Mapping operational characteristics of rotary system

Abstract

To provide a novel method and system for mapping the operational characteristics of a rotary system.SOLUTION: The invention relates to a system and a method for mapping the operational characteristics of a first rotary system as a function of one or a plurality of operation parameters. The method comprises: acquiring seed data; obtaining a plurality of setpoints; obtaining a plurality of mapping data points; and generating interpolation data using the plurality of mapping data points. The seed data constitutes an initial dataset corresponding to the operational characteristics as a function of one or the plurality of operation parameters corresponding to the first rotary system. The plurality of setpoints are obtained using a prediction model, the seed data, and a prior error setting. Each mapping data point includes an acquisition data component acquired using one or a plurality of measurements corresponding to the control of the one or the plurality of operation parameters of the first rotary system in accordance with a respective setpoint.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a system and method for mapping operating characteristics (e.g., vibration, efficiency, heat generation, etc.) of a rotating system as a function of one or more operating parameters (e.g., input power conditions, load conditions, external thermal conditions, etc.) and is applicable to a variety of rotating systems, such as electric motors, drivetrains, turbines, and internal combustion engines. [Background technology]

[0002] A prominent example of mapping the operating characteristics of a rotating system is the efficiency mapping of an electric motor. Such efficiency mapping is often obtained for two operating parameters, namely, speed (i.e., rotational speed) and torque, and is used to understand how efficiently the electric motor converts electrical energy into mechanical energy as a function of these two operating parameters. Because the efficiency of an electric motor (or other rotating system) typically varies significantly based on the conditions under which the rotating system operates, efficiency mapping of each rotating system is typically 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 the operating range of one or more operating parameters to create a detailed data set (i.e., mapping data), e.g., for illustrative purposes. Efficiency mapping of an electric motor may include establishing test conditions in which the motor is operated at various predetermined combinations of speed and load (i.e., torque). This may include, for example, using a dynamometer to control and measure speed and load. For each operating point, input power (based on voltage and current) and mechanical output (based on torque and speed) are typically measured. From these measurements, efficiency may be calculated as the ratio of output to input power at each operating point. Data obtained using one or more measurements based on an operating point may be referred to as "mapping data points" and represent data points of operating characteristics as a function of one or more measured parameters. A collection of mapping data points may constitute or form part of the mapping data. The mapping data may include interpolated data points, which are generated from the mapping data points and then added to the mapping data. The mapping data can be plotted on a graph. For example, in the example of efficiency as a function of speed and torque discussed above, with speed on one axis and torque on the other, efficiency can be represented by color coding or contours. Such a graphical representation is sometimes called an efficiency map, or in more general terms, a map. Such a map visually represents 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. Traditional approaches for efficiency mapping of electric motors involve many successive measurements. Typically, a rectangular mesh of set points (a speed-torque grid) is selected, and one measurement is taken for each set point of the grid. An example of this is shown in FIG. 1, where an exemplary efficiency map of an electric motor as a function of speed and torque is shown schematically, with the axes labeled by normalized quantities, n / n Ndenotes the normalized velocity, and M / M N indicates normalized torque. The efficiency map in Figure 1 is an example of a graphical representation with contour lines and a color gradient showing the efficiency η / % of a motor at different combinations of speed and torque. The upper right corner of the efficiency map is blank due to the motor's operating limits. A schematic example of measurement points that are within the motor's operating limits is shown by a schematic mesh of 54 circles (size 9 x 8), for which some of the mesh points have been narrowed (i.e., moved downward) with respect to torque values ​​so as not to fall outside the motor's operating limits.

[0004] Furthermore, a set of measurements, eg, in a speed-torque grid as described above, may be repeated according to different reference conditions, eg, different ambient temperatures and / or different voltage levels of 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 necessary step for validating the development progress of each rotating system. Data obtained using such mapping is referred to as "mapping data." Such mapping data, for example, from efficiency mapping, is typically essential for system designers to optimize a rotating system for a particular application. Similarly, mapping data is typically essential for operational designers to enable a rotating system to operate within a desired operating range, e.g., to operate within the most efficient operating range, e.g., for energy conservation and cost reduction. Furthermore, mapping data for a rotating system is typically essential for comparative analysis, e.g., to select a particular rotating system for a particular application based on performance. Furthermore, mapping data can be incorporated into control strategies, e.g., for electric vehicles or automated machinery, to enable the rotating system to operate efficiently. Summary of the Invention [Problem to be solved by the invention]

[0006] For example, the entire series of measurements as described above for mapping to obtain an efficiency map is usually relatively resource and time consuming, and therefore may be associated with high costs and become a bottleneck in system development, system use, etc.

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

[0008] In the context of the present invention, the term "mapping data" or "desired mapping data" refers to mapping data of desired operating characteristics as a function of one or more desired operating parameters for a desired rotating system; It can be understood as:

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

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

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

[0012] A fourth object of the present invention is to facilitate providing a balance between the second and third objects, i.e., a balance between reducing the resource requirements for obtaining mapping data and improving data quality.

[0013] Therefore, an object of the present invention may be to obtain sufficient mapping data while reducing the investment (e.g., time, cost, or other resources) associated with acquiring mapping data, e.g., to balance data quality with the investment required to acquire the data. [Means for solving the problem]

[0014] The present invention can be provided by any of the described embodiments, which are provided and / or intended to achieve one or more of the above-mentioned objects and / or additional objects.

[0015] According to a first aspect of the present invention, there is provided a method for mapping. Mapping may mean that mapping data is obtained by the method. The method may be a computer-implemented method, such that at least a part of the method, or the entire method, is computer-implemented. The method is configured to map operating characteristics of a first rotating system as a function of one or more operating parameters. The mapping may be under first reference conditions, i.e., the first rotating system may be subject to such first reference conditions during the performance of each measurement used to obtain the mapping data.

[0016] The method of the first aspect includes obtaining seed data, obtaining a plurality of set points, obtaining, e.g., receiving, a plurality of mapping data points, and optionally generating interpolated data using the plurality of mapping data points.

[0017] The seed data preferably constitutes an initial data set for or of operational characteristics as a function of one or more operational parameters for the first rotating system. In this context, the term “for” may be understood as “suitable for,” “applicable for,” or “useful for.” The seed data may be useful for an initial or preliminary description or representation of operational characteristics of the first rotating system as a function of one or more operational parameters. According to embodiments, the seed data is not necessarily measurement-based, not necessarily based on the first rotating system, and not necessarily based on measurements of the first rotating system. According to embodiments, the seed data constitutes an initial data set, which will be utilized to map operational characteristics of the first rotating system as a function of one or more operational parameters. According to embodiments, the seed data is suitable, applicable, or useful for the first rotating system. According to embodiments, the seed data is suitable, applicable, or useful for describing operational characteristics as a function of one or more operational 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 including a component for each of one or more operating parameters. According to the first aspect, the set points, i.e., the set points of the plurality of set points, are obtained using a predictive model, seed data, and a priori error settings.

