CN113984114B - Method for diagnosing abnormality of underwater structure of ocean floating platform - Google Patents
Method for diagnosing abnormality of underwater structure of ocean floating platform Download PDFInfo
- Publication number
- CN113984114B CN113984114B CN202111207655.2A CN202111207655A CN113984114B CN 113984114 B CN113984114 B CN 113984114B CN 202111207655 A CN202111207655 A CN 202111207655A CN 113984114 B CN113984114 B CN 113984114B
- Authority
- CN
- China
- Prior art keywords
- underwater structure
- floating platform
- diagnosis
- monitoring
- ocean floating
- 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
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
An abnormality diagnosis method for an underwater structure of an ocean floating platform belongs to the field of abnormality diagnosis of the underwater structure of the ocean floating platform. The invention utilizes the essential characteristic that the six-degree-of-freedom motion law of the floating platform changes when the underwater structure is damaged, and establishes the six-degree-of-freedom combined prediction model of the ocean floating platform under the lossless state of the underwater structure based on the long-time memory neural network LSTM. And based on historical monitoring database information, providing an abnormality diagnosis method based on a Principal Component Analysis (PCA) method, and providing an abnormality diagnosis threshold value. The system can accurately diagnose abnormal behaviors such as corrosion, sudden fracture, rigidity damage and the like of the underwater structure of the ocean floating platform, and can be widely applied to the field of abnormal diagnosis of the structure of the ocean floating platform.
Description
Technical Field
The invention relates to an abnormality diagnosis method for an underwater structure of an ocean floating platform, and belongs to the field of abnormality diagnosis for the underwater structure of the ocean floating platform.
Background
The underwater structure is the main stressed and positioned part of the ocean floating platform. Under the comprehensive action of external environmental forces such as wave force, ocean current force and the like, the underwater structure is easy to have behaviors such as fatigue damage and the like, and meanwhile, the underwater structure is easy to have behaviors such as corrosion and the like due to long-term service in an ocean environment. The damage of the underwater structure can directly cause the structural failure of the ocean floating platform, and great threat is generated to the safety of operators. Therefore, it is very important to the abnormality diagnosis work of the underwater structure.
Diagnosing abnormal behavior of an underwater structure typically requires the installation of various sensors on the underwater structure. And carrying out abnormity diagnosis by analyzing the change behavior of the long-term monitoring data. However, the underwater structure environment is complex, the installation difficulty of the sensor is too high, and the cost is too high. Therefore, it is very important to diagnose the abnormality of the underwater structure by an indirect method. With the continuous development of sensor technology, the monitoring technology for the six-degree-of-freedom motion of the marine environment and the floating platform is mature. Therefore, the invention provides a system and a method for diagnosing the abnormality of the underwater structure of the ocean floating platform based on the ocean environment load and the six-degree-of-freedom motion information of the floating body.
The invention effectively solves the problems of high cost and large installation difficulty of the traditional underwater structure-based monitoring method. Meanwhile, real-time diagnosis can be carried out on the state of the underwater structure on the basis of real-time monitoring information.
Disclosure of Invention
In view of the above problems, the present invention provides a method for diagnosing abnormality of underwater production structure. The diagnosis method effectively solves the problem that the underwater structure is difficult to diagnose abnormally, and carries out the underwater structure abnormal diagnosis by utilizing marine environment monitoring data and floating body six-degree-of-freedom monitoring data based on the provided abnormal diagnosis method. The defects of high cost and high construction difficulty of directly monitoring the underwater structure are effectively overcome.
