CN102520263A - Identifying method for electromagnetic environment of loader - Google Patents

Abstract

The invention discloses an identifying method for electromagnetic environment of a loader. In the method, according to the difference between acquisition data and ground station data of an electromagnetic sensor of the loader, an electromagnetic environment sample of the loader is determined, and an aggregative model and a component model of the electromagnetic environment of the loader are built. A direct current part is identified by applying a large sample average-taking method; and a special ARMA (Autoregressive Moving Average) model is adopted, the order and the parameters of the ARMA model can be determined by utilizing singular value decomposition and a minimal norm least square method, and the frequency of an electromagnetic signal alternating component of the loader is identified by utilizing a harmonic retrieval method. The identifying method has the advantages that an air plane does not need to fly, and the electromagnetic environment of the loader can be identified by the ground measuring data, so that the expense consumed by identifying the electromagnetic environment of the loader can be lowered greatly, and simultaneously, the aggregate and the component of the electromagnetic environment of the loader can be identified.

Description

The recognition methods of a kind of year machine magnetic environment

Technical field

The invention belongs to aviation magnetic survey and identification field, be specifically related to the recognition methods of a kind of year machine magnetic environment.

Background technology

The magnetic environment of the machine of carrying has significant effects to airborne magnetic survey.Carrying the machine magnetic environment comprises:

1, carries the influence of permanent magnetic material and paramagnetic material magnetic source on the machine

Permanent magnetic material in typical device such as electromagnetic relay, ferrite isolator, the motor, all ferromagnetic materials and their soft magnetic materials such as alloy formation are magnetized the back and the characteristic in the hard magnetic source that shows.

2, carry induced field on the machine

Comprise direct current and exchange lead and loop, all can produce steady magnetic field and alternating magnetic field, be referred to as stray magnetic field.

Characterizing the strong and weak physical quantity of magnetic is magnetic moment, is defined as through the electric current of loop and the product of this loop encirclement area.Unit is an ampere. rice ²

Carry the identification problem of machine magnetic environment, be the research problem of carrying the compensation of machine magnetic environment in the airborne magnetic survey specialty always.Usually carrying a machine magnetic survey subsystem is a subsystem that adds repacking, and the magnetic environment of aircraft can't carry out magnetic again and designed when adding repacking.Fixed magnet on the carrier aircraft and the ferromagnetic material that has magnetized can cause Magnetic Sensor to produce deviation.On the other hand, the current loop on the aircraft can produce the alternating magnetic field of induction at periphery.Therefore, in order to obtain real earth's magnetic field data, must carry out the magnetic compensation of carrier aircraft.Carry the machine magnetic environment and will compensate, just must discern the aircraft magnetic environment.

In theory, the identification of airborne magnetic environment can realize through the relatively actual output of machine sensor and the magnetic field model theoretically (like the IGRF model etc.) of carrying.But the resolution of existing theoretical model itself is compared when the terrestrial magnetic field with actual with precision, and the error of nT up to a hundred can be arranged.Therefore, we intend the magnetic environment data that the method that adopts difference is extracted the machine of carrying.Specifically, the aircraft magnetic environment is to obtain through more airborne magnetic survey data and land station's magnetic survey data.

The work the earliest that we learn is 18 years machine magnetic environment models that Leliak proposed in 1961.Source document can't find at present at home, and we learn this model in the improved aircraft magnetic compensation Project Summary Report through the Canadian PetRosEiKon Jia Ruizhong of company.

18 models of Leliak are following:

B _T＝c ₁cosX+c ₂cosY+c ₃cosZ+B _g(c ₄cos ²X+c ₅cosXcosY++c ₆cosXcosZ+c ₇cos ²Y+c ₈cosYcosZ+c ₉cos ²Z)++B _g(c ₁₀cosX(cosX)′+c ₁₁cosX(cosY)′+c ₁₂cosX(cosZ)′+ (14)+c ₁₃cosY(cosX)′+c ₁₄cosY(cosY)′+c ₁₅cosY(cosZ)′+c ₁₆cosZ(cosX)′+c ₁₇cosZ(cosY)′+c ₁₈cosZ(cosZ)′)

In the formula, BT is a year machine magnetic environment, (cosX, cosY, the direction cosine of cosZ) carrying machine transverse axis, the longitudinal axis and Z-axis relatively, () for earth-magnetic field vector ' be derivative to independent variable.In case obtain this model, we just can predict the magnetic field of the machine of carrying.In formula (14) right-hand member, three the permanent magnetism of year machine and the interference that paramagnetic material causes have been represented.Then represented the interference of induced field on the aircraft for six subsequently.Nine last Xiang Ze have represented the interference in the magnetic field that eddy current causes on the aircraft.