[0019] According to a first aspect, the plurality of mapping data points includes a first mapping data point, a second mapping data point, and an optional third mapping data point. The number of mapping data points typically corresponds to the number of setpoints obtained and used. Each mapping data point includes acquired data components obtained using one or more measurements of the first rotating system for control of one or more operating parameters of the first rotating system by the respective setpoint. Thus, the first mapping data point includes acquired data components obtained by the first setpoint, the second mapping data point includes acquired data components obtained by the second setpoint, and the optional third mapping data point includes acquired data components obtained by the third setpoint.

[0020] The acquired data component of each mapping data point may represent a value of an operating characteristic, such as a measurement of the operating characteristic itself, or a derived value based on one or more measurements. Each mapping data point generally includes a component corresponding to each of one or more operating parameters. Such components may be described as operational data components, control components, or input components. These operational data components may be obtained using one or more measurements of the first rotating system for control of one or more operating parameters of the first rotating system according to respective setpoints. Alternatively, or additionally, the one or more operational data components may be obtained based on respective setpoints utilized to control the first rotating system. Obtaining the plurality of mapping data points may include receiving the acquired data components or receiving data utilized to derive the acquired data components. Such data or data components may be received, for example, from an automation system utilized to control the first rotating system. The step of obtaining the plurality of mapping data points may include combining the received acquired data components with a corresponding set point or a component of a set point and / or with one or more operational data components, for example, one or more operational data components received from an automation system.

[0021] According to a second aspect of the present invention, there is provided a management method including the method according to the first aspect of the present invention. The management method includes controlling one or more operational parameters of a first rotating system according to respective setpoints and acquiring respective acquired data components, the acquiring each acquired data component including performing one or more measurements of the first rotating system utilized to acquire each respective acquired data component. Further, the management method may include acquiring one or more operational data components, such as each operational data component.

[0022] The management method may be considered to include a plurality of management steps, each of which includes controlling the first rotating system according to a respective set point. Further, each of the management steps includes one or more measurements of the first rotating system in response to the control of the first rotating system according to the respective 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 the third set point.

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

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

[0027] According to embodiments, the following steps are performed and completed in the order listed: obtaining seed data, obtaining a plurality of set points, obtaining a plurality of mapping data points, and generating interpolated data. According to embodiments, these steps begin in the order listed, but are not necessarily completed in that order. According to embodiments, the steps of obtaining a plurality of set points and obtaining a plurality of mapping data points are performed incrementally and interchangeably.

[0028] It is to be understood that both the foregoing general description and the following detailed description are exemplary and 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 with skill in the art upon examination of the drawings and disclosure of the invention, including the detailed description. All such additional systems, methods, and features are intended to be included within this description, be within the scope of the invention, and be protected by the accompanying claims.

[0029] This Summary of the Invention represents an inventive overview of some of the teachings of the present disclosure and is not intended to be an exclusive or exhaustive treatment of the present subject matter. Further details about the present subject matter can be found throughout the disclosure, including the detailed description and appended claims. The scope of the present invention is defined by the appended claims and their legal equivalents.

[0030] The accompanying drawings are included to provide a further understanding of the present invention, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the present invention and, together with the description, serve to explain the principles of the present invention. The drawings illustrate the design and utility of the embodiments, and like elements may be referred to by common reference numerals. The drawings are not necessarily drawn to scale. To better understand how the above and other advantages and objectives are obtained, a more particular description of the embodiments illustrated in the accompanying drawings will be given. The drawings may depict only typical embodiments and therefore should not be considered limiting of the scope. Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. [Brief explanation of the drawings]

[0031] [Figure 1] FIG. 1 is a diagram illustrating an exemplary efficiency map including measurement points. [Figure 2] FIG. 2 is a diagram illustrating some principles of obtaining multiple set points according to an embodiment of the present invention. [Figure 3] FIG. 3 is a diagram illustrating some principles of obtaining multiple set points according to an embodiment of the present invention. [Figure 4] FIG. 4 is a diagram illustrating some principles of obtaining multiple set points according to an embodiment of the present invention. [Figure 5] FIG. 5 is a diagram illustrating some principles of obtaining multiple set points according to an embodiment of the present invention. [Figure 6] FIG. 6 is a schematic diagram illustrating a method according to one embodiment of the present invention. [Figure 7] FIG. 7 is a schematic diagram illustrating a method according to one embodiment of the present invention. [Figure 8] FIG. 8 is a schematic diagram of a system according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] Detailed Description One, more, or all of the following definitions may be applied to interpret terms applied to features of the embodiments disclosed herein and are meant only to define elements within the present disclosure. No limitations on the terms used in the claims are necessarily intended or should be derived thereby. Terms used in the appended claims can and should be limited only by their customary meaning within the applicable technical field.

[0033] Throughout this disclosure, ordinal numbers are generally understood as merely nominal numbers unless otherwise specified. For example, terms such as "first," "second," and "third" are generally understood as arbitrary identifiers of respective features of the present invention unless otherwise specified. Throughout this disclosure, the use of ordinal numbers for events and steps does not necessarily indicate any timing and / or prioritization of the respective events or steps. Thus, one event, such as a first event, may occur before, during, or after another event, such as a second event, or one event may occur in any combination before, during, and after other events 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 a "first feature" and a "second feature," etc.

[0034] Throughout this disclosure, the term "speed" generally refers to "rotational speed."

[0035] The method for mapping 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 using a computer system. The computer system may include, for example, any one or any combination of a server, a client, and a cloud computing service. The present invention may be provided by any one or any combination of a computer program, a computer-readable medium, and a computer program product. The present invention may include any one or any combination of a computer program, a computer-readable medium, and a computer program product, where the computer program product may include means for executing the method for mapping according to the present invention. The present invention may include a computer program including instructions, when executed by a computer system, that cause the computer system to execute the method for mapping 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 a computer program according to the present invention stored thereon. The present invention may include a computer-readable medium including instructions, when executed by a computer system, that 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 may be implemented by a distributed computing system that may use or include a computer network, which may be referred to as a “distributed computing environment.” Within such a distributed computing system, a method for mapping according to the present invention may be performed by one or more or all of a number of entities, such as any combination of 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 may be provided. In accordance with the present invention, a computer program product may be provided comprising instructions which, when executed by a computer, cause the computer to carry out the method of the present invention. The computer program may be configured to run on a general-purpose computer. The computer program may be configured to communicate with a test system.

[0036] The first rotating system may include or be a drivetrain, e.g., an electric drivetrain. The first rotating system may include components for the drivetrain, e.g., electrical components for the drivetrain. The first rotating system may include, or be any of, a rotary actuator system, a rotary transmission system, or a rotary generator system. The first rotating system may be electrical, e.g., including one or more electrical components for converting electrical energy to mechanical energy or vice versa. The first rotating system may include, or be an electric machine, such as an electric motor or generator. The first rotating system may include, or be an internal combustion engine (ICE) and / or 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.

[0037] The present invention can be applied to mapping a variety of operating characteristics. The choice of which operating characteristic to map is typically a matter of interest and typically depends on the type and selection of the rotating system. The operating characteristic may be efficiency. In the context of the present invention, efficiency may be understood as the efficiency with which a first rotating system converts or transfers energy from the input to the output of the first rotating system. The operating characteristic may be power loss. Power loss typically represents the portion of 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 sound level or sound pressure generated by the first rotating system. Such sound level or sound pressure may be referred to as noise. The operating characteristic may be thermal energy generated by the first rotating system. The operating characteristic may be durability, for example, as measured by wear and tear. The operating characteristic may be torque. The operating characteristic may be inductance or a component of inductance, which may be of particular interest in the context of mapping electrical rotating systems.

