A wind turbine fault diagnosis method and system based on sensor network

Abstract

The present invention discloses a wind turbine fault diagnosis method and system based on a sensor network, which relates to the field of wind turbine fault prediction. The method comprises obtaining past wind turbine specifications and operating data, analyzing and processing the past wind turbine operating data to obtain a wind turbine database, constructing a wind turbine model and signal fluctuation curve based on the wind turbine database, obtaining current wind turbine specifications and deploying and operating sensors in combination with the wind turbine model; obtaining primary sensor data and configuring a sensor operating strategy using an improved tabu search algorithm in combination with a standard signal curve; implementing the sensor configuration operating strategy and obtaining real-time sensor data to obtain fault diagnosis results for the wind turbine. The present invention utilizes an improved tabu search algorithm to optimize and adjust sensor operation, thereby alleviating the problem of excessive external sensor installation and operation affecting wind turbine operation and improving the accuracy of wind turbine fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the field of wind turbine fault prediction, and in particular to a wind turbine fault diagnosis method and system based on a sensor network. Background Art

[0002] Wind turbines are complex systems comprised of electrical, mechanical, and automated control components, and are significantly affected by environmental conditions. To prevent major accidents and reduce turbine failure rates, the industry currently employs big data analysis of data generated during wind turbine operation to provide early warnings of turbine status and diagnose faults.

[0003] Currently, the industry primarily utilizes SCADA data, which is cleaned through analysis and sorting. Missing data can be retrieved through fitting methods. These methods improve data acquisition quality. Due to the incompleteness of early wind turbine SCADA systems, the quality of acquired information was low, resulting in low data analysis accuracy and difficulty guiding production. Therefore, the use of external data acquisition cards to supplement SCADA data, improve data quality, and enhance the accuracy of data analysis is an industry trend.

[0004] In order to solve these problems, a wind turbine fault diagnosis method and system based on sensor networks is urgently needed. Summary of the Invention

[0005] To solve the above problems, this application proposes a wind turbine fault diagnosis method and system based on a sensor network, which includes the following steps:

[0006] S1. Obtaining past specifications and operating data of wind turbines, analyzing and processing the past operating data of wind turbines to obtain a wind turbine database, wherein the wind turbine database includes a pitch unit database, a current conversion unit database, and a yaw unit database;

[0007] S2. Constructing a wind turbine model and a signal fluctuation curve based on the wind turbine database;

[0008] S3. Obtain the current wind turbine specifications and combine them with the wind turbine model to deploy and operate sensors;

[0009] S4, obtaining sensor data once and using an improved tabu search algorithm to configure an operating strategy for the sensor in combination with a standard signal curve;

[0010] S5. Implement the sensor configuration operation strategy and obtain real-time sensor data to obtain a fault diagnosis result of the wind turbine generator set.

[0011] Preferably, the specific contents of constructing the wind turbine model and the signal fluctuation curve according to the wind turbine database are:

[0012] The wind turbine model includes a variable pitch model, a variable flow model, and a yaw model;

[0013] Analyze wind turbine specifications to obtain wind turbine structural information and past sensor installation locations;

[0014] Generate a three-dimensional module of the wind turbine according to the proportion of the wind turbine structure information, and disassemble the three-dimensional module of the wind turbine to obtain a variable pitch sub-model, a variable flow sub-model, and a yaw sub-model;

[0015] The sensor nodes are obtained by counting the past sensor installation sites and marked on the corresponding propeller sub-model, variable flow sub-model, and yaw sub-model. The sensor nodes include important nodes and ordinary nodes;

[0016] Match the operation data with the sensor nodes and establish the operation curve of the corresponding sensor nodes with time as the horizontal axis;

[0017] The features of the running curve in different time periods will be extracted to obtain stable features and abnormal features;

[0018] According to the stable characteristics, the running curve is intercepted and divided to obtain the stable curve, and the stable curve is fitted to obtain the standard signal fluctuation curve and the standard fluctuation range value;

[0019] The operation curve is intercepted and divided according to abnormal characteristics to obtain abnormal curves, and the abnormal characteristics correspond to different wind turbine faults.