Consider

cos ²X+cos ²Y+cos ²Z＝1 (15)

cosX(cosX)′+cosY(cosY)′+cosZ(cosZ)′＝0 (16)

Actual following formula (14) has only 16.In addition, we notice that from formula (1), existing magnetic environment compensation technique is fully only to the terrestrial magnetic field total amount.

The total amount Bg of attention terrestrial magnetic field in formula (14) can't obtain before compensation, and we do not find the original of Leliak, can't know.It seems that have only a kind of possibility, the theoretical value of promptly using the IGRF Model Calculation to come out substitutes.

We it is pointed out that 18 models of Leliak, perhaps are the model of a practicality on engineering, yet but exist serious logic falsehood.At first, this model only is applicable to the identification and the compensation of total amount.Secondly, the induced field of year machine derives from the induction sources such as exchange current in exchange current, relay and the avionics system in the electric system on the machine of carrying.Where these induction sources have nothing to do with sensing in the magnetic field of the earth with aircraft on objective fact.The magnetic field of vortex induction refers to once more, in magnetic field, forms the magnetic field of the circuit inductance of loop, because the wiring aboard of these loops is fixed, also only follows the attitude of aircraft relevant in principle.Therefore, intuitively go up us and can think, in the Leliak compensation model, it is relevant to characterize three attitudes with aircraft of carrying machine permanent magnetism environment.We induced field and eddy current magnetism with being called stray magnetic field, we think it only with attitude with/attitude rate is relevant.

A thought-provoking practical work is that 18 models of Leliak are actually the product of the time that does not also have the comprehensive theory and practice of avionics system people.So the designer of magnetic survey subsystem directly adopts the measurement of triaxial magnetometer to carry out deciding appearance under the situation that can not get aspect information.The merchant has recognized this problem, and the method that adopts GPS to decide appearance provides independently attitude measurement, yet does not but recognize 18 limitations that model is more deep.

Summary of the invention

Deficiency to prior art exists the object of the present invention is to provide the recognition methods of a kind of year machine magnetic environment, and this method utilizes large sample to carry machine magnetic environment data, realizes carrying the identification of machine magnetic environment total amount and component.

Goal of the invention of the present invention is to realize through following technical scheme:

Aircraft with one fixedly course angle be parked on the runway, all electronic equipments are all in running order on engine and the machine.Airborne measurements earth magnetism total amount and three-component sensor also begin image data.Synchronization, the difference of the image data of machine upper sensor and land station's data are the total amount and the three-component magnetic environment of the machine of carrying.Repeatedly sampling can obtain carrying machine magnetic environment data sequence.

Change vector, remeasure, can obtain year machine magnetic environment data sequence under this course.

Because be the large sample sampling, direct current component promptly equals sample average.

Measurement data is deducted the direct current component of having discerned, promptly obtain measurement sequence with the AC portion of measuring noise; Set up ARMA (Auto-Regressive and Moving Average Model, the autoregressive moving average) model of this measurement sequence, confirm AR (autoregressive model) exponent number of this model through singular value decomposition method; Utilize minimum norm least square method to calculate the parameter of AR model again; Utilization is set up the proper polynomial of AC harmonic signal with the parameter of resolving, and separates the frequency that this proper polynomial can obtain each harmonic signal of AC compounent, realizes the identification to AC portion.

Specifically may further comprise the steps:

(a) carry the collection of machine magnetic environment data

The machine of carrying is parked on the runway, and vector is that

aircraft engine and various electronic equipment are all in running order.Synchronization, the difference of the geomagnetic data that the geomagnetic survey system that loads on geomagnetic data that magnetic survey station, ground is measured and the aircraft is measured are the measured value that carries the machine magnetic environment.Carry out N time according to this and measure, the total amount of vector when be

measured its three axial three-components measurement sequences of sequence and geographic coordinate system lower edge

Change vector record to

under this course

wherein; I=1; 2 ... N.

(b) set up additive white noise observation data model

According to the research to year machine magnetic environment, its total amount B _HWith component B _x, B _y, B _zModel all can be expressed as:

B(i)＝C+S(i)+v(i) (1)

In the formula, i measures for the i time, and N is for measuring sample size, and C is for carrying the DC component of machine magnetic environment, and S (i) is for carrying the AC compounent of machine magnetic environment, and v (i) is that zero, mean square deviation is σ for average ²The measurement noise.

(c) machine magnetic environment direct current component is carried in identification

Because setting the machine of carrying is that the flat of vectoring flies, DC component C is a constant relevant with course angle cosine, so C can be expressed as:

In the formula, C ₁, C ₂Be undetermined parameter.