[0038] The term "mapping" in the context of the present invention generally refers to a process of obtaining or providing a set of data, called "mapping data," that includes a representation of how the operating characteristics of a first rotating system vary as a function of one or more operating parameters. Such mapping data may be suitable for analytical or optimization purposes and may be used, for example, for the design, operation, and / or control of the first rotating system. Individual data points of the mapping data may be referred to as mapping data points.

[0039] The process of mapping may include generating a visual representation of the mapping data. However, the purpose of mapping is not necessarily limited to or includes such a visual representation. While the visual map may be useful for human interpretation, the underlying data may alone or additionally serve broader functions as referenced in this disclosure, such as assisting in optimizing system performance, conserving energy, and / or reducing costs. For example, torque mapping of an electric motor can reveal an optimal operating point that balances power consumption and output efficiency, which is essential for applications in, for example, electric vehicles and automated machinery.

[0040] Typically, the operating characteristic depends on various parameters of the first rotating system. These parameters may be collectively referred to as "influence parameters." The influence parameters may be divided into two groups: one or more operational parameters and a plurality of reference parameters, where the plurality of reference parameters define the reference conditions. When mapping the operating characteristic, an influence parameter that is not an operational parameter is, by definition, a reference parameter. Similarly, an operational parameter cannot be a reference parameter. Similarly, a component of a setpoint cannot be a component of the corresponding reference condition, and vice versa.

[0041] Depending on the operating characteristics and the first rotating system, the influence parameters may include various physical properties. The influence parameters may include input and / or output torque of the first rotating system. The influence parameters may include input and / or output speed of the first rotating system. The influence parameters may include a temperature of the first rotating system. The influence parameters may include an ambient temperature of the first rotating system. The influence parameters may include a heat dissipation coefficient of the first rotating system. The influence parameters may include one or more indicators of energy supply or generation, such as a supply voltage or current (e.g., for an electric motor) or a fuel octane number (e.g., for an ICE).

[0042] In practice, for rotating systems, 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. Often, for example, to facilitate graphical representation, two operating parameters are allowed to vary (such as torque and speed), while other parameters (reference parameters) can be held generally constant or ignored. Changing the value of the reference parameters affects the torque-speed-efficiency map. A user may wish to vary three or more parameters (i.e., operating parameters) and hold the remaining parameters (i.e., reference parameters) generally constant or ignore them. Finally, the question may arise as to how to utilize and / or illustrate the resulting mapping data.

[0043] The present invention can be applied to mapping various operating characteristics as a function of various operating parameter(s). The selection of which operating parameter(s) are utilized to map the operating characteristic is typically of interest and typically depends on the type of rotating system and the selection of the operating characteristic. In many cases, it is important to select one or more operating parameters that have a significant impact on the operating characteristic and can be controlled and / or designed for the first rotating system during normal use.

[0044] The present 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 three or more operating parameters.

[0045] Depending on the operating characteristics and the first rotational system, the one or more operating parameters may include one or more different physical characteristics. The one or more operating parameters may include input and / or output torque of the first rotational system. The one or more operating parameters may include input and / or output speed of the first rotational system. The one or more operating parameters may include a temperature of the first rotational system. The one or more operating parameters may include an ambient temperature of the first rotational system. The one or more operating parameters may include a heat dissipation coefficient of the first rotational system. The one or more operating parameters may include one or more indicators of energy supply or generation, such as a supply voltage or current (e.g., for an electric motor) or a fuel octane number (e.g., for an ICE).

[0046] For purposes of illustrating the mapping data, it may be desirable to select the one or more operating parameters to consist of only one or two operating parameters. However, mapping the operating characteristics as a function of three or more operating parameters may be important to obtain the resulting mapping data. Moreover, illustration of such mapping data based on three or more operating parameters is possible and is merely a matter of illustrative means. Furthermore, such mapping data based on three or more operating parameters can be readily utilized for illustration of the operating characteristics as a function of a selected one or two of the three or more operating parameters.

[0047] An operating characteristic may not be an operating parameter for the same embodiment. What constitutes an operating characteristic in one embodiment may be an operating parameter in another embodiment. For example, torque may be treated as an operating characteristic or as an operating parameter, as described in the context of various embodiments.

[0048] According to an embodiment, one or more operating parameters are controlled by a plurality of set points. A measured operating parameter typically refers to a corresponding measured value of the first rotating system when controlled according to a respective component of the set point, which component represents an intended or target value of the corresponding operating parameter and is not necessarily identical to its actual or measured value. However, in some cases, one or more respective components of the set point may be utilized as the corresponding operating parameter for each mapping data point, for example, without measuring the corresponding operating parameter.

[0049] In general, when defining a particular operating parameter, it may be implicit that any other influencing parameters may be attempted to be kept at a constant level.

[0050] For purposes of mapping operating characteristics, one or more operating parameters are typically defined within an operating range. Such an operating range typically includes one component for each of the one or more operating parameters. The operating range is typically defined to encompass the range of interest for each of the one or more operating parameters.

[0051] A method for mapping operating characteristics may be defined as being performed “under first reference conditions.” The term “under first reference conditions” is understood to mean that first reference conditions exist for the first rotating system during measurements that lead to the provision of the mapping data points. Such existence may be independent of whether such reference conditions are defined, measured, and / or ignored. Accordingly, multiple reference parameters may form part of or define each reference condition, such as the first reference condition. One or more reference parameters of each reference condition may be measured, registered, or estimated, for example, in connection with measurements of the respective rotating system to obtain the respective mapping data points.

[0052] The first reference conditions may be understood as a set of reference parameters that may affect the value of the measured operating characteristic, but that are kept constant (e.g., sufficiently constant, e.g., constant within a tolerance) when mapping the operating characteristic as a function of one or more operating parameters.

[0053] Although the reference parameters may be intended to remain unchanged, variations may occur within a given reference condition. Such variations may be ignored and / or noted as part of the experimental conditions. Practical difficulties may exist in keeping one or more reference parameters constant. However, in some cases, one or more reference parameters are not intended to be constant. In any case, the reference parameters usually form a prerequisite for the measurement. An attempt is usually made to keep the reference parameters at a constant level / value. The reference parameters are usually not the primary focus of each measurement. The reference parameters usually serve as a reference point or reference condition against which the effects of various operating parameters on operating characteristics are measured. In practice, while an attempt may often be made to keep the reference parameters constant, this is not always achievable in practice.