[0020] Preferably, the sensor nodes include a sensor influence relationship, and a first influence factor is set between the sensor nodes according to the sensor influence relationship;

[0021] The sensor node is provided with a sensor node coordinate, and the sensor node coordinate takes the bottom of one end of the wind turbine model as the origin coordinate.

[0022] Preferably, the specific contents of obtaining the current wind turbine specifications and deploying sensors in combination with the wind turbine model include:

[0023] Obtaining current wind turbine specifications, obtaining current wind turbine structural information based on the current wind turbine specifications, and generating a current wind turbine three-dimensional model based on the current wind turbine structural information;

[0024] The current wind turbine 3D model is disassembled to obtain the current variable pitch sub-model, the current variable flow sub-model, and the current yaw sub-model;

[0025] According to the current pitch sub-model, the current flow sub-model and the current yaw sub-model, the corresponding current sensor nodes and matching signal fluctuation curves are obtained in the pitch model, the flow model and the yaw model respectively;

[0026] Sensors are deployed and operated for the current wind turbine according to the current sensor nodes.

[0027] Preferably, the specific contents of acquiring sensor data once and configuring the sensor operation strategy using an improved tabu search algorithm in combination with the standard signal curve further include:

[0028] S401, setting an objective function according to the coverage and number of sensors;

[0029] S402, the current sensor node is an initial sensor network layout and the initial sensor network layout is used as a starting point;

[0030] S403, changing the positions of the sensor nodes starting from the starting point to generate a secondary layout, i.e., a neighborhood solution;

[0031] S404: Calculate the objective function values ​​corresponding to the secondary layouts, sort the objective function values, and select the secondary layout with the highest order as the current target layout;

[0032] S405: A target range is preset, and the current target layout and the layouts within the adjacent target range are added to the taboo table;

[0033] S406: A target number of iterations is preset, and S403 to S405 are repeated until the target number of iterations is reached;

[0034] S407: Select the optimal sensor network layout from the taboo table as the target result, and configure the sensor operation strategy according to the target result.

[0035] Preferably, the objective function is:

[0036]

[0037] Among them, W is the objective function value, α is the distance conversion coefficient, β is the energy consumption conversion coefficient, X max is the maximum value of node coordinates in the initial sensor network layout, x max is the maximum value of node coordinates in the secondary layout, is the coordinate x i The energy consumption value of the sensor corresponding to the node, is the coordinate x max The energy consumption value of the sensor corresponding to the node.

[0038] Preferably, in the process of changing the positions of the sensor nodes starting from the starting point to generate the secondary layout, i.e., the neighborhood solution, the specific manner of changing the positions of the sensor nodes is:

[0039] A target one-time operation time is preset, and the one-time sensor data is the sensor operation data within the one-time operation time;

[0040] The sensor operation data at different nodes are used to establish the current signal fluctuation curve with time as the coordinate;

[0041] Matching the current signal fluctuation curve with the matching signal fluctuation curve to obtain the current standard signal fluctuation curve;

[0042] Extract the current signal fluctuation curve characteristics. If the current signal fluctuation curve characteristics meet the stability characteristics of the current standard signal fluctuation curve, retain the important nodes and change the position of the sensor node.

[0043] If the current signal fluctuation curve characteristics meet the abnormal characteristics of the current standard signal fluctuation curve, the important nodes and the nodes corresponding to the abnormal characteristics are retained to obtain the position of the sensor node once;

[0044] Calculate the offset of the current signal fluctuation curve corresponding to the abnormal feature, and multiply the offset by the first impact factor between the node corresponding to the abnormal feature and other sensor nodes to obtain the node emphasis value;

[0045] A node emphasis value threshold is preset. If the node emphasis value exceeds the node emphasis value threshold, the node exceeding the node emphasis value threshold is selected as the position of the secondary changed sensor node.

