CN116756504A - Underground factory building environment monitoring model and trend early warning algorithm - Google Patents
Underground factory building environment monitoring model and trend early warning algorithm Download PDFInfo
- Publication number
- CN116756504A CN116756504A CN202310666057.4A CN202310666057A CN116756504A CN 116756504 A CN116756504 A CN 116756504A CN 202310666057 A CN202310666057 A CN 202310666057A CN 116756504 A CN116756504 A CN 116756504A
- Authority
- CN
- China
- Prior art keywords
- data
- prediction model
- trend
- early warning
- underground
- 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.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 48
- 238000012549 training Methods 0.000 claims abstract description 25
- 238000013528 artificial neural network Methods 0.000 claims abstract description 24
- 238000012545 processing Methods 0.000 claims abstract description 12
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 64
- 238000006073 displacement reaction Methods 0.000 claims description 36
- 239000011159 matrix material Substances 0.000 claims description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 7
- 238000000034 method Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- WSFSSNUMVMOOMR-UHFFFAOYSA-N Formaldehyde Chemical compound O=C WSFSSNUMVMOOMR-UHFFFAOYSA-N 0.000 description 3
- 230000035882 stress Effects 0.000 description 3
- 230000004075 alteration Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000003014 reinforcing effect Effects 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000003673 groundwater Substances 0.000 description 1
- 230000002706 hydrostatic effect Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computational Linguistics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Alarm Systems (AREA)
Abstract
The invention belongs to the technical field of underground factory building monitoring, and particularly provides an underground factory building environment monitoring model and a trend early warning algorithm, wherein the monitoring model comprises the following components: the data acquisition module is used for acquiring one or more data of different areas in the underground factory building at different moments; the data processing module is used for training the depth neural network by the same kind of historical data and the current data of the same area to obtain a trend prediction model for outputting trend information corresponding to various data; and the early warning module is used for distributing weights to each trend prediction model according to different influences of different areas to obtain a comprehensive prediction model, and early warning is carried out when the output value of the comprehensive prediction model exceeds a safety value. The same kind of data in different areas is fused and predicted according to time sequence, and a deep neural network is adopted for training, so that the prediction accuracy of trend change of single data in a certain area can be greatly improved, and accurate capturing trend is realized.
Description
Technical Field
The invention relates to the technical field of underground factory building monitoring, in particular to an underground factory building environment monitoring model and a trend early warning algorithm.
Background
The underground factory building is an underground complex in various aspects such as electricity, machinery and hydraulic construction, and the underground factory building has complex mountain structures, multiple holes and more electromechanical embedded parts, and is easy to deform, crack, locally crack concrete, sink or leak due to negligence of design, construction or structure self aging, so that the use of the factory building and the equipment and personal safety are influenced. In addition, because the underground factory is generally built in the mountain or under water, along with the longer service time, the possibility of water seepage and collapse exists, so measures such as water seepage monitoring and structure monitoring are needed for the factory building, so that water seepage points can be found in time and repaired.
The current underground structure damage mainly adopts surface strain gauge (monitoring concrete surface strain), hydrostatic level gauge (monitoring subsidence), reinforcing bar gauge (monitoring reinforcing bar stress), concrete strain gauge (monitoring concrete internal strain), displacement gauge (monitoring displacement of appointed point along appointed direction), soil pressure gauge (monitoring soil pressure), osmometer (monitoring groundwater leakage), anchor rod stress gauge (monitoring anchor rod stress), etc., has obtained better monitoring effect. However, the sensors can only reflect the change of specific monitoring content of a local area of a specific point, a large number of sensors are required to be distributed in space to reflect the state of the whole structure, and the monitoring cost and the equipment installation difficulty are greatly improved. The optical fiber sensor which is increasingly widely used at present can better cover a large range of monitoring requirements by using one continuous optical fiber, but has the advantages of high cost, easy damage and relatively single monitoring content. The Chinese patent application "a method for identifying damage to a large-span bridge" issued to 2017.04.26 discloses a method for identifying damage to a bridge structure based on temperature strain, but the method requires arranging sensors on the cross section, and only the sensors can be arranged on the surface of the built underground structure, and the sensor balls cannot be arranged in the section of the structure, so that the method cannot be applied to the underground structure.
