CN114462513A - Airplane fault early warning method and system based on airplane fault occurrence data - Google Patents
Airplane fault early warning method and system based on airplane fault occurrence data Download PDFInfo
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- CN114462513A CN114462513A CN202210056633.9A CN202210056633A CN114462513A CN 114462513 A CN114462513 A CN 114462513A CN 202210056633 A CN202210056633 A CN 202210056633A CN 114462513 A CN114462513 A CN 114462513A
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- 238000007781 pre-processing Methods 0.000 claims abstract description 12
- 238000012423 maintenance Methods 0.000 claims description 29
- 230000015654 memory Effects 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 8
- 230000001915 proofreading effect Effects 0.000 claims description 7
- 230000008439 repair process Effects 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
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- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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Abstract
The invention discloses an airplane fault early warning method and system based on airplane fault occurrence data; the method comprises the following steps: acquiring historical fault data of an airplane to be processed; classifying the airplane historical fault data to be processed according to a predefined classification standard; preprocessing the classified historical fault data of each type of airplane; and calculating the preprocessed historical fault data of each type of airplane by using a Poisson algorithm to obtain an airplane fault early warning result. Based on SDR fault occurrence data, a Poisson distribution algorithm is adopted, and calculation, prediction analysis and alarm of SDR fault occurrence are achieved.
Description
Technical Field
The invention relates to the technical field of civil aviation and airplane SDR (software defined networking) fault alarm prediction, in particular to an airplane fault early warning method and system based on airplane fault occurrence data.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The aviation company provides relatively high requirements for effectively mastering the use, maintenance conditions and safety dynamics of the aircraft and timely discovering potential safety hazards in the operation of the aircraft, and provides effective alarm data through calculation, analysis and prediction of fault information data, so that the risk occurrence probability is reduced, and the operation safety of the aircraft is improved.
The SDR fault information data volume is large, the span time is long, the abnormal conditions are more, the trend change condition of the part within a certain time can not be accurately displayed through the traditional fault rate data calculation, and the accuracy of maintenance prediction is lower.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an aircraft fault early warning method and system based on aircraft fault occurrence data; based on SDR fault occurrence data, a Poisson distribution algorithm is adopted, and calculation, prediction analysis and alarm of SDR fault occurrence are achieved.
In a first aspect, the invention provides an aircraft fault early warning method based on aircraft fault occurrence data;
the airplane fault early warning method based on the airplane fault occurrence data comprises the following steps:
acquiring historical fault data of an airplane to be processed;
classifying the airplane historical fault data to be processed according to a predefined classification standard;
preprocessing the classified historical fault data of each type of airplane;
and calculating the preprocessed historical fault data of each type of airplane by using a Poisson algorithm to obtain an airplane fault early warning result.
In a second aspect, the invention provides an aircraft fault early warning system based on aircraft fault occurrence data;
aircraft trouble early warning system based on aircraft trouble takes place data includes:
an acquisition module configured to: acquiring historical fault data of an airplane to be processed;
a classification module configured to: classifying the airplane historical fault data to be processed according to a predefined classification standard;
a pre-processing module configured to: preprocessing the classified historical fault data of each type of airplane;
a fault pre-warning module configured to: and calculating the preprocessed historical fault data of each type of airplane by using a Poisson algorithm to obtain an airplane fault early warning result.
In a third aspect, the present invention further provides an electronic device, including:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the method of the first aspect.
In a fourth aspect, the present invention also provides a storage medium storing non-transitory computer readable instructions, wherein the non-transitory computer readable instructions, when executed by a computer, perform the instructions of the method of the first aspect.
In a fifth aspect, the invention also provides a computer program product comprising a computer program for implementing the method of the first aspect when run on one or more processors.
Compared with the prior art, the invention has the beneficial effects that:
the Poisson distribution algorithm is a discrete probability distribution commonly found in statistics and probability science, is suitable for describing the occurrence frequency of random events in unit time (or space), is discovered through research and analysis that the Poisson distribution algorithm and fault information data analysis have a common part, and is capable of discovering that the result data has a consistency rule by combining the fault information data with the Poisson distribution algorithm, so that the Poisson distribution algorithm is helpful for improving the alarm accuracy of the fault information, and is further helpful for an airline company to improve the safety control level.
