Dual-reliability evaluation method based on condition control

Abstract

The invention discloses an engineering structure health monitoring method-oriented dual-reliability evaluation method based on condition control, and belongs to the technical field of engineering structure health monitoring. The method comprises the following steps: firstly, evaluating the reliability of a structural health monitoring online monitoring sensor and a monitoring system; and further introducing key service influence factors, and evaluating the structural health monitoring and online monitoring reliability under an intermediate evaluation condition. The dual-reliability evaluation method based on condition control provided by the invention can comprehensively evaluate the reliability of the new method for structural health monitoring.

Description

Technical Field

[0001] The invention relates to the technical field of engineering structure health monitoring, and in particular to a dual reliability evaluation method based on conditional control. Background Art

[0002] The Structural Health Monitoring (SHM) method uses sensors integrated inside or on the surface of the structure to obtain signals related to the structural health status online and in real time. Through specific signal processing and mechanical modeling analysis methods, the signal characteristics related to damage are extracted, and then the damage diagnosis method is used to evaluate the structural health status and predict the damage extension and remaining life, ultimately guiding structural design, mission decision-making and logistics maintenance. Therefore, in recent decades, the structural health monitoring method has received extensive attention and research.

[0003] In order to promote the large-scale engineering application of structural health monitoring methods, the reliability evaluation of structural health monitoring is a key issue that needs to be solved urgently. Most of the existing reliability evaluation studies of structural health monitoring directly refer to conventional non-destructive testing reliability evaluation methods, such as using hit / miss data and " â vs a "Data analysis technology, when evaluating the reliability of damage detection, the evaluation indicators include: Probability of Detection (POD), Probability of False Alarm (PFA) and Receiver Operating Characteristic (ROC) curve. However, the existing evaluation technology lacks attention to the evaluation conditions that are important for applying these indicators. In the evaluation of structural health monitoring reliability, since structural health monitoring methods are usually applied online during structural service, there are a large number of uncertainties. At the same time, different application scenarios may have different major key uncertainty factors. In these different situations, how to evaluate the reliability of structural health monitoring and provide users with useful reliability information remains to be further studied. The limited evaluation methods developed so far are only used for specific objects or application scenarios, and attempt to cover all factors in structural health monitoring applications, confusing a large number of factors in service, and ignoring the unified control and standardization of evaluation conditions during reliability evaluation. Summary of the invention

[0004] The present invention aims to provide a dual reliability evaluation method based on conditional control, comprising: firstly evaluating the reliability of online monitoring sensors and monitoring systems of structural health monitoring under approximate repeatability conditions by controlling service condition influencing factors; and further introducing key service influencing factors to evaluate the online monitoring reliability of structural health monitoring under intermediate evaluation conditions.

[0005] To achieve the above object, the technical solution of the present invention is: The dual reliability evaluation method based on conditional control includes: Step S1, by controlling the influencing factors of the service conditions, under the condition of approximate repeatability, firstly evaluate the reliability of the online monitoring sensor of the structural health monitoring and the monitoring system itself; Step S2, introduce key service influencing factors and evaluate the online monitoring reliability of structural health monitoring under intermediate evaluation conditions.

[0006] Furthermore, the above step S1 includes: Step S11, designing and manufacturing the same batch of identical structural parts; Step S12, controlling damage of specific shape and size; Step S13, controlling the consistency of batch sensor performance and sensor / structure integration method; Step S14, controlling the room temperature and pressure monitoring environment, using the same structural health monitoring system and its internal fixed diagnosis algorithm to monitor the structural damage, obtaining the structural health monitoring diagnosis results corresponding to the actual damage size under the approximate repeatability test conditions, and constructing a reliability evaluation data set; Step S15, evaluating the monitoring accuracy of the structural health monitoring according to the reliability evaluation data set obtained in step S14 under the condition of approximate repeatability; Step S16, under the condition of approximate repeatability, the monitoring uncertainty of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S14.

