Computer-implemented method, management method and system, and computer program product

Abstract

Computer-implemented methods, management methods and systems, and computer program products are disclosed. Systems and methods for mapping operating characteristics of a first rotating system as a function of one or more operating parameters. The method comprises the following steps: obtaining seed data; obtaining a plurality of set points; obtaining a plurality of mapping data points; and generating interpolated data using the plurality of mapped data points, where the seed data constitutes an initial dataset of operating characteristics as a function of one or more operating parameters of the first rotating system, and where the plurality of set points are obtained using a predictive model, the seed data, and a priori error setting, and where the seed data constitutes an initial dataset of operating characteristics as a function of the one or more operating parameters of the first rotating system. Each mapping data point includes an acquired data component acquired with one or more measurements of the first rotating system in response to control of one or more operating parameters of the first rotating system according to a respective setpoint.

Description

Technical Field

[0001] The present invention relates to systems and methods for mapping the operating characteristics of a rotating system (e.g., vibration, efficiency, heat generation, etc.) as a function of one or more operating parameters (e.g., input power conditions, load conditions, external thermal conditions, etc.). The present invention can be applied to various rotating systems, such as electric motors, transmissions, turbines, and internal combustion engines. Background Art

[0002] A prominent example of mapping the operating characteristics of a rotating system is the efficiency mapping of an electric motor. Such efficiency maps are typically obtained based on two operating parameters: speed (i.e., rotational speed) and torque, to understand how efficiently the electric motor converts electrical energy into mechanical energy based on these two operating parameters. Because the efficiency of an electric motor (or other rotating system) often varies significantly based on the operating conditions of the rotating system, efficiency mapping of the corresponding rotating system is often important for understanding and optimizing its performance.

[0003] For example, efficiency mapping of an electric motor typically involves testing (i.e., mapping) a rotating system at various operating points within an operating range of one or more operating parameters with the goal of creating a detailed data set (i.e., mapping data), for example, for illustrative purposes. Efficiency mapping of an electric motor may involve setting up test conditions in which the electric motor operates at various predetermined combinations of speed and load (i.e., torque). For example, this involves using a dynamometer to control and measure speed and load. For each operating point, the electrical input power (based on voltage and current) and the mechanical output power (based on torque and speed) are typically measured. From these measurements, efficiency can be calculated as the ratio of output power to input power for each operating point. Data obtained using one or more measurements based on an operating point (data points representing operating characteristics as a function of one or more operating parameters) can be referred to as "mapping data points." A collection of mapping data points can constitute or form part of mapping data. Mapping data can include interpolated data points generated from mapping data points and subsequently added to the mapping data. Mapping data can be plotted on a graph. For example, for the above example of efficiency as a function of speed and torque, where speed is on one axis and torque is on the other, efficiency can be represented by color coding or contouring. Such a graphical representation can be referred to as an efficiency map, or more generally, a map. Such a map provides a visual representation of the performance of a selected operating characteristic of a rotating system (e.g., an electric motor) over an operating range of one or more operating parameters. Conventional methods of efficiency mapping of electric motors involve multiple, continuous measurements. Typically, a rectangular grid of set points is selected—a speed-torque grid—wherein a measurement is taken for each set point of the grid. Figure 1An example is shown in FIG. 1 , which schematically illustrates an exemplary efficiency map of an electric motor as a function of speed and torque, wherein the axes are labeled with normalized quantities, wherein n / n N represents the normalized speed, and M / M N Indicates normalized torque. Figure 1 The efficiency map is an example of a graphical representation, where contour lines and color gradients represent the efficiency η / % of an electric motor at different speed and torque combinations. Due to the operating limits of the electric motor, the upper right corner of the efficiency map is blank. A schematic grid of 54 circles (size 9×8) illustrates a schematic example of measurement points within the operating limits of the electric motor. For the upper right corner, the torque values ​​of some grid points have been scaled down (i.e., shifted downward) to ensure they do not exceed the operating limits of the electric motor.

[0004] Furthermore, a set of measurements within a speed-torque grid, such as described above, may be repeated according to various reference conditions, such as for various ambient temperatures and / or various voltage levels for the electric motor.

[0005] Mapping the desired operating characteristics of a rotating system as a function of one or more desired operating parameters is often a fundamental task for verifying the development progress of the corresponding rotating system. The data obtained using such a mapping is referred to as "mapping data". Such mapping data (e.g., from an efficiency map) is often essential for system designers to optimize the rotating system for a particular application. Similarly, for operations designers, mapping data is often necessary to enable the rotating system to operate within a desired operating range (e.g., within its most efficient operating range), such as for saving energy and reducing costs. In addition, mapping data for rotating systems is often essential for comparative analysis, such as selecting a particular rotating system for a particular application based on performance. In addition, mapping data can be incorporated into control strategies, such as for electric vehicles or automated machinery, to enable the rotating system to operate efficiently. Summary of the Invention

[0006] A total measurement series for mapping (eg for obtaining an efficiency map) as described above is typically relatively resource- and time-consuming and is therefore associated with high costs and may become a bottleneck in terms of system development, system use, and so on.

[0007] The inventors of the present invention have recognized a need to overcome the shortcomings of the prior art and to provide novel methods and systems for mapping the operating characteristics of rotating systems. Furthermore, the inventors have recognized that the above-mentioned and other considerations apply not only to efficiency mapping of electric motors, but also to various rotating systems and their various operating characteristics as a function of their various operating parameters.

[0008] In the context of the present invention, the term "mapping data" or "desired mapping data" may be understood as mapping data of desired operating characteristics as a function of one or more desired operating parameters of a desired rotating system.

[0009] A first object of the invention is to facilitate the provision of mapping data.

[0010] A second object of the present invention is to facilitate a reduction in the time or effort required for measurement and / or to facilitate a further reduction in the resources required to provide mapping data. For example, such reduction can be assessed by comparison with a rectangular grid of set points for measurement as described above or similar, such as within the speed-torque grid described above.

[0011] A third object of the present invention is to facilitate the provision of mapping data with improved data quality.

[0012] A fourth object of the present invention is to facilitate providing a balance between the second and third objects, ie a balance between reducing the required resources and improving the data quality used to obtain the mapping data.

[0013] Therefore, an object of the present invention may be to enable obtaining sufficient mapping data while reducing the investment (e.g., time, cost, or other resources) associated with obtaining the mapping data, e.g., to strike a balance between data quality and the investment used to obtain the data.

[0014] The present invention may be provided according to any of the stated aspects. These aspects are provided and / or directed to achieving one or more of the above objects and / or further objects.

[0015] According to a first aspect of the present invention, a mapping method is provided. Mapping may imply that mapping data is obtained by the method. The method may be a computer-implemented method, such as at least a portion of the method or the entire method is computer-implemented. The method is configured to map an operating characteristic of a first rotating system as a function of one or more operating parameters. The mapping may be below a first reference condition, i.e., the first rotating system may be subjected to the effects of the first reference condition while being subjected to corresponding measurements used to obtain the mapping data.

[0016] The method of the first aspect comprises obtaining seed data, obtaining a plurality of set points, obtaining, for example, receiving, a plurality of mapped data points, and optionally generating interpolated data using the plurality of mapped data points.

[0017] The seed data preferably constitutes an initial data set of operating characteristics as a function of one or more operating parameters of the first rotating system. In this document, the term "for" may be understood as suitable, applicable or useful. The seed data is useful for an initial or preliminary description or representation of the operating characteristics as a function of one or more operating parameters of the first rotating system. According to an embodiment, the seed data is not necessarily based on measurements, nor is it necessarily based on the first rotating system, nor is it necessarily based on measurements of the first rotating system. According to an embodiment, the seed data constitutes an initial data set that will be used to map the operating characteristics of the first rotating system as a function of one or more operating parameters. According to an embodiment, the seed data is suitable, applicable or useful for the first rotating system. According to an embodiment, the seed data is suitable, applicable or useful for describing the operating characteristics as a function of one or more operating parameters of the first rotating system. According to an embodiment, the seed data is obtained from measurements of a second rotating system different from the first rotating system. According to an embodiment, the seed data is obtained from measurements of the first rotating system. According to an embodiment, the seed data is obtained from a software model of the first rotating system.

