic emission signal monitoring system for rock and soil, such as... Figures 2-3 As shown, the host computer monitoring method includes:

[0057] T1: The host computer continuously receives data uploaded by the FPGA acquisition card and directly accesses the uploaded data through memory mapping. Its advantage is that the data transmission latency is low, and it does not consume too many resources, which would slow down the calculation speed.

[0058] T2. Configure multi-threaded concurrent processing of acoustic emission signal data. Inter-thread communication uses a lock-free queue and a semaphore to trigger the queue to avoid polling and waiting.

[0059] T3. When the thread queue reaches the trigger condition, store the acoustic emission signal and display the pulse waveform.

[0060] In one specific embodiment, the multi-threaded execution order in T2 is as follows: acquisition loop thread, filtering loop thread, noise reduction loop thread, and trigger thread. The acquisition loop thread dynamically filters the uploaded data through the FPGA to transmit only valid signal segments, saves the first 512 samples and the last 1024 samples of the trigger point to the buffer, and notifies the host to read the data through an interrupt. This thread has the first priority.

[0061] The filtering loop thread uses IIR bandpass filtering to eliminate out-of-band noise, mainly filtering out low-frequency noise and high-frequency interference. This thread has the second priority.

[0062] The noise reduction loop thread reconstructs the signal through wavelet decomposition to highlight the key features of the acoustic emission signal from the rock and soil, and is the fourth priority.

[0063] The triggering thread determines the validity of the acoustic emission signal of rock and soil fracture based on hardware triggering, energy ratio triggering, and characteristic triggering. After the trigger is determined, it switches to T3, which is the third priority.

[0064] In one specific embodiment, the noise reduction loop thread sets several candidate wavelet bases including db6 / sym8 / coif3, and calculates the correlation coefficients between the current acoustic emission signal and all candidate wavelet basis functions:

[0065] ;

[0066] In the formula, The correlation coefficient between the signal and the wavelet basis function. For signal functions, The wavelet basis functions are selected; the wavelet basis with the highest correlation coefficient is chosen to decompose the acoustic emission signal. High-frequency subbands in the acoustic emission signal are hard-thresholded to remove random noise and retain steep rising edges, thus reconstructing the effective frequency band of the acoustic emission signal. If the CPU utilization of the noise reduction loop thread is too high, the number of wavelet decomposition layers is reduced.

[0067] In one specific embodiment, the energy ratio triggering condition and the feature triggering condition are calculated in real time in the triggering thread. The energy ratio triggering condition factors include short-term energy and long-term energy with a fixed window length.

[0068] ;

[0069] In the formula, Energy ratio For short-term energy, It is a long-term energy source; characteristic triggering factors include signal rise time, kurtosis, and waveform factor.

[0070] Specifically, determining whether a thread queue has been triggered includes the following steps:

[0071] S1. Loop through and check the FPGA trigger signal. If triggered, buffer the acoustic emission signal data packet after filtering and denoising by the filtering loop thread and the denoising loop thread, and then go to S2.

[0072] S2. Determine the energy ratio If the preset threshold is reached, proceed to S3; otherwise, if the judgment time is exceeded, return to S1.

[0073] S3. Calculate the rise time, kurtosis, and waveform factor of the acoustic emission signal data packet based on the buffer. If the rise time is lower than the threshold, the kurtosis is higher than the threshold, and the waveform factor is higher than the threshold, then the acoustic emission signal data packet is deemed valid.

[0074] Specifically, the calculation of signal rise time, kurtosis, and waveform factor includes:

[0075] ;

[0076] ;

[0077] ;

[0078] In the formula, The point in time when the signal amplitude first reaches 10% of its peak amplitude. The point at which the signal amplitude first reaches 90% of its peak amplitude. The steepness of the acoustic emission signal. Kurtosis coefficient The fourth-order center distance of the signal. The standard deviation of the signal. The peak voltage of the signal. The root mean square of the signal, Waveform factor;

[0079] Short-term energy and long-term energy include:

[0080] ;

[0081] ;

[0082] In the formula, Let n be the nth discrete value of the signal. The window length for short-time energy. Indexed by the current time. The average number of windows for long-term energy. This is a cumulative index.

[0083] In one specific embodiment, due to the time difference between the FPGA acquisition card and the concurrent threads of the host computer, the host computer in this application preferentially uses LabVIEW as the monitoring platform, and uses the backplane clock of the PXI system as a reference to calibrate the delay values ​​of the acquisition loop thread, filtering loop thread, noise reduction loop thread, and trigger thread to correct the trigger time of the acoustic emission signal event of rock and soil fracture:

[0084] ;

[0085] In the formula, This is the original timestamp of the signal recorded by the system. To filter the processing delay time of the loop thread, This refers to the processing time of the noise reduction loop thread. To determine the computation time of the trigger thread, This represents the corrected time of occurrence of soil and rock fracturing.