The invention adopts a computer system means, combines SDR fault data occurring in the civil aviation field with a Poisson distribution mathematical algorithm to carry out calculation and analysis, and carries out SDR occurrence probability prediction on the basis of the data, and carries out system alarm when the probability exceeds 95 percent. The problem of current civil aviation affairs in the aspect of fault data messy, irregular, can't carry out scientific analysis and prediction is solved.
The traditional fault SDR classification method is optimized, and classification is more accurate. Defining the airplane fault setting special topic: air-break faults, tire burst faults, operating system failures, and pressure relief faults.
The invention realizes the full-automatic classification, import and automatic calibration process of the fault SDR by self-programming. And the manual data processing process is omitted.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flowchart of a method according to a first embodiment.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
All data are obtained according to the embodiment and are legally applied on the data on the basis of compliance with laws and regulations and user consent.
Interpretation of terms:
and the SDR Service Difficulty Report is a specific crew maintenance fault set and is used for reflecting the use, maintenance condition and safety dynamics of the aircraft.
NRC Non-Routine fault Card
PO production order
Example one
The embodiment provides an aircraft fault early warning method based on aircraft fault occurrence data;
as shown in fig. 1, the method for early warning an aircraft fault based on aircraft fault occurrence data includes:
s101: acquiring historical fault data of an airplane to be processed;
s102: classifying the airplane historical fault data to be processed according to a predefined classification standard;
s103: preprocessing the classified historical fault data of each type of airplane;
s104: and calculating the preprocessed historical fault data of each type of airplane by using a Poisson algorithm to obtain an airplane fault early warning result.
Further, the step S101: acquiring historical fault data of an airplane to be processed; wherein the aircraft historical fault data comprises: non-routine fault data (i.e., NRC) and command repair data (i.e., PO).
Further, the S102: classifying the historical fault data of the airplane to be processed according to predefined classification standards, specifically: according to the fault types, dividing the historical fault data of the airplane to be processed into: idle stop faults, flat tire faults, failure of the operating system, and pressure application faults.
Further, the step S103: preprocessing the classified historical fault data of each type of airplane; the method specifically comprises the following steps: and performing automatic calibration processing and junk data elimination processing on the classified historical fault data of each type of airplane.
Further, the automatic calibration processing specifically includes: setting fault information parameters for each fault, automatically correcting fault data and the fault information parameters, if the fault data are matched with the fault information parameters, considering that the correction is passed, and recording the data which is passed through the correction into a calculation link; otherwise, the collation is deemed to fail.
The major 50 types of fault data in the system are subjected to proofreading rules, and the examples are as follows:
model 737NG airplane, failure section 331100, repair category: and the fault information parameters of the air route comprise 'the lamp is not on', and the proofreading is considered to meet the requirement and pass.
Model 737NG airplane, failure section 262601, repair category: in the air route, fault information parameters comprise 'white glue of a fire extinguishing bottle', and the air route is considered that the proofreading does not meet the requirements and does not pass.
Further, the junk data is removed, wherein the junk data is defined as that the data is not passed through the proofreading, obviously wrong data is proofread again after being manually corrected, the data still cannot be regarded as the junk data, and the junk data is not included in a subsequent calculation link.
Further, the S104: calculating the preprocessed historical fault data of each type of airplane by using a Poisson algorithm to obtain an airplane fault early warning result; the method specifically comprises the following steps:
v_poisson:＝F_GET_POISSON_SUM(v_pre_count_3m,v_sum_flight_num_3_all)*100；
wherein v _ poisson represents an SDR poisson distribution calculation result;
v _ sum _ flight _ num _3_ all represents the total number of times of faults of the actual service maintenance fault set SDR three months before the specified time point;
v _ pre _ count _3m represents the number of occurrences of a failure in the expected set of crew maintenance failures SDR three months prior to the specified point in time;
f _ GET _ POISSON _ SUM represents the POISSON distribution algorithm function of the crew repair failure set SDR.
