A Dynamic Monitoring Method for Evaluating the Execution Effect of an Aircraft Maintenance Plan

Abstract

The present invention discloses a method for dynamically monitoring the effectiveness of aircraft maintenance program execution, comprising the following steps: Step 1: Calculating monthly failure indicators before and after the maintenance program is optimized; Step 2: Testing whether the two population means are equal by first determining the distribution of the difference between the sample means of the two datasets before and after the maintenance program is optimized; Step 3: Calculating the combined standard deviation of the samples of the two datasets before and after the maintenance program is optimized; Step 4: Establishing a warning value for the fleet system failure rate by calculating the standard deviation of the fleet system failure rate (x) per thousand flight hours over the past three consecutive months using flight hours, crew-reported failures, and ground maintenance findings over the past 14 months. The present invention belongs to the technical field of aircraft maintenance reliability assessment and monitoring. By rationally classifying and integrating maintenance data, and setting null hypotheses and paradoxical hypotheses, the effectiveness of maintenance program optimization can be evaluated and dynamically monitored using long-term maintenance data.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft maintenance reliability evaluation and monitoring, and specifically refers to a dynamic monitoring method for evaluating the execution effect of an aircraft maintenance plan. Background Art

[0002] According to the Civil Aviation Administration of China (CAAC) regulations, "Large Aircraft Public Air Transport Carrier Operation Certification Rules" (CCAR-121), certificate holders must prepare a maintenance program for each aircraft they operate and continuously monitor the effectiveness of the maintenance program based on the reliability program. Section 6.4(5) of the Civil Aviation Administration of China's AC-121 / 135-53 "Civil Aircraft Maintenance Program" requires that modifications to maintenance intervals should be implemented gradually through controlled methods (such as sampling tests, statistical analysis of maintenance data, or other supporting data), but must be approved by the relevant regional civil aviation administration in advance. Maintenance interval modifications should be monitored through controlled methods to ensure the reliability of related systems after the interval modification.

[0003] According to the aforementioned Chinese Civil Aviation regulations and advisory circulars, maintenance plans must be continuously monitored through reliability programs. Modifications to maintenance intervals within maintenance plans must be monitored through controlled methods to ensure the reliability of the relevant systems after the intervals are modified. Currently, airline operators generally follow the approach outlined in "7.4 Statistical Performance Standards (Alert Values)" of Civil Aviation Administration of China Advisory Circular AC-121-54R1, "Reliability Plans," to establish corresponding alert values based on the monthly fluctuations in system or component failures within their fleets, using statistical methods such as "standard deviation" or "Poisson distribution," and conduct monthly monitoring.

[0004] For example, China Civil Aviation Advisory Circular AC-121-54R1, "Reliability Plan," requires: "For control objects (such as systems and components) that use alert value performance standards, statistical performance standard analysis methods should be used to compare the collected data with the currently adopted performance standards." The specific implementation methods are as follows:

[0005] Statistical performance standards (alert values)

[0006] (1) Aircraft performance is usually measured numerically based on crew reports, flight delays or cancellations, unplanned component replacements, confirmed component failure rates, service time, or other events, and is used as the basis for establishing standards. The following factors should be considered in determining performance standards (alert values):

[0007] (a) Past and current operational experience of independent operators and industry. If such experience is used, the operator's reliability control program must include provisions and measures for reviewing performance standards after one year of operational experience has accumulated;

[0008] (b) Performance analysis of similar equipment currently in use;

[0009] (c) Reliability engineering analysis by aircraft or equipment manufacturers;

[0010] (d) In-service experience based on reliability standards acceptable to the aviation community.

[0011] (2) The establishment and modification of control limits or warning values are usually based on acceptable statistical methods, such as standard deviation or Poisson distribution. Common performance standard calculations (but not limited to) are as follows:

[0012] (a) Unit reported faults: (at least 12 months of cumulative data)

[0013] Calculation method 1: 1.3*average reported failure rate per thousand hours

[0014] Calculation method 2: Average (rounded) of reported failure rates per thousand hours + 3 times the standard deviation (rounded)

[0015] Calculation method 3: Average reported failure rate per thousand hours + standard deviation of the adjacent month's average + 3 times the standard deviation (rounded up)

[0016] (b) Unplanned replacement rate of components: (data accumulated for at least 21 months)

[0017] Calculation method 4: average value of unplanned component replacement rate per quarter + 2 times standard deviation

[0018] Calculation method 5: Use the Poisson distribution table to find the maximum acceptable number of unplanned replacements per quarter with a probability of 95%.

