CN114660181A - Safety monitoring system, early warning system and detection method based on acoustic emission sensor - Google Patents
Safety monitoring system, early warning system and detection method based on acoustic emission sensor Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
- G01N29/4427—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with stored values, e.g. threshold values
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4463—Signal correction, e.g. distance amplitude correction [DAC], distance gain size [DGS], noise filtering
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/46—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by spectral analysis, e.g. Fourier analysis or wavelet analysis
Abstract
The invention discloses a safety monitoring system, an early warning system and a detection method based on an acoustic emission sensor for carrying out safety monitoring on traffic infrastructure. The device comprises a monitoring unit, a data acquisition unit and a judgment unit, wherein the data acquisition unit is in communication connection with the judgment unit; the monitoring unit comprises at least one acoustic emission sensor and a fixing device thereof, and the sensing surface of the acoustic emission sensor is directly or indirectly contacted with the monitored object through the fixing device and is used for sensing the vibration of the monitored object; the acoustic emission sensor is connected with the data acquisition unit; the judging unit is used for judging whether the monitored target cracks or fractures based on the data acquired by the data acquisition unit, and judging that the detected target cracks or fractures when one of a plurality of different characteristic signals is extracted by the judging unit; and when the judging unit does not extract one of the plurality of different characteristic signals, judging that the detected target is not cracked or broken.
Description
Technical Field
The invention relates to the field of traffic infrastructure safety monitoring, in particular to a safety monitoring system, an early warning system and a detection method based on an acoustic emission sensor for carrying out safety monitoring on traffic infrastructure.
Background
In recent years, with the continuous and rapid development of rail transit and some key infrastructures, such as high-speed rail, subway rail, tunnel, culvert, bridge, etc., steel structure bridges are important components of the rail transit, subway rail, tunnel, culvert, bridge, etc., and it is necessary to ensure the safety and stability in the use process; therefore, the inspection device needs to be inspected so as to check and find whether the metal component has potential safety hazards such as cracking and breaking or not in time.
At present, the mode of patrolling and examining steel structure bridges is mainly a manual patrolling and examining mode. However, the following defects exist in the inspection of the steel structure bridge in the manual mode: on the one hand, more bridge positions are built under the condition of complex geological and geographic environment, the complex and the bad conditions of the inspection working environment are caused, meanwhile, the great potential safety hazard risk is brought to the self safety of inspection workers, and great inconvenience is brought to the inspection work. On the other hand, the missing detection phenomenon is easily caused due to factors such as consciousness or experience defects possibly existing in the manual inspection, and the missing detection phenomenon brings great potential safety hazards, and the missing detection is avoided to the utmost extent by a method.
Disclosure of Invention
The invention provides a safety monitoring system, an early warning system and a detection method based on an acoustic emission sensor, which can reduce the dependence on manual inspection, reduce the omission phenomenon, improve the safety of metal structure bridges and similar infrastructures, and can timely find the potential safety hazard phenomenon, so as to solve the current situations that the safety inspection of the existing metal steel structure bridges and similar infrastructures is complicated and bad in inspection working environment due to the adoption of a manual inspection mode, and is easy to cause potential safety hazards of inspectors or cause the omission phenomenon, thereby causing the potential safety hazard problem of the steel structure bridges and the like.
The invention adopts the following specific technical scheme for solving the technical problems: a safety monitoring system based on an acoustic emission sensor is characterized in that: the device comprises a monitoring unit, a data acquisition unit and a judgment unit, wherein the data acquisition unit is in communication connection with the judgment unit;
the monitoring unit comprises at least one acoustic emission sensor and a fixing device thereof, and the sensing surface of the acoustic emission sensor is directly or indirectly contacted with the monitored object through the fixing device and is used for sensing the vibration of the monitored object; the acoustic emission sensor is connected with the data acquisition unit in a wired mode;
the data acquisition unit comprises a signal conditioning unit and a communication unit, the signal conditioning unit is connected with the acoustic emission sensor through a coaxial cable and transmits acquired data to the judgment unit, and the data acquisition unit is used for acquiring monitoring signals transmitted by the acoustic emission sensor;
the judging unit is used for judging whether the monitored target cracks or fractures based on the data acquired by the data acquisition unit, and judging that the detected target cracks or fractures when one of a plurality of different characteristic signals is extracted by the judging unit; and when the judging unit does not extract one of the plurality of different characteristic signals, judging that the detected target is not cracked or broken.
