Method for calculating design margin of power device and system for calculating design margin of power device

Abstract

Disclosed are a method for calculating a design margin of a power device and a system for calculating a design margin of a power device. The method for calculating a design margin of a power device according to the present invention comprises the steps of: preparing a database storing design values and test values of the power device; performing statistical analysis on the basis of the stored design values and test values to calculate statistical moments considering uncertainties including design errors, manufacturing deviations, and test conditions; and calculating a design margin that minimizes a difference between a customer specification and a design value and minimizes a sum of failure cost and product production cost, on the basis of the calculated statistical moments.

Description

Method for Calculating Design Margins for Power Equipment and System for Calculating Design Margins for Power Equipment

[0001] The present invention relates to a method for calculating design margins for power equipment and a system for calculating design margins for power equipment. More specifically, it relates to a method for calculating design margins for power equipment and a system for calculating design margins for power equipment that can reduce the number of MH (Man-Hour) required for design and lower the possibility of over-design or under-design during the design process by standardizing and computerizing the calculation of design margins based on design and test data.

[0002] In general, ensuring reliability while meeting various performance requirements is a critical challenge in power equipment design. In particular, power equipment design is highly susceptible to discrepancies between design values ​​and actual test values ​​due to various uncertainty factors, such as manufacturing tolerances, material inhomogeneities, and changes in the operating environment. Since such differences can degrade performance or cause failures, it is essential to establish appropriate safety margins during the design phase.

[0003] However, conventional design methods have often relied on fixed standards or the designer's experience when setting design margins. Yet, this approach can lead to unnecessary cost increases by applying excessive margins, or conversely, performance degradation or reliability issues due to insufficient margins. Particularly in power equipment such as electric motors, failure to satisfy constraints—such as starting torque, starting current, efficiency, and power factor—is highly likely to result in reduced performance or unexpected failures.

[0004] Conventional design methods have limitations in that they fail to simultaneously consider these performance conditions and reliability, and rely excessively on the designer's experience. Consequently, systematic analysis to address uncertainty is not performed, and the difference between design values ​​and test values ​​is not effectively reflected. This problem leads to over-design or under-design in the design of power equipment, resulting in an inability to strike a balance between performance and cost.

[0005] Therefore, to address the problems arising from existing design methods, the present invention proposes a method for quantitatively analyzing uncertainty based on the statistical moments of design and test values, and evaluating performance distribution and reliability based on this analysis. In this process, by calculating the optimal design margin that satisfies constraints, it will be possible to maintain stable performance while reducing unnecessary costs.

[0006] Embodiments of the present invention aim to quantitatively analyze design errors and variability by calculating statistical moments based on design values ​​and test values, thereby achieving a more reliable design.

[0007] In addition, this study aims to prevent performance degradation of manufactured products by presenting a method to appropriately calculate design margins while satisfying constraints.

[0008] In addition, we intend to propose a method to optimize performance while reducing unnecessary overdesign by evaluating performance distribution and reliability using statistical moments, calculating failure costs, and determining design margins based on this.

[0009] In addition, we aim to reduce unnecessary variables in the statistical moment calculation process, thereby lowering computational complexity during the design process while maintaining reliability.

[0010] According to one aspect of the present invention, a method for calculating a design margin of power equipment may be provided, comprising: a step of preparing a database storing design values ​​and test values ​​of the power equipment; a step of performing a statistical analysis based on the stored design values ​​and test values ​​to calculate a statistical moment considering uncertainties including design error, manufacturing deviation, and test conditions; and a step of calculating a design margin based on the calculated statistical moment to minimize the difference between customer specifications and design values ​​and to minimize the sum of failure costs and product production costs.

[0011] The step of calculating the above statistical moment can quantitatively analyze the variability of the performance distribution due to manufacturing deviation by calculating the average value, which is the first moment of the design value and the test value, and the variance, which is the second moment.

[0012] The step of calculating the above statistical moments can analyze the asymmetry and peaking of the performance distribution, including the skewness, which is the third moment, and the kurtosis, which is the fourth moment, by considering a random variable that follows a non-normal distribution.

[0013] The step of calculating the above statistical moment can perform a non-normal distribution-based analysis regarding the design value and the test value by applying a probabilistic design including a non-normal distribution.