[0007] Further, the step S2 includes: Step S21, based on the condition control requirements for stand-alone applications, consistency control is performed on the stand-alone specific monitored structure, damage, sensor performance, sensor / structure integration method, structural health monitoring system and its internal fixed diagnostic algorithm using the same control conditions as in step S1; Step S22, introducing intermediate evaluation conditions influenced by key service factors according to the single machine service scenario in the application; Step S23, controlling the influence of other service factors; Step S24, using the same structural health monitoring system and its internal fixed diagnostic algorithm to monitor structural damage, obtain structural health monitoring diagnostic results corresponding to the actual damage size under critical service conditions, and construct a reliability evaluation data set; Step S25, under the intermediate evaluation condition of introducing the influence of the key service factors, the monitoring accuracy of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S24; Step S26, under the intermediate evaluation condition of introducing the influence of the key service factors, the monitoring uncertainty of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S24.

[0008] Further, in step S15 and step S25, the POD curve is used to evaluate the monitoring accuracy of the structural health monitoring damage alarm capability; Building Structural Health Monitoring Diagnostic Results and actual damage size The relationship is as follows: in, f (.)and g (.) represents the necessary transformation function so that and There is a linear relationship; β 1 is the slope; β 0 is the intercept; is a noise term with a mean of 0 and a standard deviation of Normal distribution of Defining monitoring thresholds , POD means the diagnosis result is greater than the monitoring threshold The probability is calculated as follows: in, is a standard normal cumulative distribution with a mean of: ; Standard deviation is , represents the standard deviation of the noise term; Represents the diagnostic results of the structural health monitoring system.

[0009] Furthermore, in step S16 and step S26, the POD one-sided confidence interval is used. To evaluate the monitoring uncertainty of the structural health monitoring damage alarm capability, is the lower limit of the one-sided confidence interval of POD, which is calculated as follows: , is the confidence probability.

[0010] Further, in step S16 and step S26, using The index is used to characterize the minimum damage size that can be reliably detected by the structural health monitoring system.

[0011] Furthermore, in step S15 and step S25, the maximum absolute error is used. , Root Mean Square Error The indicator evaluates the monitoring accuracy of the damage size quantification capability of structural health monitoring; Among them, the maximum absolute error for: ; Among them, the root mean square error for: Where: Represents the total number of quantitative diagnoses of the structural health monitoring system; Indicates i The actual size of the damage created on the structure during the first diagnosis; Indicates the actual damage size The corresponding structural health monitoring diagnosis results are shown below.

[0012] Further, in step S16 and step S26, the monitoring uncertainty of the damage size quantitative capability of structural health monitoring is evaluated using standard deviation or confidence probability or confidence interval; The calculation formula of standard deviation is as follows: Where: Represents the total number of quantitative diagnoses of the structural health monitoring system; It represents the mean of multiple diagnostic results of structural health monitoring; Among them, the confidence probability The quantitative diagnosis results of damage size for structural health monitoring appear in the form of mean A confidence interval centered on The probability of being within, where represents the width of the upper confidence interval, Indicates the width of the lower half of the confidence interval; The calculation method for the upper and lower limits of the confidence interval is: In the formula, represents the width of the upper confidence interval, represents the width of the lower half confidence interval, The probability density function representing the quantitative diagnosis result of damage size in structural health monitoring, Represents the confidence probability.

[0013] The present invention also provides a device, comprising: one or more processors; a memory for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned dual reliability evaluation method based on conditional control.

[0014] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the dual reliability evaluation method based on conditional control is implemented.

[0015] Beneficial effect: The dual reliability evaluation method based on conditional control of the present invention provides a standard framework for evaluating the reliability of structural health monitoring. Under the condition of approximate repeatability, the reliability of the structural health monitoring sensor and the system itself is evaluated by controlling the monitoring influencing factors as much as possible; under the intermediate evaluation condition, according to different application scenarios, typical key service factors are introduced to evaluate the reliability of structural health monitoring under specific service conditions.

