CN112434561A - Method for automatically judging shock wave signal validity - Google Patents
Method for automatically judging shock wave signal validity Download PDFInfo
- Publication number
- CN112434561A CN112434561A CN202011207801.7A CN202011207801A CN112434561A CN 112434561 A CN112434561 A CN 112434561A CN 202011207801 A CN202011207801 A CN 202011207801A CN 112434561 A CN112434561 A CN 112434561A
- Authority
- CN
- China
- Prior art keywords
- signal
- shock wave
- time
- data
- wave signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
- G01L5/0052—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes measuring forces due to impact
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- General Engineering & Computer Science (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a method for automatically judging the effectiveness of shock wave signals. When the shock wave signal acquisition terminal is provided with a high-speed wireless transmission link, the data processing process is carried out in the upper computer, otherwise, the data processing process is carried out at the acquisition terminal. Establishing a data matching template after a series of data processing in the upper computer, and calculating the similarity of each data by using the template so as to judge the signal effectiveness; whether the shock wave signal is normal or not is judged mainly by checking whether the rising stage of the collected signal is short or not, whether the signal larger than the peak value of the rising stage appears again in the attenuation process or not and whether the attenuation process accords with the physical law from fast to slow at the signal collection terminal. The method for automatically judging the effectiveness of the shock wave signal disclosed by the invention has the advantages of high reaction speed, high real-time efficiency, high accuracy and stronger adaptability.
Description
Technical Field
The invention belongs to the technical field of pattern recognition and signal processing, particularly relates to the technical field of information processing of shock wave signals, and more particularly relates to a method for automatically judging the effectiveness of shock wave signals.
Background
In order to accurately measure and evaluate the shock wave signal, real-time measurement needs to be performed on site by means of an overpressure sensor and the like. Generally, the field condition for acquiring shock wave signal data is complex, and signals acquired by a sensor can be interfered by various unknown factors, so that the accuracy of signal measurement is influenced, for example, the sensor acquires abnormal signals due to direct impact of flying stones and debris on the sensor. In order to obtain an accurate impact signal, validity judgment needs to be performed on the acquired impact signal, and accurate evaluation on the explosion effect can be realized only after an invalid signal is eliminated.
At present, the mainstream method for judging the effectiveness of the shock wave signal mainly adopts manual interpretation, the manual interpretation has the problems of low efficiency and low accuracy, and at present, no method for automatically judging the effectiveness of the shock wave signal on a signal acquisition terminal or an upper computer exists, so that a method for automatically judging the effectiveness of the shock wave signal is urgently needed to replace the manual interpretation.
Disclosure of Invention
In view of the above, the present invention provides a method for automatically determining the validity of a shock wave signal.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for automatically judging the effectiveness of a shock wave signal comprises the steps of firstly utilizing an acquisition terminal to acquire waveform data, then carrying out data processing on an upper computer or the acquisition terminal, and finally judging the effectiveness of the shock wave signal.
Preferably, when the shock wave signal acquisition terminal has a high-speed wireless transmission link, the data processing process is performed in an upper computer, and the data processing steps are as follows:
step 1: the upper computer receives waveform data acquired by a plurality of signal acquisition terminals;
step 2: the upper computer performs noise reduction and filtering on all waveform data;
and step 3: respectively detecting the respective occurrence time of all waveform data, and aligning signals by taking the occurrence time as a center;
and 4, step 4: respectively selecting a 1ms time window for each waveform data, and carrying out normalization processing on all data in the time windows;
and 5: performing wavelet transformation on the normalized data, and converting the normalized data into corresponding time-frequency signals;
step 6: carrying out symbol mapping on the time-frequency domain signals, dividing the time-frequency domain signals into three sections according to the absolute values of the time-frequency domain signals, and then carrying out three-value mapping;
and 7: calculating the similarity of different terminal data subjected to symbol mapping, selecting three data with the highest correlation, and combining the same parts in the data to generate a data matching template;
and 8: firstly, symbol mapping is carried out on data from different acquisition terminals, then the similarity between the data subjected to symbol mapping and a data matching template is calculated, the effectiveness of the shock wave signal is judged according to the similarity, and the shock wave signal can be judged to be an effective signal if the similarity is more than 70%.
