CN111104971A - Engine parameter detection method based on probability statistics and support vector machine - Google Patents
Engine parameter detection method based on probability statistics and support vector machine Download PDFInfo
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Abstract
The invention discloses an engine parameter detection method, which is characterized by comprising the following steps: obtaining a plurality of engine parameters of a plurality of frames of an airplane; acquiring a time sequence data set formed by each engine parameter of a single frame; carrying out anomaly detection on the engine parameters of each time sequence data set through an anomaly detection algorithm based on a probability statistical model, and calculating the average value, the standard deviation and the probability statistical model parameters of each single-rack engine parameter; for each engine parameter, constructing a support vector machine classification model through a plurality of groups of calculated average values, standard deviations and probability statistical model parameters; calculating the average value and the standard deviation of the engine parameters of the new set of engines, predicting corresponding probability statistic model parameters through a support vector machine model, and obtaining abnormal data in the probability statistic model parameters of the new set of aircraft engines through an abnormal detection algorithm based on the probability statistic model according to the probability statistic model parameters. The method can effectively predict the health condition of the aircraft engine.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to an aircraft engine parameter anomaly detection method based on probability statistics and a support vector machine for a military flight big data aircraft outfield autonomous security information support system.
Background
From the last 90 years to the present, the technology of aviation equipment is rapidly developed, and particularly under the large environment that military strategy adjustment and aviation equipment combat use pattern change, the requirement on airplane ground guarantee is higher and higher, and the guarantee on an airplane engine is the most fundamental factor. The rapid development of military science and technology puts higher requirements on the guarantee and the abnormality detection of the flight engine. However, in long-term development, the technology of securing aircraft engines has always lagged behind that of other space equipment. The original aircraft engine barrier system has great challenge under the condition of new equipment, and the readiness rate of military aircraft can be greatly reduced if the barrier system cannot meet the guarantee requirement.
The anomaly detection of the aircraft engine lacks quantitative analysis, and the accumulated experience and data in the actual use and maintenance process cannot be well combined with the design data for analysis, so that the theory is separated from the reality. When the aircraft engine is abnormal, no early warning mechanism exists, and the maintenance personnel of the aircraft field equipment are difficult to count the aircraft engine in mind, so that the predictability is insufficient, and the completeness rate of the aircraft engine is reduced.
When an aircraft engine is abnormal, the aircraft field maintenance personnel are difficult to perform definite abnormality detection aiming at the comprehensive analysis of the fault phenomenon, the reliability data, the index data and the like, so that an optimal abnormality detection method is difficult to find. This increases the maintenance costs of the aircraft engine, while the failed engine is not well maintained, resulting in a waste of resources.
The anomaly detection of the flight engine is the basis for predicting the health state of the aircraft engine, and influences the operation efficiency and the maintenance guarantee efficiency of a military aircraft all the time, so that the anomaly detection has an extremely important effect in the whole army, and therefore, when the aviation equipment is vigorously developed, how to provide accurate anomaly detection for the aircraft engine is also a technical problem to be solved urgently in the field of ground guarantee.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the aircraft engine parameter anomaly detection method based on probability statistics and a support vector machine, which is applied to the aircraft outfield autonomous guarantee information support system, and can effectively detect the anomaly data in the aircraft engine parameters.
Based on the above purpose, the embodiment of the present invention provides an engine parameter detection method based on probability statistics and a support vector machine, including the following steps:
obtaining a plurality of engine parameters of a plurality of frames of an airplane;
acquiring a time sequence data set formed by each engine parameter of a single frame;
carrying out anomaly detection on the engine parameters of each time sequence data set through an anomaly detection algorithm based on a probability statistical model, and calculating the average value, the standard deviation and the probability statistical model parameters of each single-rack engine parameter;
for each engine parameter, constructing a support vector machine classification model through a plurality of groups of calculated average values, standard deviations and probability statistical model parameters;
calculating the average value and the standard deviation of the engine parameters of the new set of engines, predicting the corresponding probability statistic model parameters through a support vector machine model, and obtaining abnormal data in the probability statistic model parameters of the new set of aircraft engines through an abnormal detection algorithm based on the probability statistic model according to the probability statistic model parameters.