[0046] A wind turbine fault diagnosis system based on a sensor network, comprising:

[0047] Data acquisition unit: acquires past wind turbine specifications and operating data, analyzes and processes the past wind turbine operating data to obtain a wind turbine database, which includes a pitch unit database, a current conversion unit database, and a yaw unit database;

[0048] Model building unit: builds wind turbine model and signal fluctuation curve according to wind turbine database;

[0049] Strategy operation unit: obtains the current wind turbine specifications and combines them with the wind turbine model to deploy and operate sensors. It obtains primary sensor data and uses an improved tabu search algorithm based on the standard signal curve to configure the sensor operation strategy.

[0050] Result judgment unit: implements the sensor configuration operation strategy and obtains the real-time data of the sensor to obtain the fault diagnosis result of the wind turbine generator set.

[0051] An electronic device, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the wind turbine fault diagnosis method based on the sensor network is implemented.

[0052] A storage medium is characterized in that the storage medium stores computer executable instructions, and when the computer executable instructions are loaded and executed by a processor, the content of the wind turbine fault diagnosis method based on the sensor network is implemented.

[0053] In summary, the sensor network-based wind turbine fault diagnosis method and system of the present invention, compared to conventional technologies, utilizes an improved tabu search algorithm to optimize sensor operation. This not only mitigates the impact of excessive external sensor installation and operation on wind turbine operation, but also improves the accuracy of wind turbine fault diagnosis. This reduces fault location and resolution time. Furthermore, in-depth fault analysis allows the development of operational and maintenance technical measures, reducing overall failure rates and saving spare parts procurement expenses.

[0054] The technical method of the present invention is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a step diagram of a wind turbine fault diagnosis method based on a sensor network according to the present invention;

[0056] Figure 2 This is a module diagram of a wind turbine fault diagnosis system based on a sensor network according to the present invention. DETAILED DESCRIPTION

[0057] The technical method of the present invention is further described below through the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and values ​​described in these embodiments do not limit the scope of this application.

[0058] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0059] Technologies, systems, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0060] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0061] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0062] The present invention provides a wind turbine fault diagnosis method and system based on a sensor network. S1: obtaining past wind turbine specifications and operating data, analyzing and processing the past wind turbine operating data to obtain a wind turbine database, wherein the wind turbine database includes a pitch unit database, a current conversion unit database, and a yaw unit database;

[0063] S2. Constructing a wind turbine model and a signal fluctuation curve based on the wind turbine database;

[0064] Preferably, the specific contents of constructing the wind turbine model and the signal fluctuation curve according to the wind turbine database are:

[0065] The wind turbine model includes a variable pitch model, a variable flow model, and a yaw model;

[0066] Analyze wind turbine specifications to obtain wind turbine structural information and past sensor installation locations;

[0067] Generate a three-dimensional module of the wind turbine according to the proportion of the wind turbine structure information, and disassemble the three-dimensional module of the wind turbine to obtain a variable pitch sub-model, a variable flow sub-model, and a yaw sub-model;

[0068] The sensor nodes are obtained by counting the past sensor installation sites and marked on the corresponding propeller sub-model, variable flow sub-model, and yaw sub-model. The sensor nodes include important nodes and ordinary nodes;

[0069] Match the operation data with the sensor nodes and establish the operation curve of the corresponding sensor nodes with time as the horizontal axis;

[0070] The features of the running curve in different time periods will be extracted to obtain stable features and abnormal features;

[0071] According to the stable characteristics, the running curve is intercepted and divided to obtain the stable curve, and the stable curve is fitted to obtain the standard signal fluctuation curve and the standard fluctuation range value;

[0072] The operation curve is intercepted and divided according to abnormal characteristics to obtain abnormal curves, and the abnormal characteristics correspond to different wind turbine faults.

[0073] Preferably, the sensor nodes include a sensor influence relationship, and a first influence factor is set between the sensor nodes according to the sensor influence relationship;

[0074] The sensor node is provided with a sensor node coordinate, and the sensor node coordinate takes the bottom of one end of the wind turbine model as the origin coordinate.