[0054] The term “seed data” in the context of the present invention generally refers to data representing assumptions and / or estimates and / or approximations of operating characteristics of a first rotating system as a function of one or more operating parameters. The seed data, or portions thereof, may be readily available, for example, via one or more previous measurements of the first rotating system or another rotating system. Alternatively, or additionally, the seed data, or portions thereof, may be relatively quickly and / or relatively easily obtained, for example, from a simulation utilizing a software model of the first rotating system and / or from dynamic measurements of the first rotating system or another rotating system. Thus, 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 may be obtained, for example, by continuously varying (e.g., continuously decreasing or increasing, e.g., ramping up) one or more of the one or more operating parameters while measuring the inputs and / or outputs of the respective rotating systems. 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 measurement of the first rotating system. In such a case, the speed component value may, for example, be changed sequentially, while for each speed value, the torque may, for example, be increased successively incrementally while the input and / or output of the system are registered by measuring the input and / or output.

[0055] It is generally preferred that the seed data exhibit a reasonable similarity of the operating characteristics as a function of one or more operating parameters for the first rotating system. Good quality seed data is understood to mean good similarity. Generally, the better the quality of the seed data, the better the predictive model will perform in the process of providing useful set points for obtaining useful mapping data points.

[0056] Predictive modeling can be broadly understood as a systematic approach for generating predictions about future behavior. This can be achieved by building a model that analyzes the relationships between different variables in a dataset. Specifically, the model can use a subset of data points, called model data, selected from a larger set of data points, called seed data, where the number of data points in the model data is typically significantly smaller than the number of data points in the seed data, e.g., less than 20% of the number. To make these predictions, the model can utilize various methods, such as piecewise linear approximation or the use of orthogonal polynomials such as Chebyshev polynomials. The objective of such models is often to optimize certain parameters, such as minimizing the maximum a priori error. The a priori error typically represents the estimated deviation between the model's prediction and the actual outcome before the actual outcome is observed. This optimization typically aims to improve the model's predictive accuracy and reliability.

[0057] The predictive model can generate set points, for example, on a rectangular grid or at arbitrary locations.

[0058] The a priori error setting for the predictive 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 be of a smaller magnitude than the first value. The first value and / or the second value may be or be related to a mean error value, a median error value, a maximum error value, or any combination thereof. The a priori error setting may, for example, be or include an a priori maximum error value for the entire operating range.

[0059] The a priori error setting may be or may include a first condition. The first condition may be to minimize an a priori error, such as a maximum a priori error, a median a priori 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 limitation on the number of set points that make up the plurality of set points. For example, the a priori error setting may be or may include a first condition that minimizes the maximum value of the a priori error over the entire operating range when a maximum or specified number of set points of the plurality of set points is given as the second condition.

[0060] The preset error setting, e.g., its value, etc., may be specified. The preset error setting, e.g., its value, etc., may be set by a user and / or a system performing the method and / or an implementation of the method.

[0061] In general, a priori error relates to a measure of the difference between an estimate of a behavior characteristic provided using a predictive model and an expected, measured value of the behavior characteristic. The expected value is represented by seed data. Thus, a priori error generally refers to an error estimate made before mapping, where the estimate is based, for example, on theoretical or prior knowledge, e.g., synthetic data or data obtained from measurements under different settings, different conditions, different systems, etc. The measure of difference may be quantified by methods such as the maximum error, mean squared error, root mean squared 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 expected results and is used to train and test the predictive model.

[0062] It is emphasized that the concept of a priori error applies even when one or more mapping data points are used to derive a set point, provided that the error for each set point is evaluated based on data / information including estimated data and not solely on actual measurements for that set point.

[0063] The term "setpoint" in the context of the present invention generally refers to a control value(s) for one or more operating parameters. The setpoints may be obtained, for example, by being generated or selected from seed data. Generally, each setpoint is component-defined for one or more operating parameters. Generally, each setpoint is provided within a first operating range.

[0064] According to an embodiment, the plurality of set points includes at least four set points, such as at least five set points. According to an embodiment, the plurality of set points includes at least six set points, such as at least seven set points.

[0065] The term "mapping 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 one or more measurements of a first rotating system corresponding to control of the first rotating system by a respective set point, optionally under first reference conditions. The one or more measurements of the first rotating system can include various input and / or output measurements. In general, at least one output or input measurement is typically required. For example, for efficiency mapping of an electric motor, at least one measurement reflecting the input power provided to the motor to give a given speed and torque combination is typically required for each mapping data point. For example, if the supply voltage of the electric motor is assumed to be constant and known, simply measuring the current supply may be sufficient. The output power may be provided via one or more measurements and / or assumptions based on the settings of the system controlling the measurements, e.g., utilizing the component(s) of each corresponding set point as the corresponding component(s) (i.e., operational data component(s)) of each mapping data point in combination with the corresponding acquired data component(s) to arrive at the corresponding mapping data point.

[0066] Obtaining a data point using one or more measurements generally means that at least one component of the data point, typically at least the acquired data component, is obtained using one or more measurements.

[0067] According to an embodiment, the number of mapping data points obtained is equal to the number of set points utilized for controlling and measuring the first rotating system.

[0068] The step of generating interpolated data using a plurality of mapping data points generally means that the mapping data is supplemented with the interpolated data. Thus, the mapping data includes the mapping data points that exist before the interpolation in addition to the interpolated data provided by the interpolation. The interpolated data may include a plurality of interpolated data points.

[0069] The step of obtaining the plurality of set points may include obtaining a second set point using the first mapping data point. Further, the step of obtaining the plurality of set points may include obtaining an optional third set point using the first mapping data point and the second mapping data point. Thus, the first mapping data point may be obtained before deriving the second mapping data point. Further, the optional third mapping data point may be obtained after deriving the second mapping data point, which may be obtained after deriving the first mapping data point.

[0070] According to embodiments, certain set points of the plurality of set points may be derived using one or more previously obtained mapping data points. Thus, a subset of the plurality of set points may be derived using a predictive model, seed data, a prior error setting, and one or more previously obtained mapping data points.

[0071] According to an embodiment, one or more mapping data points may be used to generate not only one new setpoint but also a new set or subset of setpoints, and the setpoints used to obtain the one or more mapping data points may be pre-fixed for subsequent use in the predictive model. For example, if the step of obtaining multiple setpoints is conditioned on selecting, for example, exactly five setpoints, five setpoints, e.g., candidate setpoints, may first be obtained based on the predictive model, seed data, and a prior error setting that minimizes the maximum prior error. Subsequently, when a first mapping data point is obtained using the first of the five candidate setpoints, four new candidate setpoints may be obtained based on the predictive model, seed data, a prior error setting, and previously obtained mapping data points, i.e., the first mapping data point. Subsequently, when a second mapping data point is obtained using the first of the four candidate setpoints, three new candidate setpoints may be obtained based on the predictive model, seed data, a prior error setting, and previously obtained mapping data points, i.e., the first mapping data point and the second mapping data point. Subsequently, when a third mapping data point is obtained using a first setpoint of the three candidate setpoints, two new candidate setpoints can be obtained based on the predictive model, the seed data, the preliminary error settings, and the previously obtained mapping data points, i.e., the first mapping data point, the second mapping data point, and the third mapping data point. Subsequently, when a fourth mapping data point is obtained using the first setpoint of the two candidate setpoints, one new setpoint can be obtained based on the predictive model, the seed data, the preliminary error settings, and the previously obtained mapping data points, i.e., the first mapping data point, the second mapping data point, the third mapping data point, and the fourth mapping data point. Thus, obtaining a plurality of setpoints can include obtaining a corresponding first setpoint for each of the provided set of candidate setpoints in addition to the last provided setpoint, i.e., obtaining a total of five setpoints.