[0018] According to a first aspect, the plurality of set points includes a first set point, a second set point, and optionally a third set point. Each set point includes a component for each of one or more operating parameters. According to the first aspect, a set point, i.e., a set point in the plurality of set points, is obtained using a predictive model, seed data, and a priori error settings.

[0019] According to a first aspect, the plurality of mapped data points includes a first mapped data point, a second mapped data point, and optionally, a third mapped data point. The number of mapped data points generally corresponds to the number of set points obtained and utilized. Each mapped data point includes a collected data component, which is collected using one or more measurements of the first rotating system in response to controlling one or more operating parameters of the first rotating system according to the corresponding set point. Thus, the first mapped data point includes a collected data component collected according to the first set point, the second mapped data point includes a collected data component collected according to the second set point, and the third mapped data point includes a collected data component collected according to the third set point.

[0020] The collected data components of the corresponding mapped data points can represent values ​​of operating characteristics, such as their measured values ​​or derived values ​​based on one or more measurements. Each mapped data point typically includes a component for each of one or more operating parameters. Such components can be represented as operating data components, control components, or input components. These one or more operating data components can be obtained using one or more measurements of the first rotating system in response to controlling one or more operating parameters of the first rotating system according to corresponding set points. Alternatively or additionally, the one or more operating data components can be obtained based on corresponding set points used to control the first rotating system. The step of obtaining multiple mapped data points can include receiving collected data components or receiving data to be used to derive collected data components. Such data or data components can be received, for example, from an automation system used to control the first rotating system. The step of obtaining multiple mapped data points can include combining the received collected data components with the corresponding set points or components thereof and / or with one or more operating data components received, for example, from the automation system.

[0021] According to a second aspect of the present invention, a management method is provided, comprising the method according to the first aspect of the present invention. The management method comprises controlling one or more operating parameters of a first rotating system according to each corresponding setpoint; and acquiring each acquired data component, comprising performing one or more measurements of the first rotating system for acquiring each corresponding acquired data component. Furthermore, the management method may comprise acquiring one or more operational data components, such as each operational data component.

[0022] The management method can be considered to include a plurality of management steps. Each management step includes controlling the first rotating system according to a corresponding set point. Furthermore, each management step includes one or more measurements of the first rotating system in response to controlling the first rotating system according to the corresponding set point. The plurality of management steps includes a first management step according to the first set point, a second management step according to the second set point, and optionally a third management step according to a third set point.

[0023] According to a third aspect of the present invention, there is provided a management system configured to perform the method of the second aspect. The management system may be configured to accommodate a first rotation system.

[0024] According to a fourth aspect of the present invention, there is provided a computer program comprising instructions which, when executed by a computer, cause the computer to perform the method of the first aspect.

[0025] According to a fifth aspect of the present invention, there is provided a computer-readable medium comprising instructions, which, when executed by a computer, cause the computer to perform the method of the first aspect.

[0026] According to a sixth aspect of the present invention, there is provided a data carrier signal carrying the computer program of the fourth aspect.

[0027] According to an embodiment, the following steps are performed and completed in the order recited: obtaining seed data; obtaining a plurality of set points; obtaining a plurality of mapped data points; and generating interpolated data. According to an embodiment, these steps are initiated in the order recited but not necessarily completed in the order recited. According to an embodiment, the respective steps of obtaining a plurality of set points and obtaining a plurality of mapped data points are performed sequentially and interchangeably.

[0028] It should be understood that the foregoing general description and the following detailed description are exemplary and are intended to provide further explanation of the invention as claimed. Other systems, methods, and features of the invention will be or will become apparent to one of ordinary skill in the art upon examination of the drawings and disclosure of the invention, including the detailed description. It is intended that all such additional systems, methods, and features be included within this description, be within the scope of the invention, and be protected by the following claims.

[0029] This summary represents an overview of some of the teachings of the present disclosure and is not intended to be an exhaustive and / or exclusive treatment of the subject matter. Further details regarding this subject matter are found throughout this disclosure, including the detailed description and the appended claims. The scope of the present invention is defined by the appended claims and their legal equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are included to provide a further understanding of the present invention and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate embodiments of the present invention and, together with the description, serve to explain the principles of the present invention. The accompanying drawings illustrate the design and practicality of the embodiments, in which similar elements may be referred to by common reference numerals. These drawings are not necessarily drawn to scale. In order to better understand how to obtain the above and other advantages and purposes, a more specific description of the embodiments will be provided, which are illustrated in the accompanying drawings. These drawings may only depict typical embodiments and, therefore, should not be construed as limiting the scope thereof. Embodiments of the present invention will be described in more detail below in conjunction with the accompanying drawings.

[0031] Figure 1 An exemplary efficiency map with measurement points is schematically shown.

[0032] Figure 2 and Figure 3 Some principles of obtaining multiple set points according to an embodiment of the present invention are schematically illustrated.

[0033] Figure 4 Some principles of obtaining multiple set points according to an embodiment of the present invention are schematically illustrated.

[0034] Figure 5 Some principles of obtaining multiple set points according to an embodiment of the present invention are schematically illustrated.

[0035] Figure 6 A method according to an embodiment of the present invention is schematically illustrated.

[0036] Figure 7 A method according to an embodiment of the present invention is schematically illustrated.

[0037] Figure 8 A system according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0038] One, more, or all of the following definitions may be applied to interpret terms as they apply to features of the embodiments disclosed herein and are meant only to define elements within the present disclosure. Therefore, no limitations are necessarily intended or necessarily derived from the use of terms within the claims. Terms used in the appended claims may or should be limited only to their customary meanings in the applicable art.

[0039] In this disclosure, ordinal numbers are generally understood to be purely nominal numbers, unless otherwise stated. For example, the terms "first," "second," "third," etc. are generally understood to be arbitrary identifiers of corresponding features of the present invention, unless otherwise stated. Throughout this disclosure, the use of ordinal numbers for events and steps does not necessarily indicate any timing and / or priority of the corresponding events or steps. Thus, an event (such as a first event) can occur before, during, or after another event (such as a second event), or an event can occur before, during, and after any combination of another event - unless otherwise stated. Similarly, the presence of a "second feature" does not necessarily require the presence of a "first feature" of the same type, and the presence of a "third feature" does not necessarily require the presence of both the "first feature" and the "second feature," and so on.

[0040] In this disclosure, the term "speed" generally refers to "rotational speed."

[0041] The mapping method according to the present invention may be a computer-implemented method. A computer-implemented method may mean that all steps of the method are performed by a computer system. The mapping method according to the present invention may be executed and / or configured to be executed by a computer system. The computer system may, for example, include any one or any combination of the following: a server, a client, and a cloud computing service. The present invention may be provided by any one or any combination of the following: a computer program, a computer-readable medium, and a computer program product. The present invention may include any one or any combination of the following: a computer program, a computer-readable medium, and a computer program product, which may include means for executing the mapping method according to the present invention. The present invention may include a computer program comprising instructions that, when executed by a computer system, cause the computer system to execute the mapping method according to the present invention. The computer program product according to the present invention may be embodied by a computer-readable medium. The present invention may include a computer-readable medium having the computer program according to the present invention stored thereon. The present invention may include a computer-readable medium comprising instructions that, when executed by a computer system, cause the computer system to execute the mapping method according to the present invention. Any of the computer program, computer-readable medium, and computer program product according to the present invention may be distributed, for example, across multiple physical and / or computing entities. The present invention can be implemented by a distributed computing system, which can be expressed as a "distributed computing environment", such as using or including a computer network. Within such a distributed computing system, the mapping method according to the present invention can be performed by one or more or all of a plurality of entities, such as any combination of the following: one or more client computers, one or more server computers, and one or more cloud computers. A data carrier signal carrying the computer program of the present invention can be provided. According to the present invention, a computer program product can be provided, wherein the computer program product includes instructions that, when executed by a computer, cause the computer to perform the method of the present invention. The computer program can be configured to run on a general-purpose computer. The computer program can be configured to communicate with a test system.