[0075] S3. Obtain the current wind turbine specifications and combine them with the wind turbine model to deploy and operate sensors;

[0076] Preferably, the specific contents of obtaining the current wind turbine specifications and deploying sensors in combination with the wind turbine model include:

[0077] Obtaining current wind turbine specifications, obtaining current wind turbine structural information based on the current wind turbine specifications, and generating a current wind turbine three-dimensional model based on the current wind turbine structural information;

[0078] The current wind turbine 3D model is disassembled to obtain the current variable pitch sub-model, the current variable flow sub-model, and the current yaw sub-model;

[0079] According to the current pitch sub-model, the current flow sub-model and the current yaw sub-model, the corresponding current sensor nodes and matching signal fluctuation curves are obtained in the pitch model, the flow model and the yaw model respectively;

[0080] Sensors are deployed and operated for the current wind turbine according to the current sensor nodes.

[0081] S4, obtaining sensor data once and using an improved tabu search algorithm to configure an operating strategy for the sensor in combination with a standard signal curve;

[0082] Preferably, the specific contents of acquiring sensor data once and configuring the sensor operation strategy using an improved tabu search algorithm in combination with the standard signal curve further include:

[0083] S401, setting an objective function according to the coverage and number of sensors;

[0084] S402, the current sensor node is an initial sensor network layout and the initial sensor network layout is used as a starting point;

[0085] S403, changing the positions of the sensor nodes starting from the starting point to generate a secondary layout, i.e., a neighborhood solution;

[0086] S404: Calculate the objective function values ​​corresponding to the secondary layouts, sort the objective function values, and select the secondary layout with the highest order as the current target layout;

[0087] S405: A target range is preset, and the current target layout and the layouts within the adjacent target range are added to a taboo table to avoid repeated access to these layouts in future searches;

[0088] S406: A target number of iterations is preset, and S403 to S405 are repeated until the target number of iterations is reached;

[0089] S407: Select the optimal sensor network layout from the taboo table as the target result, and configure the sensor operation strategy according to the target result.

[0090] Preferably, the objective function is:

[0091]

[0092] Among them, W is the objective function value, α is the distance conversion coefficient, β is the energy consumption conversion coefficient, X max is the maximum value of node coordinates in the initial sensor network layout, x max is the maximum value of node coordinates in the secondary layout, is the coordinate x i The energy consumption value of the sensor corresponding to the node, is the coordinate x max The energy consumption value of the sensor corresponding to the node.

[0093] Preferably, in the process of changing the positions of the sensor nodes starting from the starting point to generate the secondary layout, i.e., the neighborhood solution, the specific manner of changing the positions of the sensor nodes is:

[0094] A target one-time operation time is preset, and the one-time sensor data is the sensor operation data within the one-time operation time;

[0095] The sensor operation data at different nodes are used to establish the current signal fluctuation curve with time as the coordinate;

[0096] Matching the current signal fluctuation curve with the matching signal fluctuation curve to obtain the current standard signal fluctuation curve;

[0097] Extract the current signal fluctuation curve characteristics. If the current signal fluctuation curve characteristics meet the stability characteristics of the current standard signal fluctuation curve, retain the important nodes and change the position of the sensor node.

[0098] If the current signal fluctuation curve characteristics meet the abnormal characteristics of the current standard signal fluctuation curve, the important nodes and the nodes corresponding to the abnormal characteristics are retained to obtain the position of the sensor node once;

[0099] Calculate the offset of the current signal fluctuation curve corresponding to the abnormal feature, and multiply the offset by the first impact factor between the node corresponding to the abnormal feature and other sensor nodes to obtain the node emphasis value;

[0100] A node emphasis value threshold is preset. If the node emphasis value exceeds the node emphasis value threshold, the node exceeding the node emphasis value threshold is selected as the position of the secondary changed sensor node.

[0101] S5. Implement the sensor configuration operation strategy and obtain real-time sensor data to obtain a fault diagnosis result of the wind turbine generator set.