[0014] The step of calculating the above statistical moment can evaluate the reliability of the design value and the test value by applying Reliability Based Design Optimization (RBDO).

[0015] The step of calculating the above statistical moments can improve the efficiency of statistical moment calculation by applying a univariate dimensionality reduction method to reduce the dimensionality of the design variables.

[0016] The step of calculating the above design margin can calculate a design margin corresponding to a constraint including at least one of efficiency, power factor, starting torque, and starting current.

[0017] According to another aspect of the present invention, a system for calculating a design margin of a power device may be provided, comprising: a database unit storing design values ​​and test values ​​of a power device; a statistical moment calculation unit that calculates a statistical moment considering uncertainties including design error, manufacturing deviation, and test conditions by performing statistical analysis based on the stored design values ​​and test values; and a design margin calculation unit that calculates a design margin based on the calculated statistical moment to minimize the difference between customer specifications and design values ​​and to minimize the sum of failure costs and product production costs.

[0018] The above statistical moment calculation unit can quantitatively analyze the variability of the performance distribution due to manufacturing deviation by calculating the average value, which is the first moment of the design value and the test value, and the variance, which is the second moment.

[0019] The above statistical moment calculation unit can analyze the asymmetry and peaking of the performance distribution, including skewness (the third moment) and kurtosis (the fourth moment), by considering a random variable that follows a non-normal distribution.

[0020] The above statistical moment calculation unit can apply a probabilistic design including a non-normal distribution to perform a non-normal distribution-based analysis regarding the design value and the test value.

[0021] The above statistical moment calculation unit can evaluate the reliability of the design value and the test value by applying Reliability Based Design Optimization (RBDO).

[0022] The above statistical moment calculation unit can increase the efficiency of statistical moment calculation by applying a univariate dimensionality reduction method to reduce the dimensionality of design variables.

[0023] The above design margin calculation unit can calculate a design margin corresponding to a constraint including at least one of efficiency, power factor, starting torque, and starting current.

[0024] Embodiments of the present invention calculate statistical moments based on design values ​​and test values ​​to quantitatively analyze design errors and variability, thereby enabling the achievement of a more reliable design.

[0025] In addition, by presenting a method to appropriately calculate design margins while satisfying constraints, it is possible to prevent performance degradation of manufactured products.

[0026] In addition, by evaluating performance distribution and reliability using statistical moments, calculating failure costs, and determining design margins based on this, a method can be presented to optimize performance while reducing unnecessary overdesign.

[0027] In addition, unnecessary variables are reduced during the statistical moment calculation process, thereby lowering computational complexity in the design process while maintaining reliability.

[0028] FIG. 1 is a flowchart illustrating the process of calculating a design margin of a power device according to an embodiment of the present invention.

[0029] FIG. 2 is a block diagram illustrating a system for calculating a design margin of a power device according to an embodiment of the present invention.

[0030] Figure 3 is a conceptual diagram illustrating an example of implementing statistical moments based on existing test values ​​and design values.

[0031] Figure 4 is a conceptual diagram showing the design point where the sum of product production costs and failure costs is minimized.

[0032] Figure 5 is a conceptual diagram showing the probability of failure based on the difference between the design and test values.

[0033] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings. However, the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided to ensure that the disclosed content is thorough and complete, and to ensure that the spirit of the present invention is sufficiently conveyed to those skilled in the art. Throughout the specification, the same reference numerals indicate the same components.

[0034] FIG. 1 is a flowchart illustrating the process of calculating a design margin of a power device according to an embodiment of the present invention, FIG. 2 is a block diagram illustrating a system for calculating a design margin of a power device according to an embodiment of the present invention, FIG. 3 is a conceptual diagram illustrating an example of implementing a statistical moment based on existing test values ​​and design values. FIG. 4 is a conceptual diagram illustrating a design point where the sum of product production costs and failure costs is minimized, and FIG. 5 is a conceptual diagram illustrating the probability of failure based on the difference between the design and test values.

[0035] Referring to FIGS. 1 to 5, a method for calculating a design margin for a power device according to one embodiment of the present invention may largely comprise: a step (S110) of preparing a database storing design values ​​and test values ​​of the power device; a step (120) of performing a statistical analysis based on the stored design values ​​and test values ​​to calculate a statistical moment that considers uncertainties including design error, manufacturing deviation, and test conditions; and a step (130) of calculating a design margin based on the calculated statistical moment that minimizes the difference between customer specifications and design values ​​and minimizes the sum of failure costs and product production costs.