Preferably, when the shock wave signal acquisition terminal does not have a high-speed wireless transmission link, the data processing is performed at the signal acquisition terminal, and the data processing steps are as follows:
the method comprises the following steps: carrying out shock wave starting point detection at a system acquisition terminal;
step two: after the shock wave is detected, comparing signal values at different moments, and detecting a shock wave signal peak value v _ max;
step three: calculating the attenuation time of the shock wave signal;
step four: judging signal abnormality;
step five: judging the confidence of the signal;
step six: and transmitting the shock wave signal peak data and the waveform confidence information of the effective signal to an upper computer.
Preferably, the method performed at the signal acquisition terminal during the data processing is performed on a single waveform signal.
Preferably, in the first step, a starting point of the shock wave is detected, and when v (n) -v (m) > TH0 and n-m is 2, the signal starting time T1 is n, where n and m are different sampling times, and v (n) and v (m) are sampling signal values corresponding to n and m, respectively.
Preferably, the method for calculating the attenuation time of the signal shock wave in the third step comprises: calculating the average value of the sampled signal value v (T) from the time T1, wherein when the average value is less than v _ max _ a, the corresponding time is T2, and the time is called as a first stage; calculating the mean value of the sampling signal v (T) from the time T2, and when the mean value is less than v _ max b, the corresponding time is T3, which is called a second stage; calculating the mean value of the sampling signal v (T) from the time T3, and when the mean value is less than v _ max c, the corresponding time is T4, which is called as a third stage; wherein a, b and c are signal attenuation coefficients, and the a, b and c form a decreasing arithmetic series with a value range of 0.2-0.8.
Preferably, the method for determining the abnormal signal in the fourth step is as follows: when T1 is greater than TH1, the shock wave signal is an abnormal signal; or when v _ max2 is more than v _ max 5/4, the shock wave signal is an abnormal signal, wherein TH1 is a signal rising reference time set by a user according to the system characteristics and the characteristics of the sensor; v _ max2 is the maximum value of the signal during the signal decay period.
Preferably, the confidence judgment is performed in the fifth step according to the judgment result of TH2 k2 < (T2-T1) < TH2 k 1; (T2-T1) × k4 < (T3-T2) < (T2-T1) × k 3; (T3-T2) k6 < (T4-T3) < (T3-T2) k5, wherein k1, k2, k3, k4, k5 and k6 are all constraint coefficients, the value range is 0.5-2, and TH2 is a time parameter of a first stage of signal attenuation set by a user according to sensor characteristics.
The invention has the beneficial effects that: when the signal effectiveness is judged by the upper computer, firstly, wavelet transformation is carried out on normalized data, common characteristics of multi-source data are counted according to time-frequency information when the signal is generated, a plurality of data with the highest correlation degree are selected to establish a matching template, then the template is utilized to calculate the similarity of each data, the similarity has strong universal adaptability and is small in correlation with factors such as sensor characteristics and shock wave size;
when the signal acquisition terminal judges the validity of the signal, whether the shock wave signal is abnormal is judged by judging whether a new larger peak signal is generated or not when the shock wave is generated to the time when the shock wave reaches the maximum peak value in the rise period and is attenuated, wherein the time when the shock wave reaches the maximum peak value is related to the strength of the signal and needs to be set according to the field condition; the signal confidence calculation is to divide the signal attenuation process into three stages, determine the proportionality coefficients of the three stages and the maximum peak value according to the response characteristic of the sensor, then obtain the corresponding proportional relation of the attenuation time according to the signal attenuation characteristic, obtain a confidence information by judging whether the actual data conforms to the proportional relation, and perform data processing on the acquisition terminal simply and easily;
the automatic judgment method for the shock wave signal effectiveness can select a corresponding judgment method according to whether the acquisition terminal has a high-speed wireless transmission link, and compared with the traditional manual judgment method, the automatic judgment method for the shock wave signal effectiveness is high in response speed, real-time, efficient, high in accuracy and strong in adaptability.
Drawings
Fig. 1 is a flow chart of data processing in an upper computer by the method for automatically judging the validity of a shock wave signal according to the invention.
Detailed Description
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
The invention is described in detail below with reference to the figures and specific embodiments.
A method for automatically judging the effectiveness of a shock wave signal comprises the steps of firstly utilizing an acquisition terminal to acquire waveform data, then carrying out data processing on an upper computer or the acquisition terminal, and finally judging the effectiveness of the shock wave signal.
