EP4070264A1

EP4070264A1 - Method for identifying industrial cables

Info

Publication number: EP4070264A1
Application number: EP20824444.2A
Authority: EP
Inventors: Felix Loske; Oliver Beyer
Original assignee: Harting Electric Stiftung and Co KG
Current assignee: Harting Electric Stiftung and Co KG
Priority date: 2019-12-05
Filing date: 2020-11-25
Publication date: 2022-10-12
Also published as: CN114766044A; DE102019133193A1; WO2021110208A1; US20220415062A1

Abstract

In order for a supplier of industrial cables to reduce personnel costs and to guarantee customers a consistently high quality standard promptly and reliably, even for global data traffic, a method for identifying industrial cables, comprising the following steps, is proposed: a. automatic visual identification of multiple different components (10, 2, 3, 3', 3") of an industrial cable (100), from at least one image file; b. analysis of the geometric relationships and/or functional connections between the components (10, 2, 3, 3', 3"); c. extraction of individual characteristics of the components (10, 2, 3, 3', 3") from the image file using information obtained in step b. A combination of the visual analysis with existing knowledge, also with the possible option of adding trained knowledge, allows an extremely reliably-functioning method for recognising industrial cables to be provided.

Description

Process for the identification of industrial cables

description

The invention is based on a method for identifying industrial cables according to the preamble of independent claim 1.

Processes for the identification of industrial cables are required by cable manufacturers and cable providers in order to answer product-specific customer questions and, as a result, to prepare suitable offers for the respective customer. The customer inquiries usually relate to industrial cables already available at the customer and the corresponding compatibilities. The customer usually already has one or more industrial cables. Usually, the same or alternative industrial cables with the same or similar functionality should be found. For this purpose, the industrial cables can be assigned to predetermined functional categories. Function categories can be - for example, but not limited to: electrical energy transmission, electronic signal transmission (analogue as well as digital), optical signal transmission, pneumatics, e.g. air pressure transmission, and in rare areas of application the forwarding of liquids, e.g. cooling liquid.

State of the art

In the prior art, it is common for cable providers to send customer inquiries in the form of visual media, e.g. B. to receive, analyze and respond to digital photos, image files, etc.

At present, such inquiries are answered manually and individually by experienced employees of the cable provider.

The disadvantage of this state of the art is that these methods are expensive and person-dependent and therefore especially for the customer international goods and data traffic sometimes cause undesirable waiting times. If such an employee leaves the company of the cable provider, he must record his knowledge in writing and / or train a colleague, otherwise the corresponding knowledge for the company may be lost. To the detriment of all those involved, a consistently high quality standard of customer service is at risk.

Task

The object of the invention is to present a method for identifying industrial cables which saves a cable provider personnel costs and quickly and reliably ensures a consistently high quality standard for its customers, even in global data traffic.

This object is achieved by the features of the independent claims.

Advantageous refinements of the invention are specified in the subclaims.

One method is used to identify industrial cables and has the following steps: a. Automatic visual identification of several different components of at least one industrial cable from at least one image file; b. Analysis of the geometric relationships and / or functional relationships between these components; c. Extraction of individual features of the components from the image file using information obtained from step b. This is particularly advantageous because this method can be carried out automatically and without manual, ie human, intervention. Regardless of the time of day and date, inquiries from all over the world can be processed immediately, competently and with a consistently high level of quality by a computer program that runs on a computer server in-house or, advantageously, with little maintenance in a cloud application. For this purpose, the computing server can have at least one microprocessor and a combined program / data memory. The method can be stored in the data memory as part of the computer program.

It has been found to be particularly advantageous that method step c takes place using information obtained from step b. Finally, in this way, special knowledge about the functionality and structure of industrial cables can be taken into account in the method. This knowledge relates, for example, to the current carrying capacity, the temperature resistance, protection against moisture, weathering, ozone and UV resistance as well as shielding and insulating properties and, consequently, the material and thickness of the insulation, the shielding, e.g. B. The material and design of a braided shield and in particular how individual strands, shields and / or insulation belong together to form a common electrical conductor, etc.

The knowledge can previously, i. H. have already been introduced into the process before process step a, when the computer program was created, by or with the support of a specialist. In addition, in a preferred embodiment, the system can also generate additional knowledge in a self-learning manner.