[0016] In order to make the above features and advantages of the invention more obvious and easy to understand, embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the dual reliability evaluation method based on conditional control of the present invention.

[0018] Figure 2 It is a schematic diagram of an ear piece structure in a specific embodiment.

[0019] Figure 3 Schematic diagram of the integrated piezoelectric sensor and ear piece structure.

[0020] Figure 4 Schematic diagram of the diagnostic results during the reliability evaluation process of the structural health monitoring sensor and the system itself.

[0021] Figure 5 Schematic diagram of the relationship between the false alarm rate and the monitoring threshold during the reliability evaluation process of the structural health monitoring sensor and the system itself.

[0022] Figure 6 Schematic diagram of the POD curve and the lower limit of the 95% confidence interval during the reliability evaluation process of the structural health monitoring sensor and the system itself.

[0023] Figure 7 Schematic diagram of the reliability evaluation results of the damage size quantification capability during the reliability evaluation process of the structural health monitoring sensor and the system itself.

[0024] Figure 8 Schematic diagram of the diagnostic results during the structural health monitoring reliability assessment under critical service conditions.

[0025] Fig. 9 Schematic diagram of the relationship between the false alarm rate and the monitoring threshold in the reliability evaluation process of structural health monitoring under critical service conditions.

[0026] Fig.10 Schematic diagram of the POD curve and the lower limit of the 95% confidence interval during the reliability evaluation process of structural health monitoring under critical service conditions.

[0027] Fig.11 Schematic diagram of reliability evaluation results of damage size quantification capability during reliability evaluation of structural health monitoring under critical service conditions. DETAILED DESCRIPTION

[0028] In order to make the purpose and technical solution of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] like Figure 1 As shown, the dual reliability evaluation method based on conditional control of the present invention includes the following steps.

[0030] Step S1, by controlling the influencing factors of the service conditions, under the condition of approximate repeatability, firstly evaluate the reliability of the online monitoring sensor of the structural health monitoring and the monitoring system itself; More specifically, the evaluation index of structural health monitoring reliability is obtained under approximate repeatability test conditions, the uncertainty influencing factors are controlled as much as possible, and the reliability of the integrated structural health monitoring sensor and the structural health monitoring system itself is examined.

[0031] Furthermore, the approximate repeatability test conditions include: controlling the consistency of batch structures and damage, the consistency of sensor performance, the consistency of sensor / structure integration methods, the consistency of application environment, and using the same structural health monitoring system with a fixed internal damage diagnosis algorithm. At the same time, other types of uncertainty factors are controlled as much as possible to avoid their uncertainty effects.

[0032] Furthermore, the step S1 includes the following steps.

[0033] Step S11, designing and manufacturing the same batch of identical structural parts.

[0034] More specifically, based on parameters that are completely consistent with the form and size of the specific monitored structural object of a single machine, identical structural parts from the same batch are designed and manufactured as test objects for reliability evaluation, thereby achieving consistency control of batch structural parts.

[0035] Step S12, controlling damage of specific shape and size.

[0036] More specifically, in order to control the consistency of damage morphology and damage size, damage with fixed damage morphology and size is manufactured, and the actual damage size under the approximate repeatability test condition is recorded as , expressed as the approximate repeatability test condition i The actual size of the damage created on the structure during the first diagnosis.

[0037] Optionally, the damage may be created manually or by a machine, but the present invention is not limited thereto. Step S13, controlling the consistency of batch sensor performance and sensor / structure integration method.

[0038] More specifically, the sensor / structure integrated integration method refers to the use of a vacuum-temperature-pressure-controlled surface mount co-curing integration process to solidify and integrate the sensor network on the surface of the structure after the structural parts are processed and manufactured and before assembly, and at the same time integrate a flexible protective layer on the outermost side for protective packaging.