According to an embodiment of the present invention, a method for detecting engine parameters based on probabilistic statistics and support vector machines, the plurality of engine parameters comprises: the method comprises the following steps of low-pressure turbine rear gas total temperature T4, vibration value B, engine low-pressure rotor rotating speed N1, engine high-pressure rotor rotating speed N2, accelerator position Alfa _ PYD, low-pressure inlet blade rotating angle Alfa _1, high-pressure inlet blade rotating angle Alfa _2, nozzle fish scale position Dpc, engine inlet air total temperature T1, lubricating oil inlet pressure Pm, duty ratio S1, duty ratio S8 and APII-39 secondary power supply connection V2.
According to the embodiment of the engine parameter detection method based on probability statistics and the support vector machine, the obtaining of the plurality of engine parameters of the plurality of frames of the airplane further comprises the following steps:
and preprocessing the acquired parameters of the engine.
According to the embodiment of the engine parameter detection method based on probability statistics and the support vector machine, the preprocessing comprises data denoising, missing data filling and normalization processing.
According to an embodiment of the engine parameter detection method based on probability statistics and a support vector machine of the present invention, the normalization process sets data of several engine parameters between 0 and 1 and including 0 and 1.
According to the embodiment of the engine parameter detection method based on probability statistics and a support vector machine, the engine parameter of each time sequence data set is subjected to anomaly detection through an anomaly detection algorithm based on a probability statistics model, and the calculation of the average value, the standard deviation and the probability statistics model parameter of each single-frame engine parameter further comprises the following steps:
and constructing an anomaly detection model based on probability statistics through a Chebyshev inequality.
According to the embodiment of the engine parameter detection method based on probability statistics and the support vector machine, anomaly detection based on probability statistics is carried out on the time sequence data set constructed by each transmitter parameter of a single frame through the Chebyshev inequality.
According to the embodiment of the engine parameter detection method based on probability statistics and a support vector machine, the engine parameter of each time sequence data set is subjected to anomaly detection through an anomaly detection algorithm based on a probability statistics model, and the calculation of the average value, the standard deviation and the probability statistics model parameter of each single-frame engine parameter further comprises the following steps:
and optimizing an anomaly detection model based on the probability statistic model by manually adjusting the engine parameters.
According to the embodiment of the engine parameter detection method based on probability statistics and the support vector machine, the step of constructing the support vector machine classification model through the calculated multiple groups of average values, standard deviations and probability statistics model parameters comprises the following steps: and providing attributes of the training data through multiple groups of mean values and standard deviations of the same engine parameter, and providing labels of the training data through the probability statistical model parameters.
According to the embodiment of the engine parameter detection method based on the probability statistics and the support vector machine, the method further comprises the following steps: and training the average value, the standard deviation and the probability statistical model parameters of the engine parameters for a plurality of times through a Linear SVC algorithm in the support vector machine.
The invention has at least the following beneficial technical effects: the method adopts a mode of combining probability statistics and a support vector machine model to carry out abnormity detection on the parameters of the aircraft engine, and can effectively predict the health condition of the aircraft engine.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by using the drawings without creative efforts.
FIG. 1 is a block diagram of the steps implemented by the engine parameter detection method based on probability statistics and a support vector machine according to the present invention;
FIG. 2 is a chart of engine parameter information for a single-aircraft engine based on an embodiment of a method for engine parameter detection based on probabilistic statistics and support vector machines in accordance with the present invention;
FIG. 3 is a diagram of detected data of an embodiment of the engine parameter detection method based on probability statistics and a support vector machine according to the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments and drawings. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the scope of protection of this patent.
FIG. 1 is a block diagram illustrating the steps implemented by the engine parameter detection method based on probability statistics and a support vector machine.
