A method for calculating the reliability of a wind tunnel mobile belt floor apparatus

Abstract

The present application relates to the field of reliability analysis calculation, more particularly to a reliability calculation method of a wind tunnel moving belt floor equipment, S1: obtaining fault data of the wind tunnel moving belt floor equipment, establishing a fault criterion and a whole machine fault mode network of the wind tunnel moving belt floor equipment according to the fault data; S2: performing FTA analysis, and drawing a fault tree of the wind tunnel moving belt floor equipment; S3: calculating a whole machine fault probability of the wind tunnel moving belt floor equipment and analyzing the importance of each fault event, and determining key sub-components of reliability; S4: calculating a cumulative fault probability function of each fault event, and calculating the reliability of the whole machine through the whole machine fault probability and the cumulative fault probability function; S5: comparing the reliability of the whole machine with a target reliability, if the target is not reached, adjusting and replacing the key sub-components of reliability, and re-calculating the reliability of the whole machine until the expected target is reached.

Description

Technical Field

[0001] This invention relates to the field of reliability analysis and calculation, and more specifically to a method for calculating the reliability of a wind tunnel moving floor device. Background Technology

[0002] Wind tunnel testing is crucial in aircraft development, and ground effect simulation is the first problem to be solved in wind tunnel testing. Compared with traditional fixed-floor technology, the currently used moving-floor technology significantly improves the simulation quality of ground effects in aircraft ground effect tests. However, moving-floor equipment has an extremely complex structure, a high probability of failure, and is difficult to design. Current reliability analysis methods struggle to identify the key factors leading to overall failure, and the reliability relationships between sub-components and between sub-components and the whole system are unclear, making it difficult to identify potential failure modes and predict failure risks in advance. Therefore, there is an urgent need for a reliability calculation method for wind tunnel moving-floor equipment that can link individual failure events to overall system failure events and analyze the magnitude of the impact of each failure event on the overall system failure, meeting the high stability and high safety requirements of wind tunnel moving-floor equipment. Summary of the Invention

[0003] The purpose of this invention is to provide a reliability calculation method for a wind tunnel moving floor equipment, which can solve the problem of overall reliability analysis and calculation of wind tunnel moving floor equipment.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A method for reliability calculation of a wind tunnel moving floor device, the method comprising the following steps:

[0006] S1: Obtain fault data of the wind tunnel moving floor equipment, and establish fault judgment criteria and a fault mode network for the wind tunnel moving floor equipment based on the fault data;

[0007] S2: Perform FTA analysis and draw the fault tree of the moving floor equipment in the wind tunnel;

[0008] S3: Calculate the overall failure probability of the wind tunnel moving floor equipment and analyze the importance of each failure event to determine the key sub-components for reliability;

[0009] S4: Calculate the cumulative failure probability function for each failure event, and calculate the overall reliability of the machine using the overall failure probability and the cumulative failure probability function;

[0010] S5: Compare the overall reliability with the target reliability. If the target is not met, adjust and replace the key sub-components for reliability, and recalculate the overall reliability until the expected target is achieved.

[0011] The fault data is derived from the moving belt equipment fault data in the wind tunnel test fault data.

[0012] The fault diagnosis criteria are based on theoretical analysis and are used to determine whether a component has failed and the cause of the failure.

[0013] The wind tunnel moving floor equipment whole machine failure mode network includes multiple datasets classified by the name of the component that failed. Each dataset consists of multiple data groups, which include a sequence number, the name of the failed component, the main failure mode, and the failure time.

[0014] The FTA analysis is a fault tree analysis, which takes the whole machine failure event as the top event and uses the top-down fault causal logic to find the necessary and sufficient direct causes of the failure event layer by layer. These causes include hardware, software and environment, clarify the relationship between the failures of each component and between the failures of the component and the whole machine, and use logic gates such as AND gates and OR gates to represent the fault tree of the wind tunnel moving floor equipment.