In summary, the present environment monitoring of the underground factory building has the advantages of single form, high cost, very limited monitoring range and even scattered monitoring range, cannot form a complete and comprehensive monitoring system, and cannot predict trend of the underground factory building with long service period to form early warning.
Disclosure of Invention
The invention aims at the technical problems that the existing underground factory building environment monitoring in the prior art is single in form and cannot form a complete and comprehensive monitoring system.
The invention provides an environment monitoring model of an underground factory building, which comprises a data acquisition module, a data processing module and an early warning module;
the data acquisition module is used for acquiring one or more data of different areas in the underground factory building at different moments;
the data processing module is used for training the depth neural network by the same kind of historical data and the current data of the same area to obtain a trend prediction model for outputting trend information corresponding to various data;
and the early warning module is used for distributing weights to each trend prediction model according to different influences of different areas to obtain a comprehensive prediction model, and early warning is carried out when the output value of the comprehensive prediction model exceeds a safety value.
Preferably, the data acquisition module comprises a linear displacement sensor matrix, a methane laser sensor and an ultrasonic sensor; the linear displacement sensor matrix is used for acquiring linear displacement data of underground plant structures in different areas, the methane laser sensor is used for monitoring methane content data in the underground plant, and the ultrasonic sensor is used for acquiring crack data of the top and the side wall of the underground plant.
Preferably, the early warning module specifically includes:
for linear displacement data, the influence of different areas is sequentially from big to small, namely top, side wall and bottom;
for methane content data, the influence of different areas is sequentially from the top to the bottom;
for crack data, the influences of different areas are sequentially from large to small, namely the top and the bottom of the side wall.
Preferably, the data processing module is specifically configured to: training the deep neural network according to the historical linear displacement and the current linear displacement to obtain a displacement trend prediction model; training the deep neural network according to the historical methane content and the current methane content to obtain a methane content trend prediction model; training the deep neural network according to the historical cracks and the current cracks to obtain a crack trend prediction model.
Preferably, the early warning module is specifically configured to:
respectively obtaining a displacement comprehensive prediction model, a methane content comprehensive prediction model and a crack comprehensive prediction model according to different influences of different areas;
and setting corresponding safety values for each comprehensive prediction model, and if the safety values are exceeded, carrying out corresponding early warning.
Preferably, the early warning module is specifically configured to: and setting a plurality of groups of safety values according to the safety level and aiming at each comprehensive prediction model, wherein each group of safety values corresponds to one early warning level.
Preferably, the data acquisition module further comprises a temperature sensor and a humidity sensor, wherein the temperature sensor is used for detecting the temperature outside the underground factory building, and the humidity sensor is used for detecting the humidity outside the underground factory building.
Preferably, the data processing module is specifically configured to: training the deep neural network according to the historical temperature and the current temperature to obtain a temperature trend prediction model; training the deep neural network according to the historical humidity and the current humidity to obtain a temperature trend prediction model.
The embodiment of the invention also provides an environment trend early warning algorithm for the underground factory building, which is used for an environment monitoring model of the underground factory building and comprises the following steps:
collecting one or more data of different areas in the underground factory building at different moments;
training the same kind of historical data and current data of the same area on a deep neural network to obtain a trend prediction model for outputting trend information corresponding to various data;
and according to different influences of different areas, weighting is allocated to each trend prediction model to obtain a comprehensive prediction model, and early warning is carried out when the output value of the comprehensive prediction model exceeds a safety value.
The beneficial effects are that: the invention provides an underground factory building environment monitoring model and a trend early warning algorithm, wherein the monitoring model comprises the following components: the data acquisition module is used for acquiring one or more data of different areas in the underground factory building at different moments; the data processing module is used for training the depth neural network by the same kind of historical data and the current data of the same area to obtain a trend prediction model for outputting trend information corresponding to various data; and the early warning module is used for distributing weights to each trend prediction model according to different influences of different areas to obtain a comprehensive prediction model, and early warning is carried out when the output value of the comprehensive prediction model exceeds a safety value. The same kind of data in different areas is fused and predicted according to time sequence, and a deep neural network is adopted for training, so that the prediction accuracy of trend change of single data in a certain area can be greatly improved, and accurate capturing trend is realized.