Some embodiments according to the invention specifically comprise the steps of:
s100, obtaining a plurality of engine parameters of a plurality of frames of the airplane;
s200, acquiring a time sequence data set formed by each engine parameter of a single frame;
s300, carrying out anomaly detection on the engine parameters of each time sequence data set through an anomaly detection algorithm based on a probability statistical model, and calculating the average value, the standard deviation and the probability statistical model parameters of each single-frame engine parameter;
s400, for each engine parameter, constructing a support vector machine classification model through a plurality of groups of calculated average values, standard deviations and probability statistical model parameters;
s500, calculating the average value and the standard deviation of the engine parameters of the new-time aircraft engine, predicting corresponding probability statistic model parameters through a support vector machine model, and obtaining abnormal data in the probability statistic model parameters of the new-time aircraft engine through an abnormal detection algorithm based on the probability statistic model according to the probability statistic model parameters.
In some examples of the present invention, the automatic identification method for aircraft engine over-vibration based on RBF neural network of the present invention is implemented by the following specific steps: acquiring a data set consisting of N engine parameters of a plurality of frames of an airplane; constructing a time series data set according to each aircraft engine parameter of a single frame; according to an anomaly detection algorithm based on a probability statistic model, carrying out anomaly detection on a certain aircraft engine parameter in a time sequence data set constructed by a single frame, optimizing the anomaly detection algorithm by manually adjusting a parameter k, and simultaneously calculating the average value mu and the standard deviation sigma of the certain aircraft engine parameter of the single frame; carrying out anomaly detection based on a probability statistical model on all engine parameters of a plurality of frames of the airplane to obtain a plurality of groups of average values mu, standard deviations sigma and parameters k of the probability statistical model; and calculating the average value and the standard deviation of a certain aircraft engine parameter of the new set of aircraft engines, predicting a parameter k by using the constructed support vector machine model, and then performing an anomaly detection algorithm based on a probability statistic model by using the calculated parameter k to obtain anomaly data in the aircraft engine parameter of the new set of aircraft engines.
According to an embodiment of the present invention, a method for detecting engine parameters based on probabilistic statistics and support vector machines, the plurality of engine parameters comprises: the method comprises the following steps of low-pressure turbine rear gas total temperature T4, vibration value B, engine low-pressure rotor rotating speed N1, engine high-pressure rotor rotating speed N2, accelerator position Alfa _ PYD, low-pressure inlet blade rotating angle Alfa _1, high-pressure inlet blade rotating angle Alfa _2, nozzle fish scale position Dpc, engine inlet air total temperature T1, lubricating oil inlet pressure Pm, duty ratio S1, duty ratio S8 and APII-39 secondary power supply connection V2. In the embodiment of the invention, abnormal data can occur in the engine parameters, so that the health condition of the aircraft engine is influenced.
According to the embodiment of the engine parameter detection method based on probability statistics and the support vector machine, the obtaining of the plurality of engine parameters of the plurality of frames of the airplane further comprises the following steps:
and preprocessing the acquired parameters of the engine.
In some embodiments of the invention, some processing of the data is required before the main processing to improve the quality of the data.
According to the embodiment of the engine parameter detection method based on probability statistics and the support vector machine, the preprocessing comprises data denoising, missing data filling and normalization processing.
In some embodiments of the present invention, the collected time series data set is subjected to data denoising and missing data filling, and then to normalization post-processing. The data denoising can be performed by adopting a median filtering method, and the missing data filling can be performed by adopting an interpolation method. Fig. 2 is a single-rack aircraft engine parameter information chart of the embodiment of the engine parameter detection method based on probability statistics and a support vector machine according to the present invention, and first, one type of engine parameter data of a single rack of an aircraft is extracted to construct time series data. And then sequentially extracting N engine parameters to form a time sequence data set. As shown in FIG. 2, detailed information of individual rack aircraft engine parameters is given.
According to an embodiment of the engine parameter detection method based on probability statistics and a support vector machine of the present invention, the normalization process sets data of several engine parameters between 0 and 1 and including 0 and 1.
