RU2778874C2 - Method for determination of operational conditions of fan - Google Patents

Abstract

FIELD: ventilation.

SUBSTANCE: method for determination of operational conditions of a fan, using a digital representation – a digital twin of the fan, based on physical data, and at least one algorithm characteristic for an operational parameter includes following stages: creation of the digital representation of the real fan by representation of its properties by means of mathematical calculation models and, if needed, known data, creation of the specified at least one algorithm characteristic for the operational parameter, taking into account known ratios, characteristics, etc., calculation of conditions of structural elements of the fan, using the digital representation by means of virtual sensors, transfer of conditions of structural elements to the algorithm, which, based on conditions of structural elements, calculates operational parameters of the fan and, if needed, gives predictions concerning a fan operational mode.

EFFECT: obtaining a method for determination of operational conditions of a fan.

13 cl, 8 dwg, 1 tbl

Description

The invention relates to a method for determining the operating states of a fan, the term "fan" being understood in its broadest sense. In addition, the idea of the invention can also be transferred to a system of several fans, groups of fans or fan systems. In the case of a system of several fans, it is the interaction of the fans, in particular also the coordination between the fans. At this point, it should be noted that also the term "definition" should be understood in the broadest sense. This also includes the calculation of operating states, if possible.

The invention is based on the idea that at each operating point of the fan the best possible efficiency and the best possible performance. Due to conflicting operational parameters, this is difficult.

It is known from fan practice that the ball bearing and ball bearing lubrication are critical parameters for the life of the fan. The service life of a ball bearing and ball bearing lubrication largely depends on the operating temperature in or on the motor and on the mechanical forces acting on the ball bearing. Since neither temperature sensors nor force sensors can be placed in the immediate vicinity of the bearing, neither the bearing temperature nor the bearing forces acting on the bearing can be measured. Therefore, it is required either to indirectly measure these parameters, or to determine them by calculation.

From DE 10 2010 002 294 A1 a system or method is known for determining the condition of a bearing in an electrical machine. Real sensor blocks determine the measured value, which is transferred to the simulation block. The simulation block determines the resulting value, which is either a bearing current value or a bearing current dependent value. The resulting value is passed to the next block for further calculation. The known system/method, due to the sensor technology required, is costly and, in the absence of sufficient structural space, can only be used with difficulty for fans.

The invention is based on the task, by means of simple constructive means, of ensuring the best possible efficiency and the best performance characteristics at each operating point of the fan. In addition, lifetime estimation should be possible so that new services can be implemented, such as predictive maintenance, etc. It should be taken into account that it is difficult to install the required sensor technology in the right positions.

In accordance with the invention, the above problem is solved by means of a method with the features of claim 1 of the claims, and in the particular case it is a method for determining the operating states of the fan using a digital display (digital twin) of the fan, to which at least one characteristic for the operational parameter of the algorithm.

More precisely, a digital representation of a real fan is created by displaying its properties through mathematical calculation models and, if necessary, using known data, if necessary, real measurement data. The actual measurement data may be current measurement data from the current operating mode of each individual engine (and if necessary, its history). Also, taking into account the known relationships, characteristics, etc., at least one algorithm characteristic of the operational parameter is created and involved in further calculation.

According to the digital display, the states of the structural elements of the fan are determined or, accordingly, calculated by means of virtual sensors. These states of the structural elements are entered into an algorithm specific to either the operating mode or operating parameters or the product, which determines and calculates specific operating parameters of the fan from the operating states and, if necessary, gives predictions related to the operating mode of the fan derived from them, for example, forecasts for the service life of the fan. . It is essential that the combined use of certain states of structural elements and real measurement data is possible.

In accordance with the invention, two different software components are used, namely a first software component relating to the digital twin and a second software component relating to a performance characteristic algorithm, which can be called an "intelligent" algorithm.