[0042] The first rotating system may include or may be a drive train, for example, an electric drive train. The first rotating system may include or may be components for a drive train, for example, electric components for a drive train. The first rotating system may include or may be a rotary actuator system, a rotary transmission system, or a rotary generator system. The first rotating system may be electric, such as including one or more electrical parts for converting electrical energy into mechanical energy or vice versa. The first rotating system may include or may be an electric machine, for example, an electric motor or a generator. The first rotating system may include or may be an internal combustion engine (ICE) and / or a turbine. The first rotating system may be referred to as a device under test, for example, in the context of testing and / or measuring the first rotating system.

[0043] The present invention can be applied to mapping various operating characteristics. The choice of the operating characteristic to be mapped generally depends on the context of interest and generally depends on the type and choice of rotating system. The operating characteristic may be efficiency. In the context of the present invention, efficiency may be understood as how effectively the first rotating system converts or transfers energy from an input to an output of the first rotating system. The operating characteristic may be power loss. Power loss generally represents the portion of the input power that is not converted into useful output but is lost in various forms, such as heat, sound, or vibration. The operating characteristic may be the sound level or sound pressure generated by the first rotating system. Such a sound level or pressure may be referred to as noise. The operating characteristic may be the heat energy generated by the first rotating system. The operating characteristic may be durability, for example measured by wear. The operating characteristic may be torque. The operating characteristic may be inductance or a component of inductance, which is particularly interesting in the context of mapping electric rotating systems.

[0044] The term "mapping," in the context of the present invention, generally refers to the process of obtaining or providing a set of data (referred to as "mapped data") comprising a representation of how an operational characteristic of a first rotating system varies as a function of one or more operating parameters. Such mapped data can be suitable for analysis or optimization purposes, for example, for the design, operation, and / or control of the first rotating system. Individual data points of the mapped data can be referred to as mapped data points.

[0045] The mapping process may include generating a visual representation of the mapped data. However, the purpose of mapping is not necessarily limited to, nor does it necessarily include, such visual representations. While visual mapping may facilitate human interpretation, the underlying data may, alone or in addition, provide broader functionality as described herein, such as helping to optimize system performance, save energy, and / or reduce costs. For example, a torque map of an electric motor may reveal the optimal operating point for balancing power consumption and output efficiency, which is critical for applications such as electric vehicles and automated machinery.

[0046] Typically, the operating characteristic depends on various parameters of the first rotating system. These parameters can be collectively referred to as "influencing parameters." These influencing parameters can be divided into two groups: one or more operating parameters; and multiple reference parameters, where the multiple reference parameters define the reference conditions. When mapping the operating characteristic, any influencing parameter that is not an operating parameter is, by definition, a reference parameter. Similarly, an operating parameter cannot be a reference parameter. Similarly, a setpoint component cannot be a component of the corresponding reference condition, and vice versa.

[0047] Depending on the operating characteristics and the first rotating system, the influencing parameters may include various physical properties. The influencing parameters may include the input and / or output torque of the first rotating system. The influencing parameters may include the input and / or output speed of the first rotating system. The influencing parameters may include the temperature of the first rotating system. The influencing parameters may include the temperature of the surrounding environment of the first rotating system. The influencing parameters may include the heat dissipation coefficient of the first rotating system. The influencing parameters may include one or more energy supply or power generation metrics, such as supply voltage or current (e.g., for an electric motor) or fuel octane level (e.g., for an ICE).

[0048] In practice, for rotating systems, the operating characteristics typically depend on a variety of influencing parameters. For example, the efficiency of an electric motor typically depends on several parameters, including torque, speed, system temperature, battery voltage, switching frequency, etc. In many cases, for example, for ease of graphical representation, two operating parameters are allowed to vary (such as torque and speed), while the other parameters (reference parameters) can be kept more or less constant or ignored. The change in the reference parameter value then affects the torque-speed-efficiency mapping. The user may want to change three or more parameters (i.e., operating parameters) and keep the remaining parameters (i.e., reference parameters) more or less constant or ignored. Ultimately, it may be a question of how to utilize and / or interpret the obtained mapping data.

[0049] The present invention can be applied to mapping various operating characteristics as functions of various one or more operating parameters. The selection of which operating parameter or parameters to map the operating characteristic is generally a matter of interest and typically depends on the type of rotating system and the choice of operating characteristic. Typically, it is of interest to select one or more operating parameters that have a significant impact on the operating characteristic and that can be controlled and / or designed during normal use of the first rotating system. The operating characteristic, along with the one or more operating parameters, typically represent interdependent variables of the mapping process.

[0050] The invention may include mapping the operating characteristics of the first rotating system as a function of two or more operating parameters, such as two operating parameters, exactly two operating parameters, at least two operating parameters, or more than two operating parameters, such as three operating parameters or more.

[0051] Depending on the operating characteristics and the first rotating system, the one or more operating parameters may include various one or more physical properties. The one or more operating parameters may include the input and / or output torque of the first rotating system. The one or more operating parameters may include the input and / or output speed of the first rotating system. The one or more operating parameters may include the temperature of the first rotating system. The one or more operating parameters may include the temperature of the surrounding environment of the first rotating system. The one or more operating parameters may include the heat dissipation coefficient of the first rotating system. The one or more operating parameters may include one or more energy supply or power generation metrics, such as supply voltage or current (e.g., for an electric motor) or fuel octane level (e.g., for an ICE).

[0052] For the purpose of illustrating the mapping data, it may be desirable to select one or more operating parameters consisting of only one or two operating parameters. However, it may be useful to map the operating characteristics as a function of three or more operating parameters to obtain the resulting mapping data. Furthermore, graphical illustration of such mapping data based on three or more operating parameters is possible and is simply a matter of the manner in which it is illustrated. Furthermore, such mapping data based on three or more operating parameters can be readily used to illustrate the operating characteristics as a function of one or two selected ones of the three or more operating parameters.

[0053] An operating characteristic may not be an operating parameter for the same embodiment. However, what constitutes an operating characteristic in one embodiment may be an operating parameter in another embodiment, for example, torque as an operating characteristic or as an operating parameter, as described in the context of the respective embodiments.

[0054] According to embodiments, one or more operating parameters are controlled using multiple setpoints. A measured operating parameter typically refers to a measured value of the first rotating system when controlled according to a corresponding component of the setpoint, where the component represents an expected or target value for the corresponding operating parameter and is not necessarily the same as its actual or measured value. However, in some cases, one or more corresponding components of the setpoint are used as the corresponding operating parameter for the corresponding mapped data point, e.g., without requiring any measurement of the corresponding operating parameter.

[0055] Often, when a particular operating parameter is defined, it is implicit that an attempt may be made to keep any other influencing parameters at a constant level.

[0056] In order to map the operating characteristics, one or more operating parameters are typically defined within an operating range. Such an operating range typically includes a component for each of the one or more operating parameters. An operating range is typically defined as a range of interest encompassing the corresponding one or more operating parameters.

[0057] The method for mapping an operational characteristic may be defined as being performed "under a first reference condition". The term "under a first reference condition" is to be understood as such that the first reference condition exists for the first rotating system during the measurement that results in providing the mapping data points. Such existence may be independent of whether such reference condition is defined and / or measured and / or ignored. Thus, a plurality of reference parameters may form part of a respective reference condition (such as the first reference condition) or may define the respective reference condition. For example, one or more reference parameters of the respective reference condition may be measured, recorded or estimated in conjunction with the measurement of the respective rotating system to obtain the respective mapping data points.

[0058] The first reference conditions may be understood as a set of reference parameters that may affect the value of the measured operating characteristic but remain constant (eg, sufficiently constant, such as within an acceptable range) while mapping the operating characteristic as a function of one or more operating parameters.