[0036] And the power equipment design margin calculation system (100) in which the method for calculating the design margin of such power equipment is implemented may largely comprise: a database unit (110) that stores design values ​​and test values ​​of the power equipment; a statistical moment calculation unit (120) that calculates a statistical moment considering uncertainty including design error, manufacturing deviation, and test conditions by performing statistical analysis based on the stored design values ​​and test values; and a design margin calculation unit (130) that calculates a design margin that minimizes the difference between customer specifications and design values ​​and minimizes the sum of failure costs and product production costs based on the calculated statistical moment.

[0037] In this embodiment, the calculation of design margins is explained focusing on rotating machines, particularly induction motors, among power equipment. However, the present invention is not limited to the fields of induction motors or rotating machines and can, of course, be applied to various power equipment.

[0038] First, once the customer specifications are determined based on customer requirements, the design of the corresponding power equipment is initiated. At this stage, a database storing the design and test values ​​of the power equipment is utilized.

[0039] The database for design values ​​and test values ​​is a collection of design values ​​and test values ​​accumulated over a long period of time, forming big data. In the database section (110), a database regarding the corresponding power equipment is prepared as an accumulation of these design values ​​and test values ​​(S110). The preparation of the database is carried out continuously over a long period of time and is a concept that includes updates made in the future as well as in the past.

[0040] The database unit (110) of the present invention performs the role of systematically storing and managing design values ​​and test values ​​of power equipment. A database is essential to consistently manage performance values ​​of power equipment that change according to various conditions during the design process. The database unit (110) efficiently stores design values, test values, and statistical variability of said values, and this data is used for subsequent analysis.

[0041] For example, when designing power equipment, the designer inputs expected performance values ​​(design values). Design values ​​include predicted values ​​for key performance indicators such as starting torque, starting current, efficiency, and power factor. These design values ​​are target values ​​that must be satisfied for the power equipment to operate normally, and they are subsequently compared with actual performance values ​​(test values) measured through testing.

[0042] The test value is data obtained after actually manufacturing and testing the power equipment, and is a value that reflects design errors, manufacturing tolerances, environmental variability, etc. The database unit (110) stores the design value and the test value in conjunction and systematically records the difference between these values. Through this, the correlation between the design value and the test value can be easily analyzed.

[0043] The above database unit (110) also additionally stores various factors such as design errors, deviations that occurred during testing, and external environmental conditions, thereby providing data necessary for statistical moment analysis to be described later. The stored data serves as a basis for calculating statistical moments and plays a key role in the important analysis step of the present invention. Through this, a basis is established to evaluate the performance of power equipment and calculate the optimal design margin.

[0044] The above database unit (110) can collect and store data of various formats and can classify and manage data according to the design specifications of each power device. Through this, a designer or analyst can quickly query and compare the performance of a specific power device and quantitatively evaluate the variability between the design value and the test value.

[0045] In the next step, statistical analysis is performed based on the design values ​​and test values ​​stored in the database unit (110) to calculate a statistical moment that takes into account the uncertainty including design error, manufacturing deviation, and test conditions (S120).

[0046] These statistical moments are calculated through a statistical moment calculation unit (120). As shown in FIG. 3, the statistical moment calculation unit (120) performs the role of quantitatively analyzing the performance variability of power equipment based on design values ​​and test values ​​stored in the database unit (110). Statistical moments are used to evaluate errors and variability that may occur during the design process, thereby allowing for the systematic analysis of the difference between design values ​​and test values.

[0047] Specifically, the statistical moment calculation unit (120) calculates the 'average value (first moment)' of the design value and the test value. The average value plays an important role in identifying the overall trend of the design value and the test value. Although the design value of the power device aims for ideal performance, the test value may fluctuate due to minute differences occurring during the manufacturing process or external environmental factors. By calculating the average value, the difference between the design and the test can be objectively identified, and this serves as important basic data for optimizing the performance of the device.