[0072] An advantage of utilizing one or more previously acquired mapping data points to obtain one or more subsequent set points may be an improvement in the acquisition of the set points, which may result in improved quality of the mapping data.

[0073] The seed data includes a plurality of seed data points.

[0074] The seed data may include or consist of synthetic data. Such synthetic data may be derived using a software model of the first rotating system, i.e., a model at least appropriate for the first rotating system. Thus, the synthetic data may be derived by simulating the first rotating system. The software model of the first rotating system may reference a software model of a system different from the first rotating system, but still be useful for the purpose of providing useful seed data. One or more advantages of obtaining seed data that includes or consists of synthetic data may include that such synthetic data may be obtained relatively quickly, e.g., compared to 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 represent a reasonable approximation of the actual mapping data of the first rotating system.

[0075] The seed data may include, for example, data obtained using one or more measurements of the first rotating system under second reference conditions different from the first reference conditions within the first operating range and / or outside the first operating range, and each mapping data point is obtained using one or more measurements of the first rotating system under the first reference conditions.

[0076] The seed data may include data obtained within and / or outside the first operating range using one or more measurements of the first rotating system.

[0077] The second reference condition may include a different value or setting of at least one component of the first reference condition. The second reference condition may include, for example, a different system temperature, a different supply voltage of the system, or may be based on a dynamic measurement or a different dynamic measurement.

[0078] Advantages associated with the use of measurements under second reference conditions include that they may be readily available from previous measurements and / or may be obtained relatively quickly, for example for dynamic measurements.

[0079] Generally, similar correlations between the operating characteristic and one or more operating parameters for the first and second reference conditions will result in seed data having better quality.

[0080] The seed data may include data obtained using one or more measurements of a second rotational system different from the first rotational system. Such data from the second rotational system may be obtained using one or more measurements within and / or outside the first operating range. Additionally or alternatively, the data from the second rotational system may be obtained using one or more measurements under a first reference condition, a second reference condition, or a third reference condition, the third reference condition being different from the first reference condition.

[0081] An advantage associated with using measurements of a second rotating system is that such data may be readily available from previous measurements.

[0082] Generally, similar correlations between the operating characteristics and one or more operating parameters for the first and second rotating systems will result in seed data having better quality.

[0083] Generally, it is important that the seed data include more data points than the number of set points of the plurality of set points, e.g., at least twice as many or at least four times as many. Increasing the number of data points in the seed data generally improves the accuracy of the predictive model. On the other hand, increasing the number of data points in the seed data generally results in an increase in the computational cost of obtaining the plurality of set points. Therefore, it may be desirable to balance the accuracy of the predictive model with the computational cost resulting from the use of data points in the seed data.

[0084] When synthetic data derived using a software model of the first rotating system is utilized, the desired number of data points of the seed data can be generated directly from the software model. When previously acquired data via measurements is utilized, the acquired data set can be interpolated to provide the desired number of data points in the seed data. The seed data can 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.

[0085] 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 and may utilize a set of orthogonal polynomials, e.g., Chebyshev polynomials. The plurality of candidate prediction models may include a piecewise linear model. The plurality of candidate prediction models may include a local search model.

[0086] Obtaining a predictive model from the plurality of candidate predictive models can be based on a first selection by a user and / or by the system / method of the present invention, which may be a selection of one particular predictive model from the plurality of candidate predictive models, or a selection of a subgroup thereof.

[0087] For example, a user may simply select a certain one or subgroup of candidate predictive models based on user experience that a certain method or methods work best for certain conditions.

[0088] The method may include, for example, seed data analysis, including frequency analysis and / or amplitude analysis. The method may include presenting results of the seed data analysis to a user. Such results may be an aid used in selecting a predictive model. Alternatively, or in addition to presenting results of the seed data analysis to a user, the method may include utilizing the seed data analysis in a selection process to select a particular one or subgroup of candidate predictive models.

[0089] Various predictive models may exhibit different performance. For example, given the same setting for the ex ante maximum error, one predictive model may require more set points than another to achieve this ex ante error condition. Furthermore, various predictive models may exhibit different performance in terms of computation time to obtain multiple set points. The performance of a predictive model may depend on the type of characteristic, seed data quality, etc.

[0090] Obtaining a predictive model from multiple candidate predictive models may be responsive to a performance analysis and / or a seed data analysis. The performance analysis may be performed responsive to performance settings. The performance settings include settings related to one or more of a prior error, the number of set points obtained, and the calculation time / scope / amount. The performance settings may be preset, for example, by a system executing the method, and / or may be set or adjusted by a user. The performance settings may include, for example, a maximum number of set points, a maximum prior error, and a maximum amount of calculation. Based on the performance analysis, the system / method may obtain / select a predictive model to be used to obtain the multiple set points, or may present a suggestion to the user. The performance analysis may result in obtaining the multiple set points.

[0091] The method may be configured to obtain a user selection of a predictive model to utilize to obtain a plurality of set points. The user may select that a rectangular distribution of set points be obtained. The user may select a minimum number of set points or a minimum run time to find a set point.

[0092] The method can include selecting a predictive model used to obtain the plurality of set points, the selecting being responsive to seed data analysis, for example, if the seed data includes high frequencies with high amplitudes, it can be determined to utilize a predictive model based on Chebyshev polynomials rather than one based on piecewise linear interpolation.

[0093] The step of deriving the plurality of set points may include generating one or more weights for a predictive model, for example, one or more weights for a set of orthogonal polynomials.

[0094] The step of obtaining the plurality of set points may include optimizing the predictive model by, for example, minimizing the number of set points in the plurality of set points given a constraint defined by an a priori error set. The constraint may be a maximum a priori error value. Thus, the a priori error set may be defined as the maximum a priori error value.

[0095] Generating the interpolated data may include utilizing a predictive model or one or more aspects of the predictive model. For example, the same weights or weights for the predictive model that may be generated as part of deriving the plurality of set points may be utilized in generating the interpolated data. Alternatively, or additionally, generating the interpolated data may include utilizing a first fitting model, which may be computationally simpler than utilizing a predictive model. The first fitting model may include, for example, linear or spline interpolation, e.g., cubic spline interpolation. In any case, a plurality of mapping data points may be utilized to provide the interpolated data to be included in the mapping data.

[0096] The management method can include performing a method according to the present invention for mapping operating characteristics. The first management step can include controlling and measuring the first rotating system according to a first set point. The second management step can include controlling and measuring the first rotating system according to a second set point. The third management step can include controlling and measuring the first rotating system according to a third set point. The management method can include housing and / or mounting the first rotating system in a management system configured to perform the management method.

[0097] The management system may be configured to house the first rotating system and / or attach the first rotating system to the management system. The management system may be or include a control and data acquisition system. The management system may include a test system and an automation system. The management system may be configured to acquire a plurality of mapping data points.

[0098] The present invention can be implemented to map various operating characteristics of various rotating systems as a function of one or more different operating parameters.

[0099] According to an embodiment, the first rotating system is or includes an electric machine such as an electric motor, the operating characteristic is efficiency, each set point includes a torque component and a rotational speed component, and each set point may further include a component of the supply voltage of the electric machine.

