CN110274537B - Intelligent multichannel synchronous dynamic strain sensor capable of being cooperatively calculated - Google Patents
Intelligent multichannel synchronous dynamic strain sensor capable of being cooperatively calculated Download PDFInfo
- Publication number
- CN110274537B CN110274537B CN201910658104.4A CN201910658104A CN110274537B CN 110274537 B CN110274537 B CN 110274537B CN 201910658104 A CN201910658104 A CN 201910658104A CN 110274537 B CN110274537 B CN 110274537B
- Authority
- CN
- China
- Prior art keywords
- data
- threshold
- value
- microprocessor
- strain sensor
- 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.)
- Active
Links
- 230000001360 synchronised effect Effects 0.000 title claims abstract description 24
- 230000002159 abnormal effect Effects 0.000 claims abstract description 18
- 238000001914 filtration Methods 0.000 claims abstract description 16
- 238000004364 calculation method Methods 0.000 claims abstract description 15
- 238000004891 communication Methods 0.000 claims abstract description 13
- 230000006870 function Effects 0.000 claims abstract description 12
- 230000003993 interaction Effects 0.000 claims abstract description 7
- 238000000605 extraction Methods 0.000 claims abstract description 6
- 238000012360 testing method Methods 0.000 claims abstract description 6
- 230000004044 response Effects 0.000 claims description 11
- 238000013500 data storage Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000003750 conditioning effect Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 238000000034 method Methods 0.000 abstract description 2
- 230000008569 process Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
- G01B7/18—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge using change in resistance
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention relates to the technical field of real-time monitoring of structures, and relates to an intelligent multichannel synchronous dynamic strain sensor capable of cooperatively calculating aiming at synchronous testing and monitoring application of structural multipoint strain. The microprocessor completes the collaborative computing functions of FIR data filtering, data characteristic value extraction, super-threshold online alarming and abnormal data rejection, and realizes interaction with an upper computer through a communication module. According to the invention, part of the calculation process and part of the storage function are distributed on the intelligent sensor, so that the calculation pressure of the server is reduced, the network demand is reduced, and the problem that the fault is not timely processed due to time delay caused by overlarge calculation pressure of the server is avoided.
Description
Technical Field
The invention relates to the technical field of real-time monitoring of structures, in particular to an intelligent multichannel synchronous dynamic strain sensor with distributed storage, online collaborative computing analysis and super-threshold alarming functions, aiming at dynamic strain sensors for structure monitoring, in particular to synchronous testing and monitoring application of structural multipoint strain.
Background
In the traditional structural health monitoring system, the sensor nodes mainly complete data acquisition and transmission functions, and all data processing and analysis are completed by a local monitoring center or a remote monitoring center server. When a system is connected with a large number of dynamic sensors, a large amount of data simultaneously enter a server, the network and data processing burden of a monitoring center are very heavy, the effectiveness of early warning is seriously affected, and some simple structural state anomalies can be discovered and alarmed only after being delayed for a long time, so that structural operation maintenance personnel cannot timely and emergently treat the structural state anomalies. Therefore, it is necessary to develop an intelligent sensor capable of cooperatively calculating, which performs necessary preprocessing on the collected data and filters redundant data, so that not only can the data processing and storage burden of a monitoring center be reduced, but also the alarm and disposal are more timely.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent multichannel synchronous dynamic strain sensor capable of collaborative analysis, which has the functions of distributed storage, online collaborative calculation analysis and super-threshold alarm, and is particularly used for synchronous test and monitoring of structural multipoint strain.
The technical scheme adopted by the invention is as follows: the intelligent multichannel synchronous dynamic strain sensor comprises a resistance strain sensor, an RC low-pass filter, a multichannel synchronous analog-to-digital converter, a microprocessor, a communication module, a power module, FLASH and a distributed memory, wherein the output of the resistance strain sensor is connected to the input of the RC low-pass filter, the output of the RC low-pass filter is connected to the input of the multichannel synchronous analog-to-digital converter, the output of the analog-to-digital converter is connected to the input of the microprocessor, the microprocessor is connected with the communication module, the FLASH and the distributed memory, the communication module is connected with a monitoring center PC in a wired or wireless mode, and the power module supplies power to other circuit units.
The microprocessor completes the collaborative processing function of the acquired data and realizes the interaction with the upper computer through the communication module.
