Inertial platform health assessment method and electronic device

Abstract

The application relates to the field of inertial platforms, and particularly discloses an inertial platform health assessment method, which comprises the following steps: scoring a plurality of parameters of an inertial platform, the plurality of inertial platform parameters comprising a first inertial platform parameter group, a second parameter group and a third parameter group, the first parameter group, the second inertial parameter group and the third parameter group corresponding to a first parameter category, a second parameter category and a third parameter category respectively; determining a first score corresponding to the first parameter category according to the score of the first parameter group; determining a second score corresponding to the second parameter category according to the score of the second parameter group; determining a third score corresponding to the third parameter category according to the score of the third parameter group; and determining a comprehensive score of the inertial platform according to the first score, the second score and the third score. The application can provide a scheme for inertial platform health assessment and guide inertial platform health maintenance.

Description

Technical Field

[0001] This application relates to the technical field of inertial platforms, and in particular to an inertial platform health assessment method and electronic device. Background Technology

[0002] As a key component of equipment, the health status of inertial platforms greatly affects the overall performance of the equipment. In order to meet the future needs of improving the efficiency of equipment use and management, it is urgent to carry out engineering research on inertial platform health assessment to guide the testing, storage, maintenance and use of inertial platforms and equipment systems, and improve the efficiency of equipment throughout its entire life cycle.

[0003] Inertial platforms are highly precise and complex electromechanical devices with numerous and varied monitoring variables, which have complex effects on the overall reliability and accuracy of the inertial platform. Currently, research on health status assessment in China is in its early stages, with few related research results. For engineering applications, the credibility of the assessment and the clarity of its physical meaning are even more important. Summary of the Invention

[0004] This application provides an inertial platform health assessment method and electronic device, with the aim of providing a solution for inertial platform health assessment and guiding the health maintenance of inertial platforms, such as timely repair or replacement of inertial platforms, and monitoring relevant parameters and performance of inertial platforms to maintain optimal health status.

[0005] Firstly, a method for assessing the health of an inertial platform is provided, including:

[0006] The inertial platform is scored for multiple parameters, which include a first parameter group, a second parameter group, and a third parameter group, respectively corresponding to a first parameter category, a second parameter category, and a third parameter category.

[0007] Based on the score of each parameter in the first parameter group, determine the first score corresponding to the first parameter category;

[0008] Based on the score of each parameter in the second parameter group, determine the second score corresponding to the second parameter category;

[0009] Based on the score of each parameter in the third parameter group, determine the third score corresponding to the third parameter category;

[0010] The comprehensive score of the inertial platform system is determined based on the first score, the second score, and the third score.

[0011] Compared with the prior art, the solution provided in this application has at least the following beneficial technical effects:

[0012] This provides a solution for health assessment of inertial platform systems. After scoring, a comprehensive score is obtained for the inertial platform system, which can quantitatively compare the performance of different platform systems and guide the health maintenance of inertial platform systems. For example, timely repair or replacement of inertial platform systems can be carried out, and relevant parameters and performance of inertial platforms can be monitored to maintain optimal health status. This can also assist in product selection and task decision-making under different operational tasks.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes:

[0014] If the overall score is lower than a preset score level, the system will display the hazard category indicated by the overall score, which will be used to indicate remedial measures for the inertial platform.

[0015] If product performance deteriorates rapidly or reaches a critical point of degradation, backup products should be organized promptly. This allows for timely detection of product problems and replacement of backup products when malfunctions occur. Simultaneously, a repair process should be initiated for the problematic product to prevent further losses from continued operation under abnormal conditions. Each parameter of the inertial platform system is scored individually, which quantifies the performance of the inertial platform system parameters. This helps to quickly locate product failure points, improves repair efficiency, and also provides support for product selection, allowing for the selection of products with different performance levels based on different application requirements.

[0016] In conjunction with the first aspect, in certain implementations of the first aspect, the individual scoring of each parameter of the inertial platform system includes:

[0017] The target single parameter is scored according to the first scoring formula, which satisfies the following:

[0018]

[0019] Wherein, U is the nominal value, D is the single-sided interval of the index; A1 is the full score, A2 is the maximum fault score, B1 is the adjustment coefficient used to make the score when X=U±D the maximum fault score, and B2 is the adjustment coefficient used to adjust the curvature of the first scoring formula.

[0020] Scoring inertial platform system parameters separately enables the formalization and standardization of indicators, which is the foundation for health assessment of inertial platform systems.

[0021] In conjunction with the first aspect, in some implementations of the first aspect, the first parameter group includes motor current and voltage, and the first parameter category is an analog parameter category.

[0022] In conjunction with the first aspect, in some implementations of the first aspect, the second parameter group includes one or more inertial instrument error coefficients, and the second parameter category is an accuracy parameter category.