Specifically, when the shock wave signal acquisition terminal has a high-speed wireless transmission link, the data processing process in the method for automatically judging the validity of the shock wave signal is performed in the upper computer, and the steps are as follows as shown in fig. 1:
step 1: the upper computer receives waveform data acquired by a plurality of signal acquisition terminals;
step 2: the upper computer performs noise reduction and filtering on all waveform data;
and step 3: respectively detecting the respective occurrence time of all waveform data, and aligning signals by taking the occurrence time as a center;
and 4, step 4: respectively selecting a 1ms time window for each waveform data, and carrying out normalization processing on all data in the time windows;
and 5: performing wavelet transform on the normalized data, wherein a kernel function selects Morse wavelet, a symmetric parameter (gamma) is 3, signals are expressed by s (t), and the acquired signals are converted into 2-dimensional time-frequency domain signals WT (a, b), as shown in formula (1):
WT(a，b)＝CWT(s(t)，Morse，gamma)(1)
step 6: carrying out symbol mapping on the time-frequency domain signals WT (a, b) by a symbol dynamic filtering method, dividing the signals into three sections according to the absolute values of the WT (a, b), and then carrying out ternary mapping on the WT (a, b);
and 7: calculating the similarity of different terminal data subjected to symbol mapping, selecting three data with the highest correlation, and combining the same parts in the data to generate a data matching template; two different data correlation calculation methods are shown in equation (2):
and 8: and carrying out symbol mapping on data from different acquisition terminals, calculating the similarity between the data subjected to symbol mapping and a data matching template, and judging the effectiveness of the shock wave signal according to the similarity, wherein the effective signal can be judged if the similarity is more than 70%.
The template calculation and the matching calculation in steps 7 and 8 are both based on the data after symbol mapping.
When the shock wave signal acquisition terminal does not have the high-speed wireless transmission link, the data processing process is carried out at the signal acquisition terminal, the judgment result of the shock wave signal acquisition terminal obtains peak data and waveform confidence information of the shock wave, and the equivalent evaluation of the shock wave can be finished only by transmitting the peak data and the confidence information back to the upper computer. When the shock wave signal occurs, a rapid rising signal is generated within a short time (less than 10us), and then the shock wave signal slowly attenuates, wherein the attenuation process is about 0.3-1 ms. In the invention, the signal generation stage is called a rise period, and the later 0.3-1 ms is called a decay period. The data processing steps at the signal acquisition terminal are as follows:
the method comprises the following steps: the method comprises the following steps of carrying out shock wave starting point detection at a system acquisition terminal, wherein the specific detection method comprises the following steps: sampling values of v (1), v (2), v (3), … … and v (t); when v (n) -v (m) > TH0 and n-m is 2, obtaining a signal starting time T0 ═ n, wherein n and m are different values of time T, and v (n) and v (m) are respectively signal sampling values corresponding to the time n and the time m;
step two: after the shock wave is detected, comparing signal values at different moments, and detecting a shock wave signal peak value, wherein the detection time is smaller than a threshold value T _ max, the T _ max value is related to information such as the position of a sensor, the intensity of the shock wave and the like, and is not a fixed value, the detected maximum value is shock wave peak value data (v _ max) within the T _ max time, the data can be uploaded to an upper computer, and the time corresponding to the detected signal peak value is T1;
step three: the method for calculating the attenuation time of the shock wave signal comprises the following steps: calculating the average value of the sampled signal value v (T) from the time T1, wherein when the average value is less than v _ max _ a, the corresponding time is T2, and the time is called as a first stage; calculating the mean value of the sampling signal v (T) from the time T2, and when the mean value is less than v _ max b, the corresponding time is T3, which is called a second stage; calculating the mean value of the sampling signal v (T) from the time T3, and when the mean value is less than v _ max c, the corresponding time is T4, which is called as a third stage; the a, the b and the c are signal attenuation coefficients, and form a decreasing arithmetic series with a value range of 0.2-0.8, for example, a is 0.8, b is 0.6 and c is 0.4;
step four: and judging the signal abnormity, wherein the judging method of the signal abnormity comprises the following steps: when T1 is greater than TH1, the shock wave signal is an abnormal signal; or when v _ max2 is more than v _ max 5/4, the shock wave signal is an abnormal signal, wherein TH1 is a signal rising reference time set by a user according to the system characteristics and the characteristics of the sensor; v _ max2 is the maximum value of the signal during the decay period;
step five: judging the signal confidence according to TH2 k2 < (T2-T1) < TH2 k 1; (T2-T1) × k4 < (T3-T2) < (T2-T1) × k 3; (T3-T2) k6 < (T4-T3) < (T3-T2) k5, wherein k1, k2, k3, k4, k5 and k6 are constraint coefficients and have a value range of 0.5-2, and TH2 is a time parameter of a first stage of signal attenuation set by a user according to sensor characteristics;
step six: and transmitting the shock wave signal peak data and the waveform confidence information of the effective signal to an upper computer.