But through to the regional department installation acoustic emission sensor of the position of direct or indirect contact of monitored object to acoustic emission sensor after fixed carries out the potential safety hazard detection to monitored object, detect the detected signal that obtains acoustic emission sensor and carry out data acquisition signal's collection signal processing and extract the judgement, thereby whether automatic analysis judges that there is the fracture or the phenomenon of splitting in the detected object, can reduce the reliance of patrolling and examining to the manual work, reduce the phenomenon of louing examining, promote the security of metallic structure bridge and similar infrastructure, can in time discover the potential safety hazard phenomenon.
Preferably, the plurality of different characteristic signals adopt 4 different characteristic signals, namely a characteristic signal 1, a characteristic signal 2, a characteristic signal 3 and a characteristic signal 4, and the detected target is a monitoring target 1 and a monitoring target 2; when the judging unit extracts the characteristic signal 1, the characteristic signal 2, the characteristic signal 3 or the characteristic signal 4, the judging unit judges that the monitoring target 1 or the monitoring target 2 cracks or breaks; when the judging unit does not extract the characteristic signal 1, the characteristic signal 2, the characteristic signal 3 or the characteristic signal 4, the judging unit judges that the monitoring target 1 and the monitoring target 2 are not in fault;
the monitoring target 1 comprises a metal bridge or a related metal structural part, and the monitoring target 2 refers to a fixing device of the acoustic emission sensor;
the characteristic signal 1 represents that the monitored target 1 is cracked, the characteristic signal 2 represents that the monitored target 1 is cracked, the characteristic signal 3 represents that the monitored target 2 is cracked, and the characteristic signal 4 represents that the monitored target 2 is cracked.
The monitoring effectiveness of the potential safety hazard fault type problem monitoring is improved, the potential safety hazard is timely and automatically monitored and found, the missing detection phenomenon is reduced, the safety of metal structure bridges and similar infrastructures is improved, and the potential safety hazard phenomenon can be timely found.
Preferably, each data acquisition unit is connected with one or more acoustic emission sensors, and one or more data acquisition units are in communication connection with the judgment unit. The number of acoustic emission sensors which can be acquired and detected by the data acquisition unit is increased, and the monitoring safety effectiveness and the monitoring cost control effectiveness of the monitored target are improved.
Preferably, the data acquisition unit is provided with an encryption module, and the encryption module is used for encrypting data sent to the judgment module; the judgment unit is provided with a decryption module, and the decryption module is used for decrypting the data from the data acquisition unit encryption module. The effectiveness of monitoring, collecting and encrypting data is improved.
Preferably, the judging unit is provided with a diagnosing unit, and the diagnosing unit is used for diagnosing and judging the fault type of the monitoring target, wherein the fault type comprises the cracking or breaking of the monitoring target 1 and the monitoring target 2. The comprehensive effectiveness of monitoring, detecting and judging the fault types of different monitored targets is improved.
Preferably, the judging unit is provided with an alarm unit, and the early warning unit is used for giving an alarm for a monitoring position in the judging unit, wherein the monitoring position is used for judging that a fault occurs between two objects to be monitored. The monitoring and judging timely alarming effectiveness is improved, and the occurrence of safety failure phenomena is avoided or reduced.
Preferably, the signal conditioning unit comprises a preceding-stage signal amplifier, a band-pass filter, a subsequent-stage signal amplifier, a high-speed AD sampling processor and a microprocessor which are electrically connected in sequence, and the microprocessor is used for processing a detection signal acquired by detecting a monitoring target through the acoustic emission sensor by the preceding-stage signal amplifier, the band-pass filter, the subsequent-stage signal amplifier and the high-speed AD sampling processor to obtain a characteristic signal for operation processing. The simple and effective conditioning treatment of the monitoring signals collected by the acoustic emission sensor is improved.