[0059] Although the reference parameter may be intended to remain unchanged, it may change within a given reference condition. Such changes can be ignored and / or can be recorded as part of the experimental conditions. There may be practical difficulties in keeping one or more reference parameters constant. However, for some cases, one or more reference parameters are not intended to be constant. In any case, the reference parameter usually constitutes the background of the measurement. Usually, people try to keep the reference parameter at a constant level / value. The reference parameter is not usually the main focus of the corresponding measurement. The reference parameter is usually used as a reference point or baseline condition, according to which the influence of different operating parameters on the operating characteristics is measured. In practice, it is usually possible to try to keep the reference parameter constant, but this is not always practical.

[0060] The term "seed data" in the context of the present invention generally refers to data that represents an assumption and / or estimate and / or approximation of the operating characteristics of the first rotating system as a function of one or more operating parameters. The seed data or a portion thereof can be easily obtained, for example via one or more previous measurements of the first rotating system or another rotating system. Alternatively or in addition, the seed data or a portion thereof can be obtained relatively quickly and / or relatively easily, for example from a simulation using a software model of the first rotating system and / or from dynamic measurements of the first rotating system or another rotating system. Therefore, the seed data may include or consist of data obtained via dynamic measurements of the first rotating system and / or another rotating system. The dynamic measurements can be obtained, for example, by continuously changing (e.g., continuously reducing or increasing, e.g., ramping up) one or more of the one or more operating parameters while measuring the input and / or output of the corresponding rotating system. According to an embodiment in which the operating characteristic is efficiency and each set point includes, for example, a torque component and a rotational speed component, the seed data may include or consist of data obtained by dynamic measurements of the first rotating system. In this case, the speed component values ​​may, for example, be driven sequentially, and for each speed value the torque may be ramped up continuously while recording the input and / or output of the system, for example by measuring the input and / or output.

[0061] It is generally preferred that the seed data represent a significant similarity of the operating characteristics of the first rotating system as a function of one or more operating parameters. High quality of the seed data is understood to result in a good similarity. Generally, the better the quality of the seed data, the better the predictive model will perform in providing useful set points to obtain a plurality of useful mapping data points.

[0062] A predictive model can be broadly understood as a systematic approach to generating predictions about future behavior. This can be achieved by constructing a model that analyzes the relationship between different variables within a data set. Specifically, the model can, for example, use a subset of data points called model data selected from a larger set of data points called seed data, wherein the number of data points for the model data is typically significantly smaller than the number of data points for the seed data, for example, less than 20%. The model can, for example, utilize various methods such as piecewise linear approximation or the use of orthogonal polynomials (such as Chebyshev polynomials) to make these predictions. The goal of such a model is typically to optimize certain parameters, such as minimizing the maximum a priori error. The a priori error typically represents the estimated deviation of the model's prediction from the actual outcome before the actual outcome is observed. This optimization is typically intended to improve the model's predictive accuracy and reliability.

[0063] For example, a predictive model can generate set points on a rectangular grid or at arbitrary locations.

[0064] The a priori error setting of the prediction model may be or may include a first value. The first value may apply to the entire operating range of one or more operating parameters. Additionally, the a priori error setting may include a second value. The second value may apply to the entire operating range of one or more operating parameters or to a first subset thereof. The second value may have a smaller magnitude than the first value. The first value and / or the second value may be or may be associated with: a mean error value, a median error value, a maximum error value, or any combination thereof. The a priori error setting may be or may include, for example, an a priori maximum error value for the entire operating range.

[0065] The a priori error setting may include or include a first condition. The first condition may be minimizing the a priori error, such as a maximum error, a median error, or an average a priori error. Such a first condition for minimizing the a priori error may depend on a second condition, such as a definition or limit on the number of set points comprising the plurality of set points. For example, the a priori error setting may include or include a first condition for minimizing the maximum value of the a priori error across the entire operating range, given a second condition for a maximum or specific number of set points among the plurality of set points.

[0066] An a priori error setting, such as a value thereof, may be specified.The a priori error setting, such as a value thereof, may be set by a user and / or by a system performing the method and / or by an implementation of the method.

[0067] Typically, the a priori error is related to a measure of the difference between the estimated value of the operating characteristic provided by the predictive model and the expected measured value of the operating characteristic. The expectation is represented by seed data. Therefore, the a priori error generally refers to an error estimate made before mapping, which is based on, for example, theory or a priori knowledge, such as synthetic data or data obtained from measurements in different settings, different conditions, different systems, etc. The measure of the difference can be quantified, for example, by methods such as the maximum error, mean square error, root mean square error, or mean absolute error between the model value provided by the predictive model and the expected value represented by the seed data. According to an embodiment, the seed data represents the expected result and is used to train and test the predictive model.

[0068] It is emphasized that the concept of a priori error applies even if a set point is obtained using one or more mapped data points if the error of the corresponding set point is evaluated based on data / information including estimated data, rather than solely based on the actual measured value of the set point.

[0069] The term "set point" in the context of the present invention generally refers to a control value of one or more operating parameters. A set point can be obtained, for example, by generating or selecting from seed data. Typically, each set point is defined by a component of each of the one or more operating parameters. Typically, each set point is provided within a first operating range.

[0070] According to an embodiment, the plurality of set points comprises at least four set points, such as at least five set points. According to an embodiment, the plurality of set points comprises at least six set points, such as at least seven set points.

[0071] The term "mapped data point" in the context of the present invention generally refers to a data point obtained, e.g., received and / or derived and / or measured, in the context of controlling a first rotating system according to a corresponding set point and, optionally, according to a first reference condition, in response to one or more measurements of the first rotating system. The one or more measurements of the first rotating system may include various input and / or output measurements. Typically, at least one output or input measurement is typically required. For example, for efficiency mapping of an electric motor, each mapped data point typically requires at least one measurement that reflects the input power supplied to the electric motor to deliver a given speed and torque combination. For example, if the supply voltage to the electric motor is assumed to be constant and known, then measuring only the current supply may be sufficient. The output power may be provided via one or more measurements and / or via assumptions made based on the system settings of the control measurements, e.g., by utilizing a component of each corresponding set point as a corresponding component of each mapped data point (i.e., an operating data component) and combining it with each corresponding acquired data component to arrive at the corresponding mapped data point.

[0072] Obtaining a data point using one or more measurements generally means obtaining at least one component of the data point, typically at least the acquisition data component, using the one or more measurements.

[0073] According to an embodiment, the number of obtained mapping data points is equal to the number of set points used for the control and measurement of the first rotating system.

[0074] The step of generating interpolated data using a plurality of mapped data points generally means that the mapped data is supplemented by the interpolated data. Thus, in addition to the interpolated data provided by the interpolation, the mapped data will also include the mapped data points that existed before the interpolation. The interpolated data may include a plurality of interpolated data points.

[0075] The step of obtaining a plurality of set points may include obtaining a second set point using the first mapped data point. Furthermore, the step of obtaining a plurality of set points may include obtaining an optional third set point using the first mapped data point and the second mapped data point. Thus, the first mapped data point may be obtained before deriving the second mapped data point. Furthermore, the optional third mapped data point may be obtained after deriving the second mapped data point, and the second mapped data point may be obtained after deriving the first mapped data point.

[0076] According to an embodiment, a set point in the plurality of set points is obtained using one or more previously obtained mapping data points. Thus, a subset of the plurality of set points may be obtained using the prediction model, seed data, a priori error settings, and one or more previously obtained mapping data points.