For example, the method can be carried out as follows: The image file consists of a cross-sectional representation of a Industrial cable that has, among other things, a shielded twisted pair cable. In process step a. the information is obtained that a twisted pair line surrounded at least with its own insulation is present. From this, in process step b. It can be concluded that the twisted pair line is very likely to have a shield, since experience shows that there are only very few twisted pair lines that have their own insulation, but not their own shield. This experience can, for example, have been programmed into the system beforehand. Alternatively or in addition, this knowledge can be obtained by the method itself by means of self-learning algorithms over a sufficiently long period of time, that is to say over a sufficiently large number of different analyzes.

In many cases, especially when several such high probabilities are linked to one another, it can be assumed that the facts are almost certain. For this purpose, one or more threshold values for the corresponding probabilities can be specified in the method.

In process step a. however, alternatively or in addition, it may already have been recognized that the twisted pair conductors are surrounded by some kind of screen that is arranged within the insulation, without the type of this screen being changed in method step a. could be precisely identified.

Through the said programmed knowledge from step b. can in step c. However, it can still be concluded that this shield must be a shielding film because - as known from step b - only shielding film is fundamentally suitable for shielded twisted pair lines. In other words: According to the programmed and / or self-learned knowledge to which process step b relies, there are no known shielded twisted pair cables with shielding that does not consist of shielding foil. Conversely, the recognition of a shielded twisted pair line means that the recognized shield must consist of foil.

In a preferred embodiment, at least some of the following component categories can be available for process step a:

Cables;

Strands;

Outer shield;

Outer sheathing;

Inner sheathing;

Insulation;

Individual shields.

Furthermore, relatively trivial, optically recognizable features such as the color and labeling of the lines and / or the entire industrial cable can of course also play a role in the assignment.

Specifically, the lines of the industrial cable can also be characterized by at least one of the following features: thickness and shape of the respective electrical conductor / the respective line core;

Thickness and shape of conductor insulation;

Type and shape of the cable shield.

To this end, process step a can advantageously include:

An automatic visual recognition of the components as individual objects as well as an assignment of the components to component categories of the industrial cable by means of artificial intelligence (Kl). Optionally, the identified components can also be assigned to at least one functional category in method step a.

To enable automatic identification, the system can manually, i.e., manually, to identify and characterize the components beforehand, that is, even before method step a. H. to be “learned” through a human activity. Not only are component categories such as B. "wires", "strands", "insulation",

"Shielding", "Cable sheathing", etc. are trained, but specific, internal and general type designations can also be assigned for a previously made selection of the components. These assignments are made according to those categories that were previously taught to the system when training its artificial intelligence (Kl).

Using the artificial intelligence (CI), the system learns beforehand from manually created training tables in conjunction with training images. As a result of this learning process, the system is then able to independently assign the components found therein to these component categories for newly added image files - or defined parts thereof - in said method step a.

For this purpose, the aforementioned training is initially carried out manually before the implementation of method step a.

In a simple example, a series of training images each contain a shielded twisted pair line with a twisted pair wire pair, which is surrounded by a shield and conductor insulation. The pair of wires, its wires, its conductors and their insulation, as well as a screen and its sheathing, if necessary, are entered manually into the system in the form of geometrical information as coordinates. At At this point, the Kl is only viewed as a “black box”, ie only in terms of its functionality. If the method is now applied to user data, that is to say to analyze an image of an industrial cable shown in cross section in an image file sent by the customer, the shield of the twisted pair line, for example, if it can be clearly seen in the image, can be used as such can be recognized because it is very similar in position and texture to the screens of the training images.

Although this training initially requires manual, i.e. human, effort, in principle it is only necessary once and the method can then be used as often as required, inexpensively and at any time.

The training includes the process of first reading in a large number of training images and adding the respective component categories to the associated training images, e.g. B. by means of training tables can be assigned manually.

In a preferred development, this assignment can be made by assigning a line in the training table to each training image for each component. In addition to the column for the referenced training image, the training table also has a column for the component category and four columns that show the exact position of the component, e.g. B. in the X and Y axes and / or z. B. precisely describe by vectors and / or polar coordinates and its height and width in the image.