The technical scheme adopted by the invention is as follows: an abnormality diagnosis system for an underwater structure of an ocean floating platform comprises a monitoring end and a central processing unit, wherein the monitoring end is electrically connected with an anemograph, a wave measuring radar, a current meter, an interstellar differential GPS, an inertial navigation system and a tilt angle sensor by adopting an internet of things transmission node; the transmission node of the Internet of things comprises an information mediation module, a signal transmission module and a signal storage module. The signal mediation module unifies formats of monitoring information of the anemoscope, the wave measuring radar, the current meter, the interstellar differential GPS and the tilt angle sensor, the monitoring information is transmitted to the central processing unit through electric connection by the signal transmission module, and meanwhile, the monitoring information is stored and backed up by the signal storage module; the anemometer provides wind field information for the diagnostic system; the wave measuring radar provides wave information for the diagnosis system; the ocean flow meter provides ocean flow information for the diagnosis system; the interplanetary differential GPS provides floating body translation information for the diagnosis system; the inertial navigation system and the tilt angle sensor provide floating body rotation information for the diagnosis system; the central processing unit comprises a data receiver and an industrial personal computer, and the data receiver is electrically connected with a transmission node of the Internet of things; the industrial personal computer provides the real-time diagnosis state of the underwater structure through a built-in abnormity diagnosis algorithm and displays and stores the real-time diagnosis state.
The method for diagnosing the abnormality of the underwater structure of the ocean floating platform comprises the following steps: A. the method comprises the steps that a monitoring end is installed at the corresponding position of an ocean floating platform, an anemoscope is installed at the top of a tower, a wave measuring radar and a current meter are installed underwater through a specific clamp, and an interstellar differential GPS, an inertial navigation system and an inclination angle sensor are installed at the bottom of the tower.
B. And starting working according to preset parameters, namely an anemoscope, a wave measuring radar, a current meter, an interstellar differential GPS, an inertial navigation system and an inclination angle sensor.
C. The signal adjusting module unifies the format of the information collected by the sensor and transmits the information to the signal transmission module, and the signal storage module stores and backups the monitoring information with unified format.
D. The signal transmission module transmits the monitoring information with uniform format to the signal receiver, and the signal receiver transmits the received monitoring information to the industrial personal computer.
E. The industrial personal computer calculates a real-time abnormity diagnosis index DI by using a built-in abnormity diagnosis model, and judges whether an underwater structure is abnormal when DI is larger than UCL; when DI is less than or equal to UCL, the structure is normal. And meanwhile, the industrial personal computer displays and stores the calculation result in real time.
The invention has the following advantages:
1. according to the invention, the underwater structure abnormity diagnosis system is constructed only by considering the marine environment information and the six-degree-of-freedom motion information of the floating body, so that the problems of high construction difficulty and high economic cost caused by mounting a sensor on the underwater structure are solved.
2. The abnormality diagnosis system can diagnose the health state of the underwater structure in real time by using real-time monitoring data according to the provided abnormality diagnosis method.
3. When the hardware of the abnormity diagnosis system breaks down, the installation position of the abnormity diagnosis system is low in construction difficulty, so that the abnormity diagnosis system is easy to maintain and replace.
4. The Internet of things node unifies the format of the information collected by each sensor and transmits the information to the central processing unit, and meanwhile, the collected information is stored and backed up, so that data loss caused by faults of the central processing unit is avoided.
5. The provided abnormity diagnosis system can accurately diagnose abnormal behaviors of corrosion, damage, fracture and the like of the underwater structure, and has high recognition rate.
Drawings
Fig. 1 is a schematic structural diagram of an abnormality diagnosis system for an underwater structure of an ocean floating platform.
FIG. 2 is a schematic diagram of a method for diagnosing an abnormality of an underwater structure of an ocean floating platform.
FIG. 3 is a six degree of freedom joint prediction model prediction result.
FIG. 4 shows the results of abnormality diagnosis when a lesion occurs at different positions.
In the figure: 1. anemoscope, 2, wave-measuring radar, 3, current meter, 4, interstellar difference GPS,5, inertial navigation system, 6, tilt sensor, 7, signal conditioning module, 8, signal transmission module, 9, signal storage module, 10, thing networking transmission node, 11, monitoring end, 12, signal receiver, 13, industrial computer, 14, central processing unit.