Preferably, the distributed storage configuration has a storage space of more than 16GB, when a network has a large fault, the original data can be stored on the intelligent sensor for a long time, the stability of the sensor is improved, the data backup function can be also realized, and the data can be uploaded again when the data is lost or damaged.
The FLASH memory is used for storing running programs and characteristic values of the system, and the characteristic values mainly comprise average values, maximum values and minimum values.
And the interaction between the microprocessor and the upper computer comprises a request for real-time data and a network state test request.
Specifically, the cooperative computation of the microprocessor comprises FIR data filtering, data characteristic value extraction, super-threshold online alarm and abnormal data rejection.
The FIR filtering carries out signal conditioning and FIR filtering treatment on the acquired strain signals, and the cut-off frequency of the filtering is customized according to the measured structure, so that the aliasing of high-frequency signals to low-frequency noise is reduced, and the accuracy of the signals is improved.
The upper and lower thresholds of structural strain are not symmetrical about the mean, taking into account structural strain data characteristics. For a channel Ai, the upper threshold of the mean value comparison threshold is Mi, the lower threshold is Ni, the over-threshold counter Li (initial value is 0), the non-over-threshold counter Ki (initial value is 0) and the temporary data storage address is ADi. The working flow of the microprocessor for cooperative calculation and the extraction of the superstructural response alarm waveform is described below by taking the calculation of the characteristic value of the channel Ai as an example:
s1: continuously collecting real-time data, and performing multichannel FIR parallel filtering;
s2: the average value calculation is carried out on the initial n data, and characteristic values are stored in a FLASH space;
s3: starting to calculate a weighted average value of the (n+1) th data point, wherein the weight of each new point is always 1/n;
s4: calculating new data point and characteristic value difference, and comparing with two threshold values:
(1) The absolute value of this difference does not exceed the threshold:
(1) if li=0, the data is directly stored on the temporary storage space ADi;
(2) if Li is more than 0 and less than Q (Q is the number of continuous abnormal data points for starting alarm), eliminating abnormal data (regarded as pulse interference signals or isolated point abnormal signals) before the new data point, clearing Li, and storing the new data point on a temporary storage space ADi;
(3) if Li is more than or equal to Q, judging whether Ki reaches half of the acquisition cycle number, if not, ki=Ki+1, and storing new data points into the temporary storage space ADi; if the data segment reaches the first half period of the first super threshold value and the last half period of the last super threshold value, the data segment is considered to be a complete alarm waveform, the alarm waveform is moved from the temporary storage address ADi to the fixed data storage space, the maximum and minimum values are updated, li is cleared, and new data points are stored in the temporary storage space ADi;
(2) If the absolute value of the difference between the new data point and the characteristic value exceeds the threshold value Ni or Mi, the over-threshold counter li=li+1, the non-over-threshold counter Ki is cleared, and the data is stored in the temporary storage space ADi.
Discrimination of structural anomaly response data:
when Ai obtains a complete alarm waveform, the microprocessor compares the maximum values of the waveform in the same time period of each channel, can judge which channel obtains the maximum structural response, judges whether the ordering among the maximum values is normal according to the adjacent relation and the mechanical stress characteristics of the installation of a plurality of channels, and considers that the stress relation among the channels is damaged and the structure generates abnormal response when the ordering of the maximum values is abnormal or the difference is larger. The microprocessor uploads the results of the calculation to the monitoring center and stores the waveform data for each channel during this time period in a fixed data memory.
The intelligent sensor has the beneficial effects that by distributing part of calculation processes and part of storage functions on the intelligent sensor, the calculation pressure of the server is reduced, the network requirement is reduced, and the problem that the fault treatment is not timely due to time delay caused by overlarge calculation pressure of the server is avoided.
Drawings
FIG. 1 is a block diagram of a cooperatively computable intelligent multichannel synchronous dynamic strain sensor of the present invention.
FIG. 2 is a workflow of single channel microprocessor co-computation.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
The structure diagram of the intelligent multichannel synchronous dynamic strain sensor capable of being cooperatively calculated is shown in fig. 1, and the intelligent multichannel synchronous dynamic strain sensor comprises a resistance strain sensor, an RC low-pass filter, a multichannel synchronous analog-to-digital converter, a microprocessor, a communication module, a power module, FLASH and a distributed memory, wherein the output of the resistance strain sensor is connected to the input of the RC low-pass filter, the output of the RC low-pass filter is connected to the input of the multichannel synchronous analog-to-digital converter, the output of the analog-to-digital converter is connected to the input of the microprocessor, the microprocessor is connected with the communication module, the FLASH and the distributed memory, the communication module is connected with a monitoring center PC in a wired or wireless mode, and the power module supplies power to the microprocessor.