Drawings
Fig. 1 is a schematic block diagram of an environment monitoring model for an underground plant provided by the invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
FIG. 1 is a diagram of an environment monitoring model of an underground plant, which comprises a data acquisition module, a data processing module and an early warning module; the data acquisition module is used for acquiring one or more data of different areas in the underground factory building at different moments; the data processing module is used for training the depth neural network by the same kind of historical data and the current data of the same area to obtain a trend prediction model for outputting trend information corresponding to various data; and the early warning module is used for distributing weights to each trend prediction model according to different influences of different areas to obtain a comprehensive prediction model, and early warning is carried out when the output value of the comprehensive prediction model exceeds a safety value.
The same kind of data in different areas is fused and predicted according to time sequence, and a deep neural network is adopted for training, so that the prediction accuracy of trend change of single data in a certain area can be greatly improved, and accurate capturing trend is realized. And the model is further upgraded and optimized by utilizing different influences of different areas, so that the integral change trend of the underground factory building on certain data can be simply and efficiently output, and workers can clearly know the integral safety degree.
Because of the many factors that affect the safety of underground plants, a variety of sensors are required for classified collection. The data acquisition module comprises a linear displacement sensor matrix, a methane laser sensor and an ultrasonic sensor. The linear displacement sensor matrix is used for collecting linear displacement of underground plant structures in different areas, the methane laser sensor is used for monitoring methane content in the underground plant, and the ultrasonic sensor is used for collecting crack information of the top and the side wall of the underground plant.
In a specific implementation scenario, various historical data and current data are input into a value depth neural network model together for training, so that a trend prediction model of the data can be obtained, and future change trends of the underground plant can be known through the trend prediction model, wherein the future change trends comprise displacement changes, methane content changes and crack changes.
In a specific implementation scenario, linear displacement sensor matrixes are arranged at the top and the side edges of an underground plant, and the change condition of relative displacement is monitored through an internal structure, so that whether the relative displacement change and the structural deformation of the whole structure occur can be known. Methane laser sensors are placed at the top of an underground plant where methane is a relatively light source and can be preferentially concentrated, and safety can be best controlled by measuring the density of methane at the top.
The linear displacement sensor matrix arranged in the building structure monitors the relative displacement between the internal structures, and judges the relative displacement data in the building structure, so that the deformation of the whole structure can be known by judging the relative displacement data in the factory building. The methane content in the structure is detected through the methane laser sensor, and dangerous gas is predicted, so that whether the factory building is safe or not is judged. The ultrasonic sensor is used for monitoring whether the structure has cracks and the change trend of the cracks, and timely finding out the cracks and the expanded cracks, so that accidents are prevented.
Preferably, the early warning module specifically includes:
for linear displacement data, the influences of different areas are top, side wall and bottom in sequence from large to small. The top is subjected to pressure and can be bent and deformed, the side wall is subjected to pressure and compression deformation, and the bottom is generally not deformed. Therefore, the top is most easily broken, and once the displacement change occurs, the top is broken first wherever possible, so that the influence is the greatest.
For methane content data, the influence of different areas is sequentially from the top to the bottom; since methane is relatively light, and wherever it leaks in, it will accumulate at the top, so it is most important that the top forecast of methane content, and it can be concluded that the plant is unsafe as long as the top formaldehyde content exceeds the standard.
For crack data, the influences of different areas are sequentially from large to small, namely the top and the bottom of the side wall. Cracks and displacements are weighted similarly and are not described in detail herein.
The specific weight value may be given according to the actual situation.
Training the deep neural network according to the historical linear displacement and the current linear displacement to obtain a displacement trend prediction model; training the deep neural network according to the historical methane content and the current methane content to obtain a methane content trend prediction model; training the deep neural network according to the historical cracks and the current cracks to obtain a crack trend prediction model.
Finally, according to different influences of different areas, a displacement comprehensive prediction model, a methane content comprehensive prediction model and a crack comprehensive prediction model are respectively obtained; and setting corresponding safety values for each comprehensive prediction model, and if the safety values are exceeded, carrying out corresponding early warning.
And a plurality of groups of safety values can be set for each comprehensive prediction model according to the safety level, and each group of safety values corresponds to one early warning level. For example, the safety value is A, B, C, and the corresponding early warning grades are 1, 2 and 3 respectively. The staff carries out different counter measures according to different early warning grades, can accomplish maintenance task on the basis of minimum manpower and materials, labour saving and time saving.