And (4) performing system early warning when the analysis and calculation result exceeds 95%, triggering early warning workflow, and informing relevant workers of the engineering.
Further, the failure occurrence times of the maintenance failure set SDR are expected three months before the specified time point; the method specifically comprises the following steps:
the number of occurrences of the failure in the expected crew maintenance failure set SDR three months before the specified time point is (total flight time 21 months before the specified time point/the number of occurrences of the failure in the total crew maintenance failure set SDR 21 months before the specified time point) × the flight time three months before the specified time point.
The total flight time 21 months before the appointed time point, including the total flight time of the whole airplane team, is obtained by accumulating the flight time 21 months before the appointed time point of each airplane.
The number of times of occurrence of the faults of the total maintenance fault set SDR 21 months before the specified time point is a summation result of the number of times of the faults of the maintenance fault set SDR related to the current fault type 21 months before the specified time point.
Further, the working principle of the poisson algorithm includes:
circularly traversing each of the maintenance failure sets SDR three months before the appointed time point, and calculating the expected failure occurrence probability of each maintenance failure set SDR by using a Poisson distribution function;
summing the expected failure occurrence probabilities of all the maintenance failure sets SDR to obtain the probability of the current type of maintenance failure sets SDR occurring in the next three months;
and if the probability of the faults occurring in the current type of the maintenance fault set SDR in the next three months is larger than a set threshold value, carrying out early warning control.
And if the probability of the fault is over 95 percent, the probability is high, the risk is high, and the technical unit of the engineering is reminded to perform early warning control. And performing predicted maintenance.
By calculating the pressure relief fault from 1 month in 2020 to 10 months in 2021, the change trend of the Poisson distribution analysis result of the pressure relief fault is seen, and when the change trend exceeds 95%, the pressure relief fault is high in occurrence probability, and system early warning needs to be carried out to inform engineering workers.
By calculating the pressure relief fault idle stop fault from 1 month in 2020 to 10 months in 2021, the variation trend of the Poisson distribution analysis results of the pressure relief fault and the idle stop fault is seen, and when the Poisson distribution value exceeds 95%, the pressure relief fault and the idle stop fault have extremely high occurrence probability and need to be subjected to system early warning to inform a crew. Compared with the traditional fault rate calculation method, the method and the device greatly improve the early warning accuracy of the system. By carrying out statistical analysis on random faults, the method can realize predictive maintenance and reduce the occurrence probability of airplane safety events.
Example two
The embodiment provides an aircraft fault early warning system based on aircraft fault occurrence data;
aircraft trouble early warning system based on aircraft trouble takes place data includes:
an acquisition module configured to: acquiring historical fault data of an airplane to be processed;
a classification module configured to: classifying the airplane historical fault data to be processed according to a predefined classification standard;
a pre-processing module configured to: preprocessing the classified historical fault data of each type of airplane;
a fault pre-warning module configured to: and calculating the preprocessed historical fault data of each type of airplane by using a Poisson algorithm to obtain an airplane fault early warning result.
It should be noted here that the acquiring module, the classifying module, the preprocessing module and the fault early warning module correspond to steps S101 to S104 in the first embodiment, and the modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The fourth embodiment also provides a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The airplane fault early warning method based on the airplane fault occurrence data is characterized by comprising the following steps of:
acquiring historical fault data of an airplane to be processed;
classifying the airplane historical fault data to be processed according to a predefined classification standard;
preprocessing the classified historical fault data of each type of airplane;
and calculating the preprocessed historical fault data of each type of airplane by using a Poisson algorithm to obtain an airplane fault early warning result.
2. The aircraft fault pre-warning method based on aircraft fault occurrence data according to claim 1, wherein historical fault data of the aircraft to be processed is obtained; wherein the aircraft historical fault data comprises: non-routine fault data and command repair data.