Preferably, the communication unit in the data acquisition unit is in wired connection with the judgment unit through an optical fiber, a coaxial cable, a twisted pair cable or a network cable. Improvement of
Preferably, the communication unit in the data acquisition unit is wirelessly connected with the judgment unit through one of technologies of WIFI, LoRa, Nb-IoT, Sub1GHz or UWB.
Another object of the present invention is to provide an acoustic emission sensor-based warning system, which is characterized in that: the system comprises a big data analysis early warning system and a plurality of safety monitoring systems based on the acoustic emission sensors, wherein the plurality of safety monitoring systems based on the acoustic emission sensors are arranged in a distributed mode and are all in communication connection with the big data analysis early warning system; the early warning unit is used for early warning the analysis result obtained by the analysis unit under the preset condition. The method has the advantages of improving the big data analysis and early warning effect of the safety monitoring system based on the acoustic emission sensor, greatly improving the big data analysis and the safety early warning of the fault type of the monitored target, improving the analysis, early warning and maintenance data basis of the monitored target, prolonging the service life of the whole maintenance, and avoiding the occurrence of safety accidents.
Another object of the present invention is to provide a safety monitoring method based on an acoustic emission sensor, which is characterized in that: comprises the following detection steps
A1. One or more acoustic emission sensors and fixing devices thereof in one of the technical schemes are directly or indirectly installed and fixed to be in contact with the monitored target, so that the acoustic emission sensors and the fixing devices thereof are in contact with the monitored target to sense the vibration of the monitored object and monitor to obtain a monitoring signal that the monitored target is abnormal;
A2. the data acquisition unit acquires the monitoring signal sent by the acoustic emission sensor in the step A1, and performs signal conditioning on the acquired monitoring target detection signal to obtain a characteristic signal after the signal conditioning and sends the characteristic signal to the judgment unit;
A3. the judgment unit is internally preset with standard reference values of all characteristic signals, when the judgment unit extracts the characteristic signals obtained in the step A2, the amplitude of the characteristic signals obtained in the step A2 is compared with the standard reference values of all the preset characteristic signals for diagnosis, and whether the monitored target cracks or breaks is judged through diagnosis;
A4. in the step a2, in the signal conditioning unit, the signal after signal amplification and filtering is sent to a high-speed AD sampling circuit with sampling frequency > 600KHz, and the converted digital signal is input to a microprocessor to obtain a characteristic signal after signal conditioning and sent to a judging unit;
A5. in the step a3, the characteristic signal transmitted by the data acquisition unit is processed by using one or more of the following three algorithms:
a. comparing time domain characteristic signals, namely comparing the time domain characteristic signals with the waveform of the characteristic signals stored in the device;
b. performing frequency domain analysis, namely performing Fourier expansion on the signals to obtain signal amplitudes of characteristic frequency points;
c. machine learning, namely, a trained model is built in, and an original digital signal is input for judgment;
A6. in the step a3, the diagnostic unit of the determining unit is preset with the standard reference values of the feature signal 1, the feature signal 2, the feature signal 3 and the feature signal 4, and for the algorithm 1 in the step 5, when the similarity of the waveform change trend exceeds 70%, the determining unit determines that the signal is a valid specific signal; aiming at the algorithm 2 in the step 5, judging that the signal center frequency point of the characteristic frequency point has a deviation of less than 10% from the reference value and has a signal intensity of more than 50% as an effective characteristic signal; aiming at the algorithm 3 in the step 5, when the output reliability result is more than 80%, the characteristic signal is judged to be valid;
when the judging unit extracts an effective characteristic signal 1, a characteristic signal 2, a characteristic signal 3 or a characteristic signal 4, the judging unit judges that the monitoring target 1 or the monitoring target 2 cracks or breaks;
when the judging unit does not extract the characteristic signal 1, the characteristic signal 2, the characteristic signal 3 or the characteristic signal 4, the judging unit judges that the monitoring target 1 and the monitoring target 2 are not in fault;
the monitoring target 1 comprises a metal bridge or a related metal structural part, and the monitoring target 2 refers to a fixing device of an acoustic emission sensor;
the characteristic signal 1 represents that the monitored target 1 is cracked, the characteristic signal 2 represents that the monitored target 1 is cracked, the characteristic signal 3 represents that the monitored target 2 is cracked, and the characteristic signal 4 represents that the monitored target 2 is cracked.