[0019] (c) Component confirmation failure:

[0020] Calculation method 6: (at least 21 months of cumulative data)

[0021] Adjusted mean + adjusted standard deviation of quarterly component confirmation thousand-hour failure rate

[0022] (3) The program should include procedures for adjusting and periodically reviewing the adopted performance standards. Aircraft performance standards should be adjusted based on the operator's operating experience and should reflect the effects of seasonal and environmental factors.

[0023] (4) The plan should also include a performance monitoring program for the new aircraft until sufficient operating data and experience have been accumulated to calculate performance standards, which should normally take one year.

[0024] The existing "Statistical Performance Standard (Alert Value)" approach uses short-term abnormal fluctuation alarm logic. Its advantage is that it can identify monthly trends in worsening system or component failures, allowing for timely corrective action. However, its disadvantage is that it struggles to reflect the impact of changes in maintenance intervals on the fleet. This is because monthly fleet data fluctuations can be caused by a variety of factors, such as changes in the external environment or operating conditions, repeated failures within the fleet, and policy factors. Furthermore, this approach uses a short fault alarm cycle, while the execution cycle of maintenance plan items is long. Using short-term abnormal fluctuation alarm logic is ineffective in assessing the long-term impact of fleet maintenance plan changes.

[0025] The present invention is primarily applicable to the field of aircraft maintenance reliability assessment and monitoring. It provides a method for dynamically evaluating and monitoring the execution effectiveness of aircraft maintenance plans based on hypothesis testing. Based on "7.2 Data Analysis System Requirements" in China Civil Aviation Advisory Circular AC-121-54R1, "Reliability Plan," this method, unlike the traditional reliability monitoring method of "using statistical performance standards to analyze control objects (such as systems and components) using alert value performance standards," evaluates and dynamically monitors the optimization effectiveness of maintenance plans using long-term maintenance data through the rational classification and integration of maintenance data and the setting of null hypotheses and paradoxical hypotheses. Summary of the Invention

[0026] The technical solution adopted by the present invention is as follows: A method for dynamically monitoring the execution effect of an aircraft maintenance plan proposed by the present invention comprises the following steps:

[0027] Step 1: Record the monthly failure indicators (fleet failure rate, crew reporting rate, ground discovery rate, and delay rate) before and after the maintenance plan optimization as

[0028] Step 2: Check whether the two population means are equal. First, give the distribution of the difference between the sample means of the two data sets before and after the maintenance plan optimization.

[0029] Step 3: The combined standard deviation of the two data set samples before and after the maintenance plan optimization;

[0030] Step 4: Establish a warning value for the fleet system failure rate. This value is determined by calculating the standard deviation of the fleet system failure rate (x) per thousand flight hours over the past three months using the flight hours, crew-reported failures, and ground maintenance findings over the past 14 months.

[0031] Step 5: The warning value is to use the Poisson cumulative probability distribution table to find the maximum acceptable number of unplanned replacements in a quarter with a probability of 95%, and compare it with the number of unplanned replacements in the current quarter to determine whether it exceeds the limit and issue an alarm;

[0032] Step 6: Use Poisson cumulative distribution to monitor the reliability of the attachment;

[0033] In step 1, the monthly failure indicators include fleet failure rate, crew reporting rate, ground discovery rate and delay rate. The monthly failure indicators before and after maintenance plan optimization are recorded as The sample mean and sample standard deviation of the monthly failure index before and after optimization are:

[0034]

[0035] Furthermore, in step 2, since the monthly failure indicators before and after the maintenance plan optimization come from the same data set, the variances of the samples of the two data sets are equal and unknown, and the sample variance satisfies The overall variance of the data is To test whether the two population means are equal, we first give the distribution of the difference between the sample means of the two data sets before and after the maintenance plan optimization. According to the assumption, we get:

[0036]

[0037] By the Chi-square theorem:

[0038]

[0039] Since the sample distributions of the two data sets before and after the maintenance plan optimization are independent, Independence, by chi-square additivity:

[0040]

[0041] Defined by the construction of the distribution, After simplification, we can get:

[0042]

[0043] in:

[0044]

[0045] Furthermore, in step 3, similarly, under the condition that the sample means of the two data sets before and after the maintenance plan optimization are equal to the overall mean, μ1-μ2=0;

[0046] Because the test is two-sided, the degrees of freedom are n-1, and the two-sided α gives the two-tailed critical value Establishing the hypothesis:

[0047] Null hypothesis: There is no significant change in the samples of the two data sets before and after the maintenance plan optimization

[0048]

[0049] Paradox hypothesis: There are significant changes in the samples of the two data sets before and after the maintenance plan optimization

[0050]

[0051] if:

[0052]

[0053] Then the null hypothesis is rejected, and it is believed that the sample means of the two data sets before and after the optimization of the two maintenance schemes are not equal. Otherwise, the null hypothesis is not rejected;

[0054] If the null hypothesis is rejected,

[0055]

[0056] It represents The mean is significantly less than For example, after the maintenance plan is optimized, the data set is The data set before maintenance plan optimization is The mean failure rate of the data set after the maintenance plan is optimized is significantly lower than that of the data set before the maintenance plan is optimized. The failure rate decreases significantly after the maintenance plan is optimized.

[0057] If the null hypothesis is rejected,

[0058]

[0059] It represents The mean is significantly greater than If the maintenance plan is optimized, the data set is The data set before maintenance plan optimization is The mean failure rate of the data set after the maintenance plan is optimized is significantly higher than that of the data set before the maintenance plan is optimized, and the failure rate has increased significantly after the maintenance plan is optimized.

[0060] Furthermore, in step 4, a warning value for the fleet system failure rate is established according to the performance standard "Calculation Method 2" provided in AC-121-54. That is, the warning value is determined by calculating the standard deviation of the fleet system failure rate (x) per thousand flight hours in the three consecutive months using the flight hours, crew-reported failures, and ground maintenance discoveries in the past 14 months;

[0061]

[0062] In the formula:

[0063] X = fleet system failure rate per thousand flight hours in the three consecutive months;

[0064] N = the number of samples collected in three consecutive months, normally 12;

[0065]

[0066] Σ = sum of 12 ratios;

[0067] Warning value calculation:

[0068]

[0069]

[0070] Furthermore, in step 5, according to the Poisson distribution monitoring annex of the performance standard "Calculation Method 5" provided in AC-121-54, the warning value is the maximum acceptable number of unplanned replacements in a quarter with a probability of 95% found using the Poisson cumulative probability distribution table, and compared with the number of unplanned replacements in the current quarter to determine whether it exceeds the limit and an alarm is issued;

[0071] Suppose all possible values of the random variable X are 0, 1, 2, ..., and the probability of taking each value is:

[0072] where k = 0, 1, 2, ...,

[0073] Where λ is a constant, then X is said to obey the Poisson distribution with parameter λ;

[0074] where k = 0, 1, 2, ...,

[0075] The above formula is the probability that the random variable X is less than or equal to the value k, which is called the Poisson cumulative distribution.

[0076] Furthermore, in step six, the reliability of the attachment is monitored using Poisson cumulative distribution according to calculation method five of advisory circular AC-121-54;

[0077] Warning value = the maximum acceptable number of unplanned replacements per quarter with a 95% probability, as determined by the Poisson cumulative probability distribution table;

[0078] Components: Autoflight pitch amplifier;

[0079] The number of parts per aircraft is n = 1;

[0080] The number of flight plan replacements in the first 21 months is N = 62;

[0081] Fleet usage hours H in the first 21 months = 36,840;

[0082] Component usage hours in the first 21 months T = (n × H) = 36840;

[0083] Fleet usage hours in the past three months h = 5895;

[0084] Component usage hours in the past three months t = (n × h) = 5895;

[0085] Number of unplanned replacements in the past three months x = 12;

[0086] Average unplanned replacement rate λ = N / T = 0.00168;

[0087] Expected number of unplanned replacements in the past three months = λt = 0.00168 × 5895 = 9.9, rounded to the nearest integer = 10;

[0088] Referring to the Poisson cumulative probability diagram: the intersection line of λt=10 and the 95% probability shows that the maximum acceptable number of unplanned replacements is 15.

[0089] Comparing the current number of unplanned replacements x=12, it can be concluded that the warning value has not been exceeded.