[0048] In addition, 'variance (moment of inertia)' is calculated to evaluate the variability between design and test values. Variance is an indicator representing how spread out each data point is from the mean, allowing one to assess the consistency of test values ​​compared to design values. A higher variance indicates greater performance variability, meaning there is a higher likelihood that the device will experience performance degradation or failure in unexpected situations. Therefore, calculating variance enables the design to account for variability by incorporating a sufficient margin.

[0049] 'Skewness (3rd moment)' and 'kurtosis (4th moment)' are also important factors treated in the statistical moment calculation unit (120). Skewness indicates the asymmetry of the data distribution, and kurtosis indicates the peaking of the distribution. For example, if the test value is frequently skewed toward being lower than the design value, the skewness appears as a negative value. This means that performance lower than the design value occurs frequently, and in such cases, it is necessary to increase the design margin. Kurtosis indicates whether the data is clustered near the mean or spread out widely. If the kurtosis is high, most of the data has values ​​close to the mean, which indicates that the performance is consistent.

[0050] The statistical moment calculation unit (120) comprehensively calculates these moments to systematically analyze design errors and uncertainties. In particular, the statistical moment calculation unit (120) can also analyze data that follows a non-normal distribution, thereby enabling the derivation of results that reflect various uncertainties that may occur in the design. In this process, non-normal distribution-based analysis plays an important role, and moment calculation is performed considering random variables other than the Gaussian distribution.

[0051] In addition, the present invention quantitatively evaluates the reliability of design values ​​and test values ​​by applying a Reliability Based Design Optimization (RBDO) method. RBDO is used to minimize the error between design values ​​and test values ​​and to optimize the margin so that performance can be maintained above a certain level. The data analyzed based on reliability in the statistical moment calculation unit (120) is subsequently used as important data for calculating the design margin.

[0052] In addition, univariate dimensionality reduction enables efficient calculations in complex design problems. In power equipment design, various variables interact simultaneously, and applying univariate dimensionality reduction simplifies multivariate problems. This allows for the reduction of computational complexity while maintaining the accuracy of statistical moment calculations, thereby maximizing efficiency in the design process.

[0053] In this way, the statistical moment calculation unit (120) plays an essential role in precisely analyzing the variability between the design error and the test value of the power device and, through this, calculating a highly reliable design margin. By calculating various statistical moments and reliability-based analysis, the performance of the power device can be optimized, and a reliable device can be provided by minimizing design errors.

[0054] Next, based on the statistical moment calculated above, a design margin is calculated to minimize the difference between the customer specifications and the design value and to minimize the sum of failure costs and product production costs (S130).

[0055] The design margin calculation unit (130) of the present invention is responsible for the function of analyzing the difference between the design value and the test value to optimize the performance and ensure the reliability of the power device, and calculating the optimal design margin based on this.

[0056] Design margins are determined based on reliability evaluations that consider the performance variability of the device, and the goal is to prevent cost increases caused by excessive margins and performance degradation caused by insufficient margins.

[0057] The above-described design margin calculation unit (130) utilizes data provided by the above-described statistical moment calculation unit (120). The statistical moment provides various analysis results including the mean, variance, skewness, and kurtosis of the design value and the test value, and through this data, the design error and the variability of the test value can be accurately evaluated. In particular, even in cases following a non-normal distribution, it is possible to calculate a design margin that reflects variability through the moment.

[0058] An important feature of the present invention is the calculation of a design margin based on failure costs. While conventional technology primarily calculates margins based on failure probability, the present invention comprehensively considers costs that may arise from design failure in addition to failure probability, as shown in Figure 4.

[0059] The production cost of a product generally considered is as shown in the left graph of Fig. 4. Designing with a high safety factor and allowing a large margin of error from the product's target specifications increases the unit cost of production, so efforts are made to design with a small margin of error. However, as shown in the middle graph of Fig. 4, setting a low safety factor for the product results in failure costs, such as failures due to insufficient customer specifications. Therefore, the actual cost of the product is represented as shown in the right graph of Fig. 4, which takes into account production costs and failure costs.

[0060] Therefore, as described above, by identifying the design point where the sum of failure costs and product production costs is minimized through a standardized algorithm, total production cost reduction and design effort reduction are achieved.

[0061] For example, failure costs include rework costs, maintenance costs, and losses due to equipment damage that may occur if design values ​​fail to meet customer specifications or test values ​​deviate from standards. By calculating an optimal design margin that takes these costs into account, both cost efficiency and performance can be secured simultaneously.