In a specific implementation scenario, the safety of the underground powerhouse is not only self and internal, but also the influence of the external environment. For example, the temperature of fire in the outside can be high, water flooding in the outside can form water pressure on the factory building, and the safety of the factory building can be influenced. Therefore, the data acquisition module further comprises a temperature sensor and a humidity sensor, wherein the temperature sensor is used for detecting the temperature outside the underground factory building, and the humidity sensor is used for detecting the humidity outside the underground factory building. Whether the external environment temperature is within the safe range is predicted by detecting the temperature, and whether the water content of the external environment is within the safe range is predicted by detecting the humidity.
The embodiment of the invention also provides an underground factory building environment trend early warning algorithm which is used on the underground factory building environment monitoring model and comprises the following steps:
collecting one or more data of different areas in the underground factory building at different moments;
training the same kind of historical data and current data of the same area on a deep neural network to obtain a trend prediction model for outputting trend information corresponding to various data;
and according to different influences of different areas, weighting is allocated to each trend prediction model to obtain a comprehensive prediction model, and early warning is carried out when the output value of the comprehensive prediction model exceeds a safety value.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. The environment monitoring model for the underground factory building is characterized by comprising a data acquisition module, a data processing module and an early warning module;
the data acquisition module is used for acquiring one or more data of different areas in the underground factory building at different moments;
the data processing module is used for training the depth neural network by the same kind of historical data and the current data of the same area to obtain a trend prediction model for outputting trend information corresponding to various data;
and the early warning module is used for distributing weights to each trend prediction model according to different influences of different areas to obtain a comprehensive prediction model, and early warning is carried out when the output value of the comprehensive prediction model exceeds a safety value.
2. The underground powerhouse environment monitoring model of claim 1, wherein the data acquisition module comprises a linear displacement sensor matrix, a methane laser sensor, and an ultrasonic sensor; the linear displacement sensor matrix is used for acquiring linear displacement data of underground plant structures in different areas, the methane laser sensor is used for monitoring methane content data in the underground plant, and the ultrasonic sensor is used for acquiring crack data of the top and the side wall of the underground plant.
3. The underground powerhouse environment monitoring model of claim 2, wherein the early warning module specifically comprises:
for linear displacement data, the influence of different areas is sequentially from big to small, namely top, side wall and bottom;
for methane content data, the influence of different areas is sequentially from the top to the bottom;
for crack data, the influences of different areas are sequentially from large to small, namely the top and the bottom of the side wall.
4. The underground powerhouse environment monitoring model of claim 3, wherein the data processing module is specifically configured to: training the deep neural network according to the historical linear displacement and the current linear displacement to obtain a displacement trend prediction model; training the deep neural network according to the historical methane content and the current methane content to obtain a methane content trend prediction model; training the deep neural network according to the historical cracks and the current cracks to obtain a crack trend prediction model.
5. The underground powerhouse environment monitoring model of claim 4, wherein the early warning module is specifically configured to:
respectively obtaining a displacement comprehensive prediction model, a methane content comprehensive prediction model and a crack comprehensive prediction model according to different influences of different areas;
and setting corresponding safety values for each comprehensive prediction model, and if the safety values are exceeded, carrying out corresponding early warning.
6. The underground powerhouse environment monitoring model of claim 4, wherein the early warning module is specifically configured to: and setting a plurality of groups of safety values according to the safety level and aiming at each comprehensive prediction model, wherein each group of safety values corresponds to one early warning level.
7. The underground powerhouse environment monitoring model of claim 2, wherein the data acquisition module further comprises a temperature sensor for detecting a temperature outside the underground powerhouse and a humidity sensor for detecting a humidity outside the underground powerhouse.
8. The underground powerhouse environment monitoring model of claim 7, wherein the data processing module is specifically configured to: training the deep neural network according to the historical temperature and the current temperature to obtain a temperature trend prediction model; training the deep neural network according to the historical humidity and the current humidity to obtain a temperature trend prediction model.