[0077] According to embodiments, one or more mapping data points are used not only to generate a new set point, but also to generate a new set of set points, or a subset thereof, wherein the set points used to obtain the one or more mapping data points used can be pre-fixed for subsequent use in the prediction model. For example, if the step of obtaining multiple set points involves selecting, for example, a condition of exactly five set points, five set points (e.g., denoted as candidate set points) can initially be obtained based on the prediction model, seed data, and a priori error setting (e.g., minimizing a maximum a priori error). Subsequently, when a first mapping data point is obtained based on a first of the five candidate set points, four new candidate set points (i.e., first mapping data points) can be obtained based on the prediction model, seed data, a priori error setting, and the previously obtained mapping data points. Subsequently, when a second mapping data point is obtained based on the first of the four candidate set points, three new candidate set points (i.e., a first mapping data point and a second mapping data point) can be obtained based on the prediction model, seed data, a priori error setting, and the previously obtained mapping data points. Subsequently, when a third mapping data point is obtained based on the first of the three candidate set points, two new candidate set points, namely, a first mapping data point, a second mapping data point, and a third mapping data point, can be obtained based on the prediction model, seed data, a priori error setting, and the previously obtained mapping data points. Subsequently, when a fourth mapping data point is obtained based on the first of the two candidate set points, a new set point, namely, a first mapping data point, a second mapping data point, a third mapping data point, and a fourth mapping data point, can be obtained based on the prediction model, seed data, a priori error setting, and the previously obtained mapping data points. Thus, the step of obtaining a plurality of set points can include obtaining a corresponding first set point for each of the provided candidate set of set points in addition to the last provided set point, i.e., a total of five set points.

[0078] An advantage of utilizing one or more previously obtained mapping data points to obtain one or more subsequent set points may be an improvement in the obtained set points, which in turn may improve the quality of the mapping data.

[0079] The seed data includes a plurality of seed data points.

[0080] The seed data may comprise or consist of synthetic data. Such synthetic data may be derived using a software model of the first rotating system, i.e. a model that is at least fitted to the first rotating system. Thus, the synthetic data may be derived by simulation of the first rotating system. The software model of the first rotating system may refer to a software model of a system that is different from the first rotating system but is still useful for the purpose of providing useful seed data. One or more advantages of obtaining seed data comprising or consisting of synthetic data may include that such synthetic data may be obtained relatively quickly, for example faster than seed data obtained using one or more measurements. Another advantage may be that, depending on the quality of the model and the generation of the synthetic data, such data may initially represent a considerable approximation of the actual mapping data for the first rotating system.

[0081] The seed data may include data obtained using one or more measurements of the first rotating system under second reference conditions that are different from the first reference conditions (e.g., within and / or outside the first operating range), wherein each mapping data point is obtained using one or more measurements of the first rotating system under the first reference conditions.

[0082] The seed data may include data obtained using one or more measurements within and / or outside the first operating range of the first rotating system.

[0083] The second reference condition may comprise a different value or setting of at least one component of the first reference condition.The second reference condition may, for example, comprise a different system temperature, a different system supply voltage, or be based on a dynamic measurement or a different dynamic measurement.

[0084] Advantages associated with using measurements under second reference conditions include that they may be readily obtained from previous measurements and / or may be obtained relatively quickly, for example, for dynamic measurements.

[0085] Generally, similar correlations between the operating characteristics and the one or more operating parameters of the first reference condition and the second reference condition will result in seed data of better quality.

[0086] The seed data may include data obtained using one or more measurements from a second rotation system that is different from the first rotation system. Such data obtained from the second rotation system may be obtained using one or more measurements within and / or outside the first operating range. Additionally or alternatively, such data obtained from the second rotation system may be obtained using one or more measurements under a first reference condition, a second reference condition, or a third reference condition, where the third reference condition is different from the first reference condition.

[0087] An advantage associated with measurements using a second rotation system is that such data can be readily obtained from previous measurements.

[0088] Generally, similar correlations between the operating characteristics and one or more operating parameters of the first rotating system and the second rotating system will result in seed data of better quality.

[0089] Typically, it is desirable for the seed data to include more data points than the number of set points in the plurality of set points, for example, at least twice or at least four times that number. Increasing the number of data points in the seed data generally improves the accuracy of the prediction model. On the other hand, increasing the number of data points in the seed data also generally results in an increase in the computational cost for obtaining the plurality of set points. Therefore, it may be desirable to strike a balance between the accuracy of the prediction model and the computational cost incurred by utilizing the data points in the seed data.

[0090] When utilizing synthetic data derived using a software model of the first rotating system, the desired number of data points for the seed data can be generated directly from the software model. When utilizing data previously acquired via measurements, the acquired data set can be interpolated to provide the desired number of data points within the seed data. The seed data may include a combination of synthetic data and data acquired using one or more measurements of the first rotating system and / or the second rotating system.

[0091] The method may include obtaining a prediction model from a plurality of candidate prediction models. The plurality of candidate prediction models may include a spectral model, wherein a set of orthogonal polynomials, such as Chebyshev polynomials, may be utilized. The plurality of candidate prediction models may include a piecewise linear model. The plurality of candidate prediction models may include a local search model.

[0092] Obtaining a prediction model from a plurality of candidate prediction models may be based on a selection by a user and / or by the system / method of the present invention.The first selection may be a selection of a specific prediction model or a subset thereof from a plurality of candidate prediction models.

[0093] For example, based on user experience, a certain method or methods are most suitable for certain conditions, and the user can simply select a specific one or a subgroup of candidate prediction models.

[0094] The method may include seed data analysis, for example, including frequency and / or amplitude analysis. The method may include presenting the results of the seed data analysis to a user. Such results may assist the user in selecting a predictive model. Alternatively, or in addition to presenting the results of the seed data analysis to the user, the method may include utilizing the seed data analysis to select a specific one or a subset of candidate predictive models during the selection process.

[0095] Various prediction models may perform differently. For example, given the same settings for a priori maximum error, one prediction model may generate more required setpoints than another prediction model to achieve the a priori error condition. Furthermore, various prediction models may perform differently in terms of computational time required to obtain multiple setpoints. The performance of a prediction model may depend on the type of feature, the quality of the seed data, and other factors.

[0096] The step of obtaining a prediction model from a plurality of candidate prediction models may be in response to a performance analysis and / or in response to a seed data analysis. The performance analysis may be performed in response to a performance setting. The performance setting includes settings regarding one or more of the following: a priori error, the number of set points to be obtained, the computation time / degree / workload. The performance setting may be pre-set, for example, by the system performing the method, and / or may be set or adjusted by a user. The performance setting may, for example, include a maximum number of set points, a maximum a priori error, and a maximum computational workload. Thus, the method may, for example, perform a performance analysis of various candidate prediction models, for example, from a plurality of candidate prediction models or subsets thereof, and / or optionally various settings of such models, and / or based on a seed data analysis. Based on the performance analysis, the system / method may obtain / select a prediction model for obtaining a plurality of set points or may make a recommendation to the user. The performance analysis may result in the step of obtaining a plurality of set points.

[0097] The method can be configured to obtain a user selection of a predictive model for obtaining a plurality of set points. The user can select to obtain a rectangular distribution of set points. The user can select a minimum number of set points or a minimum run time to find a set point.

[0098] The method may include selecting a prediction model for obtaining the plurality of set points, wherein the selection is responsive to an analysis of the seed data. For example, if the seed data contains high frequencies of high amplitude, a decision may be made to use a prediction model based on Chebyshev polynomials rather than a prediction model based on piecewise linear interpolation.

[0099] The step of obtaining a plurality of set points may comprise generating one or more weights for a prediction model, for example one or more weights for a set of orthogonal polynomials.

[0100] Obtaining the plurality of set points can include optimizing the prediction model, for example, by minimizing the number of set points within the plurality of set points, given a constraint defined by an a priori error setting. The constraint can be a maximum a priori error. Thus, the a priori error setting can be defined as a maximum a priori error value.

[0101] The step of generating interpolated data may include utilizing the prediction model or one or more aspects of the prediction model. For example, the step of generating interpolated data may utilize the same one or more weights of the prediction model that may be generated as part of the step of obtaining the plurality of set points. Alternatively or additionally, the step of generating interpolated data may include utilizing a first fitting model that may be computationally simpler than utilizing the prediction model. The first fitting model may, for example, include linear interpolation or spline interpolation, such as cubic spline interpolation. In any case, a plurality of mapped data points may be used to provide interpolated data to be included in the mapped data.

[0102] The management method may include executing the method for mapping operational characteristics according to the present invention. A first management step may include controlling and measuring the first rotating system according to a first set point. A second management step may include controlling and measuring the first rotating system according to a second set point. A third management step may include controlling and measuring the first rotating system according to a third set point. The management method may include housing and / or installing the first rotating system in a management system configured to execute the management method.