A7. When a big data analysis early warning requirement exists, the big data analysis early warning system according to the above technical scheme executes the real-time data and the historical data acquired by the multiple data acquisition units in one of the steps a2 to a step a6 to analyze and acquire an analysis result, and performs early warning on the analysis result acquired by the analysis unit when a preset condition is met.
Whether the detected target cracks or breaks or not is automatically analyzed and judged, so that the dependence on manual inspection can be reduced, the missing inspection phenomenon can be reduced, the safety of metal structure bridges and similar infrastructures can be improved, and the potential safety hazard phenomenon can be timely discovered; the analysis early warning maintenance data basis of the monitored target is improved, the overall maintenance service life is prolonged, and safety accidents are avoided.
The invention has the beneficial effects that: the method comprises the steps that an acoustic emission sensor is installed in a position area where a monitored target can directly or indirectly contact, the acoustic emission sensor after installation and fixation is used for detecting potential safety hazards of the monitored target, and a detection signal obtained by detection of the acoustic emission sensor is subjected to signal acquisition processing and extraction judgment of a data acquisition signal, so that whether the detected target cracks or breaks or not is automatically analyzed and judged, and monitoring, diagnosis and early warning are given; the dependence on manual inspection can be effectively reduced, the missing inspection phenomenon is reduced, the safety of metal structure bridges and similar infrastructures is improved, and the potential safety hazard phenomenon can be found in time.
Description of the drawings:
the invention is described in further detail below with reference to the figures and the detailed description.
FIG. 1 is a schematic diagram of the principle structure of the safety monitoring system based on the acoustic emission sensor of the present invention.
Fig. 2 is a schematic diagram of a schematic structure of a signal conditioning unit in the safety monitoring system based on the acoustic emission sensor according to the present invention.
Fig. 3 is a schematic structural diagram of the principle of the acoustic emission sensor-based early warning system of the present invention.
Detailed Description
Example 1:
in the embodiment shown in fig. 1 and 2, an acoustic emission sensor-based safety monitoring system 01 includes a monitoring unit 10, a data acquisition unit 20 and a determination unit 30, where the data acquisition unit 20 is in communication connection with the determination unit 30; the monitoring unit 10 comprises at least one acoustic emission sensor 11 and an acoustic emission sensor fixing device 12 corresponding to the acoustic emission sensor 11, and the acoustic emission sensor fixing device 12 is fixed on or near the monitored target in a bolt mode or a bonding mode; for example, the configuration shown in FIG. 1 provides a1 st set of 1 st-Nth acoustic emission sensors through an Nth set of 1-Nth acoustic emission sensors, where N represents a natural number; the same data acquisition unit 1 is correspondingly configured from the 1 st group of the 1 st acoustic emission sensor 1-1 to the 1 st group of the Nth acoustic emission sensor 1-N; correspondingly configuring and using the same data acquisition unit N from the Nth group of the 1 st acoustic emission sensor N-1 to the Nth group of the Nth acoustic emission sensor N-N; the sensing surface of the acoustic emission sensor 11 is directly or indirectly contacted with the monitored object through a fixing device and is used for sensing the vibration of the monitored object; the acoustic emission sensor is connected with the data acquisition unit in a wired mode; the data acquisition unit 20 comprises a signal conditioning unit 21 and a communication unit 22, the signal conditioning unit 21 is connected with the acoustic emission sensor 11 through a coaxial cable, acquired data are sent to the judgment unit 30, and the data acquisition unit 20 is used for acquiring monitoring signals sent by the acoustic emission sensor 11; the judging unit 30 is configured to judge