[0039] Step S14, control the room temperature and pressure monitoring environment, use the same structural health monitoring system and its internal fixed diagnostic algorithm to monitor the structural damage, and obtain the actual damage size under the approximate repeatability test conditions. The corresponding structural health monitoring diagnosis results are as follows: , build a data set for reliability evaluation .

[0040] Among them, the superscript Represents the total number of diagnostic times of the structural health monitoring system under approximately repetitive test conditions.

[0041] Optionally, the same structural health monitoring system and internally fixed diagnostic algorithms are used to monitor structural damage under standard conditions such as normal temperature and pressure.

[0042] Step S15, under the condition of approximate repeatability, the monitoring accuracy of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in step S14.

[0043] Step S16, under the condition of approximate repeatability, the monitoring uncertainty of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S14.

[0044] Step S2, introduce key service influencing factors and evaluate the online monitoring reliability of structural health monitoring under intermediate evaluation conditions.

[0045] More specifically, under intermediate test conditions, the special online application mode of aircraft structural health monitoring is considered, the influence of key service factors is introduced according to specific application scenarios and user needs, and the reliability of structural health monitoring under critical service conditions is evaluated.

[0046] Furthermore, the intermediate test conditions include: structure and damage type, sensor performance, sensor / structure integration method, structural health monitoring system and diagnostic algorithm, etc., which adopt the same control conditions as in step S1. Furthermore, according to the control requirements of the intermediate test conditions, the key service conditions are not controlled, and the reliability of structural health monitoring is examined under the influence of this service condition. The remaining service conditions that are not examined need to be controlled and kept unchanged as much as possible.

[0047] Furthermore, the step S2 includes the following steps.

[0048] Step S21, based on the conditional control requirements for stand-alone applications, consistency control is performed on the stand-alone specific monitored structure, damage, sensor performance, sensor / structure integration method, structural health monitoring system and its internal fixed diagnostic algorithm using the same control conditions as in step S1.

[0049] Step S22, according to the single machine service scenario in the application, introduce the intermediate evaluation conditions affected by the key service factors.

[0050] More specifically, the reliability of structural health monitoring is investigated under the influence of this service factor.

[0051] Step S23, controlling the influence of other service factors.

[0052] More specifically, other service factors that are not under investigation need to be controlled and kept constant as much as possible.

[0053] Step S24, using the same structural health monitoring system and its internal fixed diagnostic algorithm to monitor the structural damage and obtain the actual damage size under critical service conditions The corresponding structural health monitoring diagnosis results are as follows: , build a data set for reliability evaluation .

[0054] in, Indicated as the first i The actual size of the damage created on the structure during the first diagnosis, superscript Represents the total number of diagnostic times of the structural health monitoring system under critical service conditions.

[0055] Step S25, under the intermediate evaluation condition of introducing the influence of the key service factors, the monitoring accuracy of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S24.

[0056] Step S26, under the intermediate evaluation condition of introducing the influence of the key service factors, the monitoring uncertainty of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S24.

[0057] Furthermore, considering the different monitoring objects of structural health monitoring, such as damage alarm, sizing, positioning, prediction and other functions, specific evaluation indicators are used.

[0058] Optionally, the damage alarm capability of structural health monitoring and the damage size quantification capability of structural health monitoring are taken as monitoring objects, and evaluation indicators of monitoring accuracy and monitoring uncertainty are introduced.

[0059] Furthermore, in step S15 and step S25, the POD curve is used to evaluate the monitoring accuracy of the structural health monitoring damage alarm capability.

[0060] First, establish the structural health monitoring diagnostic results and actual damage size The relationship is as follows: in, f (.)and g (.) represents the necessary transformation function so that and There is a linear relationship; β 1 is the slope; β 0 is the intercept; is a noise term with a mean of 0 and a standard deviation of The normal distribution of .

[0061] More specifically, for the reliability evaluation data set obtained in step S1, for ; For the reliability evaluation data set obtained in step S2, for .