The purpose of normalizing the collected data set is mainly to reduce all data needing to be calculated to be between 0 and 1, so that the calculation is effectively simplified, and the calculation resources are saved.
According to the embodiment of the engine parameter detection method based on probability statistics and a support vector machine, the engine parameter of each time sequence data set is subjected to anomaly detection through an anomaly detection algorithm based on a probability statistics model, and the calculation of the average value, the standard deviation and the probability statistics model parameter of each single-frame engine parameter further comprises the following steps:
and constructing an anomaly detection model based on probability statistics through a Chebyshev inequality.
In some embodiments of the invention, a probabilistic statistics based anomaly detection model may be implemented using the chebyshev inequality, and the chebyshev inequality is applicable to data subject to any distribution. The chebyshev inequality is as follows.
According to the embodiment of the engine parameter detection method based on probability statistics and the support vector machine, anomaly detection based on probability statistics is carried out on the time sequence data set constructed by each transmitter parameter of a single frame through the Chebyshev inequality.
Anomaly detection based on probability statistics is performed on time series data sets constructed for each engine parameter for a single rack. For example, the column of data in which a certain attribute T4 of the data sample of FIG. 2 is located is denoted X, μ and σ2An overall mean and an overall variance are denoted by X, and k (k ═ 1,2, 3.., 10) denotes a deviation coefficient. The overall mean μ can be estimated using the sample mean:
global variance σ2The sample variance can be used to estimate:
according to the embodiment of the engine parameter detection method based on probability statistics and a support vector machine, the engine parameter of each time sequence data set is subjected to anomaly detection through an anomaly detection algorithm based on a probability statistics model, and the calculation of the average value, the standard deviation and the probability statistics model parameter of each single-frame engine parameter further comprises the following steps:
and optimizing an anomaly detection model based on the probability statistic model by manually adjusting the engine parameters.
In the embodiment of the invention, different k is selected for a certain parameter of the aircraft engine, and different anomaly point sets can be obtained. In the training process, the optimal parameter k is selected by continuously adjusting the parameter k and combining the abnormal point set obtained by the flight parameter expert.
According to the embodiment of the engine parameter detection method based on probability statistics and the support vector machine, the step of constructing the support vector machine classification model through the calculated multiple groups of average values, standard deviations and probability statistics model parameters comprises the following steps: and providing attributes of the training data through multiple groups of mean values and standard deviations of the same engine parameter, and providing labels of the training data through the probability statistical model parameters.
In the present example, we use the sample mean and the sample standard deviation to estimate μ and σ.
According to the embodiment of the engine parameter detection method based on the probability statistics and the support vector machine, the method further comprises the following steps: and training the average value, the standard deviation and the probability statistical model parameters of the engine parameters for a plurality of times through a Linear SVC algorithm in the support vector machine.
FIG. 3 is a graph of sensed data for an embodiment of the engine parameter sensing method of the present invention based on probabilistic statistics and support vector machines. In an embodiment of the present invention, for a certain parameter of an aircraft engine, such as T4 in fig. 2, multiple sets of parameters k may be calculated by performing anomaly detection based on probability statistics on multiple sets of engine data. Meanwhile, the average value mu and the standard deviation sigma of a plurality of groups are obtained, as shown in FIG. 3. In the embodiment of the invention, a LinearSVC algorithm in a support vector machine is adopted to train the average value mu, the standard deviation sigma and the parameter k of a plurality of frames of a certain aircraft engine parameter (such as T4). Among them, several important parameters in the linear svc algorithm: the error penalty parameter C is 1.0 and the minimum tolerance is 1 e-5. Then, other parameters B, N1, N2, Alfa _ PYD, Alfa _1, Alfa _2, Dpc, T1, Pm, S1, S8, APII-39 and V2 of the engine are trained for the support vector machine model to obtain a corresponding support vector machine classification model.