[0023] In conjunction with the first aspect, in some implementations of the first aspect, the third parameter group includes the star sensor grayscale mean and variance, and the third parameter category is the star sensor parameter category.

[0024] In conjunction with the first aspect, in some implementations of the first aspect, determining the i-th score corresponding to the i-th parameter category based on the score of the i-th (i = 1, 2, 3) parameter group includes:

[0025] The i-th score is determined based on the second scoring formula and the score of the i-th parameter group. The second scoring formula satisfies:

[0026]

[0027] q i Let q be the excess weight of the i-th variable. k Let c be the excess weight of the k-th variable. i Let c be the number of times the error exceeds the tolerance threshold. k Let w be the kth time of exceeding the tolerance. i Let x be the constant weight of the i-th variable. i For the evaluation score of the i-th variable, if x i If it is less than 1, then let x i =1, where a is the variable weighting coefficient, a = 0.1 to 1.

[0028] Constant-weighted processing cannot effectively highlight abnormal changes. Therefore, a variable-weight algorithm is introduced to increase the weight of parameters with low scores, while also introducing a weight for parameters exceeding the indicator range (hereinafter referred to as "out-of-range"). The weight increases as the number of out-of-range occurrences for a parameter increases. The smaller the variable-weight coefficient 'a', the greater its impact on the evaluation results, and the easier it is to differentiate scores. The out-of-range coefficients Cs and q effectively increase the weight of out-of-range parameters, significantly increasing the impact of out-of-range on the evaluation results. Larger Cs and q values ​​indicate a higher sensitivity of the overall score to out-of-range, resulting in lower scores. Variable-weighted overall scoring can more accurately describe the platform's health status compared to constant-weighted scoring.

[0029] In conjunction with the first aspect, in some implementations of the first aspect, determining the first score, the second score, and the third score includes: when the score output by the corresponding second scoring formula is located in the target scoring domain, determining the scoring level corresponding to the first score, the second score, and the third score in the target scoring domain.

[0030] By introducing fuzzy rules, the comprehensive score can be obtained by intuitively and dynamically adjusting the weight of parameters, thus realizing fuzzy inference and defuzzification.

[0031] In conjunction with the first aspect, in some implementations of the first aspect, the score is "excellent" when the score output by the second scoring formula is in the range [90, 100].

[0032] When the score output by the second scoring formula is in the range [70, 90), the score is "good";

[0033] If the score output by the second scoring formula is in the range [50, 70), the score is "average".

[0034] When the score output by the second scoring formula is in the range [30, 50), the score is "deterioration";

[0035] When the score output by the second scoring formula is in the range [0, 30), the score is "severe".

[0036] In a second aspect, an electronic device is provided for performing the method as described in any of the implementations of the first aspect above. Attached Figure Description

[0037] Figure 1 This is a schematic flowchart illustrating a health assessment method for an inertial platform system provided in an embodiment of this application.

[0038] Figure 2 This is a schematic flowchart illustrating a health assessment method for an inertial platform system provided in an embodiment of this application.

[0039] Figure 3 This is a schematic diagram of the single-parameter scoring function of the present invention.

[0040] Figure 4 This is a schematic diagram of the membership function of the fuzzy inference input / output variables in this invention. Detailed Implementation

[0041] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0042] Inertial platform health assessment is a component of health management, providing support for it. By analyzing test data from the inertial platform system, the health status characteristics of each monitored variable are described, resulting in a health score for each parameter. Then, considering the importance and health status of each monitored variable, a quantitative health assessment result for the inertial platform system is given according to certain rules to determine whether the inertial platform system is functioning normally. This application's embodiments design a multi-level variable-weighting synthesis method to comprehensively evaluate the health status of the inertial platform system.

[0043] Figure 1This is a schematic flowchart of an inertial platform system health assessment method provided in an embodiment of this application.

[0044] 110. Scoring of multiple parameters of the inertial platform system. The multiple parameters include a first parameter group, a second parameter group, and a third parameter group. The first parameter group, the second parameter group, and the third parameter group correspond to the first parameter category, the second parameter category, and the third parameter category, respectively.

[0045] 120. Based on the scores of the first parameter group, determine the first score corresponding to the first parameter category; based on the scores of the second parameter group, determine the second score corresponding to the second parameter category; based on the scores of the third parameter group, determine the third score corresponding to the third parameter category.

[0046] 130. Based on the first score, the second score, and the third score, determine the comprehensive score of the inertial platform system.

[0047] The following is combined with Figure 1 and Figure 2 The method shown will be described in detail.

