CN115660024A - Vehicle-mounted network fault diagnosis method based on intelligent optimization algorithm and machine learning - Google Patents
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Abstract
The invention provides a vehicle-mounted network fault diagnosis method based on an intelligent optimization algorithm and machine learning, which comprises the following steps of: extracting fault features; determining an objective function; initializing a seagull population position by Gaussian mapping based on a seagull algorithm, namely initializing a penalty factor C and a kernel function parameter g of each seagull, and calculating an optimal fitness value and an optimal seagull position according to a target function; introducing a position updating mechanism of a black wife optimization algorithm, improving a position updating mode of an original gull algorithm, and updating the position; performing bidirectional sine variation on the optimal gull position; constructing a vehicle-mounted network fault diagnosis model by taking the optimal penalty factor C and the kernel function parameter g as SVM model parameters; and inputting the characteristic vector of the vehicle-mounted network fault diagnosis into the trained vehicle-mounted network fault diagnosis model to obtain a fault diagnosis result and test accuracy. The method overcomes the defects of the existing gull algorithm, and can remarkably improve the effect of vehicle-mounted network fault diagnosis.
Description
Technical Field
The invention relates to the technical field of vehicle-mounted network fault diagnosis, in particular to a vehicle-mounted network fault diagnosis method based on an intelligent optimization algorithm and machine learning.
Background
The in-vehicle network is a link connecting subsystems, devices, modules and components in a vehicle platform, and is a core supporting technology for realizing a vehicle integrated electronic system, which is considered as a central nerve of the vehicle platform. However, most of the existing vehicle-mounted network fault diagnosis still depends on manual experience for troubleshooting, and the fault reason is often difficult to diagnose effectively.
A Support Vector Machine (SVM) is a machine learning technique, and is widely applied to the field of network fault diagnosis and the like. For example, dongfeng et al proposed a support vector machine-based MAC protocol recognition method based on physical layer signals (dongfeng, quemoku, liaisajing, auobe, queghai. Support vector machine-based MAC protocol recognition method [ P ]. Jiangsu province: CN107231427B, 2020-04-07.).
In the SVM training process, the penalty factor C of the SVM and the selection quality of the RBF kernel function parameter g directly influence the final fault diagnosis result. The gull algorithm is a novel intelligent optimization algorithm for simulating gull foraging behavior, can effectively optimize SVM model parameters, and can also be applied to the problem of vehicle-mounted network fault diagnosis. However, there still exist some defects in the gull optimization algorithm, so that the algorithm is easy to fall into local optimum and has low convergence accuracy, and an ideal fault diagnosis effect is often not achieved when network fault diagnosis is performed.
Disclosure of Invention
In order to solve the problems, the invention provides the vehicle-mounted network fault diagnosis method based on the intelligent optimization algorithm and the machine learning, overcomes the defects of the existing gull algorithm, and can remarkably improve the effect of vehicle-mounted network fault diagnosis.
In order to achieve the above purpose, the present invention provides the following technical solutions.
A vehicle-mounted network fault diagnosis method based on an intelligent optimization algorithm and machine learning comprises the following steps:
extracting fault characteristics in original data of the vehicle-mounted network fault to be diagnosed;
taking the classification accuracy of the 5-fold cross validation SVM as a target function, and determining a penalty factor C of an SVM model and the upper and lower limits of an RBF kernel function parameter g;
initializing a gull population position through Gaussian mapping based on a gull algorithm, namely initializing a penalty factor C and a kernel function parameter g of each gull, and calculating an optimal fitness value and an optimal gull position according to a target function;
introducing a position updating mechanism of a black-wife optimization algorithm, improving a position updating mode of an original gull algorithm, updating the position, and obtaining an updated optimal fitness value and an optimal gull position;
performing bidirectional sine variation on the optimal gull position, and taking the gull position with the optimal fitness value before and after variation as the updated optimal gull position;
sequentially updating the optimal gull position according to the preset maximum iteration times, and determining the optimal gull position, namely obtaining a corresponding optimal penalty factor C and a kernel function parameter g;
constructing a vehicle-mounted network fault diagnosis model by taking the optimal penalty factor C and the kernel function parameter g as SVM model parameters;
and inputting the characteristic vector of the vehicle-mounted network fault diagnosis into the trained vehicle-mounted network fault diagnosis model to obtain a fault diagnosis result and test accuracy.