[0100] According to an embodiment, the first rotating system is or includes an electric machine such as an electric motor, the operating characteristic is power loss, each set point includes a torque component and a rotational speed component, and each set point may further include a component of the supply voltage of the electric machine.

[0101] According to an embodiment, the first rotating system is or includes an internal combustion engine, the operating characteristic is efficiency, and each set point includes a torque component and a rotational speed component.

[0102] According to an embodiment, the first rotating system is or includes an internal combustion engine, the operating characteristic is power loss, and each set point includes a torque component and a rotational speed component.

[0103] According to an embodiment, the first rotating system is or includes an electric motor, for example a permanent magnet synchronous motor, and the operating characteristics are a direct-axis inductance (L d ), and each set point is the direct axis current (I d ) component and cross-axis current (I q ) component, and each set point may further include a component of the supply voltage of the electric motor.

[0104] According to an embodiment, the first rotating system is or includes an electric motor, for example a permanent magnet synchronous motor, and the operating characteristics are a direct-axis inductance (L q ), and each set point is the direct axis current (I d ) component and cross-axis current (I q ) component, and each set point may further include a component of the supply voltage of the electric motor.

[0105] According to an embodiment, the first rotating system is or includes an electric motor, for example, a permanent magnet synchronous motor, and the operating characteristic is torque, and each set point is a direct axis current (I d ) component and cross-axis current (I q ) component, and each set point may further include a component of the supply voltage of the electric motor.

[0106] According to an embodiment, the first rotating system is a diesel generator, the operating characteristic is output power, and each set point comprises a biodiesel to petrodiesel ratio of the fuel supplied.

[0107] According to an embodiment, the first rotating system includes an electric motor, the operating characteristic is noise generated by the first rotating system, and each set point includes a rotational speed component, a torque component, a supply voltage component, and a system temperature component.

[0108] According to an embodiment, the first rotating system includes an electric motor, the operating characteristic is efficiency, and each set point includes a rotational speed component, a torque component, a supply voltage component, and a system temperature component.

[0109] According to an embodiment of obtaining multiple set points (referred to as the "first variant"), the predictive model includes a piecewise linear method and the one or more operating parameters consist of a single operating parameter. However, the principles of the first variant apply to embodiments that map the operating characteristic as a function of two or more operating parameters, such as two or more operating parameters.

[0110] 2 and 3 are referred to for some illustrations of the principles of the first variant, both of which show an approximation of the operating characteristic, i.e., A(O), as a function of the operating parameter. The solid curve, which is the same in FIGS. 2 and 3, represents A(O) for the first software model of the first rotating system. The 19 respective intersections between the 19 evenly spaced vertical lines and the solid line, which are the same in FIGS. 2 and 3, represent 19 respective data points of the seed data, i.e., synthetic data derived using the first software model of the first rotating system. (O1, A(O1)) to (O 19 , A(O 19 These 19 data points, referred to as )), are plotted along the x-axis, i.e., the operating parameters O1 to O2. 19 The 19 data points, or seed data, are used to derive multiple set points through optimization / evaluation of the predictive model.

[0111] In a first variant, the seed data is individually (O x , A(O xThe seed data consists of 19 data points, referred to as 19 data points (see 19 data points). The selection of 19 data points is primarily for illustrative purposes. In one or more alternative embodiments, any desired number of set points can be selected. Based on the first software model of the first rotating system, the desired number and / or spacing of data points for the seed data can be obtained relatively easily. If the seed data includes data based on measurements, i.e., measurements related to mapping, and if such data includes fewer data points than desired or data points that are too far apart, interpolation can 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 calculations required to evaluate / optimize the predictive model. Therefore, to reduce the amount of calculations required, the number of data points can be reduced accordingly. However, the number of data points for the seed data, and to some extent their spacing, are also proportional, at least to a certain limit, to the accuracy of the evaluation of the predictive model. Therefore, a balance between evaluation quality and processing volume is desired.

[0112] Further, referring to FIG. 2, for an initial exemplary evaluation of a predictive model according to the first variant, four data points of seed data are initially selected. The selected data points for the predictive model constitute an initial set of model data points for the initial evaluation. Generally, the set of selected data points 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 the four real points in FIG. 2, i.e., (O1, A(O1)), (O7, A(O7)), (O 13 , A(O 13 )), and (O 19 , A(O 19 )). The first component Ox is evenly spaced along the "x-axis" (first axis, horizontal axis). The dashed lines in Figure 2 indicate continuous piecewise linear interpolation using four data points of the initial model data as boundaries, resulting in a continuous piecewise linear function consisting of three linear pieces.

[0113] According to the present invention, the step of obtaining a plurality of set points generally includes optimizing the utilized predictive model by comparison with seed data, where the predictive model is typically based on a subset of the seed data. The optimization of the predictive model is based on a prior error setting and, optionally, a limitation on the number of set points. In a first variant, the prior error setting consists of minimizing the maximum prior error value. In the first variant, optimizing the predictive model consists of minimizing the maximum prior error value given a fixed number of set points, i.e., four set points in the exemplary implementation of FIGS. 2 and 3. The principles of the first variant apply to alternative embodiments and alternative prior error settings, for example, with or without a limitation on the number of set points.

[0114] Returning to Figure 2, the respective error value of the predictive model for each data point in the seed data is visualized by the absolute distance between the predictive model and each seed data point along the "y-axis," i.e., the absolute vertical distance between the dashed line and the solid curve. This error value may be referred to as the prediction error. The prediction error value is zero for each data point in the seed data included in the model data. The maximum prediction error value E * is the maximum absolute distance between the dashed and solid lines for any data point in the seed data. For the situation shown in Figure 2, the operating parameter value O 16 There exists a maximum prediction error value for

[0115] The seed data constitutes an initial data set for the operating characteristics as a function of one or more operating parameters for the first rotating system, and a maximum prediction error value E * corresponds to the maximum a priori error value for each model and each choice of model data.

[0116] Figure 3 is similar to Figure 2. The only difference is that points are taken from the same seed data in four different sets of model data, and therefore different piecewise linear interpolations are performed based on different model data. The model data shown in Figure 3 are four data points (O1, A(O1)), (O8, A(O8)), (O16 , A(O 16 )), (O 19 , A(O 19 )). As a result, evaluation of the forecast models using the seed data results in different sets of forecast error values ​​between the seed data and the two respective forecast models for the two situations. As can be visually observed from a comparison of Figures 2 and 3, the maximum forecast error value E* is significantly smaller for the situation in Figure 3, even though the same number of model data points or boundaries are utilized in the two situations. Although both piecewise linear approximations are formed by three linear segments, the piecewise linear approximations in Figure 3 have a smaller maximum forecast error value E* because the boundaries between the segments are different. * and therefore leads to a better approximation / optimization based on the optimization criterion of minimizing the maximum a priori error value given four set points. The number and selection of set points depend on the model data points, i.e., their respective first components, O x , and therefore in the first variant as shown in FIGS. 2 and 3, 16 , and O 19 are provided as the set points available.