In a further example, a training image shows an industrial cable in cross section, which has a certain high-current line. The high-current line has a designation, for example "Delta Power 03" and / or an internal article number, e.g. B. "10470032634". The training table then has exactly one line for the combination training picture component, in which z. B. the entry "Bild4813" for the training image, an internal type designation for the respective component, and the position information, for example the numerical values "92, 25, 178, 75" for either the XY positions or corresponding numerical values for polar coordinates, as well as height and Width and / or center and radius of the component can be found in the cross section of the industrial cable. Expert, relative position and dimension (height / width; center / radius or their relationships with regard to the various components) can have been entered manually beforehand by a person skilled in the art. If article numbers are used, it can be particularly advantageous if they are systematically maintained. Thus, components that differ only slightly can also have article numbers that have a certain similarity to one another, e.g. B. differ only in their last position or several last positions. In this case, the training table can optionally also be provided with reduced article numbers or the Kl can be used in some other way, e.g. B. by assigning to general terms, make a rough assignment.

In a further preferred embodiment, an article number of a component can also be used to represent similar components in order to achieve a somewhat coarser grid and thereby a meaningful assignment. In this way, for example, different signal lines, which differ only in terms of the color of their insulation, can be assigned to a common component category despite their minimal difference.

In the course of the training, the Kl preferably adapts the weights to its neural connections via a large number of training images in such a way that it is able to include the components located on the images To determine position and dimension. The KI is able to independently extract relevant features (such as edges, textures, etc.) for the determination and can thus identify unknown images that contain the trained components even beyond the training. Of course, this principle can also be applied in the same way to any other visual identifier via a training table. In particular, a statistical evaluation is useful in this case, in which the visual characteristics of each individual training image can be viewed as a so-called “sample”, that is to say as a random selection of data from the entirety of the features relating to the respective feature.

In process step a can then be made from each image files z. B. originate from customer inquiries, all the components known to the KL identified (that is, assigned to a known category, such as "high-current line") and localized.

As an alternative or in addition, the objects can also be assigned to specific designs in the same way. If the component category is trained specifically for very specific products, not just in general z. B. to "high current line" or "signal line", but, as described in the example above, to internal article number "10470032634", the objects to be analyzed are then assigned to these component categories.

Alternatively or in addition, the training can also relate to certain, known features of the respective components.

As has been shown in numerous trials and test runs, this principle sometimes works very well for features that a cable specialist would not initially expect. For example, the Kl can, among other things, also with regard to material and / or Manufacturing processes are trained. This then makes sense with training images that correspond to the manufacturing processes and materials used in the respective area. For this purpose, the objects shown on the training images can, but do not necessarily have to show components that originate from the cable area. Rather, they only have to have the corresponding production and material-specific characteristics that are important in the respective investigation, for example “plastic”, “metal”; “Strand” “braid” and / or “foil”. Alternatively, the Kl can also exclusively with training images of components from the cable production area and in particular precisely with the relevant components, such. B. "Cable sheathing" happen, but then the focus of the features is on the material and the manufacturing process. In detail, the training can be empirically adapted to the respective learning success to be checked manually.

As a result, in a very special embodiment, it is also possible, for example, in a first step to subsequently assign objects identified as “power lines” to the respective material and / or manufacturing process, and thus to make a preselection from which a final product-specific assignment takes place in a third step .

By means of the aforementioned method, components shown in one image or in several images, e.g. B. "Litz wire", "insulation", "shielding", "cable sheathing", etc. can be identified.

These components, for example the insulation, can have geometric properties such as strength and shape, but can also be classified functionally by said programming, for example in terms of temperature resistance, flexibility, electrical creep properties. Alternatively or in addition, you can search for material and Manufacturing processes, but also, for example, special products, can be assigned.

Furthermore, the industrial cable can be characterized by one or more of the following features: o outside diameter; o Presence and, if applicable, type of an outer screen; o number of its lines; o strength of his veins; o Material and / or manufacturing process of the cores; o Thickness, shape and / or position of the individual wire insulation; o Material and / or manufacturing process for this insulation; o Presence and, if applicable, type and form of

Individual shields and / or a separate PE (Protective Earth) line; o Thickness and shape of the cable sheathing, as well as its o suitability for a certain cable gland, it being clear to the person skilled in the art that the features relating to dimensions can advantageously be considered in their relative size to one another.

In a further preferred embodiment, the individual lines can be characterized by at least one of the following features: their size and geometric shape; o at least one function category; Their geometric arrangement in the cable, it being clear to the person skilled in the art that the features which relate to dimensions can advantageously be viewed in terms of their relative size to one another The function category can include at least one of the following features:

Electrical energy transmission,

Analog and / or digital electronic signal transmission, optical and / or opto-electronic signal transmission,

Pneumatics, e.g. B. Air pressure transmission.