The microprocessor completes the collaborative processing function of the acquired data and realizes the interaction with the upper computer through the communication module.
Preferably, the distributed storage configuration has a storage space of more than 16GB, when a network has a large fault, the original data can be stored on the intelligent sensor for a long time, the stability of the sensor is improved, the data backup function can be also realized, and the data can be uploaded again when the data is lost or damaged.
The FLASH memory is used for storing running programs and characteristic values of the system, and the characteristic values mainly comprise average values, maximum values and minimum values.
And the interaction between the microprocessor and the upper computer comprises a request for real-time data and a network state test request.
Specifically, the cooperative computation of the microprocessor comprises FIR data filtering, data characteristic value extraction, super-threshold online alarm and abnormal data rejection.
The FIR filtering carries out signal conditioning and FIR filtering treatment on the acquired strain signals, and the cut-off frequency of the filtering is customized according to the measured structure, so that the aliasing of high-frequency signals to low-frequency noise is reduced, and the accuracy of the signals is improved.
In this embodiment, a total of 8 strain channels are used, and a 24-bit synchronous 8-channel analog-to-digital converter is used as the analog-to-digital converter. Setting:
8 channel numbers A1, A2, A3, A4, A5, A6, A7, A8;
the average value of the 8 channels compares the upper threshold values M1, M2, M3, M4, M5, M6, M7 and M8;
the average value of the 8 channels compares the threshold value with the threshold value N1, N2, N3, N4, N5, N6, N7 and N8;
8 channels exceed the upper threshold value and exceed the lower threshold value counter: l1, L2, L3, L4, L5, L6, L7, L8;
8 channels do not exceed threshold counter: k1, K2, K3, K4, K5, K6, K7, K8;
8 lane temporary data storage addresses AD1, AD2, AD3, AD4, AD5, AD6, AD7, AD8;
the initial values of the counters are all 0.
The upper and lower thresholds of structural strain are not symmetrical about the mean, taking into account structural strain data characteristics. The working flow of the microprocessor for collaborative computing and the extraction of the superstructure response alarm waveform is described below by taking the calculation of the characteristic value of the first channel A1 as an example:
s1: continuously collecting real-time data, and performing 8-channel FIR parallel filtering;
s2: the average value calculation is carried out on the initial n data, and characteristic values are stored in a FLASH space;
s3: starting to calculate a weighted average value of the (n+1) th data point, wherein the weight of each new point is always 1/n;
s4: calculating new data point and characteristic value difference, and comparing with two threshold values:
(1) The absolute value of this difference does not exceed the threshold:
(1) if l1=0, the data is directly stored on the temporary storage space AD 1;
(2) if 0 < L1 < Q (Q is the number of continuous abnormal data points for starting alarm), eliminating abnormal data (regarded as pulse interference signals or isolated point abnormal signals) before the new data point, clearing L1, and storing the new data point on the temporary storage space AD 1;
(3) if L1 is more than or equal to Q, judging whether K1 reaches half of the acquisition cycle number, if not, K1=K1+1, and storing new data points into the temporary storage space AD 1; if the data segment reaches the first half period of the first super threshold value and the last half period of the last super threshold value, the data segment is considered to be a complete alarm waveform, the alarm waveform is moved from the temporary storage address AD1 to the fixed data storage space, the maximum and minimum values are updated, the L1 is cleared, and new data points are stored in the temporary storage space AD 1;
(2) If the absolute value of the difference between the new data point and the characteristic value exceeds the threshold value N1 or M1, the threshold counter l1=l1+1 is not exceeded, the threshold counter K1 is cleared, and the data is stored in the temporary storage space AD 1.
Discrimination of structural anomaly response data:
when A1 obtains a complete alarm waveform, the microprocessor compares the maximum values of the waveforms in the same time period of A2, A3, A4, A5, A6, A7 and A8, can judge which channel obtains the maximum structural response, judges whether the ordering among the maximum values is normal according to the adjacent relation and mechanical stress characteristics of 8 channels, and considers that the stress relation among the 8 channels is destroyed when the ordering of the maximum values is abnormal or the difference is larger, and the structure is abnormal in response. The microprocessor uploads the results of the calculation to the monitoring center and stores the waveform data for each channel during this time period in a fixed data memory.