[0062] Then define the monitoring threshold , POD means the diagnosis result is greater than the monitoring threshold The probability is calculated as follows: in, is a standard normal cumulative distribution with a mean of: ; Standard deviation is , where the parametersβ 0. β 1. All are estimated by the maximum likelihood method; represents the standard deviation of the noise term; represents the diagnostic result of the structural health monitoring system. More specifically, for the reliability evaluation data set obtained in step S1, for ; For the reliability evaluation data set obtained in step S2, for .

[0063] Furthermore, in step S16 and step S26, the POD one-sided confidence interval is used to evaluate the monitoring uncertainty of the structural health monitoring damage alarm capability. To this end, it is necessary to follow the specified confidence probability , find the corresponding one-sided confidence interval of POD Confidence Probability It represents the probability that the true POD of the structural health monitoring system falls within the confidence interval. is the lower limit of the one-sided confidence interval of POD, which is calculated as follows: .

[0064] Furthermore, in step S16 and step S26, The index is used to characterize the minimum damage size that can be reliably detected by the structural health monitoring system. The indicator indicates that at a confidence level of 95%, the structural health monitoring system can detect the presence of damage of this size with at least a 90% probability, i.e., the confidence probability , POD confidence interval lower limit .in, The indicator can be obtained in the POD curve by the intersection between the 90% POD and the lower bound of the 95% confidence interval.

[0065] More specifically, for the reliability evaluation data set obtained in step S1, for ; For the reliability evaluation data set obtained in step S2, for .

[0066] Furthermore, in step S15 and step S25, the maximum absolute error is used. , Root Mean Square Error The indicator evaluates the monitoring accuracy of the damage size quantification capability of structural health monitoring.

[0067] Among them, the maximum absolute error for: . Among them, the root mean square error for: Where: Represents the total number of quantitative diagnoses of the structural health monitoring system; Indicates i The actual size of the damage created on the structure during the first diagnosis; Indicates the actual damage size More specifically, for the reliability evaluation data set obtained in step S1, for ; For the reliability evaluation data set obtained in step S2, for For the reliability evaluation data set obtained in step S1, for ; For the reliability evaluation data set obtained in step S2, for .

[0068] Furthermore, in step S16 and step S26, the monitoring uncertainty of the damage size quantification capability of structural health monitoring is evaluated using standard deviation or confidence probability or confidence interval.

[0069] The calculation formula of standard deviation is as follows: Where: represents the total number of quantitative diagnoses of the structural health monitoring system; for the reliability evaluation data set obtained in step S1, for ; For the reliability evaluation data set obtained in step S2, for ; represents the mean of multiple diagnostic results of structural health monitoring, for the reliability evaluation data set obtained in step S1, for The mean of the reliability evaluation data set obtained in step S2, for The mean of .

[0070] Among them, the confidence probability The quantitative diagnosis results of damage size for structural health monitoring appear in the form of mean A confidence interval centered on The probability of being within, where represents the width of the upper confidence interval, Represents the width of the lower half of the confidence interval.

[0071] The calculation method for the upper and lower limits of the confidence interval is: In the formula, represents the width of the upper confidence interval, represents the width of the lower half confidence interval, The probability density function representing the quantitative diagnosis result of damage size in structural health monitoring, Represents the confidence probability.

[0072] The present invention also provides a device, comprising: one or more processors; a memory for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors execute the dual reliability evaluation method based on conditional control.

[0073] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the dual reliability evaluation method based on conditional control is implemented.

[0074] The following is a specific embodiment to introduce the dual reliability evaluation method based on conditional control of the present invention. In this specific embodiment, the reliability evaluation is performed on the monitoring of cracks in a typical aviation ear structure.

[0075] Firstly, the influencing factors for the application are controlled and the fixed guided wave structural health monitoring diagnostic algorithm inside the calibrated structural health monitoring system is calibrated.