[0090] The beneficial effects achieved by the present invention using the above structure are as follows:

[0091] 1. The maintenance data monitoring system established by the present invention is a long-term data monitoring system, which solves the pain point that maintenance projects have long execution time spans and are difficult to monitor.

[0092] 2. The present invention can well integrate the traditional reliability monitoring system based on "statistical performance standards (warning values)", and the two monitoring systems can achieve complementary advantages.

[0093] 3. The present invention can reflect the positive or negative effects achieved before and after the execution of a maintenance project.

[0094] 4. The present invention can quantify the normal fluctuation range and margin of the fleet system failure rate, effectively reducing the impact of other factors on the fleet failure rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 Schematic diagram of the "S1-Aircraft Maintenance Plan Execution Effect Evaluation Dynamic Monitoring System" and the "S2-Statistical Performance Standard (Alert Value) Monitoring System" according to an embodiment of the present invention;

[0096] Figure 2 This is a schematic diagram of the display interface of the S1.1 monitoring system;

[0097] Figure 3 A schematic diagram of the elements of a monitoring system according to an embodiment of the present invention;

[0098] Figure 4 This is a schematic diagram of a method for characterizing the effects of a maintenance solution before and after optimization through hypothesis testing according to an embodiment of the present invention;

[0099] Figure 5 Schematic diagram of a presentation method of an embodiment of the present invention.

[0100] Among them, Figure 2 Chinese: 1. ATA chapter number; 2. ATA chapter description; 3. Maintenance plan optimization axes (fleet failure rate, crew reporting rate, ground detection rate, and delay rate); 4. Maintenance plan optimization performance evaluation interval; 5. Orange columns represent the average failure indicators (fleet failure rate, crew reporting rate, ground detection rate, and delay rate) for the period after the maintenance plan was modified; 6. Blue columns represent the average failure indicators (fleet failure rate, crew reporting rate, ground detection rate, and delay rate) for the period before the maintenance plan was modified; 7. Areas with good maintenance plan optimization results; 8. Areas with stable maintenance plan optimization results Domain; 9. Areas where the maintenance plan optimization effect is poor; 10. The maintenance plan optimization effect has improved significantly. The further the block is to the left, the better the optimization effect is. The block color is dark green. 11. The changes in various system indicators before and after the maintenance plan optimization are within a reasonable range. The block color is light blue. 12. The maintenance plan optimization effect has deteriorated significantly. The further the block is to the right, the worse the optimization effect is. The block color is dark red. 13. The progressive color bar from red to green represents the Performance scale axis. The shorter the bar, the redder it is, the worse the data performance is. The longer the bar, the greener it is, the better the data performance is.

[0101] exist Figure 3 Chinese: 1. Double-click to display the failure rate details; 2. Average failure indicators before maintenance plan optimization (fleet failure rate, crew reporting rate, ground detection rate, and delay rate); 3. Average failure indicators after maintenance plan optimization (fleet failure rate, crew reporting rate, ground detection rate, and delay rate); 4. Monthly failure indicators before maintenance plan optimization (fleet failure rate, crew reporting rate, ground detection rate, and delay rate); 5. Monthly failure indicators after maintenance plan optimization (fleet failure rate, crew reporting rate, ground detection rate, and delay rate). DETAILED DESCRIPTION

[0102] This technical solution primarily comprises the "S1 - Dynamic Monitoring System for Evaluating Aircraft Maintenance Program Implementation Effectiveness" and the "S2 - Statistical Performance Standard (Alert Value) Monitoring System" shown below. S2 primarily comprises the methods described in AC-121-54R1, "Reliability Program," including "S2.1 Alarm Monitoring System Based on Unplanned Component Replacement Abnormalities" and "S2.2 Monitoring System Based on Reliability Alert Value Performance Standards." The present invention establishes a correlation between S1 and S2 using the Air Transport Association of America (ATA) specification No. 100 and the part or group numbers of key components.