The method for processing data at the acquisition terminal is only carried out on a single waveform signal, and whether the acquired signal is a normal shock wave signal or not is mainly judged by checking whether the rising stage of the acquired signal is short, whether a peak signal larger than the rising stage occurs again in the signal attenuation process or not and whether the signal attenuation process conforms to a physical law from high to low.
In summary, when the upper computer judges the signal effectiveness, the normalized data is firstly subjected to wavelet transformation, common features of multi-source data are counted according to time-frequency information when the signal is generated, a plurality of data with the highest correlation degree are selected to establish a matching template, and then the template is utilized to calculate the similarity of each data, wherein the similarity has strong universal adaptability and small correlation with factors such as sensor characteristics, shock wave size and the like;
when the signal acquisition terminal judges the validity of the signal, whether the shock wave signal is abnormal is judged by judging whether a new larger peak signal is generated or not when the shock wave is generated to the time when the shock wave reaches the maximum peak value in the rise period and is attenuated, wherein the time when the shock wave reaches the maximum peak value is related to the strength of the signal and needs to be set according to the field condition; the signal confidence calculation is to divide the signal attenuation process into three stages, determine the proportionality coefficients of the three stages and the maximum peak value according to the response characteristic of the sensor, then obtain the corresponding proportional relation of the attenuation time according to the signal attenuation characteristic, and obtain a confidence information by judging whether the actual data conforms to the proportional relation.
The automatic judgment method for the shock wave signal effectiveness can select a corresponding judgment method according to whether the acquisition terminal has a high-speed wireless transmission link, and compared with the traditional manual judgment method, the automatic judgment method for the shock wave signal effectiveness is high in response speed, real-time, efficient, high in accuracy and strong in adaptability.
Claims (8)
1. The method for automatically judging the effectiveness of the shock wave signal is characterized in that firstly, a collecting terminal is used for carrying out waveform data collection, then data processing is carried out on an upper computer or the collecting terminal, and finally the effectiveness judgment of the shock wave signal is carried out.
2. The method for automatically judging the effectiveness of a shock wave signal according to claim 1, wherein when the shock wave signal acquisition terminal is provided with a high-speed wireless transmission link, the data processing is performed in an upper computer, and the data processing comprises the following steps:
step 1: the upper computer receives waveform data acquired by a plurality of signal acquisition terminals;
step 2: the upper computer performs noise reduction and filtering on all waveform data;
and step 3: respectively detecting the respective occurrence time of all waveform data, and aligning signals by taking the occurrence time as a center;
and 4, step 4: respectively selecting a 1ms time window for each waveform data, and carrying out normalization processing on all data in the time windows;
and 5: performing wavelet transformation on the normalized data, and converting the normalized data into corresponding time-frequency signals;
step 6: carrying out symbol mapping on the time-frequency domain signals, dividing the time-frequency domain signals into three sections according to the absolute values of the time-frequency domain signals, and then carrying out three-value mapping;
and 7: calculating the similarity of different terminal data subjected to symbol mapping, selecting three data with the highest correlation, and combining the same parts in the data to generate a data matching template;
and 8: firstly, symbol mapping is carried out on data from different acquisition terminals, then the similarity between the data subjected to symbol mapping and a data matching template is calculated, the effectiveness of the shock wave signal is judged according to the similarity, and the shock wave signal can be judged to be an effective signal if the similarity is more than 70%.