[0076] According to the typical aviation ear piece connection structure form in engineering applications, the present embodiment is designed as follows Figure 2 The lug structure shown is made of LY12 aluminum alloy with a thickness of 5 mm. The typical aviation lug connection structure used in this specific embodiment uses the same material, design, size and manufacturing process. This batch of test pieces is divided into 3 groups, named Group A, Group B, and Group C, which are used for different purposes, and 5 test pieces are prepared in each group.

[0077] The specimens in group A are numbered C1, C2, C3, C4, and C5, which are used to calibrate the diagnostic algorithm fixed inside the structural health monitoring system. Two types of crack damage were prepared for this purpose, including three artificially cut crack damage specimens and two fatigue crack specimens that naturally expanded under fatigue load.

[0078] In addition, the specimens in group B are numbered IS1, IS2, IS3, IS4, and IS5, which are used to carry out the reliability evaluation of the integrated integrated sensor structural health monitoring in step S1 to examine the reliability of the integrated structural health monitoring sensor and the structural health monitoring system itself.

[0079] In addition, the specimens in group C are numbered CSC1, CSC2, CSC3, CSC4, and CSC5, which are used to carry out the structural health monitoring reliability evaluation under the critical service conditions in step S2.

[0080] According to the characteristics of the structure and damage initiation and expansion, a sensor network consisting of 5 piezoelectric sensors is arranged on the structure to perform health monitoring and diagnosis of the guided wave structure. The consistency control method based on multi-dimensional feature-similar distance is used to select the performance of batch piezoelectric sensors, and the surface mount co-curing integration method is used to realize the integrated integration of piezoelectric sensors and structures. Figure 3 Schematic diagram of the integrated piezoelectric sensor and ear piece structure.

[0081] A structural health monitoring system with an embedded guided wave-Gaussian process diagnostic algorithm was used, and the guided wave-Gaussian process diagnostic algorithm was calibrated using group A specimens.

[0082] Secondly, under the condition of approximate repeatability, the reliability of the online monitoring sensors and the monitoring system itself for structural health monitoring is firstly evaluated.

[0083] In the reliability evaluation process of the integrated sensor structure health monitoring, the guided wave structure health monitoring environment is controlled to be a laboratory environment at normal temperature and pressure. The electric spark wire cutting method is used to create a crack damage with a fixed morphology and a specific size on one side of the through hole of the ear piece. The same guided wave structure health monitoring system and the calibrated guided wave-Gaussian process diagnosis algorithm are used to monitor the crack lengths of group B specimens IS1 to IS5. The diagnosis results are as follows: Figure 4 shown.

[0084] In order to evaluate the reliability of the damage alarm capability of structural health monitoring, the POD curve, POD confidence interval and Indicators. To determine the monitoring threshold for POD evaluation , first obtain the structural health monitoring diagnosis results in a healthy state, and then calculate the relationship between the false alarm rate of the structural health monitoring method and the monitoring threshold, such as Figure 5 As shown in the figure, the monitoring threshold is finally determined to be 0.3 mm by weighing the false alarm rate and POD. In this case, the false alarm rate is 0, and a higher POD is also guaranteed. According to the set monitoring threshold, after calculating the cumulative probability that the structural health monitoring diagnosis result is greater than the threshold, the POD curve and the lower bound of the 95% confidence interval are calculated, as shown in the figure. Figure 6 Characterizing the minimum crack length that can be reliably identified by the integrated structural health monitoring sensor and the structural health monitoring system The indicator can be Figure 6 The intersection point between the 90% POD and the lower bound of the 95% confidence interval is obtained. The results show that It is 0.8 mm, which means that when the crack length is greater than or equal to 0.8 mm, the guided wave structural health monitoring method can detect the existence of the crack with at least 90% probability and 95% confidence.

[0085] In order to evaluate the reliability of the damage size quantification capability of structural health monitoring, the maximum absolute error of the evaluation monitoring accuracy is calculated based on the obtained structural health monitoring results. 0.8 mm, RMS error is 0.3 mm; the standard deviation of the uncertainty of evaluation monitoring is calculated to be 0.3 mm; the 95% confidence interval is as follows Figure 7 The dotted line shows that there is a 95% probability that the structural health monitoring diagnosis result will be within this range.