3. The aircraft fault early warning method based on aircraft fault occurrence data according to claim 1, characterized in that the aircraft historical fault data to be processed is classified according to predefined classification criteria, specifically: according to the fault types, dividing the historical fault data of the airplane to be processed into: idle stop faults, flat tire faults, failure of the operating system, and pressure application faults.
4. The method of claim 1, wherein the classified historical failure data of each type of aircraft is preprocessed; the method specifically comprises the following steps: and performing automatic calibration processing and junk data elimination processing on the classified historical fault data of each type of airplane.
5. The aircraft fault pre-warning method based on aircraft fault occurrence data according to claim 4, wherein the automatic calibration processing specifically comprises: setting fault information parameters for each fault, automatically correcting fault data and the fault information parameters, if the fault data are matched with the fault information parameters, considering that the correction is passed, and recording the data which is passed through the correction into a calculation link; otherwise, the proofreading is not passed;
and the junk data is removed, wherein the definition of the junk data is that the data is not passed through the proofreading, obviously wrong data is proofread again after being manually corrected, the data is still not passed through the proofreading and is not regarded as the junk data, and the junk data is not included in the subsequent calculation link.
6. The aircraft fault early warning method based on the aircraft fault occurrence data as claimed in claim 1, wherein a Poisson algorithm is adopted to calculate the preprocessed historical fault data of each type of aircraft to obtain an aircraft fault early warning result; the method specifically comprises the following steps:
v_poisson:＝F_GET_POISSON_SUM(v_pre_count_3m,v_sum_flight_num_3_all)*100；
wherein v _ poisson represents an SDR poisson distribution calculation result;
v _ sum _ flight _ num _3_ all represents the total number of times of faults of the actual service maintenance fault set SDR three months before the specified time point;
v _ pre _ count _3m represents the number of occurrences of a failure in the expected set of crew maintenance failures SDR three months prior to the specified point in time;
f _ GET _ POISSON _ SUM represents the POISSON distribution algorithm function of the crew repair failure set SDR.
7. The aircraft fault pre-warning method based on aircraft fault occurrence data as claimed in claim 6, wherein the number of occurrences of the fault in the crew service fault set SDR is expected three months before the specified time point; the method specifically comprises the following steps:
specifying the number of times of occurrence of the faults in the expected crew maintenance fault set SDR three months before the time point as (total flight time 21 months before the specified time point/the number of times of occurrence of the faults in the total crew maintenance fault set SDR 21 months before the specified time point) × specifying the flight time three months before the time point;
the total flight time 21 months before the appointed time point, including the total flight time of the whole fleet, is obtained by accumulating the flight time 21 months before the appointed time point of each airplane;
the failure occurrence times of the total maintenance failure set SDR 21 months before the appointed time point are the summation result of the failure times of the maintenance failure set SDR related to the current failure type 21 months before the appointed time point;
alternatively, the first and second electrodes may be,
the working principle of the Poisson algorithm comprises the following steps:
circularly traversing each of the maintenance failure sets SDR three months before the appointed time point, and calculating the expected failure occurrence probability of each maintenance failure set SDR by using a Poisson distribution function;
summing the expected failure occurrence probabilities of all the maintenance failure sets SDR to obtain the probability of the current type of maintenance failure sets SDR occurring in the next three months;
and if the probability of the faults occurring in the current type of the maintenance fault set SDR in the next three months is larger than a set threshold value, carrying out early warning control.
8. Aircraft trouble early warning system based on aircraft trouble takes place data, characterized by includes:
an acquisition module configured to: acquiring historical fault data of an airplane to be processed;
a classification module configured to: classifying the airplane historical fault data to be processed according to a predefined classification standard;
a pre-processing module configured to: preprocessing the classified historical fault data of each type of airplane;
a fault pre-warning module configured to: and calculating the preprocessed historical fault data of each type of airplane by using a Poisson algorithm to obtain an airplane fault early warning result.
9. An electronic device, comprising:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the method of any of claims 1-7.
10. A storage medium storing non-transitory computer-readable instructions, wherein the non-transitory computer-readable instructions, when executed by a computer, perform the instructions of the method of any one of claims 1-7.
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