The following component categories can also be available for process step a:

Strands,

Isolations

Shields.

The strands can furthermore pass through at least one of the following

Features are characterized: o their geometric dimensions; o their function category; o Your position in the cable, i.e. in the cable cross-section, it being clear to a person skilled in the art that the features that relate to

Relate dimensions, can advantageously be viewed in their relative size to each other

The function category can be formed by one of the following features:

Electrical power transmission;

Analog and / or digital electronic signal transmission; optical and optoelectronic signal transmission;

Pneumatics, e.g. B. Air pressure transmission;

Data transfer.

In particular, the method can use artificial intelligence (K1) -based automatic visual recognition in the aforementioned manner (typically by means of "Convolutional Neural Networks (CNNs)" combined with a subsequent algorithmic image processing.

The learning process and the analysis of individual components are preferably carried out taking into account the physical structure of other components of the industrial cable. Based on a digital image of a cable cross-section, the proposed process can thus be used to precisely identify the components of the industrial cable and, in particular, to describe them geometrically according to their functional relationship to one another. The advantageous sequential processing chain consisting of cable identification and algorithmic analysis enables a hierarchical description of an industrial cable.

It is of particular advantage that qualitative assignments and comparisons are possible which in the prior art could not be mapped with previous automatic recognition. The method according to the invention is characterized in particular by the combination of a Kl-based automatic visual recognition, for example using "convolutional neural networks" (CNNs), and the subsequent algorithmic image processing, taking into account the physical structure of an industrial cable, i.e. the functional and geometric Relationship to each other.

Embodiment

A preferred exemplary embodiment of the invention is illustrated below with the aid of a drawing and is explained in more detail below. For this purpose, a system for identifying an industrial cable 100 from a digital image file is presented. Show it:

1 shows an image to be analyzed of an industrial cable to be identified; 2 shows a flow diagram of a method for identifying the components of the industrial cable from a digital image file belonging to the image.

The figures contain partially simplified, schematic representations. In some cases, identical reference symbols are used for identical, but possibly not identical elements. Different views of the same elements could be scaled differently.

1 shows an image to be analyzed of an industrial cable 100 to be identified. This image is the content of an image file sent by the customer. The figure shows a cable cross-section with an outer cable sheathing 101 and a shielding 103 embedded therein in the form of a braided shield, which is not shown graphically in the drawing for reasons of clarity. The following components are arranged within this cable sheathing 101: a 4-fold twisted pair line 1 with four individual twisted pair lines; two separate single twisted pair lines 1 five individual coaxial lines 2, 2 ^' , 2 ^" ; one power line 3 with three power line cores 30, 30 ^' , 30 ^" .

Here and in the following are the same, i. H. For the sake of clarity, components that are in principle repetitive are not always provided with their own reference symbols.

This rough list alone is already suitable for fundamentally characterizing the industrial cable 100 and for associating it with other industrial cables that are functionally comparable. For a more precise assignment to their respective component categories, the components already listed are in turn explained in more detail by further components and their individual properties.

Of the two separate single twisted pair lines 1 ^' , only one is explicitly provided with a reference symbol to represent the other. In these separate single twisted pair lines, each twisted pair wire pair is made up of two wires 10 ^' , which in turn are each formed from a twisted pair conductor 15 ^' and conductor insulation 14 ^' surrounding it. As a separate jacket, the pair of wires has a twisted pair sheathing 11 ^' with a twisted pair shielding film 13 ^' arranged on the inside, which is not shown in the drawing for reasons of clarity.

As already mentioned, the 4-fold twisted pair line 1 has the four individual twisted pair lines, which in turn each have two twisted pair wires 10. Each of these wires 10 is formed from a twisted pair conductor 15 and a conductor insulation 14 surrounding it. The two twisted pair wires 10 together each form a twisted pair wire pair. Each twisted pair wire pair is surrounded by its own wire pair sheathing and a shielding film located therein, this wire pair sheathing and the shielding film located therein belonging to the respective twisted pair line and are not provided with reference symbols here for the sake of clarity. The four twisted pair lines are jointly surrounded by a twisted pair sheathing 11 and by a common shielding film 13 located therein.

Of the five coaxial lines 2, 2 ^' , 2 ^″ , only three are provided with at least one reference symbol for the sake of clarity. One of these coaxial lines is fully designated to represent the others. It has an electrical one Inner conductor 25, surrounded by a coaxial insulation 24. This in turn is surrounded by an inner screen 23, which in turn is surrounded by a coaxial line sheathing 22.