[0048] By analyzing the key factors affecting the reliability and performance of the inertial platform system, a first parameter group consisting of key analog parameters such as motor current and voltage was identified, a second parameter group consisting of key accuracy parameters such as inertial instrument error coefficient, and a third parameter group consisting of key star sensor parameters such as star sensor grayscale mean and variance. Multi-level fusion evaluation was then conducted.

[0049] The first-level inertial platform system parameters are scored individually. For all key parameters of the platform's inertial platform system across the three parameter groups, a single-parameter health score is obtained based on the degree of deviation from nominal values ​​or technical specifications. Individual scoring of key inertial platform system parameters achieves the normalization and standardization of indicators, forming the basis for inertial platform system health assessment. The score for each single parameter is obtained by utilizing the relationship between measured values ​​and indicators.

[0050] Specifically, the scoring formula is as follows:

[0051]

[0052] Where U is the nominal value, D is the single-sided interval of the indicator, and the indicator range is [UD, U+D]. For single-sided indicators such as accuracy parameters, U = 0 is taken; A1 is the full score, A2 is the maximum fault score, B1 is the adjustment coefficient, which is fine-tuned according to different indicators so that the piecewise function satisfies the value of the point U±D as the maximum fault score, and B2 is the adjustment coefficient, which adjusts the curvature of the scoring formula.

[0053] In one embodiment, when each parameter is within the indicator range, the score is between 30 and 100; when each parameter is outside the indicator range but within 3 times the indicator range, the score is between 0 and 30; when each parameter is outside 3 times the indicator range, the score is 0. A result between 90 and 100 is defined as "Excellent," between 70 and 90 as "Good," between 50 and 70 as "Average," between 30 and 50 as "Deteriorated," and between 0 and 30 as "Severe." In the scoring formula, A1 is 100, A2 is 30, and B2 is 3.

[0054] The second-level classification parameter weighted comprehensive scoring uses different weight coefficients for data of the same parameter category based on the importance of each parameter. At the same time, a weighted algorithm is designed to increase the weight of adverse parameter items.

[0055] Specifically, after scoring each parameter individually, a comprehensive score is calculated for the three types of data: analog quantity, accuracy, and star sensor. A common multi-parameter evaluation method involves assigning weights to different parameters based on expert experience and then weighting all parameters. However, if there are many parameters, constant-value weighting cannot effectively highlight abnormal changes. Therefore, a variable-weighting algorithm is introduced, increasing the weight of parameters with low scores and also introducing a weight for parameters exceeding the specified range (hereinafter referred to as "out-of-range"). The weight increases as the number of out-of-range occurrences for a parameter increases. Finally, a comprehensive health score for the three different types of parameters—analog quantity, accuracy, and star sensor—is obtained.

[0056]

[0057] Where q i (q k ) represents the excess weight of the i(k)th variable, and c i (c k Let ) represent the number of out-of-range errors of the i(k)th generation, and w i Let x be the constant weight of the i-th variable. i For the evaluation score of the i-th variable, if x i If the value is less than 1, then let xi = 1, where a is the variable weighting coefficient, which takes different values ​​from 0.1 to 1. Combined with... Figure 3 Based on experience, we choose qi = 30 and q k =30.

[0058] Suppose that 20 parameters are weighted, where parameter S takes a score between 1 and 100, and all other parameters take a full score of 100, and wi = 0.05. The resulting comprehensive score is shown in Table 1.

[0059] Table 1 Comparison of comprehensive scores under different variable weighting coefficients

[0060]

[0061] When a=1, the variable weighting method degenerates into constant weight synthesis. As shown in Table 1, when the score is high, the effect of variable weighting is not obvious compared to the constant weight (a=1) synthesis method. As the score decreases, the weight of parameter S gradually increases, and the effect of variable weighting becomes more and more obvious, which is in line with our needs. At the same time, it can be seen that the smaller the value of the variable weighting coefficient a, the greater the impact on the evaluation results.

[0062] To verify the impact of the deviation coefficient, taking a = 0.5 as an example, the comprehensive scores of different deviation coefficients were compared. It was assumed that parameter S had different deviation numbers Cs, and other parameters did not have deviation phenomena. The comprehensive scores are shown in Table 2.

[0063] Table 2 Comparison of comprehensive scores under different deviation coefficients.

[0064]

[0065] As shown in Table 2, the deviation coefficients Cs and q can effectively increase the weight of the deviation parameter and significantly increase the impact of deviation on the evaluation results. The larger the Cs and q values, the higher the sensitivity of the comprehensive score to deviation, and the lower the score. The variable-weight comprehensive score can more accurately describe the health status of the platform compared with the constant score.

[0066] The third level of fuzzy inference comprehensive scoring is based on the importance of different parameter categories in different value ranges. Fuzzy rules are designed to perform fuzzy inference on the weighted scoring results of the three parameter categories to obtain the comprehensive health score of the inertial platform and finally determine the health status of the inertial platform.