[0062] The above design margin calculation unit (130) also calculates the design margin by considering various constraints. In power equipment, particularly rotating machines, important performance indicators such as efficiency, power factor, starting torque, and starting current are important factors that determine the success of the design. The present invention calculates a design margin that can satisfy all of these constraints.

[0063] For example, if the starting torque is insufficient, the starting performance of the motor may decrease, and if the efficiency is low, it leads to unnecessary power consumption. Therefore, the design margin calculation unit (130) adjusts the design value so that performance can be stably maintained according to each constraint, and calculates the margin required to satisfy these constraints.

[0064] Furthermore, reliability based on design variability can be evaluated during the process of calculating design margins. After analyzing the performance distribution using statistical moments, a reliability index is calculated, and design margins are determined based on this. The reliability index reflects the probability of a difference occurring between design values ​​and test values, thereby allowing for an assessment of the stability of the design. For example, if the difference between design and test values ​​is large, the reliability index decreases; in such cases, it is necessary to increase the margin to enhance reliability.

[0065] The present invention also employs a method that simultaneously optimizes manufacturing costs and failure costs when calculating design margins. Based on statistical moments and reliability analysis results, it is possible to minimize design errors and reduce costs incurred due to failure while efficiently managing manufacturing costs. This prevents unnecessary increases in manufacturing costs caused by setting excessive margins, while conversely reducing the risk of performance degradation or failures resulting from insufficient margins.

[0066] Furthermore, the design margin calculation unit (130) simplifies multi-variable problems by using a single-variable dimension reduction method in the calculation of statistical moments as described above. The performance of power equipment is determined by various variables, and considering all of them can significantly increase calculation costs. By applying a single-variable dimension reduction method, complex design variables can be processed efficiently, allowing for accurate calculation of the design margin while reducing computational complexity. This increases efficiency in the design process and enables the calculation of an optimal design margin.

[0067] Meanwhile, Figure 5 is a graph representing the concept of the probability of performance falling short of customer specifications due to the difference between design and test values. This graph provides an overview of various factors that serve as important criteria when calculating design margins and is used to analyze performance variability between design and test values, as well as the probability of failure.

[0068] The curve located in the center of the graph in Fig. 5 represents the average of the performance values ​​obtained during the design and manufacturing process. Although the designer sets the performance target of the device based on this average value, the actual performance of the manufactured device may fluctuate around this average value due to non-uniformity occurring during the manufacturing process. Such manufacturing deviations affect the performance of the power device, and designers must analyze this variability through statistical moments to set an appropriate design margin.

[0069] Customer specification values ​​are represented by the vertical line shown on the right side of the graph in Fig. 5, which signifies the performance standards required by the customer. Customer specification values ​​are target values ​​that the designer must satisfy; if the device fails to meet these standards, performance degradation or failure may occur. Therefore, during design and manufacturing, the customer's required specifications must be fully considered, and a design margin must be set so that the device's performance does not exceed these standards.

[0070] The shaded area at the right end of the graph in Fig. 5 represents the probability of failure. This visually illustrates the likelihood that the device will fail to meet customer requirements due to deviations occurring during the design and manufacturing processes. This area implies that the probability of device failure is high if the design margin is insufficient, and designers must ensure sufficient margins to minimize this. This analysis is an important process for reducing failure costs and ensuring performance stability.

[0071] According to the method for calculating design margins of power equipment and the system for calculating design margins of power equipment according to the embodiments of the present invention described so far, statistical moments are calculated based on design values ​​and test values ​​to quantitatively analyze design errors and variability, thereby enabling a more reliable design.

[0072] In addition, by presenting a method to appropriately calculate design margins while satisfying constraints, it is possible to prevent performance degradation of manufactured products. Furthermore, by evaluating performance distribution and reliability using statistical moments, calculating failure costs, and determining design margins based on this, it is possible to present a method to optimize performance while reducing unnecessary over-design.

[0073] In addition, unnecessary variables are reduced during the statistical moment calculation process, thereby lowering computational complexity in the design process while maintaining reliability.