[0103] The management system can be configured to accommodate the first rotation system and / or have the first rotation system mounted to the management system. The management system can be or include a control and data acquisition system. The management system can include a test system and an automation system. The management system can be configured to collect a plurality of mapping data points.

[0104] The present invention may be implemented to map various operating characteristics of various rotating systems as functions of various one or more operating parameters.

[0105] According to an embodiment, the first rotating system is or comprises an electrical machine, such as an electric motor, the operating characteristic is efficiency, each setpoint comprises a torque component and a rotational speed component, and each setpoint may also comprise a component of the supply voltage of the electrical machine.

[0106] According to an embodiment, the first rotating system is or comprises an electrical machine, such as an electric motor, the operating characteristic is power loss, each setpoint comprises a torque component and a rotational speed component, and each setpoint may also comprise a component of the supply voltage of the electrical machine.

[0107] According to an embodiment, the first rotating system is or comprises an internal combustion engine, the operating characteristic is efficiency, and each setpoint comprises a torque component and a rotational speed component.

[0108] According to an embodiment, the first rotating system is or comprises an internal combustion engine, the operating characteristic is power loss, and each setpoint comprises a torque component and a rotational speed component.

[0109] According to an embodiment, the first rotating system is or comprises an electric motor, for example a permanent magnet synchronous motor, the operating characteristic of which is the direct axis inductance (L d ), each set point includes the direct axis current (I d ) component and the quadrature axis current (I q ) component, and each set point may also include a component of the supply voltage to the electric motor.

[0110] According to an embodiment, the first rotating system is or comprises an electric motor, for example a permanent magnet synchronous electric motor, the operating characteristic of which is the quadrature axis inductance (L q ), each set point includes the direct axis current (I d ) component and the quadrature axis current (I q ) component, and each set point may also include a component of the supply voltage to the electric motor.

[0111] According to an embodiment, the first rotating system is or comprises an electric motor, for example a permanent magnet synchronous electric motor, the operating characteristic of which is torque, and each set point comprises a direct axis current (I d ) component and the quadrature axis current (I q ) component, and each set point may also include a component of the supply voltage to the electric motor.

[0112] According to an embodiment, the first rotating system is a diesel generator, the operating characteristic is output power, and each set point consists of a ratio of biodiesel to petrodiesel of the supplied fuel.

[0113] According to an embodiment, the first rotating system comprises an electric motor, the operating characteristic is noise generated by the first rotating system, and each setpoint comprises a rotational speed component, a torque component, a supply voltage component, and a system temperature component.

[0114] According to an embodiment, the first rotating system comprises an electric motor, the operating characteristic is efficiency, and each setpoint comprises a rotational speed component, a torque component, a supply voltage component, and a system temperature component.

[0115] According to an embodiment of the step of obtaining multiple set points (referred to as the "first variant"), the prediction model includes a piecewise linear method, wherein the one or more operating parameters are composed of a single operating parameter. However, the principles of the first variant are applicable to embodiments in which the operating characteristic is mapped as a function of more than one operating parameter (such as two or more operating parameters).

[0116] refer to Figure 2 and Figure 3 To illustrate some of the principles of the first variant graphically, both figures show an approximation of the operating characteristic as a function of an operating parameter, ie A(O) is shown. Figure 2 and Figure 3The solid curve in is the same and represents A(O) of the first software model of the first rotation system. Figure 2 and Figure 3 The same thing is that the 19 corresponding connection points between the 19 evenly distributed vertical lines and the solid curve represent the 19 corresponding data points of the seed data, that is, the synthetic data derived using the first software model of the first rotation system. These 19 data points are respectively referred to as (O1, A(O1)) to (O 19 , A(O 19 )), uniformly distributed along the x-axis, i.e., operating parameters O1 to O 19 The 19 data points (i.e., seed data) are used to obtain multiple set points through optimization / evaluation of the prediction model.

[0117] For the first variant, the seed data consists of 19 data points, which are respectively referred to as (O x , A(O x )). 19 data points were chosen primarily for illustrative purposes. For one or more alternative embodiments, any desired number of set points may be chosen. Based on the first software model of the first rotating system, the desired number and / or spacing of data points for the seed data may be obtained relatively easily. If the seed data comprises data based on measurements (i.e., measurements associated with mapping), and if such data comprises fewer data points than desired or data points that are too far apart, an interpolation thereof may be performed to obtain the desired number and / or spacing of data points for the seed data. The number of data points for the seed data is proportional to the amount of computation required for evaluating / optimizing the prediction model. Therefore, in order to reduce the computational effort required, the number of data points may be reduced accordingly. However, the number and to some extent the spacing of the data points for the seed data are also proportional, at least within certain limits, to the accuracy of the evaluation of the prediction model. Therefore, a balance between the quality of the evaluation and the processing effort is desired.

[0118] In addition, reference Figure 2 , initially four data points of seed data are selected for an initial exemplary evaluation of the predictive model according to the first variant. The data points selected for the predictive model constitute an initial set of model data points for the initial evaluation. Typically, the set of data points selected for the predictive model is referred to as model data. The initial model data (i.e., the four model data points initially selected from the seed data) are, as described by Figure 2 As shown by the four solid points on the graph, that is, at the values ​​(O1, A(O1)), (O7, A(O7)), (O 13 , A(O 13 )) and (O 19 , A(O 19 )) at. These values ​​are calculated according to the first component O x Evenly distributed along the "x-axis" (first axis, horizontal axis). Figure 2 The dashed line on φ shows a continuous piecewise linear interpolation using the four data points of the initial model data as boundaries, resulting in a continuous piecewise linear function consisting of three linear parts.

[0119] According to the invention, the step of obtaining a plurality of set points generally comprises optimizing the prediction model used by comparison with the seed data, wherein the prediction model is generally based on a subset of the seed data. The optimization of the prediction model is based on an a priori error setting and optionally a constraint related to the number of set points. For a first variant, the a priori error setting consists of minimizing a maximum a priori error value. For a first variant, the optimization of the prediction model consists of minimizing a maximum a priori error value given a fixed number of set points (i.e., Figure 2 and Figure 3 The principle of the first variant is applicable to alternative embodiments and alternative a priori error settings - for example, with or without restrictions on the number of set points.

[0120] Back to Figure 2 , the corresponding error value of the prediction model relative to each data point of the seed data is visualized by the absolute distance between the prediction model and the corresponding seed data point along the "y-axis", that is, the absolute vertical distance between the dotted line and the solid curve. This error value can be called the prediction error. For each data point of the seed data included in the model data, the prediction error value is zero. The maximum prediction error value E* is the maximum absolute distance between the dotted line and the solid line for any data point of the seed data. For Figure 2 In the case shown in FIG, the maximum prediction error value exists when the operating parameter value O 16 .

[0121] Since the seed data constitute an initial data set of operating characteristics as a function of one or more operating parameters of the first rotating system, the maximum prediction error value E* corresponds to the maximum a priori error value for the corresponding model and the corresponding model data selection.

[0122] Figure 3 and Figure 2 The only difference is the different sets of four model data points from the same seed data, and therefore, different piecewise linear interpolations based on the different model data. Figure 3 The model data shown in FIG consists of four data points (O1, A(O1)), (O8, A(O8)), (O 16 , A(O 16 )) and (O 19 , A(O 19 )). Therefore, the evaluation of the prediction model using the seed data will result in a different set of prediction error values ​​between the seed data and the corresponding two prediction models for the two cases. By comparing Figure 2and Figure 3 It can be intuitively observed that even though the same number of model data points or boundaries are used in both cases, Figure 3 The maximum prediction error value E* in the case of is also significantly smaller. Both piecewise linear approximations are formed by three linear segments, but due to the different boundaries between the segments, Figure 3 The piecewise linear approximation in leads to a smaller maximum prediction error value E* and thus a better approximation / optimization based on the optimization condition of minimizing the maximum a priori error value given four set points. The number and choice of set points corresponds to the number and choice of model data points, i.e., the corresponding first component O x Therefore, for Figure 2 and Figure 3 In the first variant shown, the set points O1, O8, O 16 and O 19 is provided as the obtained multiple set points.