[0015] The formula for calculating the overall machine failure probability is:

[0016]

[0017] In the above formula, T represents a complete machine failure event; K i K j K k These are the i-th, j-th, and k-th minimum cut sets, respectively; N is the number of minimum cut sets.

[0018] The cut set is defined as a set of bottom events in a fault tree, such that when these bottom events occur simultaneously, the top event will inevitably occur. The minimal cut set is defined as a set that no longer becomes a cut set if any one of the bottom events in the cut set is removed.

[0019] The method for analyzing the importance of each failure event and determining the key sub-components for reliability is as follows:

[0020] The formula for calculating the relative probability importance of each failure event is as follows:

[0021]

[0022] In the above formula, F represents the relative probability importance of the i-th fault event; i Let F be the probability of the i-th failure event occurring. i =P(x i );F s Let F be the failure probability function for the whole machine failure event. s =P(T);

[0023] By mapping each fault event to its corresponding sub-component, the relative probability importance of each sub-component can be obtained:

[0024]

[0025] In the above formula, Let be the relative probability importance of the i-th sub-component; Let be the relative probability importance of the i-th failure event corresponding to the sub-component; N is the number of failure events corresponding to the sub-component.

[0026] The cumulative failure probability function for each failure event is calculated as follows:

[0027] Based on the fault mode network of the wind tunnel moving floor equipment, for each fault event, the shape parameter β and scale parameter η in a two-parameter Weibull distribution are calculated using the least squares method; the cumulative fault probability function for each fault event is then calculated based on the shape parameter β and scale parameter η, as shown in the following formula:

[0028]

[0029] In the above formula, t represents time in seconds; the reliability r(t) for each failure event is:

[0030]

[0031] The process of calculating the shape parameter β and scale parameter η of a Weibull distribution using the least squares method is as follows:

[0032] First, performing a logarithmic transformation on the cumulative failure probability function yields the following equation:

[0033]

[0034] Then, the least squares method is used to fit a straight line, transforming the above linear relationship into a standard linear regression form:

[0035] Y = βX + C

[0036] In the above formula, Y = ln(-ln(1-r(t))); X = ln(t); C = -βln(η).

[0037] The shape parameter β and the scale parameter η are obtained by this linear regression.

[0038] The reliability calculation method for the entire machine is as follows: The cumulative failure probability function F(t) of the entire machine is calculated using the formula for calculating the failure probability of the entire machine and the cumulative failure probability function F(t) of each failure event. S If (t), then the reliability of the whole machine is R(t) = 1 - FS (t).

[0039] The beneficial effects of this invention are as follows:

[0040] I. Through FTA analysis, the reliability relationships between various sub-components of the wind tunnel moving floor equipment and between the sub-components and the whole machine were clarified. It was possible to analyze the impact of each failure event on the failure of the whole machine and identify the key sub-components that affect the reliability of the whole machine.

[0041] Second, it can calculate the reliability of the whole machine through the overall failure probability and the cumulative failure probability function of each failure event. When the reliability of the whole machine does not meet the target reliability, it can also recalculate the reliability of the whole machine by adjusting and replacing the key sub-components of reliability until the expected target is achieved. Attached Figure Description

[0042] The present invention will now be described in further detail with reference to the accompanying drawings and specific implementation methods.

[0043] Figure 1 This is the reliability calculation method flow of the wind tunnel moving floor device of the present invention;

[0044] Figure 2 This is a schematic diagram of the FTA analysis process of the present invention;

[0045] Figure 3 This is a schematic diagram of a fault tree example of the present invention;

[0046] Figure 4 This is a schematic diagram of a fault tree example for the wind tunnel moving floor device of the present invention;

[0047] Figure 5 This is a schematic diagram of the two-parameter Weibull distribution fitting curve of the present invention. Detailed Implementation

[0048] The present invention will now be described in further detail with reference to the accompanying drawings.