3. The method for automatically determining the validity of a shockwave signal according to claim 1, wherein when the shockwave signal collection terminal does not have a high-speed wireless transmission link, the data processing is performed at the signal collection terminal, and the data processing comprises the following steps:
the method comprises the following steps: carrying out shock wave starting point detection at a system acquisition terminal;
step two: after the shock wave is detected, comparing signal values at different moments, and detecting a shock wave signal peak value v _ max;
step three: calculating the attenuation time of the shock wave signal;
step four: judging signal abnormality;
step five: judging the confidence of the signal;
step six: and transmitting the shock wave signal peak data and the waveform confidence information of the effective signal to an upper computer.
4. A method for automatically determining the validity of a shockwave signal according to claim 3, wherein said method is performed on a single waveform signal.
5. The method for automatically determining the validity of a shockwave signal according to claim 4, wherein the first step detects the starting point of the shockwave, and when v (n) -v (m) > TH0 and n-m is 2, the starting point time T1 is n, where n and m are different sampling time points, and v (n) and v (m) are the corresponding sampled signal values of n and m, respectively.
6. The method for automatically determining the validity of a shockwave signal according to claim 5, wherein the method for calculating the decay time of a shockwave signal in step three comprises: calculating the average value of the sampled signal value v (T) from the time T1, wherein when the average value is less than v _ max _ a, the corresponding time is T2, and the time is called as a first stage; calculating the mean value of the sampling signal v (T) from the time T2, and when the mean value is less than v _ max b, the corresponding time is T3, which is called a second stage; calculating the mean value of the sampling signal v (T) from the time T3, and when the mean value is less than v _ max c, the corresponding time is T4, which is called as a third stage; wherein a, b and c are signal attenuation coefficients, and the a, b and c form a decreasing arithmetic series with a value range of 0.2-0.8.
7. The method for automatically determining the validity of a shockwave signal according to claim 6, wherein the method for determining an abnormal signal in the fourth step comprises: when T1 is greater than TH1, the shock wave signal is an abnormal signal; or when v _ max2 is more than v _ max 5/4, the shock wave signal is an abnormal signal, wherein TH1 is a signal rising reference time set by a user according to the system characteristics and the characteristics of the sensor; v _ max2 is the maximum value of the signal during the signal decay period.
8. The method of claim 7, wherein the confidence level determination in step five is based on TH2 k2 < (T2-T1) < TH2 k 1; (T2-T1) k4 < (T3.T2) < (T2-T1) k 3; (T3-T2) k6 < (T4-T3) < (T3-2) k5, wherein k1, k2, k3, k4, k5 and k6 are all constraint coefficients, the value range is 0.5-2, and TH2 is a time parameter of a first stage of signal attenuation set by a user according to sensor characteristics.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011207801.7A CN112434561B (en) | 2020-11-03 | 2020-11-03 | Method for automatically judging effectiveness of shock wave signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011207801.7A CN112434561B (en) | 2020-11-03 | 2020-11-03 | Method for automatically judging effectiveness of shock wave signal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112434561A true CN112434561A (en) | 2021-03-02 |
| CN112434561B CN112434561B (en) | 2023-09-22 |
Family
ID=74695123
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011207801.7A Active CN112434561B (en) | 2020-11-03 | 2020-11-03 | Method for automatically judging effectiveness of shock wave signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112434561B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11142526A (en) * | 1997-11-12 | 1999-05-28 | Matsushita Electric Ind Co Ltd | Seismoscope |
| JP2005098800A (en) * | 2003-09-24 | 2005-04-14 | Kansai Electric Power Co Inc:The | Method and device for searching discharge source |
| US20110245669A1 (en) * | 2010-04-01 | 2011-10-06 | Siemens Medical Solutions Usa, Inc. | System for Cardiac Condition Detection and Characterization |
| CN104373820A (en) * | 2014-09-19 | 2015-02-25 | 北京化工大学 | Method for lowering false alarm rate of pipeline leakage monitoring |
| CN108701219A (en) * | 2017-03-14 | 2018-10-23 | 华为技术有限公司 | The method and device of waveform signal processing |