Preferably, the initializing the gull population position through gaussian mapping includes the following steps:
random numbers are generated by gaussian mapping:
using generated Gaussian random numbers x t Initializing seagull position P s (t) is:
P s (t)＝(UB-LB)×x t +LB
in the formula, mod (·) is a complementation function, and LB is a gull optimizing lower boundary; UB optimizes the upper boundary for gull.
Preferably, the introducing the position updating mechanism of the black oligowomen optimization algorithm improves the position updating mode of the original gull algorithm, and performs position updating, and includes the following steps: updating the seagull position through the seagull migration behavior and the seagull global attack behavior.
Preferably, the gull migration behavior comprises the steps of:
the new position of the gull is calculated using the additional variable a to avoid collision with other gulls:
C s (t)＝A×P s (t)
A＝f c -(t×f c /Miter)
in the formula: c s (t) is a new position that does not conflict with other seagulls; p s (t) is the current position of the seagull; t is the current iteration number; miter is the maximum iteration number; a is the movement behavior of the seagull in a given search space; f. of c For controlling the coefficient, the value is reduced from 2 to 0;
moving towards the direction of the optimal position:
M s (t)＝B×(P bs (t)-P s (t))
B＝2×A 2 ×r d
in the formula: m s (t) is the direction in which the optimal position is located; p bs (t) is the optimal position; b is a random number responsible for balancing global and local search; r is a radical of hydrogen d Is [0,1 ]]A random number within a range;
arrival at the new location:
D s (t)＝|C s (t)+M s (t)|
in the formula: d s (t) is the distance the gull has moved to the new position.
Preferably, the gull global attack behavior includes the following steps:
by changing the attack angle and speed continuously through the spiral motion, the spiral motion behavior is expressed as:
in the formula: r is the radius of each helix, θ is a random angle value in the range of [0,2 π ]; u and v are the correlation constants of the helical shape; e is the base number of the natural logarithm;
introducing a position updating mechanism of a black oligogyne optimization algorithm, wherein an improved gull position updating formula is as follows:
in the formula: p s (t + 1) is the gull position of the t +1 th iteration after updating; p bs (t) represents the optimal position for the t-th iteration; alpha is [0,1 ]]A random number in between; m is [0.4,0.9 ]]A random number in between; beta is [ -1,1]A random number within; r is 1 Is [1, N ]]A random integer between the number of the first and second integers,
is the r th randomly selected 1 The position of the gull; p s (t) is the current gull position;
calculating the updated seagull pheromone value according to the following formula:
in the formula: pholomone (P) s (t + 1)) is the pheromone value of the current gull；fitness max And fitness min For worst and best fitness function values, fitness (P) s (t + 1)) is the fitness value of the current gull;
when the pheromone value of a seagull is equal to or less than 0.3, the seagull cannot be selected, and the formula of the update position of the seagull is as follows:
in the formula: gamma is [0,1 ]]A random number in between; r is a radical of hydrogen 1 And r 2 Is [1, N ]]A random integer between the number of the first and second integers,
and
respectively, randomly selected r 1 And r 2 Position of gull, r 1 ≠r 2 (ii) a σ is a random binary number {0,1};
calculating a fitness value:
fitness(t)＝F f (P s (t+1))
in the formula, F f (. Cndot.) is a fitness function in calculating the fitness value.