whether the monitoring target cracks or fractures based on the data acquired by the data acquiring unit, and when the judging unit extracts the characteristic signal 1, the characteristic signal 2, the characteristic signal 3, or the characteristic signal 4, the judging unit judges that the monitoring target 1 or the monitoring target 2 cracks or fractures; when the judging unit does not extract the characteristic signal 1, the characteristic signal 2, the characteristic signal 3 or the characteristic signal 4, the judging unit 30 judges that the monitoring target 1 and the monitoring target 2 are not in fault; the monitoring target 1 comprises a metal bridge or a related metal structural part, and the monitoring target 2 refers to a fixing device of the acoustic emission sensor; the characteristic signal 1 represents that the monitored target 1 is cracked, the characteristic signal 2 represents that the monitored target 1 is cracked, the characteristic signal 3 represents that the monitored target 2 is cracked, and the characteristic signal 4 represents that the monitored target 2 is cracked. The data acquisition unit 20 is connected with one or more acoustic emission sensors 11 by adopting each data acquisition unit, and the one or more data acquisition units are in communication connection with the judgment unit. The data acquisition unit 20 is provided with an encryption module, and the encryption module is used for encrypting data sent to the judgment module; the judgment unit is provided with a decryption module, and the decryption module is used for decrypting the data from the data acquisition unit encryption module. The judging unit 30 is configured with a diagnosing unit 31, and the diagnosing unit 31 is configured to diagnose and judge the fault type of the monitoring target, where the fault type includes cracking or breaking of the monitoring target 1 and the monitoring target 2. The judging unit is provided with an alarm unit 32, and the early warning unit is used for giving an alarm for the monitoring position of the judging unit, which judges that a fault occurs between the two objects to be monitored. The signal conditioning unit 21 includes a preceding stage signal amplifier 2A, a band pass filter 2B, a succeeding stage signal amplifier 2C, a high-speed AD sampling processor 2D, and a microprocessor 2E, which are electrically connected in sequence, and the microprocessor 2E is configured to perform operation processing on a detection signal acquired by detecting a monitoring target through the acoustic emission sensor 11, and obtain a characteristic signal after the detection signal is processed by the preceding stage signal amplifier 2A, the band pass filter 2B, the succeeding stage signal amplifier 2C, and the high-speed AD sampling processor 2D. The communication unit 22 of the data acquisition unit 20 is in wired connection with the determination unit through an optical fiber, a coaxial cable, a twisted pair cable or a network cable. Of course, the communication unit 22 in the data acquisition unit and the determination unit may be wirelessly connected through one of technologies of WIFI, LoRa, Nb-IoT, Sub1GHz, or UWB. Or a communication connection mode combining wired connection and wireless connection.
Example 2:
in the embodiment shown in fig. 3, an early warning system based on an acoustic emission sensor includes a big data analysis early warning system and a plurality of safety monitoring systems 01 based on an acoustic emission sensor according to embodiment 1, the plurality of safety monitoring systems 01 based on an acoustic emission sensor are distributed and are all in communication connection with a big data analysis early warning system 02, the big data analysis early warning system 02 includes an analysis unit 40 and an early warning unit 50, the analysis unit 40 is configured to analyze real-time data and historical data acquired by a plurality of data acquisition units 20 in the safety monitoring system 01 based on an acoustic emission sensor and acquire an analysis result; the early warning unit 50 is used for early warning the analysis result obtained by the analysis unit under the preset condition, and the early warning of the analysis result includes early warning prediction such as system overall service life prediction 51, specific position damage probability and time prediction 52.