[0102] A wind turbine fault diagnosis system based on a sensor network, comprising:

[0103] Data acquisition unit: acquires past wind turbine specifications and operating data, analyzes and processes the past wind turbine operating data to obtain a wind turbine database, which includes a pitch unit database, a current conversion unit database, and a yaw unit database;

[0104] Model building unit: builds wind turbine model and signal fluctuation curve according to wind turbine database;

[0105] Strategy operation unit: obtains the current wind turbine specifications and combines them with the wind turbine model to deploy and operate sensors. It obtains primary sensor data and uses an improved tabu search algorithm based on the standard signal curve to configure the sensor operation strategy.

[0106] Result judgment unit: implements the sensor configuration operation strategy and obtains the real-time data of the sensor to obtain the fault diagnosis result of the wind turbine generator set.

[0107] An electronic device, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the wind turbine fault diagnosis method based on the sensor network is implemented.

[0108] A storage medium is characterized in that the storage medium stores computer executable instructions, and when the computer executable instructions are loaded and executed by a processor, the content of the wind turbine fault diagnosis method based on the sensor network is implemented.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical method of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical method to deviate from the spirit and scope of the technical method of the present invention.

Claims

1. A wind turbine fault diagnosis method based on a sensor network, characterized in that: The following steps are involved: S1. Obtaining past specifications and operating data of wind turbines, analyzing and processing the past operating data of wind turbines to obtain a wind turbine database, wherein the wind turbine database includes a pitch unit database, a current conversion unit database, and a yaw unit database; S2. Constructing a wind turbine model and a signal fluctuation curve based on the wind turbine database; S3. Obtain the current wind turbine specifications and combine them with the wind turbine model to deploy and operate sensors; S4, obtaining sensor data once and using an improved tabu search algorithm to configure an operating strategy for the sensor in combination with a standard signal curve; S5, implementing the sensor configuration operation strategy and acquiring real-time sensor data to obtain a fault diagnosis result of the wind turbine generator set; The specific contents of constructing the wind turbine model and signal fluctuation curve based on the wind turbine database are as follows: The wind turbine model includes a variable pitch model, a variable flow model, and a yaw model; Analyze wind turbine specifications to obtain wind turbine structural information and past sensor installation locations; Generate a three-dimensional module of the wind turbine according to the proportion of the wind turbine structure information, and disassemble the three-dimensional module of the wind turbine to obtain a variable pitch sub-model, a variable flow sub-model, and a yaw sub-model; The sensor nodes are obtained by counting the past sensor installation sites and marked on the corresponding propeller sub-model, variable flow sub-model, and yaw sub-model. The sensor nodes include important nodes and ordinary nodes; Match the operation data with the sensor nodes and establish the operation curve of the corresponding sensor nodes with time as the horizontal axis; The features of the running curve in different time periods will be extracted to obtain stable features and abnormal features; According to the stable characteristics, the running curve is intercepted and divided to obtain the stable curve, and the stable curve is fitted to obtain the standard signal fluctuation curve and the standard fluctuation range value; The operation curve is intercepted and divided according to abnormal characteristics to obtain abnormal curves, and the abnormal characteristics correspond to different wind turbine faults.

2. A wind turbine fault diagnosis method based on a sensor network according to claim 1, characterized in that: The sensor nodes include a sensor influence relationship, and a first influence factor is set between the sensor nodes according to the sensor influence relationship; The sensor node is provided with a sensor node coordinate, and the sensor node coordinate takes the bottom of one end of the wind turbine model as the origin coordinate.

3. A wind turbine fault diagnosis method based on a sensor network according to claim 2, characterized in that: The specific contents of obtaining the current wind turbine specifications and combining them with the wind turbine model for sensor deployment include: Obtaining current wind turbine specifications, obtaining current wind turbine structural information based on the current wind turbine specifications, and generating a current wind turbine three-dimensional model based on the current wind turbine structural information; The current wind turbine 3D model is disassembled to obtain the current variable pitch sub-model, the current variable flow sub-model, and the current yaw sub-model; According to the current pitch sub-model, the current flow sub-model and the current yaw sub-model, the corresponding current sensor nodes and matching signal fluctuation curves are obtained in the pitch model, the flow model and the yaw model respectively; Sensors are deployed and operated for the current wind turbine according to the current sensor nodes.