[0086] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the division of units described above is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The aforementioned units may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring acoustic emission signals in rock and soil, characterized in that: The host computer monitoring methods include: T1. The host computer continuously receives data uploaded by the FPGA acquisition card and directly accesses the uploaded data through memory mapping. T2. Set up multi-threaded concurrent processing of acoustic emission signal data, and use lock-free queues for inter-thread communication, using semaphores to trigger the queues; T3. When the thread queue reaches the trigger condition, the acoustic emission signal is stored and the pulse waveform is displayed; The execution order of the multi-threaded T2 is as follows: acquisition loop thread, filtering loop thread, noise reduction loop thread, and trigger thread. The acquisition loop thread dynamically filters the uploaded data through the FPGA to transmit only valid signal segments and is in the first priority. The filtering loop thread uses IIR bandpass filtering to eliminate out-of-band noise and is the second priority thread. The noise reduction loop thread reconstructs the signal through wavelet decomposition to highlight the key features of the acoustic emission signal from the rock and soil, and is the fourth priority. The triggering thread determines the validity of the acoustic emission signal of rock and soil fracture based on hardware triggering, energy ratio triggering, and characteristic triggering. After the trigger is determined, it switches to T3, which is the third priority.

2. The method for monitoring acoustic emission signals in rock and soil according to claim 1, characterized in that: The noise reduction loop thread sets several candidate wavelet bases and calculates the correlation coefficients between the current acoustic emission signal and all candidate wavelet basis functions: ； In the formula, The correlation coefficient between the signal and the wavelet basis function. For signal functions, These are wavelet basis functions; The wavelet basis with the largest correlation coefficient is selected to decompose the acoustic emission signal, and the high-frequency subbands in the acoustic emission signal are hard-thresholded to reconstruct the acoustic emission signal and retain the effective frequency band.

3. The method for monitoring acoustic emission signals in rock and soil according to claim 2, characterized in that: In the triggering thread, the energy ratio triggering condition and the feature triggering condition are calculated in real time. The energy ratio triggering condition factors include short-term energy and long-term energy with a fixed window length. ； In the formula, Energy ratio For short-term energy, For long-term energy; Characteristic triggering factors include signal rise time, kurtosis, and waveform factor; Determining whether a thread queue has been triggered includes the following steps: S1. Loop through and determine the FPGA trigger signal. If triggered, buffer the acoustic emission signal data packet after filtering and denoising by the filtering loop thread and the denoising loop thread, and then proceed to S2. S2. Determine the energy ratio If the preset threshold is reached, proceed to S3; otherwise, if the judgment time is exceeded, return to S1. S3. Calculate the rise time, kurtosis, and waveform factor of the acoustic emission signal data packet based on the buffer. If the rise time is lower than the threshold, the kurtosis is higher than the threshold, and the waveform factor is higher than the threshold, then the acoustic emission signal data packet is deemed valid.

4. The method for monitoring acoustic emission signals in rock and soil according to claim 3, characterized in that: The calculation of signal rise time, kurtosis, and waveform factor includes: ； ； ； In the formula, The point in time when the signal amplitude first reaches 10% of its peak amplitude. The point at which the signal amplitude first reaches 90% of its peak amplitude. The steepness of the acoustic emission signal. Kurtosis coefficient The fourth-order center distance of the signal. The standard deviation of the signal. The peak voltage of the signal. The root mean square of the signal, Waveform factor; The short-time energy and long-time energy include: ； ； In the formula, Let n be the nth discrete value of the signal. The window length for short-time energy. Indexed by the current time. The average number of windows for long-term energy. This is a cumulative index.

5. The method for monitoring acoustic emission signals in rock and soil according to claim 2, characterized in that: The host computer uses LabVIEW as the monitoring platform and the backplane clock of the PXI system as the reference to calibrate the delay values ​​of the acquisition loop thread, filtering loop thread, noise reduction loop thread, and trigger thread to correct the trigger time of the acoustic emission signal event of rock and soil fracture. ； In the formula, This is the original timestamp of the signal recorded by the system. To filter the processing delay time of the loop thread, This refers to the processing time of the noise reduction loop thread. To determine the computation time of the trigger thread, This represents the corrected time of occurrence of soil and rock fracturing.

6. A rock and soil acoustic emission signal monitoring system, based on the rock and soil acoustic emission signal monitoring method according to any one of claims 1-5, characterized in that: It includes an acoustic emission probe, a signal processing module, and a host computer. The acoustic emission probe is in real-time contact with the rock and soil to listen to the fracture sound waves and transmit them to the signal processing module. The signal processing module filters the acoustic emission signal and then transmits it to the host computer. The host computer extracts and displays the characteristics of the acoustic emission signal of the rock and soil fracture.

7. A rock and soil acoustic emission signal monitoring system according to claim 6, characterized in that: The acoustic emission probe has a built-in pre-trigger buffer. When a threshold trigger is detected, the buffered data and subsequent signals are automatically packaged and uploaded to the signal processing module. The buffered data range is adjusted according to the characteristics of the acoustic emission signals of soil and rock. The signal processing module is a high-speed digitizer that uses a pre-amplifier to filter the acoustic emission signal and transmits it to the host computer after synchronizing multiple channels.

8. A rock and soil acoustic emission signal monitoring system according to claim 7, characterized in that: The host computer uses an FPGA acquisition card to process acoustic emission signal data packets. The FPGA acquisition card triggers data upload based on the signal rise time and kurtosis. The signal rise time threshold and kurtosis threshold are adjusted in real time based on historical noise statistics.

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