Example 3:
in the embodiment shown in fig. 1, fig. 2, and fig. 3, a safety monitoring method based on an acoustic emission sensor includes the following steps:
A1. the acoustic emission sensor or sensors and the fixing device thereof in the embodiment 1 are directly or indirectly installed and fixed to be in contact with the monitored target, so that the acoustic emission sensor or sensors and the fixing device thereof can sense the vibration of the monitored object in a contact manner and monitor to obtain a monitoring signal that the monitored target is abnormal;
A2. the data acquisition unit acquires the monitoring signal sent by the acoustic emission sensor in the step A1, and performs signal conditioning on the acquired monitoring target detection signal to obtain a characteristic signal after the signal conditioning and sends the characteristic signal to the judgment unit;
A3. the judgment unit is internally preset with standard reference values of all characteristic signals, when the judgment unit extracts the characteristic signals obtained in the step A2, the amplitude of the characteristic signals obtained in the step A2 is compared with the standard reference values of all the preset characteristic signals for diagnosis, and whether the monitored target cracks or breaks is judged through diagnosis;
A4. in the step a2, in the signal conditioning unit, the signal after signal amplification and filtering is sent to a high-speed AD sampling circuit with sampling frequency > 600KHz, and the converted digital signal is input to a microprocessor to obtain a characteristic signal after signal conditioning and sent to a judging unit;
A5. in the step a3, the characteristic signal transmitted by the data acquisition unit is processed by one or more of the following three algorithms:
a. comparing time domain characteristic signals, namely comparing the time domain characteristic signals with the waveform of the characteristic signals stored in the device;
b. performing frequency domain analysis, namely performing Fourier expansion on the signals to acquire the signal amplitude of the characteristic frequency point;
c. machine learning, namely, a trained model is built in, and an original digital signal is input for judgment;
A6. in the step a3, the diagnostic unit of the determining unit is preset with the standard reference values of the feature signal 1, the feature signal 2, the feature signal 3 and the feature signal 4, and for the algorithm 1 in the step 5, when the similarity of the waveform change trend exceeds 70%, the determining unit determines that the signal is a valid specific signal; aiming at the algorithm 2 in the step 5, judging that the signal center frequency point of the characteristic frequency point has a deviation of less than 10% from the reference value and has a signal intensity of more than 50% as an effective characteristic signal; aiming at the algorithm 3 in the step 5, when the output reliability result is more than 80%, judging as an effective characteristic signal;
when the judging unit extracts an effective characteristic signal 1, a characteristic signal 2, a characteristic signal 3 or a characteristic signal 4, the judging unit judges that the monitoring target 1 or the monitoring target 2 cracks or breaks;
when the judging unit does not extract the characteristic signal 1, the characteristic signal 2, the characteristic signal 3 or the characteristic signal 4, the judging unit judges that the monitoring target 1 and the monitoring target 2 are not in fault;
the monitoring target 1 comprises a metal bridge or a related metal structural part, and the monitoring target 2 refers to a fixing device of an acoustic emission sensor;
the characteristic signal 1 represents that the monitored target 1 is cracked, the characteristic signal 2 represents that the monitored target 1 is cracked, the characteristic signal 3 represents that the monitored target 2 is cracked, and the characteristic signal 4 represents that the monitored target 2 is cracked.
A7. When there is a big data analysis early warning requirement, the big data analysis early warning system described in embodiment 2 performs analysis on the real-time data and the historical data acquired by the plurality of data acquisition units in embodiment 1 and acquires an analysis result, and performs early warning on the analysis result acquired by the analysis unit when a preset condition is met.
When in use, the utility model is used,
the present invention is not limited to the above-described specific embodiments, and may have other structural embodiments, for example: all fall within the scope of the invention.
The foregoing summary and structure are provided to explain the principles, general features, and advantages of the product and to enable others skilled in the art to understand the invention. The foregoing examples and description have been presented to illustrate the principles of the invention and are intended to provide various changes and modifications within the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A safety monitoring system based on an acoustic emission sensor is characterized in that: the device comprises a monitoring unit, a data acquisition unit and a judgment unit, wherein the data acquisition unit is in communication connection with the judgment unit;
the monitoring unit comprises at least one acoustic emission sensor and a fixing device thereof, and the sensing surface of the acoustic emission sensor is directly or indirectly contacted with the monitored object through the fixing device and is used for sensing the vibration of the monitored object; the acoustic emission sensor is connected with the data acquisition unit in a wired mode;
the data acquisition unit comprises a signal conditioning unit and a communication unit, the signal conditioning unit is connected with the acoustic emission sensor through a coaxial cable and transmits acquired data to the judgment unit, and the data acquisition unit is used for acquiring monitoring signals transmitted by the acoustic emission sensor;
the judging unit is used for judging whether the monitored target cracks or fractures based on the data acquired by the data acquisition unit, and judging that the detected target cracks or fractures when one of a plurality of different characteristic signals is extracted by the judging unit; and when the judging unit does not extract one of the plurality of different characteristic signals, judging that the detected target is not cracked or broken.