[0086] Finally, the reliability evaluation of structural health monitoring under critical service conditions is carried out.

[0087] During the reliability evaluation of structural health monitoring under critical service conditions, the same guided wave-Gaussian process structural health monitoring diagnostic algorithm was used to monitor the fatigue crack propagation of group C specimens CSC1 to CSC5 under flight load spectrum conditions. In this evaluation, two important service uncertainty factors were introduced, namely the influence of service load and damage. Structural health monitoring was carried out under the critical service load spectrum, and the damage was a real fatigue crack with uncertainty in angle, depth, width and morphology. The propagation crack length of group C specimens CSC1 to CSC5 was monitored, and the diagnostic results are shown in Figure 2. Figure 8 shown.

[0088] In order to determine the monitoring threshold of POD, the reliability evaluation of structural health monitoring damage alarm capability is carried out. First, we obtain multiple diagnostic results of the structure health monitoring method on the structure in the non-crack extension state under the influence of the key service load spectrum, and then calculate the relationship between the false alarm rate of the structure health monitoring method and the monitoring threshold, such as Fig. 9 As shown. By weighing the false alarm rate and POD, the monitoring threshold is determined to be 0.9 mm. According to the set monitoring threshold, after calculating the cumulative probability that the structural health monitoring diagnosis result is greater than the threshold, the POD curve and the lower bound of the 95% confidence interval under critical service conditions are obtained, as shown in Fig.10 The minimum crack size that can be reliably detected by structural health monitoring is obtained based on the intersection between the 90% POD and the lower limit of the 95% confidence interval. It is 1.2 mm, indicating that under the influence of critical service loads and damage, when the hole edge crack extension length of the ear structure is greater than or equal to 1.2 mm, the guided wave structural health monitoring method can detect the existence of the damage with at least 90% probability and 95% confidence level.

[0089] In terms of the reliability evaluation of the damage size quantification capability of structural health monitoring, the maximum absolute error of the evaluation monitoring accuracy is first calculated based on the obtained structural health monitoring results. The root mean square error is 1.9 mm. is 0.6 mm. Further, the standard deviation of the evaluation monitoring uncertainty is 0.4 mm, and the 95% confidence interval is as follows: Fig.11 shown.

[0090] Although the present invention has been disclosed as above by way of embodiments, it is not intended to limit the present invention. Any person having ordinary knowledge in the technical field may make some changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the scope of the attached patent application.

Claims

1. The dual reliability evaluation method based on conditional control is characterized by: include, Step S1, by controlling the influencing factors of the service conditions, under the condition of approximate repeatability, firstly evaluate the reliability of the online monitoring sensor of the structural health monitoring and the monitoring system itself; Step S2, introduce key service influencing factors and evaluate the online monitoring reliability of structural health monitoring under intermediate evaluation conditions.

2. The dual reliability evaluation method based on conditional control as claimed in claim 1, characterized in that: The step S1 comprises: Step S11, designing and manufacturing the same batch of identical structural parts; Step S12, controlling damage of specific shape and size; Step S13, controlling the consistency of batch sensor performance and sensor / structure integration method; Step S14, controlling the room temperature and pressure monitoring environment, using the same structural health monitoring system and its internal fixed diagnosis algorithm to monitor the structural damage, obtaining the structural health monitoring diagnosis results corresponding to the actual damage size under the approximate repeatability test conditions, and constructing a reliability evaluation data set; Step S15, evaluating the monitoring accuracy of the structural health monitoring according to the reliability evaluation data set obtained in step S14 under the condition of approximate repeatability; Step S16, under the condition of approximate repeatability, the monitoring uncertainty of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S14.