Furthermore, the industrial cable 100 has a power line 3 with three power line cores 30, 30 ^' , 30 ^" . In the aforementioned manner, only one power line core 30 is completely designated here. The power line cores 30, 30 ^' , 30 ^" each have a power line strand 35 as an electrical conductor, which each surrounded by a core insulation 34. These three power line cores 30, 30 ^′ , 30 ^″ are jointly surrounded by a power line sheathing 31, which is also part of the power line 3.

FIG. 2 shows a flow chart of a method for the exemplary automatic identification of the industrial cable 100 from this image file.

The method is carried out by means of a computer program on a computer server and comprises the following steps: a. Automatic identification of the basic components 10, 10 ^' , 2, 2 ^' , 2 ^" , 30, 30 ^' , 30 ^{" of} the industrial cable 100 from the image file through automatic visual recognition and assignment of the basic components 10, 10 ^' , 2, 2 ^' , 2 ^" , 30, 30 ^' , 30 ^" as individual objects by means of artificial intelligence (Kl); b. Analysis of the geometric relationships and / or functional relationships between the individual components 10, 10 ^' , 2,

2 ^' , 2 ^" , 30, 30 ^' , 30 ^" ; c. Extraction of individual characteristics of the basic components 10, 10 ^' , 2,

2 ^' , 2 ^" , 30, 30 ^' , 30 ^" from the image file using information obtained from step b.

In process step a, the following components are initially determined by means of a so-called "convolutional neural network" (CNN) separated from one another, ie recognized as different objects and assigned to the various component categories.

In method step a, the system recognizes, for example, the following on the basis of training that preceded the method:

A first object 101 of the category “cable sheathing”;

A second object 1 of the category “4-way twisted pair line”, two third objects 10 ^'of the category “1-way twisted pair line”; Five fourth objects 2, 2 ^' , 2 ^"of the" Coaxial cable "category;

A fifth object 3 in the “Power line” category.

However, depending on the type of previous training, the objects can also be assigned to specific, company-internal or public products, namely their product names and / or article numbers.

In method step b, the program first recognizes the geometric relationships, namely that the cable sheathing 101 encloses the other objects. Using its programmed knowledge, the program deduces from this that further insulation and strands, and possibly also further shields, are located within the identified cable sheathing 101, even if these were not visually recognized.

In method step c, further individual features of the components 10, 10 ^' , 2, 2 ^' , 2 ^" , 30, 30 ^' , 30 ^{" are} identified. This includes, for example, the assignment of the power line 3 and the associated power lines 30, 30 ^' , 30 ^″ for electrical energy transmission.

Further visual / geometric features can be derived according to their hierarchy according to the same principles. The algorithmic image processing relates to special physical characteristics, such as B. repeated arrangements as well as the proportions of the components.

For example, the procedure can be carried out as follows:

The image file of the image shown in FIG. 1 consists of a cross-sectional representation of the industrial cable 100, which has the third object, namely the shielded single twisted pair line 1 ^' . In process step a. for this purpose, the information is first obtained that a twisted pair line 1 ^' surrounded by a separate jacket is present. From this it can be concluded in method step b that the single twisted pair line 1 ^' with a high probability also has a screen 13 ^' , since experience shows - and this knowledge is finally programmed into the system - only rarely twisted pair lines with insulation but exist without an umbrella.

In a further embodiment, such knowledge can also be generated by the system in a self-learning manner from its own experience.

In process step a. but - for example, in the case of the 4-way twisted pair line 1, it has already been recognized by the visual analysis that the twisted pair - conductors 1 are surrounded by any twisted pair screen 13 which is arranged within the twisted pair sheathing 11 , but without the type of this twisted pair screen 13 in method step a. can be accurately identified.

Using this information obtained from step b, it can be concluded in step c in both cases that there is a twisted pair screen 13, 13 ^' and that it must be a screen foil, because for screened twisted pair lines 1, 1 ^' Basically, only shielding foil is possible. In this way, a very reliably functioning system for the detection of industrial cables is provided through the combination of existing knowledge, and optionally also supplemented by learned knowledge, in connection with the visual analysis.

Even if various aspects or features of the invention are each shown in combination in the figures, it is clear to the person skilled in the art - unless stated otherwise - that the combinations shown and discussed are not the only possible ones. In particular, corresponding units or feature complexes from different exemplary embodiments can be exchanged with one another.