2. The acoustic emission sensor-based safety monitoring system of claim 1, wherein: the plurality of different characteristic signals are 4 different characteristic signals, namely a characteristic signal 1, a characteristic signal 2, a characteristic signal 3 and a characteristic signal 4, and the target to be detected is a monitoring target 1 and a monitoring target 2; when the judging unit extracts the characteristic signal 1, the characteristic signal 2, the characteristic signal 3 or the characteristic signal 4, the judging unit judges that the monitoring target 1 or the monitoring target 2 cracks or breaks; when the judging unit does not extract the characteristic signal 1, the characteristic signal 2, the characteristic signal 3 or the characteristic signal 4, the judging unit judges that the monitoring target 1 and the monitoring target 2 are not in fault;
the monitoring target 1 comprises a metal bridge or a related metal structural part, and the monitoring target 2 refers to a fixing device of an acoustic emission sensor;
the characteristic signal 1 represents that the monitored target 1 is cracked, the characteristic signal 2 represents that the monitored target 1 is cracked, the characteristic signal 3 represents that the monitored target 2 is cracked, and the characteristic signal 4 represents that the monitored target 2 is cracked.
3. The acoustic emission sensor-based safety monitoring system of claim 1, wherein: the data acquisition unit adopts each data acquisition unit to connect one or more acoustic emission sensors, and one or more data acquisition units are in communication connection with the judgment unit.
4. The acoustic emission sensor-based safety monitoring system of claim 1, wherein: the data acquisition unit is provided with an encryption module which is used for encrypting data sent to the judgment module; the judgment unit is provided with a decryption module which is used for decrypting the data from the data acquisition unit encryption module.
5. The acoustic emission sensor-based safety monitoring system of claim 1, wherein: the judging unit is provided with a diagnosing unit, and the diagnosing unit is used for diagnosing and judging the fault type of the monitoring target, wherein the fault type comprises cracking or breaking of the monitoring target 1 and the monitoring target 2.
6. The acoustic emission sensor-based safety monitoring system of claim 1, wherein: the judging unit is provided with an alarm unit, and the early warning unit is used for alarming the monitoring position of the judging unit, which judges that a fault occurs between the two objects to be monitored.
7. The acoustic emission sensor-based safety monitoring system of claim 1, wherein: the signal conditioning unit comprises a preceding-stage signal amplifier, a band-pass filter, a rear-stage signal amplifier, a high-speed AD sampling processor and a microprocessor which are sequentially and electrically connected, and the microprocessor is used for processing a detection signal acquired by detecting and collecting a monitoring target through an acoustic emission sensor through the preceding-stage signal amplifier, the band-pass filter, the rear-stage signal amplifier and the high-speed AD sampling processor to obtain characteristic signal operation processing.
8. The acoustic emission sensor-based safety monitoring system of claim 1, wherein: the communication unit in the data acquisition unit is in wired connection with the judgment unit through an optical fiber, a coaxial cable, a twisted pair wire or a network cable; or the communication unit in the data acquisition unit is wirelessly connected with the judgment unit through one of the technologies of WIFI, LoRa, Nb-IoT, Sub1GHz or UWB.
9. The utility model provides an early warning system based on acoustic emission sensor which characterized in that: the system comprises a big data analysis early warning system and a plurality of safety monitoring systems based on acoustic emission sensors and defined in any one of claims 1 to 8, wherein the plurality of safety monitoring systems based on acoustic emission sensors are distributed and are in communication connection with the big data analysis early warning system, each big data analysis early warning system comprises an analysis unit and an early warning unit, and the analysis unit is used for analyzing real-time data and historical data acquired by a plurality of data acquisition units in the safety monitoring system based on acoustic emission sensors and acquiring analysis results; the early warning unit is used for early warning the analysis result obtained by the analysis unit under the preset condition.