Detailed Description
The present invention is described in detail below with reference to the attached drawings. It is to be understood, however, that the drawings are provided solely for the purposes of promoting an understanding of the invention and that they are not to be construed as limiting the invention.
As shown in figure 1, the system for diagnosing the abnormality of the underwater structure of the ocean floating platform provided by the invention comprises a monitoring end 11 and a central processing unit 14. The monitoring end 11 comprises an anemoscope 1, a wave-measuring radar 2, a current meter 3, an interstellar differential GPS4, an inertial navigation system 5, an inclination angle sensor 6 and an internet of things transmission node 10; the transmission node of the internet of things comprises a signal adjusting module 7, a signal transmission module 8 and a signal storage module 9; the system comprises an anemoscope 1, a wave measuring radar 2, a current meter 3, an interstellar differential GPS4, an inertial navigation system 5 and an inclination angle sensor 6, wherein the inclination angle sensor is electrically connected with a transmission node 10 of the Internet of things; the central processing unit 14 comprises a signal receiver 12 and an industrial personal computer 13, wherein the signal receiver 12 is electrically connected with the industrial personal computer.
The anemometer 1 is used to collect wind field data. The wave radar 2 is used for collecting wave data. Current meter 3 is used to collect ocean current data. The interstellar differential GPS4 is used for collecting floating body translation data. The inertial navigation system 5 and the tilt angle sensor are used for collecting rotation data of the floating body. The signal conditioning module 7 is used for unifying the formats of the data collected by the sensors 1-6. The signal transmission module 8 is used for transmitting the monitoring data with uniform format to the signal receiver 12 in the central processing unit 14. The signal storage module 9 is used for storing and backing up the monitoring data with uniform format.
The signal receiver 12 is used for receiving the monitoring information transmitted by the signal transmission module 8 and transmitting the monitoring information to the industrial personal computer 13. And the industrial control unit 13 is used for giving the real-time health state of the underwater structure through a built-in abnormity diagnosis algorithm, and displaying and storing the real-time health state.
The signal storage module 9 can be a high-capacity high-performance SD card. The industrial personal computer 13 can also be a high-performance computer. As shown in fig. 2, a schematic diagram of the anomaly diagnosis method in the present invention is shown, wherein the method mainly includes two parts, namely a platform six-degree-of-freedom motion joint prediction model and an anomaly diagnosis model. The six-degree-of-freedom motion joint prediction model of the platform mainly relates to an anemoscope, a wave measuring radar, a current meter, an interstellar differential GPS, an inertial navigation system, an inclination angle sensor and an internet of things transmission node. Wherein anemoscope, wave-measuring radar, current meter gather and obtain marine environment load, and interplanetary difference GPS, inertial navigation system and angular transducer gather and obtain body six degree of freedom motion information, and thing networking transmission node unifies the data format. The method mainly comprises the processes of data set manufacturing, model building, super-parameter selection, model training, error analysis and the like. Historical monitoring data X of marine environment load in an underwater structure under a nondestructive state (such as the initial stage of service of a platform) 1 As input, X 1 ∈R N And N is the input dimension. The six-freedom motion data Y of the floating body 1 As output, wherein Y 1 ∈R M And M belongs to the output dimension. And constructing a floating body six-degree-of-freedom motion joint prediction model based on an LSTM neural network (Long-short-term memory neural network). And constructing an underwater structure abnormity diagnosis model on the basis of the floating body six-degree-of-freedom motion joint prediction model. Monitoring data X of historical load under lossless state without participating in training 2 Inputting the combined prediction model to obtain predicted six-degree-of-freedom information Y p And monitoring data Y 2 Comparing to obtain a residual error sequence D,D＝Y 2 -Y p ，D∈R M . Principal component analysis was performed on the residual sequence D, which was first centered and the covariance matrix calculated. Performing SVD on the covariance matrix, and solving corresponding eigenvalue and eigenvector; taking the maximum k eigenvalues to obtain a corresponding eigenvalue matrix W = [ W = 1 ,w 2 ,…,w k ](ii) a The k value selection principle is as follows:
wherein S ii The diagonal elements of the S matrix produced for the SVD decomposition, q the rank of the S matrix, and P the proportion of the principal components retained by the PCA algorithm. The PCA algorithm is a Principal Component Analysis (PCA).