[0074] Although the present invention has been described above with reference to an embodiment thereof, those skilled in the art may modify and change the present invention in various ways without departing from the spirit and scope of the invention as described in the claims below. Therefore, if a modified embodiment basically includes the components of the claims of the present invention, it should be considered to be included within the technical scope of the present invention.

[0075] [Explanation of the symbol]

[0076] 100: Power Equipment Design Margin Calculation System

[0077] 110: Database Department 120: Statistical Moment Calculation Department

[0078] 130: Design Margin Calculation Unit

Claims

1. In the method for calculating the design margin of power equipment, A step of preparing a database storing design values ​​and test values ​​of the above-mentioned power device; A step of performing a statistical analysis based on the stored design values ​​and test values ​​to calculate a statistical moment that considers uncertainties including design error, manufacturing deviation, and test conditions; and, A method for calculating a design margin for power equipment, comprising the step of calculating a design margin based on the above-calculated statistical moment to minimize the difference between customer specifications and design values ​​and to minimize the sum of failure costs and product production costs.

2. In Paragraph 1, The step of calculating the above statistical moment is, A method for calculating a design margin of power equipment characterized by calculating the average value, which is the first moment of the design value and the variance, which is the second moment of the test value, and quantitatively analyzing the variability of the performance distribution due to manufacturing deviation.

3. In Paragraph 1, The step of calculating the above statistical moment is, A method for calculating design margins for power equipment, characterized by analyzing the asymmetry and peaking of a performance distribution, including skewness as the third moment and kurtosis as the fourth moment, by considering a random variable that follows a non-normal distribution.

4. In Paragraph 1, The step of calculating the above statistical moment is, A method for calculating the design margin of a power device, characterized by applying a probabilistic design including a non-normal distribution to perform a non-normal distribution-based analysis regarding the design value and the test value.

5. In Paragraph 1, The step of calculating the above statistical moment is, A method for calculating a design margin of power equipment characterized by evaluating the reliability of the design value and the test value by applying Reliability Based Design Optimization (RBDO).

6. In Paragraph 1, The step of calculating the above statistical moment is, A method for calculating design margins for power equipment characterized by increasing the efficiency of statistical moment calculations by reducing the dimensionality of design variables through the application of a univariate dimensionality reduction method.

7. In Paragraph 1, The step of calculating the above design margin is, A method for calculating a design margin for a power device, characterized by calculating a design margin corresponding to a constraint including at least one of efficiency, power factor, starting torque, and starting current.

8. A database section storing design and test values ​​of power equipment; A statistical moment calculation unit that calculates a statistical moment considering uncertainties including design error, manufacturing deviation, and test conditions by performing statistical analysis based on the stored design values ​​and test values; and, A design margin calculation system for power equipment comprising: a design margin calculation unit that calculates a design margin based on the statistical moment calculated above, which minimizes the difference between customer specifications and design values ​​and minimizes the sum of failure costs and product production costs.

9. In Paragraph 8, A system for calculating design margins of power equipment, characterized in that the above statistical moment calculation unit calculates the average value, which is the first moment of the design value and the variance, which is the second moment, of the above design value and test value, and quantitatively analyzes the variability of the performance distribution according to manufacturing deviation.

10. In Paragraph 8, A system for calculating design margins for power equipment, characterized in that the above-mentioned statistical moment calculation unit analyzes the asymmetry and peaking of a performance distribution, including skewness (a third moment) and kurtosis (a fourth moment), by considering a random variable that follows a non-normal distribution.

11. In Paragraph 8, A system for calculating design margins of power equipment, characterized in that the above-mentioned statistical moment calculation unit applies a probabilistic design including a non-normal distribution to perform a non-normal distribution-based analysis regarding the design value and the test value.

12. In Paragraph 8, The above-mentioned statistical moment calculation unit is characterized by applying Reliability Based Design Optimization (RBDO) to evaluate the reliability of the design value and the test value, thereby providing a system for calculating the design margin of a power device.

13. In Paragraph 8, The above-mentioned statistical moment calculation unit is characterized by increasing the efficiency of statistical moment calculation by reducing the dimensionality of design variables through the application of a univariate dimensionality reduction method.

14. In Paragraph 8, A system for calculating design margins of power equipment, characterized in that the above-mentioned design margin calculation unit calculates a design margin corresponding to a constraint including at least one of efficiency, power factor, starting torque, and starting current.

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