The invention is not limited to the embodiments described above, but any obvious modifications or alterations to the above embodiments may be made by a person skilled in the art without departing from the spirit of the invention and the scope of the appended claims.
Claims (5)
1. The intelligent multichannel synchronous dynamic strain sensor capable of being cooperatively calculated is characterized by comprising a resistance strain sensor, an RC low-pass filter, a multichannel synchronous analog-to-digital converter, a microprocessor, a communication module, a power module, FLASH and a distributed memory, wherein the output of the resistance strain sensor is connected to the input of the RC low-pass filter, the output of the RC low-pass filter is connected to the input of the multichannel synchronous analog-to-digital converter, the output of the analog-to-digital converter is connected to the input of the microprocessor, the microprocessor is connected with the communication module, the FLASH and the distributed memory, the communication module is connected with a monitoring center PC in a wired or wireless mode, and the power module supplies power for other circuit units; the microprocessor completes the collaborative computing functions of FIR data filtering, data characteristic value extraction, super-threshold online alarming and abnormal data rejection, and realizes interaction with an upper computer through a communication module;
the work flow of the cooperative calculation of the microprocessor is as follows:
for a channel A i An upper threshold of the average value comparison threshold is set as M i The lower threshold is N i Super threshold counter L i ，L i Initial value is 0, and the threshold counter K is not exceeded i ，K i Initial value of 0, temporary data storage address of AD i ：
S1: continuously collecting real-time data, and performing multichannel FIR parallel filtering;
s2: calculating the average value of the initial n data, and storing the average value into a FLASH space;
s3: starting to calculate a weighted average value of the (n+1) th data point, wherein the weight of each new point is always 1/n;
s4: calculating new data points and mean differences, and comparing with two threshold values:
(1) The absolute value of this difference does not exceed the threshold:
(1) if L i =0, the data is directly stored into the temporary storage space AD i Applying;
(2) if 0 < L i Q is the number of continuous abnormal data points for starting alarm, then the abnormal data before the new data point is removed, and L is taken as the value of the new data point i Clearing, storing new data points in the temporary storage space AD i Applying;
(3) if L i Judging K if Q is not less than i Whether half the acquisition cycle number is reached, if not, K i ＝K i +1, storing new data points into temporary storage space AD i Applying; if it is reached, consider the firstThe data segment from the first half period of the super threshold value to the last half period of the super threshold value is a complete alarm waveform, and the alarm waveform is sent from the temporary storage address AD i Moving to fixed data storage space, updating maximum and minimum values, clearing Li, storing new data points in temporary storage space AD i Applying;
(2) If the absolute value of the difference between the new data point and the mean exceeds the threshold value N i Or M i Then the threshold value counter L is exceeded i ＝L i +1, not exceeding threshold counter K i Clearing, and storing the data in the temporary storage space AD i Applying;
discrimination of structural anomaly response data: when A is i And the microprocessor compares the maximum values of waveforms in the same time period of other channels to judge which channel obtains the maximum structural response, judges whether the ordering among the maximum values is normal according to the adjacent relation and the mechanical stress characteristics of all the channels, and considers that the stress relation among all the channels is destroyed and the structure generates abnormal response when the ordering of the maximum values is abnormal or the difference is larger.
2. The intelligent multi-channel synchronous dynamic strain sensor capable of being cooperatively calculated according to claim 1, wherein the distributed memory is configured with a storage space of more than 16GB, when a network has a large fault, original data can be stored on the intelligent sensor for a long time, the stability of the sensor is improved, the function of data backup can be achieved, and the data can be uploaded again after being lost or damaged.
3. The cooperatively calculated intelligent multichannel synchronous dynamic strain sensor of claim 1, wherein the FLASH memory is used for storing running programs and characteristic values of a system, and the characteristic values mainly comprise an average value, a maximum value and a minimum value.
4. The cooperatively calculated intelligent multichannel synchronous dynamic strain sensor of claim 1, wherein the interaction of the microprocessor with the host computer comprises a request for real-time data, a network status test request.