10. A safety monitoring method based on an acoustic emission sensor is characterized in that: comprises the following detection steps
A1. One or more acoustic emission sensors and the fixing devices thereof as claimed in any one of claims 1 to 8 are directly or indirectly mounted and fixed in contact with the monitored object, so that the acoustic emission sensors and the fixing devices thereof are in contact with the monitored object to sense the vibration of the monitored object, and monitor to obtain a monitoring signal indicating that the monitored object is abnormal;
A2. the data acquisition unit acquires the monitoring signal sent by the acoustic emission sensor in the step A1, and performs signal conditioning on the acquired monitoring target detection signal to obtain a characteristic signal after the signal conditioning and sends the characteristic signal to the judgment unit;
A3. the judgment unit is internally preset with standard reference values of all characteristic signals, when the judgment unit extracts the characteristic signals obtained in the step A2, the amplitude of the characteristic signals obtained in the step A2 is compared with the standard reference values of all the preset characteristic signals for diagnosis, and whether the monitored target cracks or breaks is judged through diagnosis;
A4. in the step a2, in the signal conditioning unit, the signal after signal amplification and filtering is sent to a high-speed AD sampling circuit with sampling frequency > 600KHz, and the converted digital signal is input to a microprocessor to obtain a characteristic signal after signal conditioning and sent to a judging unit;
A5. in the step a3, the characteristic signal transmitted by the data acquisition unit is processed by using one or more of the following three algorithms:
a. comparing time domain characteristic signals, namely comparing the time domain characteristic signals with the waveform of the characteristic signals stored in the device;
b. performing frequency domain analysis, namely performing Fourier expansion on the signals to obtain signal amplitudes of characteristic frequency points;
c. machine learning, namely, a trained model is built in, and an original digital signal is input for judgment;
A6. in the step a3, the diagnostic unit of the determining unit is preset with standard reference values of the feature signal 1, the feature signal 2, the feature signal 3 and the feature signal 4, and for the algorithm 1 in the step 5, when the similarity of the waveform change trend exceeds 70%, the determining unit determines that the specific signal is valid; aiming at the algorithm 2 in the step 5, the deviation between the signal center frequency point of the characteristic frequency point and the reference value is less than 10%, the signal intensity is more than 50%, and the characteristic signal is judged to be an effective characteristic signal; aiming at the algorithm 3 in the step 5, when the output reliability result is more than 80%, judging as an effective characteristic signal;
when the judging unit extracts an effective characteristic signal 1, a characteristic signal 2, a characteristic signal 3 or a characteristic signal 4, the judging unit judges that the monitoring target 1 or the monitoring target 2 cracks or breaks;
when the judging unit does not extract the characteristic signal 1, the characteristic signal 2, the characteristic signal 3 or the characteristic signal 4, the judging unit judges that the monitoring target 1 and the monitoring target 2 are not in fault;
the monitoring target 1 comprises a metal bridge or a metal structural part, and the monitoring target 2 refers to a fixing device of the acoustic emission sensor;
the characteristic signal 1 represents that the monitored target 1 is cracked, the characteristic signal 2 represents that the monitored target 1 is cracked, the characteristic signal 3 represents that the monitored target 2 is cracked, and the characteristic signal 4 represents that the monitored target 2 is cracked.
A7. When a big data analysis early warning requirement exists, the big data analysis early warning system according to claim 9 performs analysis on real-time data and historical data acquired by a plurality of data acquisition units in one of the steps a2 to a step a6 to acquire an analysis result, and performs early warning on the analysis result acquired by the analysis unit when a preset condition is met.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116095131A (en) * | 2023-04-11 | 2023-05-09 | 四川三思德科技有限公司 | Reservoir safety monitoring method based on Internet of things |
| CN117470968A (en) * | 2023-11-10 | 2024-01-30 | 武汉路通市政工程质量检测中心有限公司 | Method and system for monitoring fracture of prestressed reinforcement in prestressed concrete structure |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116095131A (en) * | 2023-04-11 | 2023-05-09 | 四川三思德科技有限公司 | Reservoir safety monitoring method based on Internet of things |
| CN116095131B (en) * | 2023-04-11 | 2023-06-30 | 四川三思德科技有限公司 | Reservoir safety monitoring method based on Internet of things |
| CN117470968A (en) * | 2023-11-10 | 2024-01-30 | 武汉路通市政工程质量检测中心有限公司 | Method and system for monitoring fracture of prestressed reinforcement in prestressed concrete structure |
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