Calculating an abnormality diagnosis index
Wherein D is a residual sequence matrix of n x m, n is the number of training samples, m is the characteristic dimension of the samples, c represents a threshold coefficient, and 1 is taken. And when D is a residual sequence in the nondestructive state of the underwater structure, obtaining an abnormal diagnosis threshold UCL. And finishing establishing the abnormity diagnosis model. In actual application, inputting the real-time monitoring information into an abnormality diagnosis model to obtain a real-time abnormality diagnosis index DI, and if DI is larger than UCL, prejudging that the underwater structure is abnormal; if DI is less than UCL, the structure is normal.
Example 1
A. And installing a monitoring end at a corresponding position of the ocean floating platform.
B. Working is started according to preset parameters of an anemoscope 1, a wave measuring radar 2, a current meter 3, an interstellar differential GPS4, an inertial navigation system 5 and an inclination angle sensor 6.
C. The signal adjusting module 7 unifies the format of the information collected by the sensor and transmits the information to the signal transmission module 8, and the signal storage module 9 stores and backups the monitoring information with unified format.
D. The signal transmission module 8 transmits the monitoring information with uniform format to the signal receiver 12, and the signal receiver 12 transmits the received monitoring information to the industrial personal computer 13.
The marine environment load data is used as input, the platform six-degree-of-freedom motion is used as output, and the six-degree-of-freedom joint prediction model is established to obtain a prediction result as shown in fig. 3.
E. The industrial personal computer 13 calculates a real-time abnormity diagnosis index DI by using a built-in abnormity diagnosis model, and judges whether the underwater structure is abnormal when DI is larger than UCL; when DI is less than or equal to UCL, the structure is normal. And meanwhile, the industrial personal computer displays and stores the calculation result in real time.
Calculating an abnormality diagnosis threshold value by using an abnormality diagnosis algorithm of an underwater structure of an ocean floating platform to obtain the abnormality diagnosis threshold value UCL of 8.5 x 10 -8 。
Taking different anchor chain damages as an example, an abnormal diagnosis index DI is given when the anchor chain damage is 5% under 5 random ocean working conditions. It can be seen that when the underwater structure is damaged, the abnormality diagnosis index DI is greater than the abnormality diagnosis threshold value UCL, which indicates that the abnormality diagnosis system of the present invention can accurately identify the underwater structure with an identification rate of 100%.
The above embodiments are only used for illustrating the present invention, and the structure, connection manner, manufacturing process and the like of each component can be changed, and equivalent changes and improvements made on the basis of the technical scheme of the present invention should not be excluded from the protection scope of the present invention.