The digital twin is a digital representation of a real, individual object, in the case of the idea of a fan or fan system proposed by the invention. The digital twin displays the properties of the ventilator through a calculation model and, if necessary, using known ventilator data. The task of the digital twin is to calculate the states of the fan structural elements depending on each operational state using virtual sensors. The states of the components determined from this calculation are fed into a performance-specific algorithm that determines/calculates from the performance parameters of the digital twin the performance parameters or operating states of the fan, such as bearing life and/or bearing grease life. On the basis of the result, a control adaptation appropriate to the situation is possible. Therefore, operating parameters and operating states are just as relevant when they are design values.

The above-described combination of a digital twin and a performance-specific algorithm with respect to a digital twin algorithm can be implemented on a microprocessor to be dedicated to a fan motor and thus be dedicated to that fan as a permanent component.

The idea proposed by the invention, which can be summarized under the term "digital twin algorithm", is a combination of a digital twin describing a fan with a kind of intelligent algorithm, which is made characteristic of an operating parameter.

With a suitably designed fan, predictive maintenance can be performed to avoid failure of the fan due to, for example, a damaged bearing or bearing lubrication. We strive to adapt the system parameters to the appropriate situation, so that almost the maximum possible service life of the fan can be realized.

With the use of a digital fan display and a performance-specific algorithm, predictive maintenance aims to exhaust the life of the fan components as much as possible and at the same time avoid any fan failure. The service life of the fan is calculated by taking as a basis the design conditions of the structural elements and the resulting operating parameters.

The digital twin uses physical and/or mathematical and/or statistical and/or empirical and/or combined models to calculate the thermal and mechanical states of structural elements. In any case, they include both mathematical and physical and non-physical models. The performance-specific algorithm (intelligent algorithm) requires the digital twin-determined states of the structural elements to determine any performance parameters, for example also to predict the failure of a fan. Since the service life of a fan is primarily dependent on ball bearings and ball bearing lubrication, a focus on ball bearing lubrication and ball bearing lubrication of the performance calculation plays a very important role.

It is known from practice that the service life of a bearing grease is largely dependent on the operating temperature. The higher the operating temperature during the entire service life, the faster the bearing lubricant is consumed. Therefore, the temperature of the bearing should be determined in order to determine the service life of the bearing grease.

To determine the bearing temperature, a temperature sensor would have to be placed in the immediate vicinity of the bearing. Due to the geometrical and functional conditions of the fan/motor, this is not possible. According to the method proposed by the invention, structural element states such as bearing temperature are calculated by means of a digital twin together with an algorithm specific to the operating parameter .

The calculation is based on a mathematical model, which, in turn, is based on a reduced coupled thermomagnetic calculation model. The combination of the digital twin, together with an algorithm specific to the operating parameter, calculates heat sources, heat sinks and the thermal state of the overall system touching the fan motor. Thus, using virtual sensors of the digital twin, the bearing lubrication temperature can be determined depending on the operating state of the fan/motor and included as an operating state in the algorithm characteristic of the operating parameter.

Both the digital twin, including its virtual sensors, and the performance-specific algorithm can be implemented in machine code (C-code) on an existing microprocessor, which gives the fan a certain machine intelligence.

Although the method of development and operation of the method according to the invention is further discussed in terms of bearing life and bearing lubrication, this method can be related to all possible operating parameters of the fan. The use of the method proposed by the invention is expedient when the corresponding parameters cannot be measured directly, however, their knowledge can be used to optimize the operation mode of the fan.

There are various possibilities to implement and improve the idea of the present invention in an advantageous manner. For this, on the one hand, reference must be made to the claims dependent on claim 1 and, on the other hand, to the following explanation of preferred embodiments of the invention with the help of the drawing. In connection with the explanation of preferred embodiments of the invention with the aid of the drawing, general preferred embodiments and improved embodiments of the invention are also explained. The drawing shows:

Fig.1-8: the steps of the method of implementing the invention of the idea of the invention, with special differences.

1 shows the principle of the method according to the invention, wherein the combination of a digital twin with at least one performance-specific algorithm (intelligent algorithm) is called a digital twin algorithm, in the following example for bearing and/or bearing lubrication life.