Process for the identification of industrial cables

List of reference symbols

100 industrial cables

101 Cable sheathing 103 Outer shielding / outer shielding / braided shield

1 4-way twisted pair cable r 1-way twisted pair cable

10, 10 ^' twisted pair cores 11, 11 ^' twisted pair sheath

13, 13 ^' twisted pair screen / screen foil

14, 14 ^' twisted pair conductor insulation

15, 15 ^' twisted pair head

2, 2 ^' , 2 coaxial line

22 Coaxial Cable Sheathing

23 inner screen

24 Coaxial Isolation

25 electrical inner conductor

3 power line

30, 30 ^' , 30 ^" power lines

31 Power cable sheathing

34 Core insulation

35 power cables

Claims

Process for the identification of industrial cables claims

1. Method for the identification of industrial cables (100) with the following steps: a. Automatic visual identification of several different components (10, 2, 3, 3 ^' , 3 ^" ) of at least one industrial cable (100) from at least one image file; b. Analysis of the geometric relationships and / or functional relationships between the components (10, 2, 3 , 3 ^' , 3 ^" ); c. Extraction of individual features of the components (10, 2, 3, 3 ^' , 3 ^" ) from the image file using information obtained from step b.

2. The method according to claim 1, wherein method step a. at least the automatic visual recognition of the components (10, 2, 3, 3 ^' , 3 ^" ) as individual objects and the assignment of the components (10, 2, 3, 3 ^' , 3 ^" ) to component categories by means of artificial intelligence (Kl) ;

3. The method according to claim 2, wherein at least the following component categories are available for method step a:

Lines (10, 10 ^' , 2, 2 ^' , 2 ^" );

Outer shield (103);

Cable sheathing (101).

4. The method according to claim 3, wherein the lines (10, 10 ^' , 2, 2 ^' ,

2 ^" ) are further characterized by one or more of the following features:

Thickness and shape of at least one line core (10, 10 ^' , 2, 2 ^' , 30, 30 ^' , 30 ^" ) with an electrical conductor (15, 15 ^' , 25, 35) and a conductor insulation (14, 22) surrounding it;

Thickness and shape of the conductor insulation (14, 14 ^' , 24, 34); possibly the type and shape of a cable shield (13, 23).

5. The method according to any one of claims 3 to 4, wherein the industrial cable (100) is further characterized by one or more of the following features: o outer diameter; o Presence and possibly type of an outer screen (103); o number of its lines (1, 1 ^' , 2, 3); o Strength of their veins (15, 15 ^' , 25, 35); o Material and / or manufacturing process of the cores (15, 15 ^' , 25, 35); o Thickness, shape and / or position of the individual insulation of the wires (14, 14 ^' , 24, 34); o Material and / or manufacturing process of these insulations (14, 14 ^' , 24, 34); o Presence and, if applicable, type and form of individual shields (23, 13) and / or a separate PE (Protective Earth) line; o Thickness and shape of the cable sheathing (101), as well as its o suitability for one or more specific cable glands.

6. The method according to any one of claims 3 to 5, wherein the lines (1, 1 ^' , 2, 2 ^' , 2 ^'" , 3, 3 ^' , 3 ^" ) are further characterized by at least one of the following features: o Their size and geometric shape; o at least one function category; o Your geometric arrangement in the industrial cable (100).

7. The method according to claim 6, wherein the function category comprises at least one of the following features:

Electrical power transmission;

Pneumatics, e.g. B. Air pressure transmission.

8. The method according to any one of claims 3 to 7, wherein the following component categories are also available for method step a: electrical conductors / strands (15, 15 ^' , 25, 35)

Insulations (14, 14 ^' , 24, 34)

Shields (13, 13 ^' , 23).

9. The method according to claim 8, wherein the lines (1, 1 ^' , 2, 2 ^' , 2 ^" , 3) of the industrial cable (100) are further characterized by at least one of the following features: o their geometric dimensions; o their functional category.

10. The method according to claim 9, wherein at least one of the components in method step a is additionally assigned to at least one of the following functional categories:

Electrical power transmission;

Pneumatics, e.g. B. Air pressure transmission.

11. The method according to any one of claims 2 to 10, wherein article numbers are used as component categories.

12. The method according to any one of claims 2 to 11, wherein product names are used as component categories.