Claims (1)
1. A method for diagnosing the abnormality of an underwater structure of an ocean floating platform is characterized by comprising the following steps:
the abnormity diagnosis system adopted by the diagnosis method comprises a monitoring end (11) and a central processing unit (14), wherein the central processing unit (14) is electrically connected with a signal receiver (12) by adopting an industrial personal computer (13); the signal receiver (12) is electrically connected with the transmission node (10) of the Internet of things in the monitoring end (11); an industrial personal computer (13) is internally provided with an underwater structure abnormity diagnosis algorithm of the ocean floating platform;
the diagnosis method specifically comprises the following steps:
A. installing a monitoring end (11) at a corresponding position of the ocean floating platform; the monitoring end (11) is electrically connected with a sensor system by adopting an internet of things transmission node (10), and the sensor system comprises an anemoscope (1), a wave measuring radar (2), a current meter (3), an interplanetary differential GPS (4), an inertial navigation system (5) and an inclination angle sensor (6);
B. starting to work according to preset parameters by the six-degree-of-freedom sensor; the preset parameters comprise sampling frequency and a collection mode;
C. the signal adjusting module (7) unifies the format of the information acquired by the six-degree-of-freedom sensor and transmits the information to the signal transmission module (8), and the signal storage module (9) stores and backups the monitoring information with unified format;
D. the signal transmission module (8) transmits the monitoring information with uniform format to the signal receiver (12), and the signal receiver (12) transmits the received monitoring information to the industrial personal computer (13);
E. the industrial personal computer (13) calculates a real-time abnormity diagnosis index DI by using a built-in abnormity diagnosis model and compares the real-time abnormity diagnosis index DI with an abnormity diagnosis threshold UCL; when the DI is larger than the UCL, prejudging that the underwater structure is abnormal; when DI is less than or equal to UCL, the structure is normal; meanwhile, the industrial personal computer displays and stores the calculation result in real time;
the abnormal diagnosis algorithm for the underwater structure of the ocean floating platform comprises the processes of data set manufacturing, model building, hyper-parameter selection, model training and error analysis; the method specifically comprises the following steps:
(1) Historical monitoring data X of marine environment load under nondestructive state of underwater structure 1 As input, X ∈ R N N is the input dimension; the six-freedom motion data Y of the floating body 1 As output, where Y ∈ R M M belongs to the output dimension;
(2) Historical load monitoring data X under the lossless state without participating in training 2 Inputting the combined prediction model to obtain predicted six-degree-of-freedom information Y p And monitoring data Y 2 Comparing to obtain a residual sequence D, D = Y 2 -Y p ，D∈R M ；
(3) Carrying out principal component analysis on the residual sequence D, centralizing the residual sequence D and calculating a covariance matrix; carrying out SVD on the covariance matrix, and solving corresponding eigenvalue and eigenvector;
taking the maximum k eigenvalues to obtain a corresponding eigenvalue matrix W = [ W = 1 ,w 2 ,…,w k ]；
The k value selection principle is as follows:
wherein S ii Diagonal elements of an S matrix generated by SVD decomposition are provided, q is the rank of the S matrix, and P represents the proportion of main components reserved by a PCA algorithm;
calculating an abnormality diagnosis index
D is an n x m residual sequence matrix, n is the number of training samples, m is the characteristic dimension of the samples, c represents a threshold coefficient, and 1 is selected;
(4) When D is a residual sequence in the nondestructive state of the underwater structure, obtaining an abnormal diagnosis threshold UCL; completing establishing an abnormality diagnosis model;
(5) Inputting the real-time monitoring information into an abnormality diagnosis model to obtain a real-time abnormality diagnosis index DI, and if DI is larger than UCL, prejudging that the underwater structure is abnormal; when DI is less than or equal to UCL, the structure is normal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111207655.2A CN113984114B (en) | 2021-10-18 | 2021-10-18 | Method for diagnosing abnormality of underwater structure of ocean floating platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111207655.2A CN113984114B (en) | 2021-10-18 | 2021-10-18 | Method for diagnosing abnormality of underwater structure of ocean floating platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113984114A CN113984114A (en) | 2022-01-28 |
| CN113984114B true CN113984114B (en) | 2022-12-06 |
Family
ID=79739001