Preferably, the performing bidirectional sine variation on the optimal gull position includes the following steps:
calculating a sine chaotic value according to the current iteration times, and switching positive and negative directions at equal probability:
Sin Value＝sin(πx 0 )
wherein rand is a random number from 0 to 1; x is the number of 0 Is an iterative sequence value;
carrying out variation disturbance on the optimal position:
P bs(j) (t+1)'＝P bs(j) (t+1)+SinValue×P bs(j) (t+1)
in the formula: p is bs(j) (t + 1) represents the optimal position P for the t-th iteration bs The j-th dimension of (t + 1);
greedy update:
after mutation in each dimension, mutation was stopped.
The invention provides a vehicle-mounted network fault diagnosis method based on an intelligent optimization algorithm and machine learning, which has the following beneficial effects:
(1) By introducing Gaussian mapping to initialize the gull population position, the uniformity and diversity of population position distribution can be improved, and the stability of the algorithm is enhanced.
(2) The method improves the seagull position updating mode, introduces a position updating mechanism of a black oligowomen optimization algorithm to improve the seagull position updating mode, comprehensively considers factors such as different position updating modes, the position updating mode when the fitness value is too low and changed, the optimal position of the seagull in the iteration, other seagull positions in the population and the like to update the seagull position, realizes the increase of the algorithm search range, and enhances the adaptability of the algorithm.
(3) The capability of jumping out the local optimal solution in the later stage of the algorithm is realized by utilizing the bidirectional sine chaotic mapping variation on the optimal seagull.
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FIG. 1 is an embodiment of the present invention: a flow chart of a vehicle-mounted network fault diagnosis method based on an intelligent optimization algorithm and machine learning;
FIG. 2 is a vehicle network experimental platform of an embodiment of the present invention;
fig. 3 is a diagram of the results of the fault diagnosis in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The invention relates to a vehicle-mounted network fault diagnosis method based on an intelligent optimization algorithm and machine learning, which aims at several problems existing in SOA (service oriented architecture), provides an enhanced gull optimization algorithm (ISOA) optimized SVM and is used for fault diagnosis of a vehicle-mounted network, namely: the flow of the vehicle-mounted network fault diagnosis method based on the intelligent optimization algorithm and the machine learning is shown in fig. 1, and the specific steps are as follows:
s1: the method comprises the steps of extracting fault characteristics of original data of vehicle-mounted network faults to obtain characteristic vectors of vehicle-mounted network fault diagnosis, constructing a data set of the vehicle-mounted network fault diagnosis, and dividing the data set into a training data set and a testing data set.
S2: and establishing an objective function funtion of vehicle-mounted network fault diagnosis based on the enhanced gull algorithm optimization SVM. Because the classification accuracy of the SVM is calculated by using the training data set, the classification accuracy of the SVM cross validation by 5 folds of the training data set can be used as a target function, and the penalty factor C of the SVM model and the upper and lower limits of the RBF kernel function parameter g are determined.
S3: and setting parameters. The size of the population of seagulls (i.e., the number of individual seagulls) N; the maximum number of iterations (i.e., the condition under which the iteration stops) Miter; optimizing a lower boundary LB of the gull; seagull optimizing upper boundary UB
S4: based on a gull algorithm, initializing gull population positions through Gaussian mapping, namely initializing a penalty factor C and a kernel function parameter g of each gull, and calculating an optimal fitness value and an optimal gull position according to a target function:
mapping random number x by gaussians t Generating:
using generated Gaussian random numbers x t Initializing seagull position P s (t) is:
P s (t)＝(UB-LB)×x t +LB
in the formula, mod (·) is a complementation function, and LB is a gull optimizing lower boundary; UB optimizes the upper boundary for gull.
S5: seagull migration behavior:
during migration, the algorithm simulates how gull clusters move from one location to another. Three behaviors are mainly involved in this phase: avoid collision, move to the optimal position direction and approach the optimal position.