As already mentioned above, the service life of the bearing and bearing grease depends on the operating temperature and engine speed. Since a temperature sensor cannot be placed in the immediate environment of the bearing, the temperature of the bearing must be calculated by means of a model according to the invention with a digital twin algorithm, which is obtained from a combination of the digital twin and a performance-specific algorithm (intelligent algorithm).

The digital twin is nothing more than a mathematical model based on a reduced coupled thermomagnetic and mechanical calculation model. The digital twin calculates the thermal and mechanical state of the overall system in contact with the engine. With virtual sensors belonging to the digital twin, the digital twin can determine the temperature of the bearing lubricant depending on the operating state of the engine.

For further data processing, an intelligent algorithm needs the states of structural elements in order, for example, to predict the failure of a fan. From the failure characteristics, engine failure can be calculated, or at least estimated. All software related to the digital twin algorithm is implemented in machine code (C-code) on the engine's microprocessor, so no other electronics are required.

Figure 2 shows the sequence of calculating the service life of the bearing grease in the fan motor bearing. As part of creating a digital representation of a real fan, numerical detailed models are required, in a specific case, thermal models, magnetic circuit models, etc. In addition, algorithms are compiled to calculate the life of the lubricant.

After that, the detailed models are reduced to obtain behavior models so that the amount of data can be manipulated.

After that, the behavior models and the algorithm that calculates the life of the bearing grease in the system simulation are connected, namely, within the framework of the combination of a digital twin with a characteristic algorithm for the performance parameter, which in the present case calculates the life of the bearing grease. On the basis of system simulation, a C-code is generated, and the C-code is directly implemented on the engine microprocessor.

As mentioned above, the reduction of a detailed model to a behavior model is required in order to reduce the computation time. Thanks to this measure, the digital twin algorithm can be implemented on the engine microprocessor. To reduce the thermal model, different methods can be used, for example, the Krylov method. In this case, the data of the detailed model are reduced by lowering the order of the model.

The magnetic detailed model can be reduced using an algorithm or using a table. The table defines results that are pre-calculated for certain situations, so that complex calculations can be replaced by a quick search for values. Using suitably reduced models, the bearing lubrication temperature and the bearing temperature can be calculated. The calculated values use a performance-specific algorithm, here a bearing grease life calculation algorithm, to calculate the life of the bearing grease on the one hand and the bearing life on the other hand.

In addition, the used bearing life/bearing lubricant can be preferably exponentially weighted as a function of the operating temperature.

Figure 3 shows how such a weighting factor changes over the course of temperature change, with example parameters such as continuous operation, type of bearing, viscosity, rotational speed, lubricant temperature and time taken as the basis for calculating the life of the bearing lubricant. operation/service life. This calculation example, with a lifetime of four minutes, gives a usable lifetime of 15 minutes.

According to the invention, the reduced models in accordance with the digital twin and the performance-specific algorithm regarding the service life of the bearing lubricant are included in the system simulation and linked to each other. System simulation can be compiled, for example, in the MATLAB program. Using the MATLAB code generator, you can translate the system simulation into C-code and implement it on the engine's microprocessor.

Figure 4 illustrates the application of the method according to the invention using the calculation of the service life of a bearing lubricant, using the thermal model and the magnetic circuit model as the basis for considering the digital twin model. These two models are translated into a reduced thermal model and a reduced magnetic circuit model.

The digital twin is combined with a performance-specific algorithm, namely an algorithm that finds the bearing grease life.

Based on the detailed model, this model is reduced, that is, a reduced model is derived. This reduced model may be in the form of a matrix, the matrix being translated into a C-code in the system simulation, namely by a C-code generator. The reduction of the detailed model can also be carried out in a different way.

A reduced model is thus obtained from the detailed model. Given the algorithm that calculates the life of the bearing, a system simulation takes place, from which the code is generated. This code is transferred to the engine's microprocessor, preferably in real-time computation, as opposed to time-shifted processing in the cloud.

Fig . 5 shows the bearing life calculation, in addition to the bearing lubrication life calculation shown in Fig. 4, a mechanical model is added and converted into a reduced mechanical model.

For further calculation, two algorithms characteristic of the operational parameter are used, namely, firstly, to evaluate vibrations, and secondly, to calculate the service life.

As in the calculation of bearing grease life, the data is linked by means of system simulation, and a C-code is generated from the matrices thus obtained. This code is transferred to the engine microprocessor.

The foregoing discussions about calculating bearing lubrication life and bearing life make it clear that the method of the invention and the resulting digital twin algorithm is a key function for "digitizing" the fan, since the decisive information is calculated and analyzed, and moreover, while reducing all incoming data .

Finally, the essential information is the operating conditions, which are determined within the digital twin by means of virtual sensors so that the service life of the bearing grease and the life of the bearing in the fan can be calculated.

At this point, it should be noted that the method proposed by the invention, i. By means of a digital twin algorithm, every possible operational state can be calculated with virtual sensors, which is not possible with real sensors due to design limitations and the costs associated with such sensors.

The method according to the invention makes it possible, based on the data reduction discussed above, to produce a compact C-code that can run on standard microprocessors. On the microprocessor, a kind of data enhancement (big data → smart data) takes place, and this is the result of the calculation. Only the compressed enhanced data is further processed or sent to the cloud, for example. Of course, due to this, the volume of streaming communication to the cloud is greatly reduced (reduced).

In addition, it is only required at any or certain time intervals to query the remaining life of the fan or motor, from which a linear behavior or a drift can be recognized. As mentioned above, real sensors can be abandoned in favor of structural space and to reduce costs.

Also, the performance parameters determined based on the digital twin and performance characteristic algorithm can be used for predictive maintenance and preventive maintenance of the fan on the one hand, and for optimizing the design and operation of the fan on the other hand.

The image in Fig.6 and 7 serves to further explain the idea of the claimed invention, which has already been explained as an example with reference to Fig.4 and 5.

Essential is the use of a digital display, namely a digital fan twin. The digital twin is obtained through data processing. It is obtained, on a case-by-case basis, from a combination of known input values or sensor measurement values with calculated values and calculations/models. On the basis of the digital twin, the temperature of structural elements, currents, losses, etc. is determined in certain specified places of the fan. On the basis of the digital twin, real values are virtually determined, such as, for example, specific temperatures of structural elements, even if there is no economically/structurally feasible measurement possibility by means of sensor technology at this specific location of the fan.

Also important for the idea of the claimed invention is the algorithm characteristic of the operational parameter. On the basis of the results or data provided by the digital twin, for example the bearing temperature, indicators such as, for example, the probability of failure or the used lifetime of the fan or fan bearing are determined. These figures depend on the current operating parameters of the fan and their history, i.e. in which operating points and environments the fan is/was running.

FIGS. 6 and 7 illustrate with a specific example the method of the invention for determining fan operating conditions using a digital fan display based on the foregoing considerations.

In the left column of Fig.6 are the measured and calculated input values, including the units of measurement marked on the arrows. These input quantities are measured with available standard sensors or are known.

From these input values, heat sources and heat sinks are calculated. It is based on simulation-based models that take into account heat sources such as losses in copper, iron, electronics, and heat sinks such as engine cooling (cooling wheel, circulating air and ambient temperature). This results in input quantities for a reduced thermal model having virtual sensors. All this corresponds to the digital twin in relation to the thermal model.

From the reduced thermal model with virtual sensors, the temperatures of structural elements are calculated. At the same time, the thermal model displays the physics of the fan and, using virtual sensors, calculates the temperature in the bearing, in the winding, on the magnet and in various electronic components, depending on the need.

Fig. 7, as a continuation of Fig. 6, clearly shows that the output values from the reduced thermal model, optionally having other parameters, are used as input values for calculating the aging process. The underlying aging models are based on historical data and can be embedded in the form of characteristics. So the aging-limited residual life can be individually calculated or adjusted on site based on the actual history of the fan and the current operating condition.

The output of these aging calculation models is an estimated lifetime in days or hours, which, taken separately, can be pure information. Then the information related to this can be involved in a further forecast, namely, in the forecast of the remaining service life of individual structural elements or the entire fan. This prediction can then be used to intelligently optimize the residual life. In order to extend the remaining service life, measures can be taken, such as speed reduction or intelligent load sharing among several fans. These measures can be associated with any control variable.

Figure 8 once again shows the digital twin down to a reduced thermal model having virtual sensors, the fan being mapped to it along with the motor. As mentioned above, the thermal model displays the physics (physical characteristics) of the fan and calculates with the help of virtual sensors different temperatures, which are involved for various purposes/purposes/applications, in keywords as follows.

For control : Determination of operational parameters using virtual sensors and use them for control. These can be: warning messages, status LEDs, comments in a readable error code, displays in the cloud or software application, an image on the user interface.

For predictive maintenance : a way to determine the aging of a fan composed of numerous individual systems such as ball bearings, windings, electronic components, magnets, and predict the remaining life. Use, for example, for scheduling maintenance intervals, to achieve the longest possible service life before a maintenance interval (i.e. not too early maintenance), automatic maintenance scheduling, maintenance notification, automatic ordering of spare parts.

For optimization : a way to determine the operational states relating to the performance of the product, i.e. efficiency, temperatures of structural elements, rotational speed, output power, flow, noise, vibrations, etc.

To create an intelligent fan: reaction to certain operating conditions to improve behavior or to achieve certain goals:

- changing the operating point / changing the control parameter for optimal efficiency;

- changing the operating point to achieve the longest possible service life;

- reduction of rotational speed when the probability of failure is very high;

- changing the operating point during the day-night rhythm for the quietest possible night application;

- output of a control value for additional devices or customer devices, for example, output of a temperature for use in the control of a heat pump or for additional cooling;

- purposeful bypass of the critical states of the system (for example, resonances, elevated temperatures, etc.).

The following is relevant for creating detailed models:

the model, in principle, is a reflection or approximation of reality, that is, by definition, an approximation. The model is always limited to some fragment that is interesting for the desired display. In addition, the model is fundamentally incomplete because either it is reduced for easier application with respect to its required inputs, or the individual physical behaviors are not known when the model is built. Depending on the later use and goal setting, a different kind of simulation is needed, ie, for example, a different area to be considered, a different accuracy in the results required, or a speed of calculation. There are many types of models, and in the technical field, most often the model is associated with a mathematical representation, for example, with algebraic equalities or inequalities, systems of ordinary or partial differential equations, spatial representations of states, tables, graphs.

An integral part of today's product development is virtual product development using Finite Element Simulation (FE) simulation. Classically, here the physical domain (eg, strength or heat or magnetic circuit) is mapped into a very large (on the order of 100 Gigabytes) and computationally intensive model, and the results are defined in this model in millions of places (nodes). This is one of the detailed models. A rough sequence of these detailed models can be outlined as follows:

1. import of 3D geometry, for example from the CAD area,

2. assignment of boundary conditions, i.e. permanent fixings, material definitions, contact conditions (glue points, sliding joints, thermal insulation),

3. union (dividing the geometry into a million small elements that are connected to each other),

4. creating a load, that is, forces, sources / receivers of heat, magnetic fields.

5. automatic solution of the resulting differential equations for each individual element and reduction into one large result for the general model,

6. evaluation of results.

The reduced models are created in the following way.

Model reduction describes an already existing model in general terms in order to reduce other information, for example, to optimize it for memory requirements or computational speed. Here, depending on the specific application, there are many options, such as for example:

- approximation of simple mathematical functions, such as, for example, polynomial functions, and only storing coefficients in memory;

- writing tables for different input values and then either applying these discrete values or interpolating between values;

- Approximation of statistical models that give predictions from previous values;

- graphs/logic diagrams, example: if T ˃ 200°C, then the fan is faulty.

Creating Reduced Models - Example a)

The starting point for the reduced model is the thermal FE model, which displays the temperatures at each point in the model as a function of heat input and heat removal. In the following example, the reduction of the model is simplified to only one heat input and one heat rejection, only one detectable temperature at location A, and only "high" and "low" respectively. To do this, a study of the parameters is carried out, at which this so-called "reference table" is found:

Temperature at location A Heat Input=Low=1W Heat Dissipation=High=11W Heat Dissipation=Low=1W 40°C 80°C Heat Dissipation=High=5W 20°C 60°C

After that, there are several possibilities for using the results:

- direct or discrete use of the table. Example: when a temperature for heat input of 4 W and heat removal of 1 W is predicted at location A, the value of 40°C is directly assumed;

- using a table and linear interpolation between values. Example: when a temperature for heat input of 5 W and heat removal of 1 W is predicted at location A, a value of 60°C is assumed by linear interpolation;

- using a table to determine the temperature prediction function through regression. Examples of objective functions are polynomial functions, linear functions, exponential functions, statistical functions, differential equations, etc. Then determine the temperature using this function.

Creating reduced models - example b)

The starting point for the reduced model is the thermal FE model, which displays the temperatures at each point in the model as a function of heat input and heat removal. After that, with the help of mathematical assumptions, calculations and transformations (for example, the LSS system (linear stationary system) or the Krylov subspace method), compact spatial models of states can be approximated. These models consist of two essential differential or, respectively, integral equations and four matrices describing the entire system (for example, 200 x 200 matrices filled with scalar numerical values). However, they no longer represent temperatures in millions of knots, but only in a small number of selected locations. In addition, due to approximation, there is a deviation of the results depending on the size of the spatial model of the state. In principle, it is believed that the larger the model and its matrices, the smaller the deviation.

Spatial state models are standardly available as procedures, modules, or objects in many computer programs such as Matlab, or in programming languages, so such models can be calculated by simply importing matrices. The input values here are, for example, the thermal power that enters the system and the heat sinks by convection, the output values are, for example, determined component temperatures (eg three different component temperatures).

Creating reduced models - example c)

The starting point for model reduction in this example is the results of experiments. Here, as in example a), a table would be created from the measurement results and then dealt with in an equivalent way (discrete use, linear interpolation, or regression by means of mathematical functions.

Relationships between physical domains or different models can have additional value.

Classically, in virtual product development, domains are considered separately, since the overall consideration is very computationally and memory intensive and not practical. Due to the reduction of models, it becomes possible to link models of different domains. For example, the connection of a detailed model of a magnetic circuit, the computation time of which on a high-performance computing cluster ranges from several days to weeks, with a thermal model is undesirable. In many cases, it is necessary to represent the real behavior as accurately as possible. With the reduction of detailed models, the required computing power and memory requirements become lower, and thus it becomes possible to link the models taking into account the economic aspect. In many cases, it is necessary to represent the real behavior as accurately as possible.

Communication of physical domains or different models - examples:

- the resistance of the winding is approximately linearly dependent on the temperature of the copper. Depending on the resistance of the winding, the power loss in the winding varies approximately linearly. Depending on the power loss, the thermal behavior, for example the temperature of the winding and the bearing, changes strongly non-linearly, which in turn affects the resistance of the winding. Thus, here, depending on the requirement for the results of the model, a connection is needed;

- the required torque and fan speed are highly dependent on the resistance of the installation and, for example, the pressure difference and temperature of the discharged medium. Depending on the load moment, the behavior of the magnetic circuit changes, i.e. winding currents, magnetic field, speed, etc. Then, depending on this, the power consumption, losses and achievable speed also change. And here, if the customer's requirements are taken into account, it is possible to link the behavior of the fan with the installation situation, depending on the application.