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111207655.2A Active CN113984114B (en) | 2021-10-18 | 2021-10-18 | Method for diagnosing abnormality of underwater structure of ocean floating platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113984114B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117404258B (en) * | 2023-12-13 | 2024-03-29 | 江苏道达风电设备科技有限公司 | Composite cylindrical foundation offshore construction cloud monitoring system |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4762088B2 (en) * | 2006-08-31 | 2011-08-31 | 株式会社東芝 | Process abnormality diagnosis device |
| JP2012137934A (en) * | 2010-12-27 | 2012-07-19 | Hitachi Ltd | Abnormality detection/diagnostic method, abnormality detection/diagnostic system, abnormality detection/diagnostic program and company asset management/facility asset management system |
| CN104102773B (en) * | 2014-07-05 | 2017-06-06 | 山东鲁能软件技术有限公司 | A kind of equipment fault early-warning and state monitoring method |
| CN106338372A (en) * | 2016-09-19 | 2017-01-18 | 中国海洋大学 | Offshore platform damage positioning method based on residual strain energy and system thereof |
| CN106482781A (en) * | 2016-09-20 | 2017-03-08 | 中国海洋石油总公司 | A kind of marine floating type platform prototype measuring system |
| US11146579B2 (en) * | 2018-09-21 | 2021-10-12 | General Electric Company | Hybrid feature-driven learning system for abnormality detection and localization |
| KR102051791B1 (en) * | 2018-11-16 | 2020-01-08 | 고려대학교 산학협력단 | System and method of monitoring soil gas and processing correspondence |
| CN111400850B (en) * | 2018-12-28 | 2024-04-09 | 阿里巴巴集团控股有限公司 | Equipment fault analysis method, device, equipment and storage medium |
| CN111768082A (en) * | 2020-06-02 | 2020-10-13 | 广东电网有限责任公司 | Power equipment state evaluation method based on big data analysis |
| CN112232447B (en) * | 2020-12-14 | 2021-06-04 | 国网江西省电力有限公司电力科学研究院 | Construction method of complete sample set of power equipment state monitoring data |
-
2021
- 2021-10-18 CN CN202111207655.2A patent/CN113984114B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113984114A (en) | 2022-01-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113569445A (en) | Steel structure health monitoring system and method based on digital twinning technology | |
| CN116643517B (en) | Accident prevention equipment for underwater manned submersible vehicle | |
| KR20170066099A (en) | Apparatus for inspecting rotating device and method thereof | |
| CN104880217B (en) | A kind of fault sensor signal reconstruct method based on the measured value degree of association | |
| CN111259990A (en) | Sea water pump fault monitoring device and diagnosis method based on multi-source information fusion | |
| CN113984114B (en) | Method for diagnosing abnormality of underwater structure of ocean floating platform | |
| Moeinifard et al. | Lost vibration test data recovery using convolutional neural network: a case study | |
| CN110243497A (en) | A kind of sensor fault diagnosis method and system based on principal component analysis | |
| CN110987396B (en) | Intelligent fault diagnosis and service life prediction method for coal mining machine rocker arm | |
| JPWO2018051568A1 (en) | Plant abnormality diagnosis device and plant abnormality diagnosis system | |
| CN115655717A (en) | Bearing fault diagnosis method based on depth domain generalization network | |
| CN110196165B (en) | Rotary machine ball bearing fault diagnosis method based on K-means clustering and evidence reasoning | |
| CN115994320A (en) | Intelligent friction pendulum vibration isolation support and state monitoring and fault diagnosis system | |
| KR101711606B1 (en) | A similarity-based damage estimating method of offshore structures | |
| JP2024045515A (en) | Structure diagnosis system, structure diagnosis method, and structure diagnosis program | |
| WO2020230422A1 (en) | Abnormality diagnosis device and method | |
| KR102267811B1 (en) | System for predicting present state and future state of agriculture solar power generation structure based on rnn | |
| Yanez-Borjas et al. | Convolutional neural network-based methodology for detecting, locating and quantifying corrosion damage in a truss-type bridge through the autocorrelation of vibration signals | |
| CN116907772A (en) | Self-diagnosis and fault source identification method and system of bridge structure monitoring sensor | |
| US11339763B2 (en) | Method for windmill farm monitoring | |
| CN116383750A (en) | Rolling bearing early-stage abnormality detection method based on windowed differential health index | |
| CN112231849B (en) | Axle temperature fault detection method based on NEST and SPRT fusion algorithm | |
| CN209878248U (en) | Bridge structure health monitoring system based on CAN bus and wireless communication | |
| CN114235257A (en) | Bolt axial force online accurate monitoring method and device based on data fusion | |
| CN103471870A (en) | Method for researching sensibility and clustering performance for recognizing states of coal mine equipment based on two-dimensional projection characteristic indexes |
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 |