5. The cooperatively calculated intelligent multichannel synchronous dynamic strain sensor of claim 1, wherein the FIR filtering performs signal conditioning and FIR filtering processing on the acquired strain signal, and the cut-off frequency of the filtering is customized according to the measured structure, so that aliasing of high-frequency signals to low-frequency noise is reduced, and accuracy of the signals is improved.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910658104.4A CN110274537B (en) | 2019-07-20 | 2019-07-20 | Intelligent multichannel synchronous dynamic strain sensor capable of being cooperatively calculated |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910658104.4A CN110274537B (en) | 2019-07-20 | 2019-07-20 | Intelligent multichannel synchronous dynamic strain sensor capable of being cooperatively calculated |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110274537A CN110274537A (en) | 2019-09-24 |
| CN110274537B true CN110274537B (en) | 2024-04-02 |
Family
ID=67964984
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910658104.4A Active CN110274537B (en) | 2019-07-20 | 2019-07-20 | Intelligent multichannel synchronous dynamic strain sensor capable of being cooperatively calculated |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110274537B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005069727A (en) * | 2003-08-28 | 2005-03-17 | Kyowa Electron Instr Co Ltd | Multi-channel strain measuring circuit |
| RU90596U1 (en) * | 2009-07-20 | 2010-01-10 | Открытое акционерное общество "Научно-производственное предприятие "Рубин" (ОАО "НПП "Рубин") | COMPLEX OF INFORMATION EXCHANGE OF CLOSED DOCUMENTAL INFORMATION ON IMULSE COMMUNICATION CHANNELS (COMPLEX I-300 STATIONARY) |
| KR20100006230A (en) * | 2008-07-09 | 2010-01-19 | 금오기전 주식회사 | Alarm and monitor device of diesel engine status which is enable to measure multi-channel temperature and pressure signal |
| CN102735331A (en) * | 2011-11-30 | 2012-10-17 | 重庆大学 | Network node of wireless sensor with on-sheet processing capability |
| CN105137864A (en) * | 2015-07-31 | 2015-12-09 | 上海卫星工程研究所 | Lower computer coordinative control SoC chip for spacecraft |
| CN105554059A (en) * | 2015-11-25 | 2016-05-04 | 北京华油信通科技有限公司 | Logistics transportation intelligent perception and position service system based on Beidou navigation technology |
| CN107941388A (en) * | 2018-01-11 | 2018-04-20 | 晋江万芯晨电子科技有限公司 | Distributed intelligence scaffold base stress monitors system |
| CN108254670A (en) * | 2017-12-06 | 2018-07-06 | 中国航空工业集团公司西安航空计算技术研究所 | For exchanging the health monitoring circuit structure of SoC at a high speed |
| CN109556849A (en) * | 2018-12-17 | 2019-04-02 | 交通运输部公路科学研究所 | Simply supported girder bridge structure damage monitoring system based on machine learning |
| CN109741483A (en) * | 2018-12-11 | 2019-05-10 | 成都路行通信息技术有限公司 | A kind of car crass detection method based on car networking platform |
| CN209991933U (en) * | 2019-07-20 | 2020-01-24 | 交通运输部公路科学研究所 | Intelligent multi-channel synchronous dynamic strain sensor capable of realizing cooperative computing |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9756699B2 (en) * | 2013-08-30 | 2017-09-05 | Avago Technologies General Ip (Singapore) Pte. Ltd | Low-power data acquisition system and sensor interface |
| US20170160243A1 (en) * | 2015-12-02 | 2017-06-08 | Broadsens Corp. | Device and System for Structural Health Monitoring |
-
2019
- 2019-07-20 CN CN201910658104.4A patent/CN110274537B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005069727A (en) * | 2003-08-28 | 2005-03-17 | Kyowa Electron Instr Co Ltd | Multi-channel strain measuring circuit |
| KR20100006230A (en) * | 2008-07-09 | 2010-01-19 | 금오기전 주식회사 | Alarm and monitor device of diesel engine status which is enable to measure multi-channel temperature and pressure signal |
| RU90596U1 (en) * | 2009-07-20 | 2010-01-10 | Открытое акционерное общество "Научно-производственное предприятие "Рубин" (ОАО "НПП "Рубин") | COMPLEX OF INFORMATION EXCHANGE OF CLOSED DOCUMENTAL INFORMATION ON IMULSE COMMUNICATION CHANNELS (COMPLEX I-300 STATIONARY) |