| CN110148402A (en) * | 2019-05-07 | 2019-08-20 | 平安科技（深圳）有限公司 | Method of speech processing, device, computer equipment and storage medium |
| CN111579938A (en) * | 2020-04-14 | 2020-08-25 | 杭州柯林电气股份有限公司 | Multi-sensor-based multi-source partial discharge signal separation method and monitoring system |
-
2020
- 2020-11-03 CN CN202011207801.7A patent/CN112434561B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11142526A (en) * | 1997-11-12 | 1999-05-28 | Matsushita Electric Ind Co Ltd | Seismoscope |
| JP2005098800A (en) * | 2003-09-24 | 2005-04-14 | Kansai Electric Power Co Inc:The | Method and device for searching discharge source |
| US20110245669A1 (en) * | 2010-04-01 | 2011-10-06 | Siemens Medical Solutions Usa, Inc. | System for Cardiac Condition Detection and Characterization |
| CN104373820A (en) * | 2014-09-19 | 2015-02-25 | 北京化工大学 | Method for lowering false alarm rate of pipeline leakage monitoring |
| CN108701219A (en) * | 2017-03-14 | 2018-10-23 | 华为技术有限公司 | The method and device of waveform signal processing |
| CN110148402A (en) * | 2019-05-07 | 2019-08-20 | 平安科技（深圳）有限公司 | Method of speech processing, device, computer equipment and storage medium |
| CN111579938A (en) * | 2020-04-14 | 2020-08-25 | 杭州柯林电气股份有限公司 | Multi-sensor-based multi-source partial discharge signal separation method and monitoring system |
Non-Patent Citations (4)
| Title |
|---|
| HAIBIN ZHOU等: "Signal analysis and waveform reconstruction of shock waves generated by underwater electrical wire explosion with piezoelectric pressure probes", 《SENSORS》, vol. 2016, no. 04, pages 1 - 18 * |
| XIAODONG DING等: "Machine learning-based signal quality evaluation of single-period radial artery pulse waves：model development and validation", vol. 8, no. 6, pages 1 - 11 * |
| 刘嘉慧: "战斗部***冲击波超压测试与数据处理技术研究", no. 9, pages 3 - 4 * |
| 马彦: "复杂薄壁铝合金零件冲击液压成形技术与设备研究", 《中国博士学位论文全文数据库 工程科技I辑》, vol. 2019, no. 08, pages 022 - 118 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112434561B (en) | 2023-09-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112101174A (en) | LOF-Kurtogram-based mechanical fault diagnosis method | |
| CN101856225B (en) | Method for detecting R wave crest of electrocardiosignal | |
| CN102319063B (en) | Device and method for improving acceleration identification accuracy of fetal heart rate data | |
| CN110852201B (en) | Pulse signal detection method based on multi-pulse envelope spectrum matching | |
| CN111238814A (en) | Rolling bearing fault diagnosis method based on short-time Hilbert transform | |
| CN110987434A (en) | Rolling bearing early fault diagnosis method based on denoising technology | |
| CN109633633B (en) | Life signal enhancement method based on segmented classification enhancement processing | |
| CN111273336B (en) | Gaussian forming method for digital nuclear pulse signal | |
| CN110376575B (en) | Low-frequency line spectrum detection method based on damping parameter matching stochastic resonance | |
| CN112380992B (en) | Method and device for evaluating and optimizing accuracy of monitoring data in machining process | |
| CN113326782B (en) | Rolling bearing fault feature automatic extraction method based on envelope spectrum form fitting | |
| CN102988041A (en) | Selective cardiac-magnetic signal averaging method in signal noise suppression | |
| CN103852525A (en) | Acoustic emission signal identification method based on AR-HMM | |
| CN114486263A (en) | Noise reduction and demodulation method for vibration signal of rolling bearing of rotary machine | |
| CN104125050A (en) | Ultrahigh-frequency RFID (radio frequency identification) reader protocol conformance testing method | |
| CN112434561B (en) | Method for automatically judging effectiveness of shock wave signal | |
| CN113723521A (en) | Motor abnormal sound identification method and identification system | |
| CN103886868A (en) | Impact sound detection method and detection system | |
| CN117169346A (en) | High-altitude building damage identification method based on wavelet packet energy spectrum analysis | |
| CN110333506B (en) | Method for extracting inhaul cable position parameters of cable force measurement radar | |
| CN112332807A (en) | Weak envelope signal detection method and system | |
| CN110299926B (en) | Underwater acoustic signal detection method oriented to low signal-to-noise ratio environment | |
| CN111012306B (en) | Sleep respiratory sound detection method and system based on double neural networks | |
| CN109061725B (en) | Automatic identification method of elastic wave data acquisition jumping point and equipment used by same | |
| CN114137364A (en) | Interference wave elimination method based on time domain reflection method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |