Method and machine controller for temperature monitoring of electromechanical machine

Abstract

According to the invention, structural data (SD) relating to the geometry, thermal conductivity and electrical conductivity of the elements of the machine (M) to be monitored are read. Furthermore, a predefined temperature-critical point (P1, P2) of the machine is positioned with reference to the structure data (SD). Furthermore, a plurality of temperature profiles (TV) in the machine are simulated in a spatially resolved manner by means of a first thermal simulation model (ST) comprising a plurality of spatially resolved shape functions (FE1, FE2,...). On this basis, a reduced thermal simulation model (RST) comprising a smaller number of shape functions (F1, F2,...) is generated and set such that it reproduces the temperature profile (TV) at the temperature critical point (P1, P2). Then, electrical operating data (U, I) of the machine are detected during the ongoing operation of the machine, and the electrical energy loss (QE) in the machine is continuously simulated in a spatially resolved manner by means of an electrical simulation model (SE) of the machine on the basis of the structural data (SD) and the electrical operating data (U, I). Furthermore, on the basis of the structural data (SD) and the simulated electrical energy loss (QE), the temperatures (RT1, RT2) at the temperature critical points (P1, P2) are continuously determined by means of the reduced thermal simulation model (RST) and output for temperature monitoring of the machine.

Description

Background Art

[0001] The safe operation of electromechanical machines such as electric motors requires in many cases, especially in the higher power range, temperature monitoring of key machine components or temperature-critical parts of the machine. For example, the insulation of the stator windings of an electric motor should not be exposed to relatively high temperatures in order not to jeopardize its functionality.

[0002] However, the temperature of many temperature-critical locations or machine components cannot be measured directly or can only be measured directly with difficulty. Therefore, installing temperature sensors on the motor rotor and transmitting the sensor signals from there to a monitoring device is often technically very complex.

[0003] In order to at least obtain an estimate of the expected operating temperature, historical operating data of the machine are usually used. However, for this purpose, a large amount of historical operating data for the machine in question is usually required, which covers a large number of operating conditions.

[0004] Furthermore, it is known to evaluate the measured values ​​of temperature sensors placed at more accessible locations of the machine to be monitored. Based on these measured values, the temperature of less accessible machine components can then be inferred with the aid of a physical simulation model. Summary of the invention

[0005] The object of the invention is to specify a method for temperature monitoring of an electromechanical machine and a machine controller which allow more efficient and / or less complex temperature monitoring.

[0006] This object is achieved by a method having the features of patent claim 1 , a machine controller having the features of patent claim 10 , a computer program product having the features of patent claim 11 , and a computer-readable storage medium having the features of patent claim 12 .

[0007] Structural data of the thermal conductivity, electrical conductivity and geometry of the machine elements are read in for temperature monitoring of an electromechanical machine based on electrical operating data of the machine. Furthermore, predetermined temperature-critical locations of the machine are located with reference to the structural data. Furthermore, based on the structural data, a plurality of temperature curves in the machine are simulated in a spatially resolved manner by means of a first thermal simulation model comprising a plurality of spatially resolved shape functions. On this basis, a reduced thermal simulation model comprising a plurality of shape functions that are fewer than the first thermal simulation model is generated and arranged in such a way that it can substantially reproduce the temperature curves at the temperature-critical locations. Then, the electrical operating data are detected during the ongoing operation of the machine, and based on the structural data and the detected electrical operating data, the electrical energy losses in the machine are continuously simulated in a spatially resolved manner by means of the electrical simulation model of the machine. Furthermore, based on the structural data and the simulated electrical energy losses, the temperatures at the temperature-critical locations are continuously determined by means of the reduced thermal simulation model and output for temperature monitoring of the machine.

[0008] In order to carry out the method according to the invention, a machine control, a computer program product and a computer-readable, preferably non-volatile storage medium are provided.

[0009] For example, the method according to the invention and the machine controller according to the invention can be executed or implemented by one or more computers, processors, application specific integrated circuits (ASICs), digital signal processors (DSPs) and / or so-called field programmable gate arrays (FPGAs).

[0010] One advantage of the invention is that the temperature prevailing in or on the machine can be determined based on the electrical operating data which are usually present in the machine controller anyway. In many cases, it is therefore no longer necessary to install temperature sensors on or in the machine itself. Furthermore, the simulation also makes it possible to determine and / or monitor the temperature at difficult-to-access locations of the machine, in particular inside the machine.

[0011] Since the temperature determination is concentrated on the predetermined temperature-critical locations, in many cases, a reduced thermal simulation model can be generated which is significantly simplified compared to the first thermal simulation model. Such a reduced thermal simulation model usually requires significantly fewer computing resources. Therefore, in many cases, the reduced thermal simulation model can be executed in real time with relatively low computing power for real-time monitoring of the machine.

[0012] Advantageous embodiments and refinements are described in the dependent claims.

[0013] According to an advantageous embodiment of the invention, the operating current and / or the operating voltage of the machine or its components can be quantified by means of electrical operating data. Alternatively or additionally, the electrical operating data can quantify the intermediate circuit voltage, the frequency and / or the degree of modulation. The above-mentioned electrical operating data are already present in many machine controllers, so that generally no additional installation is required on or in the machine to be monitored for temperature monitoring of the machine.

[0014] According to another preferred embodiment of the present invention, the shape function of the reduced thermal simulation model can be selected from the shape function of the first thermal simulation model and / or parameterized in such a way that the deviation between the temperature curve determined by the reduced thermal simulation model and the temperature curve determined by the first thermal simulation model is minimized specifically at the temperature critical part. By focusing on the temperature critical part or ignoring the non-critical part, or considering the non-critical part in a simplified manner, the number of shape functions required for the reduced thermal simulation model is usually significantly reduced. The reduction process can be carried out in particular in such a way that the deviation is specifically, preferably or uniquely minimized at the temperature critical part, and the minimized deviation does not exceed a predetermined deviation threshold.

[0015] In addition, the ambient temperature of the machine and / or the air flow rate in the environment of the machine can be continuously detected with sensors. The temperature at the temperature-critical part can then be determined based on the ambient temperature and / or the air flow rate. In this way, the simulation accuracy can be significantly improved in many cases.

[0016] Furthermore, based on the structural data and mechanical operating data of the machine, mechanical energy losses in the machine can be simulated in a spatially resolved manner with the aid of a mechanical simulation model of the machine. The temperature at the temperature-critical locations can then be determined based on the simulated mechanical energy losses. The mechanical simulation model can in particular simulate friction losses, for example friction losses in machine bearings. For this purpose, the structural data can include friction coefficients for machine elements that are affected by friction losses.

[0017] In addition, the machine operating data can quantify the rotational speed, torque, movement speed and / or applied force of the machine or its components. Such machine operating data is usually already present in many machine controllers, so that it is usually not necessary to install additional equipment on or in the machine to be monitored for temperature monitoring of the machine.

[0018] According to an advantageous development of the invention, structural data relating to the electrical and / or thermal conductivity of the machine element can be modified as a function of the determined temperature. A simulation of the electrical energy loss and / or a determination of the temperature at temperature-critical locations can then be performed based on the modified structural data. Thus, in particular, the temperature dependency of the electrical resistance can be taken into account when simulating the electrical energy loss and / or the temperature dependency of the thermal conductivity can be taken into account when determining the temperature. In this way, the simulation accuracy can generally be significantly increased.

[0019] Furthermore, based on the determined temperature at the temperature-critical points, the machine can be turned down, instructions for optimized operation of the machine can be output, recommendations for optimized operation of the machine can be output, and / or cooling devices can be controlled. In many cases, the wear of the machine can be significantly reduced and / or its service life can be extended by the above measures.

[0020] According to a further advantageous embodiment of the invention, the position of a predetermined machine component can be determined based on the structural data. The determined position can then be used as a temperature-critical location, wherein the temperature determined at this location is output as a component-specific temperature value. In this way, one or more critical machine components, such as rotors, stators, windings, bearings and / or insulation of the electric machine, can be monitored individually. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The embodiments of the present invention are described in more detail below with reference to the accompanying drawings, in which they are respectively shown in schematic diagrams:

[0022] Figure 1 shows the temperature monitoring of the motor by the motor controller, and

[0023] Figure 2 The generation of a reduced thermal simulation model for a motor controller is shown. DETAILED DESCRIPTION

[0024] Figure 1 The temperature monitoring of an electric machine M as an electromechanical machine by a motor controller CTL as a machine controller according to the invention is shown. Alternatively, the electromechanical machine M to be monitored can also include a robot, a machine tool, a turbine, a production machine, a motor vehicle, a 3D printer or a component thereof, or can include such a machine or component. The motor controller CTL can in particular include an inverter.

[0025] Furthermore, the motor controller CTL has one or more processors PROC for carrying out the method according to the invention and one or more memories MEM for storing data to be processed.

[0026] Figure 1The motor controller CTL shown in FIG. 4 is located outside the electromechanical machine M and is coupled thereto. Alternatively, the motor controller CTL can also be fully or partially integrated into the electromechanical machine M.

[0027] The motor controller CTL is used to operate and control the motor M. For this purpose, the motor controller CTL can in particular predetermine a motor speed RPM for the motor M and / or supply the motor M with a corresponding operating voltage U and / or a corresponding operating current I. In this case, the motor M can be supplied with energy in particular via an inverter (not shown) of the motor controller CTL. In addition, the motor controller CTL can detect the motor speed RPM, the operating voltage U and / or the operating current I currently measured by sensors of the motor M. For reasons of clarity, the measured and predetermined operating data in the figures are each denoted by the same reference numerals.

[0028] As an alternative or in addition to the motor speed RPM, other mechanical operating data of the electromechanical machine M, such as torque, movement speed and / or applied force of the machine M or its components, can be detected or used by the motor controller CTL. Accordingly, in addition to the operating voltage U or the operating current I, other current electrical operating data of the electromechanical machine M or its components can also be detected or used by the motor controller CTL.

[0029] For the present exemplary embodiment it is assumed that the machine M has two temperature-critical locations P1 and P2 whose temperatures are to be specifically monitored. In the case of an electric machine, such temperature-critical locations may in particular be predetermined locations of the rotor, stator, windings, bearings or insulation of the electric machine.

[0030] In addition to P1 and P2 , one or more further temperature-critical locations may be provided for monitoring in or on the machine M. However, for reasons of clarity, only the locations P1 and P2 are explicitly shown in the figure to represent all temperature-critical locations to be monitored.

[0031] Furthermore, the motor controller CTL is coupled to a database DB in which structural data SD are stored regarding the geometry, thermal conductivity and electrical conductivity of elements of the machine M. The thermal conductivity can be equivalently expressed or represented by thermal resistance, while the electrical conductivity can be equivalently expressed or represented by electrical resistance.

[0032] Furthermore, the structural data SD include friction coefficients of elements of the machine M that are subject to friction losses. These friction coefficients may be, in particular, coefficients for the friction between the rotor shaft and the bearings.

[0033] Examples of machine elements described by means of structure data SD are in particular machine components, such as rotors, stators, windings, bearings or insulators of electric machines, or parts of such machine components. In particular, machine elements which have a specific influence on the electrical conduction, thermal conduction and / or friction during operation of the machine M are described by means of the structure data SD. For example, these machine elements may be coatings, electrical lines, switching elements, thermal bridges or other structural elements of the machine M. The structure data SD preferably specify the thermal conductivity, electrical conductivity and / or friction in spatially resolved form.

[0034] The motor controller CTL comprises: a first simulation module S1, which includes an electrical simulation model SE of the machine M; a second simulation module S2, which includes a mechanical simulation model SM of the machine M; and a third simulation module S3, which includes a reduced thermal simulation model RST of the machine M. The reduced thermal simulation model RST is generated by a predetermined detailed thermal simulation model of the machine M. Figure 2 The generation of the reduced thermal simulation model RST is further explained in detail.

[0035] The first simulation module S1 is used for continuous spatially and temporally resolved simulation of the electrical energy losses QE in the machine M based on the electrical simulation model SE. The second simulation module S2 is used for continuous spatially and temporally resolved simulation of the mechanical energy losses QM in the machine M by means of the mechanical simulation model SM. Finally, the third simulation module S3 is used for continuous determination of the temperatures RT1 and RT2 at the temperature-critical parts P1 and P2 by means of the reduced thermal simulation model RST.

[0036] In this case, the electrical simulation model SE and the mechanical simulation model SM can be combined into an electromechanical simulation model, if necessary. A real-time simulation which is also carried out during the operation of the machine M is respectively carried out by means of these simulation modules S1 , S2 and S3 .

[0037] To initialize these simulation models SE, SM and RST, the motor controller CTL reads the structure data SD from the database DB and inputs the read structure data SD at least partially into the simulation modules S1, S2 and S3, respectively. Thus, the electrical simulation model SE is initialized by the structure data SD related to the geometry and electrical conductivity of the machine elements. Similarly, the mechanical simulation model SM is initialized by the structure data SD related to the geometry and friction of the machine elements. Finally, the reduced thermal simulation model RST is initialized by the structure data SD related to the geometry and thermal conductivity of the machine elements.

[0038] In order to perform the above-mentioned simulation, there are many effective methods and models for physical simulation available. In particular, the finite element method can be used for simulation. Therefore, the electrical simulation model SE, the mechanical simulation model SM, the detailed thermal simulation model and / or the possible reduced thermal simulation model can be configured as a finite element model respectively and implemented with the help of a plurality of known simulation libraries.

[0039] After the simulation models SE, SM and RST are initialized, the described simulation can be performed in real time based on the detected current operating data (here U, I and RPM of the machine M). For this purpose, the current electrical operating data (here operating voltage U and operating current I) are continuously input to the first simulation module S1. Then, the first simulation module S1 continuously simulates the electrical energy loss QE in the machine M in a spatially resolved and time-resolved form based on the electrical operating data U and I with the help of the initialized electrical simulation model SE. The simulated electrical energy loss QE is input by the first simulation module S1 to the third simulation module S3.

[0040] In addition, the current mechanical operating data (here, the current rotational speed RPM) is continuously input to the second simulation module S2. Therefore, the second simulation module S2 continuously simulates the mechanical energy loss QM in the machine M in a spatially resolved and time-resolved form based on the mechanical operating data RPM with the help of the initialized mechanical simulation model SM. The simulated mechanical energy loss QM is input from the second simulation module S2 to the third simulation module S3.

[0041] The energy losses QE and QM obviously act as heat sources in the machine M, the heat of which is distributed in the machine M according to the local thermal conductivity of the machine M.

[0042] In addition, the air flow rate and / or the ambient temperature in the environment of the machine M are detected by a sensor and are also input into the third simulation module S3.

[0043] Finally, the third simulation module S3 continuously determines the temperatures RT1 and RT2 at the temperature critical parts P1 and P2 in a time-resolved form through the initialized reduced thermal simulation model RST based on the simulated spatially resolved and time-resolved energy losses QE and QM and the ambient temperature or air flow rate.

[0044] Preferably, the determined temperatures RT1 and RT2 can be fed back to the simulation models SE, SM and RST to modify the structural data SD on which the models SE, SM and RST are based. In this way, it is possible to take into account the temperature-dependent electrical resistance in the electrical simulation model SE, the temperature-dependent thermal resistance in the reduced thermal simulation model RST, and / or the temperature-dependent mechanical properties of the machine elements in the mechanical simulation model SM. Thus, in many cases, the accuracy of the simulation can be significantly improved.

[0045] The determined temperatures RT1 and RT2 are transmitted from the third simulation module S3 to the monitoring module MON of the motor controller CTL. The monitoring module MON continuously checks, based on the transmitted temperatures RT1 and RT2, whether the maximum permissible temperature at the relevant temperature-critical part P1 or P2 is exceeded and / or whether the corresponding target temperature is met. This can be done, for example, by comparing with a predetermined threshold value and / or a predetermined temperature interval.

[0046] Based on these checks, the monitoring module MON can reduce the machine M, control the cooling device of the machine M, and / or output instructions or suggestions for an optimized operation of the machine M. In order to control the machine M accordingly, the monitoring module MON forms a suitable control signal CS and transmits it (e.g. Figure 1 The data are then transmitted to the machine M as shown by the dashed arrow.

[0047] The present invention enables efficient and accurate temperature monitoring of a machine or electric machine M based on operating data (here U, I, RPM) which are predefined or currently measured by a motor controller CTL and which are present in many machine controllers, motor controllers or inverters. In this way, in many cases, temperature monitoring can be achieved without complex sensors to be installed on or in the motor.

[0048] Furthermore, by reducing the detailed thermal simulation model to a reduced thermal simulation model RST in the following manner, the computational effort for the thermal simulation can be significantly reduced in many cases. Since a sufficiently accurate thermal simulation usually accounts for the main or dominant share of the total computational effort, this can be significantly reduced in many cases.

[0049] Figure 2 The diagram shows the generation of a reduced thermal simulation model RST for the motor controller CTL based on a predetermined detailed thermal simulation model ST. Figure 2 The reference numerals used in Figure 1 In the figures, the reference numerals denote identical or corresponding entities, which can in particular be implemented or configured as described above. For reasons of overview, Figure 2 It is no longer explicitly stated Figure 1 Some components and data flows of the motor controller CTL and the machine M shown in FIG.

[0050] The detailed thermal simulation model ST is implemented in the third simulation module S3 as a finite element model, which contains a plurality of spatially resolved shape functions FE1, FE2, ... For the implementation, at least a portion of the structural data SD is provided to the third simulation module S3. Within the scope of the finite element model, the region of the machine M on which the thermal simulation is to be performed is decomposed into a plurality of spatial finite elements. Then, shape functions located within the scope of the finite element model are defined on these spatial finite elements, here FE1, FE2, ... . In this case, it is known that each shape function FE1, FE2, ... describes the behavior to be simulated (here the temperature behavior) in the corresponding spatial finite element. There are a plurality of efficient program libraries available for decomposing spatial finite elements, configuring shape functions FE1, FE2, ... and performing finite element-based simulations.

[0051] In the present case, the decomposition into spatial finite elements is performed based on the geometrical data of the machine M contained in the provided structural data SD. On this basis, the heat conduction equation discretized into spatial finite elements is established based on the spatially resolved data related to the thermal conductivity of the elements of the machine M contained in the provided structural data SD.

[0052] There are several known and effective methods for formulating and solving this discrete heat conduction equation.

[0053] However, for sufficiently accurate thermal simulations, a large number of finite elements are usually required, which usually leads to considerable computational complexity. For this reason, the detailed thermal simulation model ST is transformed by a transformation T into a simplified thermal simulation model, here RST, which contains significantly fewer shape functions F1, F2, ...

[0054] The reduced thermal simulation model RST is first initialized geometrically based on the geometric data contained in the provided structural data SD or the geometric structure of the detailed thermal simulation model ST. In addition, the temperature-critical parts P1 and P2 are located with reference to the structural data SD. In this case, the coordinates of the temperature-critical parts P1 and P2 are determined in particular with reference to the coordinate system defined in the structural data SD.

[0055] On this basis, shape functions F1, F2, ... are selected and / or configured which are specifically focused on or adapted to the temperature-critical locations P1 and P2. In this case, the shape functions F1, F2, ... can be generated at least partially by means of a transformation T from the shape functions FE1, FE2, ... When selecting and / or configuring the shape functions F1, F2, ..., the temperature-critical locations P1 and P2 are preferred over other, less temperature-critical locations of the machine M or locations that have a smaller influence on heat conduction. In many cases, by thinning, roughening and / or ignoring shape functions at less critical locations in this way, the number of shape functions F1, F2, ... can be significantly reduced compared to the number of shape functions FE1, FE2, ...

[0056] In order to further adapt the reduced thermal simulation model RST, a plurality of spatially resolved and time-resolved energy losses Q acting as heat sources are provided as input data to the reduced thermal simulation model and the detailed thermal simulation model ST, respectively. For example, the energy losses Q may include a plurality of recorded or simulated electrical energy losses QE and / or mechanical energy losses QM of the machine M. Furthermore, the air flow rate and / or the ambient temperature in the environment of the machine M may also be represented as a heat source or heat sink via the energy losses Q.

[0057] Based on the provided plurality of energy losses Q, a plurality of respectively associated spatially resolved and time-resolved temperature profiles TV in the machine M are simulated with the aid of a detailed thermal simulation model ST. The simulated temperature profiles TV are transmitted from the third simulation module S3 to an evaluation module EV of the motor controller CTL. The evaluation module EV evaluates the temperature profiles TV at the temperature-critical locations P1 and P2, respectively, and determines the thus simulated profiles of the temperatures T1 and T2 at the temperature-critical locations P1 and P2.

[0058] Analogously, based on the provided plurality of energy losses Q, a plurality of curves of respectively associated temperatures RT1 and RT2 are determined at the temperature-critical locations P1 and P2 with the aid of the reduced thermal simulation model RST.

[0059] The reduced thermal simulation model RST and / or its shape functions F1, F2, ... are now set and / or parameterized in such a way that these temperatures RT1 and RT2 reproduce the temperatures T1 and T2 as accurately as possible. For this purpose, the deviation D between the curves of the temperatures RT1 and RT2 and the associated curves of the temperatures T1 and T2 is determined. This deviation D can be determined in particular as a time and or time integral of the square or absolute value of the difference between the corresponding temperature pair (RT1, RT2) and the respectively associated temperature pair (T1, T2). For example, according to D=integral[(RT1(t)-T1(t))2+(RT2(t)-T2(t))2]dt.

[0060] like Figure 2 As shown by the dashed arrow, the deviation D is fed back to the third simulation module S3. The reduced thermal simulation model RST and / or its shape functions F1, F2, etc. are set and / or parameterized based on the deviation D so that the deviation D is minimized. For such minimization problems, there are a variety of effective optimization methods available.

[0061] Preferably, the minimum deviation D obtainable in this way is compared with a predetermined deviation threshold value. As long as the minimum deviation D does not exceed the deviation threshold value, the reduction process of the reduced thermal simulation model RST can be continued; in particular, by further thinning, coarsening or ignoring the shape functions F1, F2, ... and continuing.

[0062] In this way, the reduced thermal simulation model RST can be significantly simplified in many cases with a predeterminable accuracy, so that in many cases only relatively few computing resources are required for a real-time simulation of the thermal behavior of the machine M.

Claims

1. A computer-implemented method for temperature monitoring of an electromechanical machine (M) based on electrical operating data (U, I) of the machine (M), wherein a) reading structural data (SD) about the thermal conductivity, electrical conductivity and geometry of elements of the machine, b) locating predetermined temperature-critical points (P1, P2) of the machine with reference to the structural data (SD), c) simulating a plurality of temperature profiles (TV) in the machine in a spatially resolved manner based on the structural data (SD) by means of a first thermal simulation model (ST) comprising a plurality of spatially resolved shape functions (FE1, FE2, . . . ), d) generating and setting a reduced thermal simulation model (RST) comprising fewer shape functions (F1, F2, . . . ) than the first thermal simulation model (ST) in such a way that it substantially reproduces the temperature profile (TV) specifically at the temperature-critical points (P1, P2), e) detecting the electrical operating data (U, I) during ongoing operation of the machine, f) based on the structural data (SD) and the electrical operating data (U, I), continuously simulating the electrical energy losses (QE) in the machine in a spatially resolved manner with the aid of an electrical simulation model (SE) of the machine, g) continuously determining the temperatures (RT1, RT2) at the temperature-critical locations (P1, P2) by means of a reduced thermal simulation model (RST) based on the structural data (SD) and the simulated electrical energy losses (QE), and h) Outputting the determined temperatures (RT1, RT2) for temperature monitoring of the machine.

2. The method according to claim 1, It is characterized in that An operating current (I) and / or an operating voltage (U) of the machine (M) is quantified by means of the electrical operating data (I, U).

3. The method according to any one of the preceding claims, It is characterized in that The shape functions (F1, F2, ...) of the reduced thermal simulation model (RST) are selected from the shape functions (FE1, FE2, ...) of the first thermal simulation model (ST) and / or are parameterized in such a way that a deviation (D) between a temperature curve determined by the reduced thermal simulation model (RST) and a temperature curve determined by the first thermal simulation model (ST) is minimized, specifically at the temperature critical locations (P1, P2).

4. The method according to any one of the preceding claims, It is characterized in that The ambient temperature of the machine and / or the air flow rate in the environment of the machine are continuously detected by a sensor, and the temperature at the temperature critical part (P1, P2) is determined according to the ambient temperature and / or the air flow rate.

5. The method according to any one of the preceding claims, It is characterized in that Based on the structural data (SD) and mechanical operating data (RPM) of the machine (M), mechanical energy losses (QM) in the machine are simulated in a spatially resolved manner using a mechanical simulation model (SM) of the machine, and The temperature at the temperature critical locations (P1, P2) is determined based on the simulated mechanical energy loss (QM).

6. The method according to claim 5, It is characterized in that The machine operating data (RPM) quantify the rotational speed, torque, movement speed and / or applied force of the machine (M).

7. The method according to any one of the preceding claims, It is characterized in that Structural data (SD) relating to the electrical and / or thermal conductivity of the machine element are modified as a function of the determined temperature (RT1, RT2), and A simulation of the electrical energy loss (QE) and / or a determination of the temperature (RT1, RT2) at the temperature-critical locations (P1, P2) are performed based on the modified structural data.

8. The method according to any one of the preceding claims, It is characterized in that According to the determined temperature at the temperature critical parts (P1, P2), - lowering the machine (M), - outputting information about the optimized operation of the machine (M), - outputting a proposal for an optimized operation of the machine (M), and / or -Control cooling device.

9. The method according to any one of the preceding claims, It is characterized in that determining a position of a predetermined machine component based on the structural data (SD), The determined locations are used as temperature critical locations (P1, P2), and The temperatures (RT1, RT2) determined at the locations are output as component-specific temperature values.

10. A machine controller (CTL) for operating and temperature monitoring an electromechanical machine (M), comprising means for performing the steps of the method according to any of the preceding claims.

11. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to perform the method according to any one of claims 1 to 9.

12. A computer-readable storage medium comprising the computer program product according to claim 11.

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