[0049] like Figures 1 to 5 As shown, in order to solve the technical problem of "reliability analysis and calculation of wind tunnel moving floor equipment", the steps and functions of a reliability calculation method for wind tunnel moving floor equipment are explained in detail below;

[0050] A method for reliability calculation of a wind tunnel moving floor device, the method comprising the following steps:

[0051] S1: Obtain fault data of the wind tunnel moving floor equipment, and establish fault judgment criteria and a fault mode network for the wind tunnel moving floor equipment based on the fault data;

[0052] The fault data comes from the moving belt equipment fault data in the wind tunnel test fault data;

[0053] The fault diagnosis criteria are based on theoretical analysis and established using the fault data. They are used to determine whether a component has failed and the cause of the failure.

[0054] The wind tunnel moving floor equipment whole machine failure mode network includes multiple datasets classified by the name of the component that failed. Each dataset consists of multiple data groups, which include a sequence number, the name of the failed component, the main failure mode, and the failure time.

[0055] S2: Perform FTA analysis and draw the fault tree of the moving floor equipment in the wind tunnel;

[0056] FTA analysis is fault tree analysis, and the analysis process is as follows: Figure 2 As shown, one should first be sufficiently familiar with and understand the structural composition of the wind tunnel moving floor equipment, and determine the purpose of the analysis: to ensure that the reliability of the whole machine reaches the expected target.

[0057] Then, the top event is determined to be a system failure event, and the logical relationships between the bottom events and the top event are analyzed.

[0058] Furthermore, by working backward from the top down along the causal logic of the fault, the necessary and sufficient direct causes of the fault event are identified layer by layer. These causes include hardware, software, and environmental factors, and are represented using logic gates such as AND and OR gates. A fault tree diagram of the wind tunnel moving floor equipment is then drawn. An example fault tree diagram is shown below. Figure 3 As shown in the figure, the fault tree is a series-parallel system consisting of three base events and one intermediate event. The three base events are X1, X2, and X3.

[0059] The meaning of a cut set is: a set of some bottom events in a fault tree, when these bottom events occur simultaneously, the top event must occur; the meaning of a minimal cut set is: if any one of the bottom events contained in the cut set is removed, it will no longer be a cut set, such a cut set is a minimal cut set.

[0060] Then the fault tree has three cut sets: {X1}, {X2, X3}, and {X1, X2, X3}.

[0061] Two minimal cut sets: {X1}, {X2, X3};

[0062] S3: Calculate the overall failure probability of the wind tunnel moving floor equipment and analyze the importance of each failure event to determine the key sub-components for reliability;

[0063] For ease of explanation, Figure 4The fault tree in the example is only a part of the embodiments of the present invention, not all of them. A drive motor failure will directly lead to a failure of the wind tunnel moving belt floor equipment; a failure of the offset monitoring sensor and an excessive offset of the moving belt occurring at the same time will cause the moving belt to be thrown out, which in turn will lead to a failure of the wind tunnel moving belt floor equipment; a failure of the tension monitoring sensor and an excessive tension of the moving belt occurring at the same time will cause the moving belt to tear, which in turn will lead to a failure of the wind tunnel moving belt floor equipment.

[0064] The formula for calculating the overall machine failure probability is:

[0065]

[0066] In the above formula, T represents a complete machine failure event; K i K j K k These are the i-th, j-th, and k-th minimum cut sets, respectively; N is the number of minimum cut sets.

[0067] Suppose that before a certain time point t0, the probability of drive motor failure is P(X1) = 0.02, the probability of offset monitoring sensor failure is P(X2) = 0.1, the probability of excessive conveyor belt offset is P(X3) = 0.2, the probability of tension monitoring sensor failure is P(X4) = 0.2, and the probability of excessive conveyor belt tension is P(X5) = 0.3. Then the overall machine failure probability is:

[0068] P(T)=P(X1)+P(X2)P(X3)+P(X4)P(X5)=0.1

[0069] Furthermore, the relative probability importance of each fault event is calculated to obtain the impact of small changes in the probability of each fault event on the overall probability of fault events. The calculation formula is as follows:

[0070]

[0071] In the above formula, F represents the relative probability importance of the i-th fault event; i Let F be the probability of the i-th failure event occurring. i =P(X) i );F s Let F be the failure probability function for the whole machine failure event. s =P(T). Then we have:

[0072]

[0073] Similarly, we can obtain The relative probability importance of drive motor failure, offset monitoring sensor failure, and excessive moving belt offset are all 0.2, while the relative probability importance of tension monitoring sensor failure and excessive moving belt tension are both 0.6. Clearly, the tension monitoring sensor failure and excessive moving belt tension have a greater impact on the overall machine failure than other failure events.

[0074] Furthermore, each fault event is mapped to a corresponding sub-component to obtain the relative probability importance of each sub-component:

[0075]

[0076] In the above formula, Let represent the relative probability importance of the i-th sub-component; N is the number of failure events corresponding to that sub-component.

[0077] The relative probability importance of the drive motor Relative probability importance of offset monitoring sensors Relative probability importance of tension monitoring sensors Relative probability importance of the moving strip

[0078] The relative probability importance of a sub-component reflects the degree to which its reliability is important to the overall reliability of the machine. The higher the relative probability importance, the more important the reliability of the sub-component. The sub-components with the highest relative probability importance are the key sub-components for the overall reliability of the machine. Obviously, the moving belt is the key sub-component for the overall reliability of this embodiment.

[0079] S4: Calculate the cumulative failure probability function for each failure event, and calculate the overall reliability of the machine using the overall failure probability and the cumulative failure probability function;

[0080] The process of calculating the cumulative failure probability function for each failure event includes: based on the overall failure mode network of the wind tunnel moving floor equipment, for each failure event, calculating its shape parameter β and scale parameter η in a two-parameter Weibull distribution using the least squares method. Figure 5 The curve shown is the general form of the two-parameter Weibull distribution curve; the cumulative failure probability function for each failure event is calculated based on the shape parameter β and the scale parameter η, and the calculation formula is as follows:

[0081]

[0082] In the above formula, t represents time in seconds. The reliability r(t) for each failure event is:

[0083]

[0084] Furthermore, the process of calculating the shape parameter β and scale parameter η of each fault event using the least squares method, which conform to the Weibull distribution, is as follows:

[0085] First, performing a logarithmic transformation on the cumulative failure probability function yields the following equation:

[0086]

[0087] at this time Figure 5 The two-parameter Weibull distribution curve shown will be converted to a horizontal axis of... A straight line graph with the vertical axis being ln(-ln(1-r(t))).

[0088] Then, the least squares method is used to fit a straight line, transforming the above linear relationship into a standard linear regression form:

[0089] Y = βX + C

[0090] In the above formula, Y = ln(-ln(1-r(t))); X = ln(t); C = -βln(η).

[0091] The shape parameter β and the scale parameter η are obtained by using this linear regression.

[0092] Furthermore, the process of calculating the overall reliability is as follows: using the formula for calculating the overall failure probability and the cumulative failure probability function F(t) of each failure event, the cumulative failure probability function F of the entire machine is calculated. S If (t), then the reliability of the whole machine is R(t) = 1 - F S (t);

[0093] S5: Compare the overall reliability with the target reliability. If the target is not met, adjust and replace the key sub-components for reliability, and recalculate the overall reliability until the expected target is achieved.

[0094] Furthermore, the adjustment of the sub-component involves redesigning the sub-component; the replacement involves replacing it with a new sub-component or replacing it with another component that has the same function; then, the fault data of the new sub-component is obtained, and the new overall reliability is calculated until the expected goal is achieved.