[0067] Different parameters of the inertial platform have varying importance across different numerical ranges. To better describe their impact, fuzzy rules are introduced to intuitively and dynamically adjust parameter weights to obtain a comprehensive score. The variable-weighted comprehensive scores of three parameter categories—accuracy, analog quantity, and star sensor—are used as input variables for the fuzzy rules, with the overall inertial platform score as the output variable. The universe of discourse is [0, 100]. Five linguistic values ​​are selected: excellent, good, average, deteriorated, and severe. The input and output variables are fuzzified using a membership function such as... Figure 4 As shown.

[0068] The fuzzy rules are shown in Table 3. If any item is severe, the score is severe. The Mamdani method is used to perform fuzzy inference and defuzzification to obtain the comprehensive score of the inertial platform.

[0069] Table 3 Fuzzy Rule Table

[0070]

[0071]

[0072] After fuzzy inference-based comprehensive scoring, the overall score of the inertial platform is obtained, which can quantitatively compare the performance of different platforms and provide a reference for product maintenance and use. If the product performance deteriorates sharply or reaches a critical point of degradation, backup products should be organized in a timely manner. When a product malfunctions, the problem can be detected promptly, and the backup product can be replaced. At the same time, the repair process should be initiated for the problematic product to avoid greater losses due to continued operation under abnormal conditions. Inertial platform parameters are scored individually, which can quantify the performance of the inertial platform parameters, help to quickly locate the product failure point, improve repair efficiency, and also provide support for product selection, allowing for the selection of products with different performance levels based on different application requirements.

[0073] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope defined in the claims of the present invention.

Claims

1. A method for assessing the health of an inertial platform, characterized in that, include: The inertial platform is scored for multiple parameters, which include a first parameter group, a second parameter group, and a third parameter group, respectively corresponding to a first parameter category, a second parameter category, and a third parameter category. Based on the scores of the first parameter group, determine the first score corresponding to the first parameter category; Based on the scores of the second parameter group, determine the second score corresponding to the second parameter category; Based on the scores of the third parameter group, determine the third score corresponding to the third parameter category; The comprehensive score of the inertial platform is determined based on the first score, the second score, and the third score. The method further includes: If the overall score is lower than a preset score level, the system will display the hazard category indicated by the overall score, which will be used to indicate the remedial measures for the inertial platform. The scoring of multiple parameters of the inertial platform includes: Each parameter in each parameter group is scored according to the first scoring formula, which satisfies the following: Where U is the nominal value and D is the one-sided interval of the index; For full marks, This represents the maximum fault score. The adjustment coefficient is used to make the score when X=U±D the maximum value of the fault score. This is an adjustment coefficient used to adjust the curvature of the first scoring formula; The step of determining the first score corresponding to the first parameter category based on the score of the first parameter group includes: The first score is determined based on the second scoring formula and the score of each parameter in the first parameter group. The second scoring formula satisfies: ， Let be the excess weight of the i-th variable. Let k be the excess weight of the k-th variable. Let i be the number of out-of-tolerance errors. Let k be the number of times the error exceeds the tolerance threshold. The constant weight of the i-th variable. Let i be the evaluation score of the i-th variable, if If it is less than 1, then let =1, where a is the variable weighting coefficient, and a = 0.1~1; The second score is determined based on the second scoring formula and the score of each parameter in the second parameter group; The third score is determined based on the second scoring formula and the score of each parameter in the third parameter group; The first parameter group, the second parameter group, and the third parameter group are determined such that, when the score output by the second scoring formula is within the target scoring range, the scoring level corresponding to the first score, the second score, and the third score in the target scoring range is determined respectively. When the score output by the second scoring formula is in the range [90, 100], the score is "Excellent"; When the score output by the second scoring formula is in the range [70, 90), the score is "good"; If the score output by the second scoring formula is in the range [50, 70), the score is "average". When the score output by the second scoring formula is in the range [30, 50), the score is "deterioration"; When the score output by the second scoring formula is in the range [0, 30), the score is "serious".

2. The method according to claim 1, characterized in that, The first parameter group includes motor current and voltage, and the first parameter category is the analog parameter category.

3. The method according to claim 1, characterized in that, The second parameter group includes one or more inertial instrument error coefficients, and the second parameter category is the accuracy parameter category.

4. The method according to claim 1, characterized in that, The third parameter group includes the mean and variance of the star sensor grayscale, and the third parameter category is the star sensor parameter category.

5. The method according to claim 1, characterized in that, The inertial platform system's comprehensive health score is obtained through fuzzy reasoning based on the scores and levels of the first, second, and third scores.

6. An electronic device, characterized in that, The electronic device is used to perform the method as described in any one of claims 1 to 4.

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