The new position of the gull is calculated using the additional variable a to avoid collision with other gulls:
C s (t)＝A×P s (t)
A＝f c -(t×f c /Miter)
in the formula: c s (t) is a new position that does not conflict with other seagulls; p is s (t) is the current position of the seagull; t is the current iteration number; miter is the maximum iteration number; a is the movement behavior of the seagull in a given search space; f. of c For controlling the coefficient, the value is reduced from 2 to 0;
moving towards the direction of the optimal position:
M s (t)＝B×(P bs (t)-P s (t))
B＝2×A 2 ×r d
in the formula: m s (t) is the direction in which the optimal position is located; p is bs (t) is the optimal position; b is a random number responsible for balancing global and local search; r is a radical of hydrogen d Is [0,1 ]]A random number within a range;
arrival at the new location:
D s (t)＝|C s (t)+M s (t)|
in the formula: d s (t) is the distance the gull has moved to the new position.
S6: gull attack behavior:
by changing the attack angle and speed continuously through the spiral motion, the spiral motion behavior is expressed as:
in the formula: r is the radius of each helix, θ is a random angle value in the range of [0,2 π ]; u and v are the correlation constants of the helical shape; e is the base of the natural logarithm.
In the original seagull algorithm, the optimal seagull position is only used for guiding to update the seagull position, in order to effectively improve the global search capability of the seagull, a position update mechanism of a black-wife optimization algorithm is introduced to improve a seagull position update mode, the seagull position is updated by comprehensively considering factors such as different position update modes, a time-varying update mode with too low fitness value, the optimal seagull position of the iteration, other seagull positions in the population and the like, the local optimal seagull position in each iteration is avoided, and the global search capability of the seagull algorithm is further improved.
Introducing a position updating mechanism of a black oligogyne optimization algorithm, wherein an improved gull position updating formula is as follows:
in the formula: p s (t + 1) is the gull position of the t +1 th iteration after updating; p bs (t) represents the optimal position for the t-th iteration; alpha is [0,1 ]]A random number in between; m is [0.4,0.9 ]]A random number in between; beta is [ -1,1 [ ]]A random number within; r is 1 Is [1, N ]]A random integer between the number of the first and second integers,
is the r th randomly selected 1 The position of the gull; p is s (t) is the current gull position;
calculating the updated pheromone value of the seagull according to the following formula:
in the formula: pholomone (P) s (t + 1)) is that of the current gullAn pheromone value; fitness max And fitness min For the worst and optimal fitness function value, fitness (P) s (t + 1)) is the fitness value of the current seagull;
when the pheromone value of the gull is equal to or less than 0.3, the gull is not selected, and the formula of the update position is as follows:
in the formula: gamma is [0,1 ]]A random number in between; r is a radical of hydrogen 1 And r 2 Is [1, N ]]A random integer between the number of the first and second integers,
and
respectively, randomly selected r 1 And r 2 Position of gull, r 1 ≠r 2 (ii) a σ is a random binary number {0,1};
calculating a fitness value:
fitness(t)＝F f (P s (t+1))
in the formula, F f (. Cndot.) is a fitness function in calculating a fitness value.
S7: the optimal gull in the current iteration is recorded.
S8: and performing bidirectional sine variation on the optimal seagull position, and taking the seagull position with the optimal fitness value before and after variation as the updated optimal seagull position.
Calculating a sine chaotic value according to the current iteration times, and switching positive and negative directions at equal probability:
Sin Value＝sin(πx 0 )
wherein rand is a random number from 0 to 1; x is a radical of a fluorine atom 0 Is an iterative sequence value;
carrying out variation disturbance on the optimal position:
P bs(j) (t+1)'＝P bs(j) (t+1)+Sin Value×P bs(j) (t+1)
in the formula: p bs(j) (t + 1) represents the optimal position P for the t-th iteration bs The j-th dimension of (t + 1);
greedy update:
after mutation is performed for each dimension, the mutation is stopped.
S9: the optimal gull in the current iteration is recorded.