[0123] Often, a function can be well approximated by a piecewise linear function consisting of several linear segments. Using a larger number of segments generally leads to better approximation / optimization. Furthermore, given a fixed number of segments, changing the boundaries between segments can lead to better or worse approximations. Therefore, given a fixed number of linear segments in a piecewise linear approximation, the maximum prediction error depends on the location of the boundaries.

[0124] In general, for piecewise linear models, if the bounds are collected in the vector , where n is the number of boundaries, and if we want to optimize the model by minimizing the maximum prediction error, the problem to be solved can be to find the function The minimum value of , where e(·) gives the boundary In the case of , it is the maximum prediction error, which is a standard optimization problem with multiple solutions.

[0125] If we want to optimize the model by minimizing the number of set points (i.e., minimizing the number of bounds) subject to the desired maximum a priori error value, then the problem we want to solve might be to find the function The lowest possible maximum prediction error is obtained by minimizing the number of n, where the minimum value is lower than the desired maximum a priori error value. Therefore, given an initial number of boundary points, n, the resulting lowest possible maximum prediction error may be too large. By increasing n and solving the optimization problem, the lowest possible maximum prediction error can be reduced until it is less than the desired value. Similarly, if the lowest maximum prediction error is less than the desired setting, n should be decreased.

[0126] According to an embodiment of the present invention, for example, using the first variant, the method may include the following steps: considering A as a function of O, wherein the boundary x i is the set point:

[0127] • Obtaining seed data comprising a first approximation A of an operating characteristic of the first rotating system as a function of one or more operating parameters.

[0128] • Select n data points from the seed data, where n may be at least 3, as an initial selection of the mapping data.

[0129] • Optimizing the first prediction model G using piecewise linear approximation by minimizing the maximum prediction error based on the first prediction model by modifying the selection of n data points from the seed data used as model data.

[0130] Continue increasing / decreasing n and modifying the selection of model data points in the seed data until the prediction error E* is less than the target maximum a priori error value to obtain the lowest possible number n

[0131] • Selecting a number of n set points P based on a model data set that meets the conditions of the previous bullet.

[0132] • Obtaining a plurality of n mapped data points based on a plurality of n set points P.

[0133] • Generating interpolated data using a plurality of the n mapped data points, for example, by using a first prediction model G as a piecewise linear function using a plurality of the n mapped data points.

[0134] The piecewise linear interpolation is based on a first order interpolation function, using points that are directly adjacent to the set point value for which the mapped data point value must be obtained. The method of the invention can be extended by utilizing higher order interpolation functions.

[0135] According to an embodiment of the step of obtaining multiple set points (referred to as the "second variant"), the prediction model includes an iterative piecewise linear method, wherein the one or more operating parameters are composed of a single operating parameter. The principles of the second variant are applicable to embodiments in which the operating characteristic is mapped as a function of more than one operating parameter (such as two or more operating parameters).

[0136] Figure 4 As a graphical illustration of some of the principles of the second variant, an approximation of the operating characteristic as a function of the operating parameter, i.e., A(O), is shown. The solid curve represents A(O) for the first software model of the first rotating system. Seed data consisting of synthetic data derived using the first software model of the first rotating system can be obtained. The data points of the seed data can be referred to as (O). x , A(O x )). The seed data is used to obtain multiple set points through optimization / evaluation of the predictive model.

[0137] Similar to some implementations of the first variant, the implementation goal of the second variant is to find the boundary points between linear segments so that the maximum prediction error E* is smaller than the expected value of the a priori maximum error.

[0138] For the second variant, the boundaries are obtained one by one.

[0139] For the value O x , the value A(O x ).

[0140] The first boundary point (O1, A(O1)) is Figure 4 The goal is to find the distance x to the next boundary. Figure 4 In the example, the maximum prediction error E* is represented by the dot on the right, so that the maximum prediction error E* is equal to (and / or does not exceed) the required a priori maximum error E. Obviously, when x is chosen to be very small, the prediction error E* will be less than E, and when x is chosen to be very large, the maximum prediction error E* will be greater than E. Given a value x, a first boundary point and an approximation A(O), the maximum prediction error E*(x) can be determined, thereby defining the function e(x)=E*(x)-E. The optimal value of x is E*(x)-E=0, so x can be obtained by finding the zero point of the function e(x). In the art, many methods for finding the zero point of a function are known, such as the Newton-Raphson method or the secant method.

[0141] According to an embodiment of the invention, for example using the second variant, the method may comprise the following steps: considering A as a function of O, wherein the boundaries form the set points:

[0142] Define the error function e(O) = E*-E.

[0143] Obtain multiple of the n set points using the zero-finding method to obtain each set point O y ,

[0144] So that e(O y )=0, so that the desired operating range is approximated by the set point.

[0145] Obtain a plurality of n mapped data points, each mapped data point being associated with each corresponding set point O y consistent.

[0146] Generate interpolated data using mapped data points.

[0147] According to an embodiment of the step of obtaining a plurality of set points (called "third variant"), the prediction model comprises using

[0148] Chebyshev polynomials.

[0149] Functions on bounded intervals can be well approximated by weighted sums of Chebyshev polynomials of the first kind T i (x). If the function g(x) represents the seed data, for example, by a software model of the first rotation system, then the approximation of the function g(x) using N Chebyshev polynomials can be given by

[0150]

[0151] Among them, the value c k Is the weight. If at N points x i In the evaluation function g(x), the weight can be determined as

[0152]

[0153] If more Chebyshev polynomials and therefore more weights are used, and if g(x) is a smooth function (which can be assumed to be a smooth function), the approximation error of g(x) will decrease. m (x) after which the sum of the weighted polynomials will be truncated to the value c m+1 This assumes that the series converges uniformly and rapidly, with no high-frequency components or discontinuities, which is usually assumed to be the case for g(x), which represents an approximation of the operating characteristics of the first rotating system as a function of one or more operating parameters.

[0154] According to an embodiment of the present invention, for example, using the third variant, the method may include the following steps: considering A as a function of O, where A represents an approximate value of the operating characteristic and O represents one or more operating parameters:

[0155] Obtain seed data A(O).

[0156] • Ensure that the seed data A(O) has a relatively large number of data points, such as more than 50, for example, by obtaining the required data points from a software model of the first rotating system or by interpolating from existing data points of the seed data.

[0157] Use A(O) to determine the weight c k .

[0158] Determine if index m satisfies the optimization condition, for example, where c k The value of is less than the a priori maximum error E, and it is used to obtain multiple of the m set points P

[0159] • Based on a plurality of m set points P, a plurality of m data points are obtained.

[0160] Generate interpolated data using an updated prediction model that uses multiple of the m data points to determine m weights ck And use the weighted sum of Chebyshev polynomials T0 to T m-1 and weights c0 to c m-1 .

[0161] Compared to some other spectral methods, Chebyshev polynomials significantly reduce the Gibbs phenomenon at the extremes of the domain. Therefore, compared to some other spectral methods, Chebyshev polynomials may be more suitable for approximating functions over finite fields.

[0162] Utilizing one or more previously obtained mapped data points to obtain one or more subsequent set points (e.g., including obtaining a set of candidate set points) may be performed in various ways. Variations thereof may be implemented in embodiments of the present invention, such as by Figure 5 shown.

[0163] Figure 5 Schematically shows the Figure 4 The same situation. In addition, according to the set point O x The obtained mapping data points 51 are shown by solid squares 51. Based on the mapping data points 51, the software model of the rotating system is updated or installed. Such updated A(O) of the first software model of the first rotating system is shown by the bold dashed curve 52. Thus, starting from point 51, an iterative piecewise linear method can be performed to obtain a second set point. Subsequently, the second mapping data points obtained based on the second set point can be used for further updating or fitting of the software model.