3. The dual reliability evaluation method based on conditional control as claimed in claim 2, characterized in that: The step S2 comprises: Step S21, based on the condition control requirements for stand-alone applications, consistency control is performed on the stand-alone specific monitored structure, damage, sensor performance, sensor / structure integration method, structural health monitoring system and its internal fixed diagnostic algorithm using the same control conditions as in step S1; Step S22, introducing intermediate evaluation conditions influenced by key service factors according to the single machine service scenario in the application; Step S23, controlling the influence of other service factors; Step S24, using the same structural health monitoring system and its internal fixed diagnostic algorithm to monitor structural damage, obtain structural health monitoring diagnostic results corresponding to the actual damage size under critical service conditions, and construct a reliability evaluation data set; Step S25, under the intermediate evaluation condition of introducing the influence of the key service factors, the monitoring accuracy of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S24; Step S26, under the intermediate evaluation condition of introducing the influence of the key service factors, the monitoring uncertainty of the structural health monitoring is evaluated according to the reliability evaluation data set obtained in the step S24.

4. The dual reliability evaluation method based on conditional control as claimed in claim 3, characterized in that: In step S15 and step S25, the POD curve is used to evaluate the monitoring accuracy of the structural health monitoring damage alarm capability; Building Structural Health Monitoring Diagnostic Results and actual damage size The relationship is as follows: in, f (.)and g (.) represents the necessary transformation function to make and There is a linear relationship; β 1 is the slope; β 0 is the intercept; is a noise term with a mean of 0 and a standard deviation of Normal distribution of Defining monitoring thresholds , POD means the diagnosis result is greater than the monitoring threshold The probability is calculated as follows: in, is a standard normal cumulative distribution with a mean of: ; Standard deviation is , represents the standard deviation of the noise term; Represents the diagnostic results of the structural health monitoring system.

5. The dual reliability evaluation method based on conditional control as claimed in claim 4, characterized in that: In step S16 and step S26, the POD one-sided confidence interval is used To evaluate the monitoring uncertainty of the structural health monitoring damage alarm capability, is the lower limit of the one-sided confidence interval of POD, which is calculated as follows: ， is the confidence probability.

6. The dual reliability evaluation method based on conditional control as claimed in claim 5, characterized in that: In step S16 and step S26, the The index is used to characterize the minimum damage size that can be reliably detected by the structural health monitoring system.

7. The dual reliability evaluation method based on conditional control as claimed in claim 3, characterized in that: In step S15 and step S25, the maximum absolute error is used , Root Mean Square Error The indicator evaluates the monitoring accuracy of the damage size quantification capability of structural health monitoring; Among them, the maximum absolute error for: ； Among them, the root mean square error for: Where: Represents the total number of quantitative diagnoses of the structural health monitoring system; Indicates i The actual size of the damage created on the structure during the first diagnosis; Indicates the actual damage size The corresponding structural health monitoring diagnosis results are shown below.

8. The dual reliability evaluation method based on conditional control as claimed in claim 7, characterized in that: In step S16 and step S26, the monitoring uncertainty of the damage size quantitative capability of structural health monitoring is evaluated using standard deviation or confidence probability or confidence interval; The calculation formula of standard deviation is as follows: Where: Represents the total number of quantitative diagnoses of the structural health monitoring system; It represents the mean of multiple diagnostic results of structural health monitoring; Among them, the confidence probability The quantitative diagnosis results of damage size for structural health monitoring appear in the form of mean A confidence interval centered at The probability of being within, where represents the width of the upper confidence interval, Indicates the width of the lower half of the confidence interval; The calculation method for the upper and lower limits of the confidence interval is: In the formula, represents the width of the upper confidence interval, represents the width of the lower half confidence interval, The probability density function representing the quantitative diagnosis result of damage size in structural health monitoring, Represents the confidence probability.

9. A device, characterized in that: The device includes: one or more processors; and a memory for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors execute the dual reliability evaluation method based on conditional control as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the dual reliability evaluation method based on conditional control as described in any one of claims 1 to 8 is implemented.

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