S10: repeatedly executing S6-S9, sequentially updating the optimal gull position according to the preset maximum iteration times, and determining the optimal gull position to obtain the optimal parameter C of the SVM best And g best 。
S11: constructing a vehicle-mounted network fault diagnosis model by taking the optimal penalty factor C and the kernel function parameter g as SVM model parameters; and inputting the characteristic vector of the vehicle-mounted network fault diagnosis into the trained vehicle-mounted network fault diagnosis model to obtain a fault diagnosis result and test accuracy.
In this embodiment:
as shown in fig. 2, the vehicle-mounted network experimental platform adopts a dual-bus topology structure, a terminator is connected to the end of a bus to prevent the emission phenomenon caused by impedance mismatching, and 1 vehicle-mounted network protocol analyzer, 1 vehicle-mounted network fault injection device, 6 network nodes, and the like are respectively attached to a network. In a verification experiment, the platform simulates 6 typical states of an on-board network, which are respectively: (1) the network is normal; (2) network disconnection; (3) a near end termination failure; (4) a distal termination failure; (5) double-sided termination failure; (6) intermittent disconnection.
2000 groups of samples of each state of the vehicle-mounted network are collected, 1600 groups of samples are taken as training samples at random, and the rest 400 groups of samples are taken as testing samples. And fault diagnosis is carried out on the vehicle-mounted network by adopting the SOA-SVM and the ISOA-SVM respectively. The parameters in the SOA algorithm are: n =50, maximum =200, the search ranges for c and g are both between 0-100, i.e. LB =0, ub =100; the parameters in the ISOA algorithm are: n =50, maximer =200, the search ranges for c and g are each between 0-100, i.e. LB =0, ub =100.
As shown in fig. 3, compared with the SOA-SVM, the ISOA-SVM has higher independent diagnosis accuracy for 6 states of the vehicle-mounted network; for the average diagnostic accuracy, the ISOA-SVM is improved by 6.57% compared with the SOA-SVM. That is, SVM parameters obtained by the ISOA search are better than SVM parameters obtained by the SOA search. Simulation results show that the ISOA algorithm has stronger searching capability than the SOA algorithm, the ISOA-SVM has higher diagnosis accuracy than the SOA-SVM, and the effectiveness of the method is verified.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The vehicle-mounted network fault diagnosis method based on the intelligent optimization algorithm and the machine learning is characterized by comprising the following steps of:
extracting fault characteristics in original data of the vehicle-mounted network fault to be diagnosed;
taking the classification accuracy of the 5-fold cross validation SVM as a target function, and determining a penalty factor C of the SVM model and the upper and lower limits of an RBF kernel function parameter g;
initializing a gull population position through Gaussian mapping based on a gull algorithm, namely initializing a penalty factor C and a kernel function parameter g of each gull, and calculating an optimal fitness value and an optimal gull position according to a target function;
introducing a position updating mechanism of a black oligowomen optimization algorithm, improving a position updating mode of an original gull algorithm, updating the position, and obtaining an updated optimal fitness value and an optimal gull position;
performing bidirectional sine variation on the optimal gull position, and taking the gull position with the optimal fitness value before and after variation as the updated optimal gull position;
sequentially updating the optimal gull position according to the preset maximum iteration times, and determining the optimal gull position, namely obtaining a corresponding optimal penalty factor C and a kernel function parameter g;
constructing a vehicle-mounted network fault diagnosis model by taking the optimal penalty factor C and the kernel function parameter g as SVM model parameters;
and inputting the characteristic vector of the vehicle-mounted network fault diagnosis into the trained vehicle-mounted network fault diagnosis model to obtain a fault diagnosis result.
2. The vehicle network fault diagnosis method based on the intelligent optimization algorithm and the machine learning of claim 1, wherein the initializing gull population positions through Gaussian mapping comprises the following steps:
random numbers are generated by gaussian mapping:
using generated Gaussian random numbers x t Initializing seagull position P s (t) is:
P s (t)＝(UB-LB)×x t +LB
in the formula, mod (·) is a complementation function, and LB is a gull optimizing lower boundary; UB optimizes the upper boundary for gull.