Specification of the technical implementation - example a):

- compilation of a thermal FE model of a fan → a computationally intensive and memory intensive FE model with 1,000,000 additional elements for the polynomial function. Heat sources and sinks are displayed as polynomial functions depending on the input current and speed;

- compilation of a reduced thermal model by statistical methods, which displays the temperature of the electronic components depending on the input current and speed → a polynomial function that describes the temperature depending on the input current and speed = virtual temperature sensor;

- characteristic from the data sheet on the service life of an electronic component depending on its temperature → an algorithm specific to the operational parameter, which, using a virtual temperature sensor, calculates the probability of failure;

- use of preventive maintenance to control or to optimize the operating point → intelligent algorithm.

Specification of the technical embodiment - example b):

- registration of the indicator of current and frequency of rotation of the engine by means of the integrated electronics/regulation. From here, the electromagnetic operating point is derived;

- Based on this operating point, the losses of the motor and power electronics are obtained by means of look-up tables or polynomial functions;

- the thermal model processes the loss values and determines the temperatures of critical system components such as ball bearings or semiconductor components;

- at the same time, vibrations of structural elements are recorded by means of a real sensor. Local vibrations are virtually projected by behavior models onto the overall system, as a result of which, for example, the load on the bearing from vibrations is estimated;

- with the help of algorithms specific to the operational parameter, certain temperatures and vibration values are recalculated into an assessment of the service life of structural elements and the fan;

- thus, further measures such as preventive maintenance can be ensured;

- at the same time, knowing the losses, the operating point and the efficiency of the system can be optimized by adaptations carried out according to the control technology, such as, for example, by varying the preliminary control angle.

Finally, it should be directly pointed out that the above-described embodiments serve only to explain the idea of the claimed invention, however, it is not limited to these embodiments.

Claims (19)

1. A method for determining the operational states of a fan, wherein a digital display - a digital twin - of the fan and at least one algorithm characteristic of the operational parameter is used to determine the operational states of the fan, the method contains the following steps, in which: - generate a digital twin of a real fan by reproducing the properties of the fan with the help of mathematical calculation models and real sensor measurement data, - creating said at least one performance-specific algorithm based on the input data or, respectively, said measurement data, - determine the state of the structural elements of the fan using a digital twin through virtual sensors, - transfer the states of structural elements to the algorithm, - from the states of structural elements on the basis of the algorithm determine the operating parameters of the fan and control and, if necessary, regulate the mode of operation of the fan. 2. The method according to claim 1, characterized in that the digital display is based on a thermal and/or mechanical calculation model and/or a magnetic calculation model as a mathematical and/or physical and/or empirical and/or statistical or combined model. chains. 3. The method according to claim 1 or 2, characterized in that each calculation model is transferred from a detailed model to a reduced model. 4. Method according to any one of claims 1 to 3, characterized in that the digital display serves to calculate the thermal and/or mechanical state of the fan, in particular the fan motor. 5. The method according to any one of claims 1 to 4, characterized in that with the help of virtual digital display sensors, using calculation models, the temperatures of structural elements are determined depending on the operational state. 6. The method according to any one of claims 1 to 5, characterized in that said at least one performance-specific algorithm, taking into account the states of the structural elements, if necessary taking into account the failure criteria or the failure characteristic, calculates the failure criteria fan failure or predicts an expected fan failure. 7. Method according to any one of claims 1 to 6, characterized in that the digital display and its preferably reduced models and said at least one performance-specific algorithm are included in the system simulation by means of a simulation program. 8. The method according to claim 7, characterized in that the digital display software and said at least one performance-specific algorithm, preferably in the form of a simulation program, are implemented on a microprocessor, preferably in machine code (C-code). 9. Method according to claim 8, characterized in that the microprocessor is designed directly for the fan electric motor. 10. Method according to any one of claims 1 to 9, characterized in that the operating parameters are the life of the bearing and/or the life of the bearing grease. 11. Method according to any one of claims 1 to 10, characterized in that certain operating parameters are used for proactive preventive maintenance of the fan. 12. Method according to any one of claims 1 to 11, characterized in that the real measurement data is the current measurement data from the current operating mode of each individual engine and, if necessary, its history. 13. A method according to any one of claims 1 to 12, characterized in that the input data is data sheet data and/or historical fan data.

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