4. A wind turbine fault diagnosis method based on a sensor network according to claim 3, characterized in that: The specific contents of obtaining sensor data once and using the improved tabu search algorithm in combination with the standard signal curve to configure the sensor operation strategy also include: S401, setting an objective function according to the coverage and number of sensors; S402, the current sensor node is an initial sensor network layout and the initial sensor network layout is used as a starting point; S403, changing the positions of the sensor nodes starting from the starting point to generate a secondary layout, i.e., a neighborhood solution; S404: Calculate the objective function values ​​corresponding to the secondary layouts, sort the objective function values, and select the secondary layout with the highest order as the current target layout; S405: A target range is preset, and the current target layout and the layouts within the adjacent target range are added to the taboo table; S406: A target number of iterations is preset, and S403 to S405 are repeated until the target number of iterations is reached; S407: Select the optimal sensor network layout from the taboo table as the target result, and configure the sensor operation strategy according to the target result.

5. A wind turbine fault diagnosis method based on a sensor network according to claim 4, characterized in that: The objective function is: Among them, W is the objective function value, α is the distance conversion coefficient, β is the energy consumption conversion coefficient, X max is the maximum value of node coordinates in the initial sensor network layout, x max is the maximum value of node coordinates in the secondary layout, is the coordinate x i The energy consumption value of the sensor corresponding to the node, is the coordinate x max The energy consumption value of the sensor corresponding to the node.

6. A wind turbine fault diagnosis method based on a sensor network according to claim 5, characterized in that: In the process of changing the position of the sensor node from the starting point to generate the secondary layout, i.e. the neighborhood solution, the specific way to change the position of the sensor node is as follows: A target one-time operation time is preset, and the one-time sensor data is the sensor operation data within the one-time operation time; The sensor operation data at different nodes are used to establish the current signal fluctuation curve with time as the coordinate; Matching the current signal fluctuation curve with the matching signal fluctuation curve to obtain the current standard signal fluctuation curve; Extract the current signal fluctuation curve characteristics. If the current signal fluctuation curve characteristics meet the stability characteristics of the current standard signal fluctuation curve, retain the important nodes and change the position of the sensor node. If the current signal fluctuation curve characteristics meet the abnormal characteristics of the current standard signal fluctuation curve, the important nodes and the nodes corresponding to the abnormal characteristics are retained to obtain the position of the sensor node once; Calculate the offset of the current signal fluctuation curve corresponding to the abnormal feature, and multiply the offset by the first impact factor between the node corresponding to the abnormal feature and other sensor nodes to obtain the node emphasis value; A node emphasis value threshold is preset. If the node emphasis value exceeds the node emphasis value threshold, the node exceeding the node emphasis value threshold is selected as the position of the secondary changed sensor node.

7. A wind turbine fault diagnosis system based on a sensor network, implementing the wind turbine fault diagnosis method based on a sensor network as claimed in any one of claims 1 to 6, characterized in that: include: Data acquisition unit: acquires past wind turbine specifications and operating data, analyzes and processes the past wind turbine operating data to obtain a wind turbine database, which includes a pitch unit database, a current conversion unit database, and a yaw unit database; Model building unit: builds wind turbine model and signal fluctuation curve according to wind turbine database; Strategy operation unit: obtains the current wind turbine specifications and combines them with the wind turbine model to deploy and operate sensors. It obtains primary sensor data and uses an improved tabu search algorithm based on the standard signal curve to configure the sensor operation strategy. Result judgment unit: implements the sensor configuration operation strategy and obtains the real-time data of the sensor to obtain the fault diagnosis result of the wind turbine generator set.

8. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the content of the wind turbine fault diagnosis method based on the sensor network as claimed in any one of claims 1 to 6 when calling the computer program in the memory.

9. A storage medium, characterized in that: The storage medium stores computer-executable instructions, which, when loaded and executed by the processor, implement the contents of the wind turbine fault diagnosis method based on a sensor network as claimed in any one of claims 1 to 6.

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