3. The vehicle network fault diagnosis method based on intelligent optimization algorithm and machine learning of claim 1, wherein the position update mechanism of the introduced black oligowomen optimization algorithm improves the position update mode of the raw gull algorithm and performs position update, and comprises the following steps: updating the gull position through the gull migration behavior and the gull global attack behavior.
4. The vehicle network fault diagnosis method based on intelligent optimization algorithm and machine learning of claim 3, wherein the gull migration behavior comprises the following steps:
the new position of the gull is calculated using the additional variable a to avoid collision with other gulls:
C s (t)＝A×P s (t)
A＝f c -(t×f c /Miter)
in the formula: c s (t) is a new position that does not conflict with other seagulls' positions; p s (t) is the current position of the seagull; t is the current iteration number; miter is the maximum iteration number; a is the movement behavior of the seagull in a given search space; f. of c For controlling the coefficient, the value is reduced from 2 to 0;
moving towards the direction of the optimal position:
M s (t)＝B×(P bs (t)-P s (t))
B＝2×A 2 ×r d
in the formula: m is a group of s (t) is the direction in which the optimal position is located; p bs (t) is the optimal position; b is a random number responsible for balancing global and local search; r is d Is [0,1 ]]A random number within a range;
arrival at the new location:
D s (t)＝|C s (t)+M s (t)|
in the formula: d s (t) is the distance the gull has moved to the new position.
5. The vehicle-mounted network fault diagnosis method based on the intelligent optimization algorithm and the machine learning, as claimed in claim 3, wherein the gull global attack behavior comprises the following steps:
by changing the attack angle and speed continuously through the spiral motion, the spiral motion behavior is expressed as:
in the formula: r is the radius of each helix, θ is a random angle value in the range of [0,2 π ]; u and v are the correlation constants of the helical shape; e is the base number of the natural logarithm;
introducing a position updating mechanism of a black oligogyne optimization algorithm, wherein an improved gull position updating formula is as follows:
in the formula: p is s (t + 1) is the gull position of the t +1 th iteration after updating; p bs (t) represents the optimal position for the t-th iteration; alpha is [0,1 ]]A random number in between; m is [0.4,0.9 ]]A random number in between; beta is [ -1,1 [ ]]A random number within; r is 1 Is [1, N ]]A random integer in between, and a random integer,
is the r th randomly selected 1 The position of the gull; p s (t) is the current gull position;
calculating the updated pheromone value of the seagull according to the following formula:
in the formula: pholomone (P) s (t + 1)) is the pheromone value of the current gull; fitness max And fitness min For worst and best fitness function values, fitness (P) s (t + 1)) is the fitness value of the current gull;
when the pheromone value of the gull is equal to or less than 0.3, the gull is not selected, and the formula of the update position is as follows:
in the formula: gamma is [0,1 ]]A random number in between; r is a radical of hydrogen 1 And r 2 Is [1, N ]]A random integer in between, and a random integer,
and
respectively of randomly selected r-th 1 And r 2 Position of gull, r 1 ≠r 2 (ii) a σ is a random binary number {0,1};
calculating a fitness value:
fitness(t)＝F f (P s (t+1))
in the formula, F f (. Cndot.) is a fitness function in calculating a fitness value.
6. The vehicle-mounted network fault diagnosis method based on the intelligent optimization algorithm and the machine learning of claim 1, wherein the bidirectional sine variation of the optimal gull position comprises the following steps:
calculating a sine chaotic value according to the current iteration times, and switching positive and negative directions at equal probability:
SinValue＝sin(πx 0 )
wherein rand is a random number from 0 to 1; x is a radical of a fluorine atom 0 Is an iterative sequence value;
carrying out variation disturbance on the optimal position:
P bs(j) (t+1)'＝P bs(j) (t+1)+SinValue×P bs(j) (t+1)
in the formula: p bs(j) (t + 1) represents the optimal position P for the t-th iteration bs The j-th dimension of (t + 1);
greedy update:
after mutation is performed for each dimension, the mutation is stopped.
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