In some embodiments of the invention, for a new set of aircraft engine parameters, μ and σ are estimated by calculating a sample mean and a sample standard deviation. And predicting a parameter k required in the anomaly detection model based on probability statistics according to the estimated mu and sigma and the trained classification model of the support vector machine. And carrying out anomaly detection on the aircraft engine parameters according to the calculated mu and sigma and the calculated k to obtain anomaly data in the new-time aircraft engine parameters.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An engine parameter detection method based on probability statistics and a support vector machine is characterized by comprising the following steps:
obtaining a plurality of engine parameters of a plurality of frames of an airplane;
obtaining a time series data set of each of said engine parameters for a single frame;
carrying out anomaly detection on the engine parameters of each time sequence data set through an anomaly detection algorithm based on a probability statistical model, and calculating the average value, the standard deviation and the probability statistical model parameters of the engine parameters of each single frame;
for each engine parameter, constructing a support vector machine classification model through the calculated multiple groups of the average values, the standard deviations and the probability statistic model parameters;
and calculating the average value and the standard deviation of the engine parameters of the new frame, predicting the corresponding probability statistic model parameters through the support vector machine model, and obtaining abnormal data in the probability statistic model parameters of the new frame aircraft engine through the abnormal detection algorithm based on the probability statistic model according to the probability statistic model parameters.
2. The probabilistic statistics and support vector machine based engine parameter detection method of claim 1, wherein the number of engine parameters comprises: the method comprises the following steps of low-pressure turbine rear gas total temperature T4, vibration value B, engine low-pressure rotor rotating speed N1, engine high-pressure rotor rotating speed N2, accelerator position Alfa _ PYD, low-pressure inlet blade rotating angle Alfa _1, high-pressure inlet blade rotating angle Alfa _2, nozzle fish scale position Dpc, engine inlet air total temperature T1, lubricating oil inlet pressure Pm, duty ratio S1, duty ratio S8 and APII-39 secondary power supply connection V2.
3. The probabilistic statistics and support vector machine based engine parameter detection method of claim 1, wherein the obtaining a plurality of engine parameters for a plurality of aircraft frames further comprises:
and preprocessing a plurality of acquired engine parameters.
4. The probabilistic statistics and support vector machine based engine parameter detection method of claim 3, wherein the preprocessing comprises data denoising, missing data padding and normalization processing.
5. The probabilistic statistic and support vector machine based engine parameter detection method according to claim 4, wherein the normalization process sets data of several engine parameters between 0 and 1 and including 0 and 1.
6. The probabilistic statistic and support vector machine based engine parameter detection method according to claim 1, wherein the calculating the mean, standard deviation and probabilistic statistical model parameter of the engine parameter for each single rack by performing anomaly detection on the engine parameter of each time series data set through an anomaly detection algorithm based on a probabilistic statistical model further comprises:
and constructing the anomaly detection model based on the probability statistics through a Chebyshev inequality.
7. The probabilistic statistic and support vector machine based engine parameter detection method according to claim 6, wherein the probability statistic based anomaly detection is performed on the time series data set constructed for each of the transmitter parameters of a single rank by the chebyshev inequality.
8. The probabilistic statistic and support vector machine based engine parameter detection method according to claim 1, wherein the calculating the mean, standard deviation and probabilistic statistical model parameter of the engine parameter for each single rack by performing anomaly detection on the engine parameter of each time series data set through an anomaly detection algorithm based on a probabilistic statistical model further comprises:
and optimizing the anomaly detection model based on the probability statistic model by manually adjusting the engine parameters.
9. The probabilistic statistic and support vector machine based engine parameter detection method according to claim 1, wherein the constructing a support vector machine classification model by the calculated plurality of sets of the mean, the standard deviation and the probabilistic statistical model parameter comprises: and providing attributes of training data through a plurality of groups of average values and standard deviations of the same engine parameter, and providing labels of the training data through the probability statistic model parameter.
10. The probabilistic statistics and support vector machine based engine parameter detection method of claim 1, further comprising: training the average value, the standard deviation and the probability statistic model parameter of the engine parameter for a plurality of times through a Linear SVC algorithm in the support vector machine.
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