[0164] If the seed data includes data from previous measurements obtained under different settings, such as for different rotation systems and / or for different reference conditions, such data can be fitted based on the obtained one or more mapped data points. According to an embodiment using Chebyshev polynomials as a prediction model, the function A(O) can, for example, be scaled so that the resulting A(O) is closer to or intersects the mapped data points.

[0165] Figure 6 A mapping method 70 according to a first embodiment of the present invention is schematically illustrated. The method 70 is a computer-implemented method for mapping an operating characteristic of a first rotating system as a function of one or more operating parameters. The method 70 includes obtaining 72 seed data; obtaining 74 a plurality of set points; obtaining 76 a plurality of mapped data points; and generating 78 interpolated data using the plurality of mapped data points.

[0166] The arrows from one box to another indicate that the output or result of the corresponding step is utilized or required to perform another step. Thus, the seed data obtained by step 72 is utilized by step 74. The plurality of set points obtained by step 74 forms the basis for obtaining the plurality of mapped data points of step 76. The plurality of mapped data points obtained by step 76 are utilized by step 78.

[0167] The seed data constitutes an initial data set of operating characteristics as a function of one or more operating parameters of the first rotating system.

[0168] The plurality of set points includes a first set point, a second set point, and a third set point. Each set point includes a component for each of one or more operating parameters. The plurality of set points is obtained 74 using a prediction model, seed data, and a priori error settings.

[0169] The plurality of mapped data points includes a first mapped data point, a second mapped data point, and a third mapped data point. Each mapped data point includes an acquired data component acquired using one or more measurements of the first rotating system in response to controlling one or more operating parameters of the first rotating system according to a corresponding set point, such that the first mapped data point includes an acquired data component acquired according to the first set point, the second mapped data point includes an acquired data component acquired according to the second set point, and the third mapped data point includes an acquired data component acquired according to the third set point.

[0170] Figure 7 The method 80 according to the second embodiment of the present invention is schematically shown. Figure 6 The method 80 is similar to the method 70. The method 80 differs from the method 70 in that the following steps are replaced with the following steps: obtaining 84 multiple set points; and obtaining 86 multiple mapping data points.

[0171] Steps 84 and 86 are different from steps 74 and 76. One difference is the dependency, e.g. Figure 7 As shown by the arrow between blocks 84 and 86, the step 84 of obtaining a plurality of set points includes: obtaining a second set point using the first mapped data point (obtained as part of step 86); and obtaining a third set point using the first mapped data point and the second mapped data point (obtained as part of step 86).

[0172] Figure 8An embodiment of a management system 90 according to the present invention is schematically illustrated. Management system 90 includes a mapping system 92, an automation system 94, and a test system 96. Mapping system 92 provides a plurality of set points to automation system 94. Automation system 94 controls test system 96 based on the plurality of set points. Test system 96 acquires one or more measurements from a first rotating system 98, which it controls, based on each of the plurality of set points. First rotating system 98 is connected to the test system, allowing it to be controlled and measured by test system 96. When test system 96 acquires measurement data from first rotating system 98, the data can be forwarded back to mapping system 92. Mapping system 92 utilizes this input measurement data to generate corresponding mapping data points. Thus, mapping system 92 obtains corresponding mapping data points based on the measurement data acquired by test system 96.

[0173] Reference Signs List

[0174] The following shows a list of reference numerals used in the drawings.

[0175] 70: Implementation method of the mapping method.

[0176] 72: Get seed data.

[0177] 74: Get multiple set points.

[0178] 76: Get multiple mapping data points.

[0179] 78: Generate interpolation data.

[0180] 80: Implementation method of the mapping method.

[0181] 84: Get multiple set points.

[0182] 86: Get multiple mapping data points.

[0183] 90: How the management system is implemented.

[0184] 92: Mapping system.

[0185] 94: Automation system.

[0186] 96: Test system.

[0187] 98: A device under test, for example, a first rotating system.

[0188] Although specific embodiments have been shown and described, it should be understood that these embodiments are not intended to limit the claimed invention. Accordingly, the description and drawings should be regarded as illustrative rather than restrictive. The claimed invention is intended to cover alternatives, modifications, and equivalents. It should be emphasized that the terms "comprise" and "comprising," when used in this disclosure, specify the presence of a recited feature, integer, step, component, etc., but do not necessarily preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof. For any claim that enumerates several features, it is contemplated that several of these features may be embodied by the same item of hardware and / or software. The fact that certain measures are recited in mutually different dependent claims or described in different embodiments does not indicate that a combination of these measures cannot be used to advantage. It will be apparent to those skilled in the art that various modifications and variations may be made to the structure of the present invention without departing from the scope of the present invention. In view of the foregoing, the present invention is intended to cover modifications and variations of the present invention as long as they fall within the scope of the appended claims and their equivalents. The scope of the invention is determined by the claims, in which reference numerals are used for clarity and not for limitation. Any embodiment or implementation not included in the claims is provided to assist in understanding the present invention and does not constitute a part of the claimed invention.

Claims

1. A computer-implemented method for mapping an operational characteristic of a first rotating system as a function of one or more operating parameters, the method comprising: Get seed data; Obtain multiple set points; obtaining a plurality of mapping data points; as well as generating interpolated data using the plurality of mapped data points, said seed data constituting an initial data set of said operating characteristics as a function of said one or more operating parameters of said first rotating system, the plurality of set points including a first set point, a second set point, and a third set point, each set point including a component for each of the one or more operating parameters, the plurality of set points being obtained using a predictive model, the seed data, and an a priori error setting, The multiple mapping data points include a first mapping data point, a second mapping data point and a third mapping data point, each mapping data point including an acquisition data component, and the acquisition data component is acquired using one or more measurements of the first rotation system in response to controlling the one or more operating parameters of the first rotation system according to a corresponding set point, so that the first mapping data point includes an acquisition data component acquired according to the first set point, the second mapping data point includes an acquisition data component acquired according to the second set point, and the third mapping data point includes an acquisition data component acquired according to the third set point.

2. The method according to claim 1, wherein The step of obtaining the plurality of set points comprises: obtaining the second set point using the first mapped data point; and The third set point is obtained using the first mapped data point and the second mapped data point.

3. The method according to claim 1 or 2, wherein: The seed data includes synthetic data derived using a software model of the first rotating system.

4. A method according to any one of the preceding claims, wherein The one or more measurements of the first rotation system used to acquire each corresponding acquired data component are performed under first reference conditions, and wherein the seed data includes first reference data acquired under second reference conditions different from the first reference conditions using the one or more measurements of the first rotation system.

5. A method according to any one of the preceding claims, wherein The seed data includes second reference data obtained using one or more measurements of a second rotation system, the second rotation system being different from the first rotation system.

6. A method according to any preceding claim, comprising deriving the prediction model from a plurality of candidate prediction models.

7. A method according to any one of the preceding claims, wherein The step of obtaining the plurality of set points includes generating one or more weights for the prediction model.

8. A method according to any one of the preceding claims, wherein The step of obtaining the plurality of set points includes optimizing the prediction model by minimizing the number of set points in the plurality of set points given constraints defined by the a priori error settings, the constraints including a maximum a priori error value.

9. A method according to any one of the preceding claims, wherein The step of generating interpolation data includes utilizing a combination of the prediction model and the plurality of mapped data points.

10. The method according to any one of the preceding claims, wherein: The operating characteristic is efficiency; Each set point includes a torque component and a rotational speed component; and The first rotation system includes an actuator.

11. The method according to any one of claims 1 to 9, wherein: The operating characteristic is direct-axis inductance, quadrature-axis inductance, or torque; Each set point includes a direct-axis current component and a quadrature-axis current component; and The first rotation system includes an electric motor.

12. A management method comprising the method according to any one of the preceding claims, the management method comprising: controlling the one or more operating parameters of the first rotating system according to each corresponding set point; as well as Acquiring each acquisition data component includes performing the one or more measurements of the first rotation system used to acquire each respective acquisition data component.

13. A management system configured to perform the method according to claim 12, the management system being configured to accommodate the first rotation system.

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

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