[0103] The monthly failure indicators include fleet failure rate, crew reporting rate, ground discovery rate and delay rate. The monthly failure indicators before and after maintenance plan optimization are recorded as The sample mean and sample standard deviation of the monthly failure index before and after optimization are:

[0104]

[0105] Because the monthly failure indicators before and after the maintenance plan optimization come from the same data set, the variances of the samples of the two data sets are equal and unknown, and the sample variance satisfies The overall variance of the data is To test whether the two population means are equal, we first give the distribution of the difference between the sample means of the two data sets before and after the maintenance plan optimization. According to the assumption, we get:

[0106]

[0107] By the Chi-square theorem:

[0108]

[0109] Since the sample distributions of the two data sets before and after the maintenance plan optimization are independent, Independence, by chi-square additivity:

[0110]

[0111] Defined by the construction of the distribution, After simplification, we can get:

[0112]

[0113] in:

[0114]

[0115] is the pooled standard deviation of the two data sets before and after the maintenance plan is optimized.

[0116] Similarly, under the condition that the sample means of the two data sets before and after the maintenance plan optimization are equal to the overall mean, μ1-μ2=0.

[0117] Because the test is two-sided, the degrees of freedom are n-1, and the two-sided α gives the two-tailed critical value

[0118] Establishing the hypothesis:

[0119] Null hypothesis: There is no significant change in the samples of the two data sets before and after the maintenance plan optimization

[0120]

[0121] Paradox hypothesis: There are significant changes in the samples of the two data sets before and after the maintenance plan optimization

[0122]

[0123] if:

[0124]

[0125] Then the null hypothesis is rejected, and it is believed that the sample means of the two data sets before and after the optimization of the two maintenance schemes are not equal. Otherwise, the null hypothesis is not rejected.

[0126] If the null hypothesis is rejected,

[0127]

[0128] It represents The mean is significantly less than For example, after the maintenance plan is optimized, the data set is The data set before maintenance plan optimization is The mean failure rate of the data set after the maintenance plan is optimized is significantly lower than that of the data set before the maintenance plan is optimized, and the failure rate has decreased significantly after the maintenance plan is optimized.

[0129] If the null hypothesis is rejected,

[0130]

[0131] It represents The mean is significantly greater than If the maintenance plan is optimized, the data set is The data set before maintenance plan optimization is The mean failure rate of the data set after the maintenance plan is optimized is significantly higher than that of the data set before the maintenance plan is optimized, and the failure rate has increased significantly after the maintenance plan is optimized.

[0132] S2-Statistical Performance Standard (Alarm Value) Monitoring System S2.1 Alarm Monitoring System Based on Unplanned Component Replacement Abnormality

[0133] Determination of Failure Rate Warning Value for Fleet System

[0134] The alert value for the fleet system failure rate is established according to the performance standard "Calculation Method 2" provided in AC-121-54. That is, it is determined by calculating the standard deviation of the fleet system failure rate (x) per thousand flight hours in the three consecutive months using the flight hours, crew-reported failures and ground maintenance findings in the past 14 months.

[0135]

[0136] In the formula:

[0137] X = fleet system failure rate per thousand flight hours in the three consecutive months N = number of samples in the three consecutive months, normally 12

[0138]

[0139] Σ=12 times the sum of ratios

[0140] Warning value calculation:

[0141]

[0142] S2.2 Monitoring system based on reliability warning value performance standard

[0143] According to the Poisson distribution monitoring annex of the performance standard "Calculation Method 5" provided in AC-121-54, the warning value is to use the Poisson cumulative probability distribution table to find the maximum acceptable number of unplanned replacements in a quarter with a probability of 95%, and compare it with the number of unplanned replacements in the current quarter to determine whether it exceeds the standard and issue an alarm.

[0144] Suppose all possible values of random variable X are 0, 1, 2, ..., and the probability of taking each value is:

[0145] where k = 0, 1, 2, ...,

[0146] Where λ is a constant, then X is said to obey the Poisson distribution with parameter λ.

[0147] where k = 0, 1, 2, ...,

[0148] The above formula is the probability that the random variable X is less than or equal to the value k, which is called Poisson cumulative distribution. S2.2.1 Monitoring method

[0149] Based on calculation method five of advisory circular AC-121-54, Poisson cumulative distribution is used to monitor accessory reliability. The specific method is shown in the following example.

[0150] Warning value = the maximum acceptable number of unplanned replacements per quarter with a probability of 95% as determined by the Poisson cumulative probability distribution table.