| CN102735331A (en) * | 2011-11-30 | 2012-10-17 | 重庆大学 | Network node of wireless sensor with on-sheet processing capability |
| CN105137864A (en) * | 2015-07-31 | 2015-12-09 | 上海卫星工程研究所 | Lower computer coordinative control SoC chip for spacecraft |
| CN105554059A (en) * | 2015-11-25 | 2016-05-04 | 北京华油信通科技有限公司 | Logistics transportation intelligent perception and position service system based on Beidou navigation technology |
| CN108254670A (en) * | 2017-12-06 | 2018-07-06 | 中国航空工业集团公司西安航空计算技术研究所 | For exchanging the health monitoring circuit structure of SoC at a high speed |
| CN107941388A (en) * | 2018-01-11 | 2018-04-20 | 晋江万芯晨电子科技有限公司 | Distributed intelligence scaffold base stress monitors system |
| CN109741483A (en) * | 2018-12-11 | 2019-05-10 | 成都路行通信息技术有限公司 | A kind of car crass detection method based on car networking platform |
| CN109556849A (en) * | 2018-12-17 | 2019-04-02 | 交通运输部公路科学研究所 | Simply supported girder bridge structure damage monitoring system based on machine learning |
| CN209991933U (en) * | 2019-07-20 | 2020-01-24 | 交通运输部公路科学研究所 | Intelligent multi-channel synchronous dynamic strain sensor capable of realizing cooperative computing |
Non-Patent Citations (2)
| Title |
|---|
| 基于片上系统协同计算的实时数据自动检测电化学传感器;周剑;李跃新;;计算机测量与控制;20160325(第03期);第21-23、27页 * |
| 基于片上系统协同计算的实时自动检测电化学传感器;魏斌等;《传感器与微系统》;第34卷(第10期);第44-47页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110274537A (en) | 2019-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109858104B (en) | Rolling bearing health assessment and fault diagnosis method and monitoring system | |
| CN111427753B (en) | Capacity prediction device based on ARIMA model and control method thereof | |
| WO2018092073A1 (en) | Fast automated detection of seasonal patterns in time series data without prior knowledge of seasonal periodicity | |
| CN102223264B (en) | Alarm processing method and alarm processing system for monitoring system | |
| CN112650200B (en) | Method and device for diagnosing plant station equipment faults | |
| CN110469496B (en) | Intelligent early warning method and system for water pump | |
| CN108644752A (en) | Method, apparatus and machine readable storage medium for analyzing four main tubes of boiler leakage | |
| CN110274537B (en) | Intelligent multichannel synchronous dynamic strain sensor capable of being cooperatively calculated | |
| KR20180020626A (en) | Method and device for monitoring multi-profile | |
| CN110795324B (en) | Data processing method and device | |
| CN111443263B (en) | Cable analysis device based on ring main unit and detection method thereof | |
| KR102266182B1 (en) | Method and apparatus for monitoring a plurality of streaming data | |
| CN103293017B (en) | A kind of condensate polishing treatment fore filter filter core operation life apparatus for evaluating | |
| CN209991933U (en) | Intelligent multi-channel synchronous dynamic strain sensor capable of realizing cooperative computing | |
| RU121944U1 (en) | AUTOMATED SYSTEM FOR IDENTIFYING FAULT SENSORS AMONG SENSORS INTENDED FOR CONTROL OF TECHNOLOGICAL PROCESSES | |
| CN110274683B (en) | Three-axis intelligent vibration sensor capable of being cooperatively calculated | |
| CN116448234A (en) | Power transformer running state voiceprint monitoring method and system | |
| US9945825B2 (en) | Predictive analysis of complex datasets and systems and methods including the same | |
| CN115310490A (en) | Rotating equipment fault analysis method based on multi-domain feature and sensitive feature selection | |
| CN114200213A (en) | Capacitance online monitoring method and device of low-frequency power transmission matrix converter | |
| CN113793014A (en) | Energy consumption monitoring and evaluation analysis system and method based on carbon peak | |
| KR102359985B1 (en) | A system and method for data collection | |
| CN113254253A (en) | Data processing method, system and equipment | |
| CN112597440A (en) | Power consumption transaction analysis method based on financial technical indexes | |
| CN112785142A (en) | Intelligent work order early warning method and system for sewage treatment equipment based on Internet of things |
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 |