[0095] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for calculating reliability of a wind tunnel moving belt floor apparatus, characterized by: The method comprises the following steps: S1: obtaining fault data of the wind tunnel moving belt floor equipment, establishing a fault criterion and a wind tunnel moving belt floor equipment whole machine fault mode network according to the fault data; S2: performing FTA analysis to draw a fault tree of the wind tunnel moving belt floor equipment; S3: calculating a whole machine fault probability of the wind tunnel moving belt floor equipment and analyzing the importance of each fault event to determine key sub-components of reliability; S4: calculating a cumulative fault probability function of each fault event, and calculating the reliability of the whole machine through the whole machine fault probability and the cumulative fault probability function; S5: comparing the reliability of the whole machine with a target reliability, and if the target is not reached, adjusting and replacing the key sub-components of reliability, and recalculating the reliability of the whole machine until the expected target is reached.

2. The reliability calculation method of a wind tunnel mobile belt floor apparatus according to claim 1, characterized by: The fault data is obtained from moving belt equipment fault data in wind tunnel test fault data.

3. The method of claim 1, wherein: The fault criterion is based on theoretical analysis and is used to determine whether a component fails and the failure cause.

4. The method of claim 1, wherein: The wind tunnel moving belt floor equipment whole machine fault mode network comprises a plurality of data sets classified according to component names that have failed, and each data set comprises a plurality of data groups, wherein the data groups comprise a serial number, a failed component name, a main fault mode and a fault time.

5. The method of claim 1, wherein: The FTA analysis is fault tree analysis, the whole machine fault event is taken as a top event, the necessary and sufficient direct causes of the fault event are found layer by layer through top-down fault cause and effect logical backstepping, the causes include hardware, software and environment, the relationships between component faults and between component faults and the whole machine fault are clarified, and a wind tunnel moving belt floor equipment fault tree is drawn.

6. The method of claim 5, wherein: The calculation formula of the whole machine fault probability is: In the above formula, T is the whole machine fault event; K i , K j , K k are the i, j, k smallest cut sets respectively; and N is the number of the smallest cut sets.

7. The method of claim 6, wherein: The meaning of the cut set is that when the bottom events in the cut set occur simultaneously, the top event will occur; the meaning of the minimum cut set is that if any bottom event in the cut set is removed, the cut set will no longer exist.

8. The method of claim 7, wherein: The method for analyzing the importance of each fault event and determining the key sub-components of reliability is as follows: The calculation formula of the relative probability importance of each fault event is as follows: In the above formula, is the relative probability importance of the ith failure event; F i is the occurrence probability of the ith failure event, i.e. F i = P(x i ); F s is the failure probability function of the whole machine failure event, i.e. F s = P(T); The relative probability importance of each sub-component is obtained by corresponding each fault event to the corresponding sub-component: In the above formula, is the relative probability importance of the ith subcomponent; is the relative probability importance of the ith failure event corresponding to the subcomponent; N is the number of failure events corresponding to the subcomponent.

9. The method of claim 8, wherein: The calculation method of the cumulative fault probability function of each fault event is as follows: According to the wind tunnel moving belt floor equipment whole machine fault mode network, the shape parameter β and the scale parameter η conforming to the double-parameter Weibull distribution are calculated for each fault event by the least square method; the cumulative fault probability function of each fault event is calculated according to the shape parameter β and the scale parameter η, and the calculation formula is as follows: In the formula, t is time, the unit is second; and the reliability r(t) of each fault event is as follows: The process of calculating the shape parameter β and the scale parameter η conforming to the Weibull distribution by the least square method is as follows: First, the cumulative fault probability function is logarithmically transformed to obtain the following formula: Then, a straight line is fitted by using the least square method to convert the above linear relationship into a standard linear regression form: Y = βX + C In the above equation, Y = ln(-ln(l-r(t))); X = ln(t); C = -βln(η), The shape parameter β and the scale parameter η are solved by the linear regression.

10. The method of claim 9, wherein: The reliability calculation method of the whole machine is: through the calculation formula of the whole machine failure probability and the cumulative failure probability function F(t) of each failure event, the cumulative failure probability function F(t) of the whole machine is calculated S If the cumulative failure probability function F(t) of the whole machine is calculated, the reliability of the whole machine is R(t)=1-F S (t).

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