[0151] Component: Autoflight pitch amplifier

[0152] Number of parts per aircraft n = 1

[0153] The number of flight plan replacements in the first 21 months is N = 62

[0154] Fleet usage hours H in the first 21 months = 36840

[0155] Component usage hours in the first 21 months T = (n × H) = 36840

[0156] Fleet usage hours in the past three months h=5895

[0157] Component usage hours in the past three months t = (n × h) = 5895

[0158] Number of unplanned replacements in the past three months x = 12

[0159] Average unplanned replacement rate λ = N / T = 0.00168

[0160] Expected number of unplanned replacements in the past three months = λt = 0.00168 × 5895 = 9.9 rounded to the nearest integer = 10

[0161] Referring to the Poisson cumulative probability diagram: the intersection line of λt=10 and the 95% probability shows that the maximum acceptable number of unplanned replacements (A value) is 15.

[0162] Comparing the current number of unplanned replacements x=12, it can be concluded that the warning value has not been exceeded.

[0163] The calculation formula in Section 3.2.1 shows that the Poisson cumulative distribution is positively correlated with the number of failures. The greater the number of failures, the larger the Poisson cumulative distribution. The greater the number of failures, the greater the failure rate and the operational support pressure. Therefore, the Poisson cumulative distribution reflects the following two operational significances: 1. The Poisson cumulative distribution reflects the level of accessory failure rate; 2. The Poisson cumulative distribution reflects the level of accessory operational support pressure.

[0164] The relationship between S1 and S2

[0165] 1. Perform a systematic chapter-by-chapter analysis of maintenance plan items using the Air Transport Association of America (ATA) ATA100 specification. Obtain the ATA100 chapter numbers for the inspection contents associated with the maintenance plan items. Use the S1 and S2 algorithms to monitor the fault indicators (fleet failure rate, crew reporting rate, ground detection rate, and delay rate) corresponding to the ATA100 chapter numbers.

[0166] 2. Associate by key component number or part number group number

[0167] Analyze the maintenance plan items, obtain the component part numbers or part number group numbers of the inspection contents related to the maintenance plan items, and use the "S2.2 Monitoring system based on reliability warning value performance standards" method for monitoring.

[0168] The above is the entire process of using a dynamic monitoring method for evaluating the implementation effect of an aircraft maintenance plan.

[0169] This paper establishes a set of dynamic monitoring and analysis processes and methods for evaluating the execution effect of aircraft maintenance plans based on the two-sample equal variance assumption. The key points include:

[0170] (1) An analytical framework that integrates the “S1-Dynamic Monitoring System for Aircraft Maintenance Program Implementation Effectiveness Evaluation” with the “S2-Statistical Performance Standard (Alert Value) Monitoring System”.

[0171] (2) S1.1 Monitoring system display interface, element composition and meaning

[0172] (3) S1.2 Algorithm principle and actual presentation method, setting logic of null hypothesis and paradox hypothesis before and after maintenance plan optimization.

[0173] (4) The mutual relationship between S1 and S2 is established through the Air Transport Association of America (ATA100) specification and key component part numbers or group numbers.

[0174] The description of the dynamic monitoring and analysis process and method for evaluating the effectiveness of aircraft maintenance program optimization from S1 to S2 is relatively specific and detailed, but this should not be construed as limiting the scope of the present invention. It should be noted that the process sequence may be modified and improved in various ways without departing from the scope of the present invention, and all such modifications and improvements fall within the scope of protection of the present invention.

[0175] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A method for dynamically monitoring the effectiveness of aircraft maintenance program execution, characterized by: The following steps are involved: Step 1: Record monthly failure indicators before and after maintenance plan optimization; Step 2: Check whether the two population means are equal. First, give the distribution of the difference between the sample means of the two data sets before and after the maintenance plan optimization. Step 3: The combined standard deviation of the two data set samples before and after the maintenance plan optimization; Step 4: Establish a warning value for the fleet system failure rate. This value is determined by calculating the standard deviation of the fleet system failure rate (x) per thousand flight hours over the past three months using the flight hours, crew-reported failures, and ground maintenance findings over the past 14 months. Step 5: The warning value is to use the Poisson cumulative probability distribution table to find the maximum acceptable number of unplanned replacements in a quarter with a probability of 95%, and compare it with the number of unplanned replacements in the current quarter to determine whether it exceeds the limit and issue an alarm; Step 6: Use Poisson cumulative distribution to monitor the reliability of the attachment; In step 1, the monthly failure indicators include fleet failure rate, crew reporting rate, ground discovery rate and delay rate. The monthly failure indicators before and after maintenance plan optimization are recorded as The sample mean and sample standard deviation of the monthly failure index before and after optimization are:

2. The method for dynamically monitoring the execution effect of an aircraft maintenance plan according to claim 1, characterized in that: In step 2, because the monthly failure indicators before and after the maintenance plan optimization come from the same data set, the variances of the samples of the two data sets are equal and unknown, and the sample variance satisfies The overall variance of the data is To test whether the two population means are equal, we first give the distribution of the difference between the sample means of the two data sets before and after the maintenance plan optimization. According to the assumption, we get: By the Chi-square theorem: Since the sample distributions of the two data sets before and after the maintenance plan optimization are independent, Independence, by chi-square additivity: Defined by the construction of the distribution, After simplification, we can get: in:

3. The method for dynamically monitoring the execution effect of an aircraft maintenance plan according to claim 2, characterized in that: In the step 3, similarly, under the condition that the sample means of the two data sets before and after the maintenance plan optimization are equal to the overall mean, μ1-μ2=0; Because the test is two-sided, the degrees of freedom are n-1, and the two-sided α gives the two-tailed critical value Establishing the hypothesis: Null hypothesis: There is no significant change in the samples of the two data sets before and after the maintenance plan optimization Paradox hypothesis: There are significant changes in the samples of the two data sets before and after the maintenance plan optimization if: Then the null hypothesis is rejected, and it is believed that the sample means of the two data sets before and after the optimization of the two maintenance schemes are not equal. Otherwise, the null hypothesis is not rejected; If the null hypothesis is rejected, It represents The mean is significantly less than For example, after the maintenance plan is optimized, the data set is The data set before maintenance plan optimization is The mean failure rate of the data set after the maintenance plan is optimized is significantly lower than that of the data set before the maintenance plan is optimized. The failure rate decreases significantly after the maintenance plan is optimized. If the null hypothesis is rejected, It represents The mean is significantly greater than If the maintenance plan is optimized, the data set is The data set before maintenance plan optimization is The mean failure rate of the data set after the maintenance plan is optimized is significantly higher than that of the data set before the maintenance plan is optimized, and the failure rate has increased significantly after the maintenance plan is optimized.

4. The method for dynamically monitoring the execution effect of an aircraft maintenance plan according to claim 3, characterized in that: In step 5, according to the Poisson distribution monitoring annex of the performance standard "Calculation Method 5" provided in AC-121-54, the warning value is to use the Poisson cumulative probability distribution table to find the maximum acceptable number of unplanned replacements in a quarter with a probability of 95%. The warning value is compared with the number of unplanned replacements in the current quarter to determine whether it exceeds the limit and issue an alarm; Suppose all possible values of random variable X are 0, 1, 2, ..., and the probability of taking each value is: where k = 0, 1, 2, ..., Where λ is a constant, then X is said to obey the Poisson distribution with parameter λ; where k = 0, 1, 2, ..., The above formula is the probability that the random variable X is less than or equal to the value k, which is called the Poisson cumulative distribution.

5. The method for dynamically monitoring the execution effect of an aircraft maintenance plan according to claim 4, characterized in that: Step 6: Based on calculation method 5 of advisory circular AC-121-54, the reliability of the attachment is monitored using Poisson cumulative distribution; Warning value = the maximum acceptable number of unplanned replacements per quarter with a 95% probability, as determined by the Poisson cumulative probability distribution table; Components: Autoflight pitch amplifier; The number of parts per aircraft is n = 1; The number of flight plan replacements in the first 21 months is N = 62; Fleet usage hours H in the first 21 months = 36,840; Component usage hours in the first 21 months T = (n × H) = 36840; Fleet usage hours in the past three months h = 5895; Component usage hours in the past three months t = (n × h) = 5895; Number of unplanned replacements in the past three months x = 12; Average unplanned replacement rate λ = N / T = 0.00168; Expected number of unplanned replacements in the past three months = λt = 0.00168 × 5895 = 9.9, rounded to the nearest integer = 10; Referring to the Poisson cumulative probability diagram: the intersection line of λt=10 and the 95% probability shows that the maximum acceptable number of unplanned replacements is 15.

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