[0117] In general, a function can be well approximated by a piecewise linear function composed of several linear segments. Using a larger number of segments generally results in a better approximation / optimization. Furthermore, given a fixed number of segments, varying the boundaries between the segments can lead to a better or worse approximation. Thus, given a fixed number of linear segments in a piecewise linear approximation function, the maximum prediction error depends on the location of the boundaries.

[0118] In general, for piecewise linear models, a vector where n is the number of bounds

number

number

number

[0119] If a model is to be optimized by minimizing the number of set points (i.e., minimizing the number of boundaries) given a desired maximum a priori error value, the problem to be solved is the function

number

[0120] According to an embodiment of the present invention, for example an embodiment utilizing the first variant, the method comprises the steps of: i is the set point, and the following steps can be included to consider A as a function of O: acquiring seed data including a first approximation A of an operating characteristic of the first rotating system as a function of one or more operating parameters; Selecting n data points from the seed data, which may be at least three, as an initial selection of mapping data. by minimizing the maximum prediction error based on the first forecast model by modifying the selection of n data points from the seed data to be used as model data; optimizing a first prediction model G that utilizes interval linear approximation; ·Prediction error E with the smallest possible number of n * and continuing to increase / decrease n and modify the selection of model data points from the seed data until σ is below the target maximum a priori error value. Selecting a number of n set points P based on a set of model data that satisfies the condition in the preceding paragraph. obtaining a plurality of n mapping data points based on a plurality of n set points P; generating interpolated data utilizing the plurality of n mapping data points, for example, by utilizing a first prediction model G as a piecewise linear function utilizing the plurality of n mapping data points;

[0121] Piecewise linear interpolation is based on a first-order interpolation function that uses points immediately adjacent to the setpoint value from which the mapping data point value must be derived. The method of the present invention can be extended by utilizing higher-order interpolation functions.

[0122] According to an embodiment of the step of obtaining multiple set points (referred to as the "second variant"), the predictive model includes an iterative piecewise linear method and the one or more operating parameters consist of a single operating parameter. The principles of the second variant apply to embodiments for mapping operating characteristics as a function of more than one operating parameter, for example, two or more operating parameters.

[0123] FIG. 4 is referred to for some illustration of the principles of the second variant, showing an approximation of the operating characteristic, i.e., A(O), as a function of the operating parameters. The solid curve represents A(O) for a first software model of the first rotating system. Seed data can be obtained that is composed of synthetic data derived using the first software model of the first rotating system. The data points of the seed data are (O x , A(O x The seed data is used to derive multiple set points through the optimization / evaluation of the predictive model.

[0124] As with some implementations of the first variant, the goal of implementations of the second variant is to achieve a maximum prediction error E * The goal is to find the boundary points between the linear segments such that is less than the desired value of the a priori maximum error.

[0125] In a second variant, the boundaries are obtained one by one.

[0126] Value O x If , the value A(O x ) can be efficiently obtained from the first software model.

[0127] The first boundary point (O1, A(O1)) is indicated by the round dot on the left in Figure 4. The goal is to achieve the maximum prediction error E * The goal is to find the distance x to the next boundary, shown by the round dot on the right in Figure 4, such that the predicted error E is equal to (and / or does not exceed) the desired maximum a priori error E. If a very small x is chosen, the predicted error E * is smaller than E, and if a very large x is chosen, the maximum prediction error E * It is clear that is larger than E. Given a value x, the first boundary point, and the approximation A(O), the maximum prediction error E * (x) and hence the function e(x)=E * It is possible to define (x)-E. E * Since the best value of x is obtained when (x)-E=0, x can be obtained by finding the zeros of the function e(x). Many methods are known in the art for finding the zeros of the function e(x), such as the Newton-Raphson's method or the secant method.

[0128] According to an embodiment of the invention, for example an embodiment making use of the second variant, the method may comprise the following steps, in which the boundaries form set points and consider A as a function of O: Error function e(O)=E * Steps to define -E ·e(O y ) = 0 at each set point O y obtaining a plurality of n set points using a zero-point finding method to obtain a set point such that the desired operating range is approximated by the set points; Each mapping data point corresponds to each set point. y obtaining a plurality of n mapping data points, - A step of generating interpolated data using mapping data points

[0129] According to an embodiment of the step of obtaining multiple set points (referred to as the "third variant"), the predictive model includes the use of Chebyshev polynomials.

[0130] A function on a bounded interval is a Chebyshev polynomial of the first kind T i If the function g(x) represents, for example, seed data in a software model of the first rotating system, then an approximation of the function g(x) using N Chebyshev polynomials can be given as:

number

number

[0131] According to an embodiment of the present invention, for example an embodiment utilizing the third variant, the method may comprise the following steps, where A represents an approximation of the operating characteristic and O represents one or more operating parameters, and A is considered as a function of O: Step to obtain seed data A(O) Ensuring that the seed data A(O) has a relatively large number of data points, such as more than 50, for example by obtaining the necessary data points from a software model of the first rotating system or by interpolating from existing data points of the seed data. ·A(O) is used to calculate the weight c k Steps to determine Optimization conditions, e.g., c k determining an index m that satisfies an optimization condition, such as a value of m being less than the a priori maximum error E, and using the index m to obtain a plurality of m set points P; acquiring a plurality of m data points based on a plurality of m set points; generating interpolated data using an updated prediction model, the updated prediction model using a plurality of m data points to generate m weights c k Determine the Chebyshev polynomials T0 to T m-1 and weights c0 to c m-1 generating interpolated data, the interpolated data being provided by utilizing a weighted sum up to

[0132] Compared to some other spectral methods, Chebyshev polynomials significantly reduce the Gibbs phenomenon at the edges of the domain, and therefore may be preferred for approximating functions over limited domains compared to other spectral methods.

[0133] For example, utilizing one or more previously acquired mapping data points to obtain one or more subsequent setpoints, including obtaining a set of candidate setpoints, may be performed in a variety of ways, and this variation, which may be implemented in embodiments of the present invention, is illustrated with reference to FIG.

[0134] Figure 5 shows the same schematic as Figure 4. Additionally, set point O x The mapping data points 51 obtained by A(O) are shown by solid squares 51. Based on the mapping data points 51, a software model of the rotating system is updated or fitted. Such updated A(O) for the first software model of the first rotating system is shown by the rough-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 by the second set point can be used for further updating or fitting the software model.

[0135] If the seed data includes data from previous measurements taken under different settings, e.g., for a different rotating system and / or for different reference conditions, such data may be fitted with the resulting mapping data point or points. According to embodiments utilizing Chebyshev polynomials in the predictive model, the function A(O) may be amplified, for example, so that the resulting A(O) approaches or intersects with the mapping data points.

[0136] 6 generally illustrates a method 70 for mapping in accordance with a first embodiment of the present invention. The method 70 is a computer-implemented method for mapping operating characteristics of a first rotating system as a function of one or more operating parameters. The method 70 includes obtaining seed data 72, obtaining a plurality of set points 74, obtaining a plurality of mapping data points 76, and generating interpolated data 78 using the plurality of mapping data points.