9. An underground powerhouse environment trend early warning algorithm, which is used on an underground powerhouse environment monitoring model according to any one of claims 1-8, and comprises the following steps:
collecting one or more data of different areas in the underground factory building at different moments;
training the same kind of historical data and current data of the same area on a deep neural network to obtain a trend prediction model for outputting trend information corresponding to various data;
and according to different influences of different areas, weighting is allocated to each trend prediction model to obtain a comprehensive prediction model, and early warning is carried out when the output value of the comprehensive prediction model exceeds a safety value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310666057.4A CN116756504A (en) | 2023-06-06 | 2023-06-06 | Underground factory building environment monitoring model and trend early warning algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310666057.4A CN116756504A (en) | 2023-06-06 | 2023-06-06 | Underground factory building environment monitoring model and trend early warning algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116756504A true CN116756504A (en) | 2023-09-15 |
Family
ID=87960085
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310666057.4A Pending CN116756504A (en) | 2023-06-06 | 2023-06-06 | Underground factory building environment monitoring model and trend early warning algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116756504A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117542169A (en) * | 2023-11-07 | 2024-02-09 | 国网江苏省电力有限公司镇江供电分公司 | Automatic equipment temperature abnormality early warning method based on big data analysis |
| CN118548930A (en) * | 2024-07-24 | 2024-08-27 | 中交第三公路工程局有限公司 | Comprehensive waterproof and anti-seepage monitoring method and system for building |
-
2023
- 2023-06-06 CN CN202310666057.4A patent/CN116756504A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117542169A (en) * | 2023-11-07 | 2024-02-09 | 国网江苏省电力有限公司镇江供电分公司 | Automatic equipment temperature abnormality early warning method based on big data analysis |
| CN118548930A (en) * | 2024-07-24 | 2024-08-27 | 中交第三公路工程局有限公司 | Comprehensive waterproof and anti-seepage monitoring method and system for building |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116756504A (en) | Underground factory building environment monitoring model and trend early warning algorithm | |
| CN113779835A (en) | AI and intelligent monitoring system based deep and large foundation pit safety early warning method | |
| CN102330645A (en) | Health monitoring system and method for wind generator system structure | |
| CN103604642A (en) | Problematic cable/concentrated load progressive recognition method on basis of generalized displacement cable power monitoring | |
| CN113900381A (en) | Steel structure remote health monitoring platform based on Internet of things and application method | |
| CN102735459A (en) | Progressive generalized displacement identification method for problem cables and support based on temperature change tension force monitoring | |
| CN103616245A (en) | Progressive concentrated load support angular displacement identification method for damaged cable in process of cable force monitoring | |
| CN103604554A (en) | Problematic cable/concentrated load/generalized displacement progressive recognition method on basis of cable power monitoring | |
| CN103604627A (en) | Problematic cable/concentrated load/generalized displacement progressive recognition method on basis of hybrid monitoring | |
| CN103604631A (en) | Problematic cable/concentrated load progressive recognition method on basis of linear displacement cable power monitoring | |
| CN103616247A (en) | Generalized displacement cable tension monitoring identification method for defective cable and concentrated loads | |
| CN104807661B (en) | A kind of high-rise and tall and slender structure Dynamic testing evaluation on bearing capacity method | |
| CN103604651A (en) | Damaged cable/concentrated load/generalized displacement recognition method on basis of cable power monitoring | |
| CN103604550A (en) | Damaged cable/concentrated load/generalized displacement progressive recognition method on basis of cable power monitoring | |
| CN103604656A (en) | Damaged cable/concentrated load/linear displacement progressive recognition method on basis of cable power monitoring | |
| CN103616233A (en) | Generalized displacement cable tension monitoring progressive identification method for damaged cable and concentrated loads | |
| CN103616114A (en) | Progressive identifying method for problem cable and concentrated loads based on cable force monitoring in time of angular displacement | |
| CN103616211A (en) | Method for identifying concentrated loads of damaged cables in linear displacement process based on cable force monitoring | |
| CN103604650A (en) | Problematic cable/concentrated load/linear displacement recognition method on basis of cable power monitoring | |
| CN103616230A (en) | Damaged cables in process of cable force monitoring and progressive concentrated load identification method | |
| CN103604653A (en) | Damaged cable/concentrated load/generalized displacement progressive recognition method on basis of strain monitoring | |
| CN102735461A (en) | Identification method for problem cables and angular support displacement on basis of temperature variation and cable force monitoring | |
| CN103616238A (en) | Concentrated load angular displacement identification method for problem cables in process of cable force monitoring | |
| CN103616235A (en) | Method for recognizing concentrated loads of damaged cable under condition of generalized displacement based on cable force monitoring | |
| CN103604626A (en) | Damaged cable/concentrated load/generalized displacement progressive recognition method on basis of hybrid monitoring |
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 |