[0137] The arrows from one box to another indicate that the output or results of each step are used or required to perform another step. Thus, the seed data obtained by step 72 is used by step 74. The set points obtained by step 74 form the basis for obtaining the mapping data points of step 76. The mapping data points obtained by step 76 are used by step 78.

[0138] The seed data constitutes an initial data set for operating characteristics as a function of one or more operating parameters for the first rotating system.

[0139] The plurality of set points includes a first set point, a second set point, and a third set point, each set point having a component corresponding to each of the one or more operating parameters, and the plurality of set points are derived (74) using a predictive model, seed data, and a priori error settings.

[0140] The plurality of mapping data points includes a first mapping data point, a second mapping data point, and a third mapping data point, each mapping data point including an acquired data component obtained utilizing one or more measurements of the first rotating system in response to controlling one or more operating parameters of the first rotating system with a respective setpoint, such that the first mapping data point includes an acquired data component obtained with the first setpoint, the second mapping data point includes an acquired data component obtained with the second setpoint, and the third mapping data point includes an acquired data component obtained with the third setpoint.

[0141] Figure 7 shows a schematic diagram of a method 80 according to a second embodiment of the present invention. Method 80 is similar to method 70 of Figure 6. Method 80 differs from method 70 by including a step 84 of obtaining a plurality of set points and a step 86 of obtaining a plurality of mapping data points in place of step 74 of obtaining a plurality of set points and step 76 of obtaining a plurality of mapping data points, respectively.

[0142] Steps 84 and 86 differ from steps 74 and 76, one difference being the dependency, as indicated by the arrow between box 84 and step 86 in Figure 7. Step 84 of obtaining multiple set points includes utilizing a first mapped data point (obtained as part of step 86) to obtain a second set point, and utilizing the first and second mapped data points (obtained as part of step 86) to obtain a third set point.

[0143] FIG. 8 schematically illustrates an embodiment of a management system 90 according to the present invention. The management system 90 includes a mapping system 92, an automation system 94, and a test system 96. The mapping system 92 provides a plurality of set points to the automation system 94. The automation system 94 controls the test system 96 based on the plurality of set points. The test system 96 acquires one or more measurements from a first rotation system 98 based on its control by each of the plurality of set points. The first rotation system 98 is connected to a test system that enables control and measurement of the first rotation system 98 using the test system 96. Once the test system 96 acquires measurement data from the first rotation system 98, the measurement data can be transmitted back to the mapping system 92. The mapping system 92 uses the input measurement data to generate respective mapping data points. Thus, the mapping system 92 obtains respective mapping data points based on the measurement data acquired by the test system 96.

[0144] While particular embodiments have been shown and described, it will be understood that these embodiments are not intended to limit the claimed invention. Accordingly, the specification and drawings should be regarded in an illustrative rather than a restrictive sense. The claimed invention is intended to encompass alternatives, modifications, and equivalents. It should be emphasized that, as used in this disclosure, the term "comprises" specifies the presence of a stated feature, integer, step, component, etc., but does not necessarily exclude the presence or addition of one or more other features, integers, steps, components, or groups thereof. For any claim enumerating several features, it is contemplated that several of these features can be embodied by the same hardware and / or software. The mere fact that certain measures are recited in mutually different dependent claims or 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 can be made to the structure of the invention without departing from the scope of the invention. In view of the foregoing, it is intended that the present invention cover the modifications and variations of this invention insofar as they come within the scope of the following claims and their equivalents. The scope of the present invention is to be defined by the following claims, and any reference signs in the claims are for clarity only and not to be construed as limiting the scope of the invention. Any embodiment or implementation not included in the claims is intended to aid in the understanding of the invention and does not form part of the claimed invention.

[0145] Below is a list of reference numbers used in the accompanying drawings: [Explanation of symbols]

[0146] 70 Method Embodiments for Mapping 72 Obtaining seed data 74 Getting multiple setting values 76 Acquiring Multiple Mapping Data Points 78 Generating Interpolated Data 80 Method Embodiments for Mapping 84 Get multiple setting values 86 Acquiring Multiple Mapping Data Points 90 Management System Implementation Examples 92 Mapping System 94 Automation Systems 96 Test System 98 Device under test (e.g., first rotation system)

Claims

1. 1. A computer-implemented method for mapping operating characteristics of a first rotating system as a function of one or more operating parameters, comprising: Obtaining seed data; Obtaining multiple set points; obtaining a plurality of mapping data points; generating interpolated data using the plurality of mapping data points; A method comprising: the seed data constituting an initial data set for the operating characteristics as a function of the one or more operating parameters for the first rotating system; the plurality of set points include a first set point, a second set point, and a third set point, each set point including a component corresponding to each of the one or more operating parameters, and the plurality of set points are obtained utilizing a predictive model, the seed data, and a priori error setting; the plurality of mapping data points include first mapping data points, second mapping data points, and third mapping data points, each mapping data point including acquired data components obtained utilizing one or more measurements of the first rotating system in response to controlling the one or more operating parameters of the first rotating system with a respective set point, such that the first mapping data points include acquired data components obtained with the first set point, the second mapping data points include acquired data components obtained with the second set point, and the third mapping data points include acquired data components obtained with the third set point; method.

2. obtaining the plurality of set points utilizing the first mapping data points to obtain the second set point; obtaining the third set point utilizing the first mapping data point and the second mapping data point; and The method of claim 1 , comprising:

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

4. 4. The method of claim 1, wherein the one or more measurements of the first rotational system utilized for acquiring each respective acquired data component are performed at a first reference condition, and the seed data includes first reference data acquired using one or more measurements of the first rotational system at a second reference condition different from the first reference condition.

5. 5. The method of claim 1, wherein the seed data comprises second reference data obtained using one or more measurements of a second rotational system, the second rotational system being different from the first rotational system.

6. The method of any one of claims 1 to 5, comprising deriving the predictive model from a plurality of candidate predictive models.

7. The method of any one of claims 1 to 6, wherein obtaining the plurality of set points comprises generating one or more weights for the predictive model.

8. 8. The method of claim 1, wherein obtaining the plurality of set points comprises optimizing the predictive model by minimizing a number of set points in the plurality of set points under constraints defined by the a priori error settings, the constraints including a maximum a priori error value.

9. The method of any one of claims 1 to 8, wherein generating the interpolated data comprises utilizing the predictive model in combination with the plurality of mapping data points.

10. The operating characteristic is efficiency, Each set point includes a torque component and a rotational speed component, the first rotation system comprises an actuator, such as an electric motor or an internal combustion engine; The method according to any one of claims 1 to 9.

11. the operating characteristic is direct-axis inductance, cross-axis inductance, or torque; Each set point includes a direct current component and an alternating current component; the first rotational system includes an electric motor, such as a permanent magnet synchronous motor; The method according to any one of claims 1 to 9.

12. A method of management comprising the method according to any one of claims 1 to 11, controlling the one or more operating parameters of the first rotating system according to each respective set point; acquiring each acquired data component, including performing the one or more measurements of the first rotational system utilized to acquire each respective acquired data component; A method comprising:

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

14. A computer program comprising instructions that, when said computer program is executed by a computer, cause said computer to carry out the method of any one of claims 1 to 11.

15. A computer readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 11.

16. A data carrier signal carrying a computer program according to claim 14.

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