Because sample size N is very big; Carrying the machine magnetic environment is the large sample sampling; So the size of its DC component is sample average; The sample

that is obtained with the x direction is an example, and DC component can be expressed as:

Can obtain the DC component parameters C of x direction through separating this equation ₁, C ₂All available this equation of the parameter of the DC component of y and z direction and total amount obtains, and does not do too much narration here.This method only is identified as example with the machine magnetic environment that carries of x direction.

So far, we have discerned the DC component of year machine magnetic environment.

(d) set up a year machine magnetic environment AC portion model

DC component by under the definite course of step (c) knowledge is discerned order:

S _m(n)＝B(n)-C，n＝1，2，…，N (4)

Study known AC compounent S (n), n=1,2 ... N is the process by p humorous wave component.

$S\left(n\right)=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}{A}_i\sin (2\mathrm{\π}{f}_i\·\frac{n}{N}+{\mathrm{\θ}}_i)$

Wherein, p is a harmonic wave number undetermined, A _i, f _i, θ _iBe respectively amplitude, frequency and the phase place of i harmonic wave.

Corresponding with it difference equation is:

$S\left(n\right)+\underset{i=1}{\overset{2p}{\mathrm{\Σ}}}{a}_{i}x(n-i)=0---\left(5\right)$

Get by (1) and (4) formula:

S _m(n)＝S(n)+v(n) (6)

Be the measurement sequence S of AC compounent S (n) at additive white noise v (n) _m(n) observed in.

(6) substitution (5) is got:

$S_m+\underset{i=1}{\overset{2p}{\mathrm{\Σ}}}{a}_i{S}_m(n-i)=v\left(n\right)+\underset{i=1}{\overset{2p}{\mathrm{\Σ}}}{a}_{i}v(n-i)---\left(7\right)$

Formula (7) is measures sequence S _m(n) (exponent number of its AR model is all consistent with the exponent number and the parameter of MA (running mean) model with parameter for 2p, 2p) model for ARMA.

(e) identification exchanges the parameter and the exponent number of model

(d) can know S by step _m(n) observation sample is: [S _m(1), S _m(2) ... S _m(N)]

The autocorrelation function of definition sample $\widehat{R_S}\left(k\right)=S_m\left(n\right)\·S_m(n-k),$ K＞2p

Structure expansion N-p _e* p _e/ 2 dimension autocorrelation matrixes:

$R_e=\left[\begin{array}{cccc}\widehat{R_S}(p_e+1)& \widehat{R_S}(p_e-1)& \·\·\·& \widehat{R_S}\left(1\right)\\ \widehat{R_S}(p_e+2)& \widehat{R_S}\left(p_e\right)& \·\·\·& \widehat{R_S}\left(2\right)\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \widehat{R_S}\left(N\right)& \widehat{R_S}(N-2)& \·\·\·& \widehat{R_S}(N-p_e)\end{array}\right]---\left(8\right)$

In the formula, p _eGet certain value greater than 2p, and N-p _e＞＞p _e

To R _eDo svd: R _e=U ∑ V ^H

∑ is N-p in the formula _e* p _e/ 2 dimension diagonal matrixs, the element on its principal diagonal is non-negative, and presses following order and arrange:

${\mathrm{\σ}}_{11}\≥{\mathrm{\σ}}_{22}\≥\·\·\·\≥{\mathrm{\σ}}_{\frac{p_e}{2}\frac{p_e}{2}}\≥0---\left(9\right)$

Order $\mathrm{\α}\left(k\right)={\left[\frac{{\mathrm{\σ}}_{11}^2+\·\·\·+{\mathrm{\σ}}_{\mathrm{Kk}}^2}{{\mathrm{\σ}}_{11}^2+\·\·\·+{\mathrm{\σ}}_{\frac{p_e}{2}\frac{p_e}{2}}^2}\right]}^{1/2},$ $1\≤k\≤\frac{p_e}{2}$

And confirm one in advance and be in close proximity to 1 threshold value, constantly approach with k

As p when being α (k), p is confirmed as matrix R more than or equal to the smallest positive integral of this threshold value _eEffective order, the AR exponent number that can obtain arma modeling is p.

After the AR of arma modeling exponent number p confirms, for formula (7):

Order $\stackrel{\‾}{v}\left(n\right)=(v\left(n\right)-a_{1}v(n-1)-..-a_{2p}v(n-2p)),$ Because v (n) is for having variances sigma ²The zero-mean white noise, so Also be the zero-mean white noise, have unknown variance $(1+a_1^2+\·\·\·a_{2p}^2){\mathrm{\σ}}^2.$

Make x=[a ₁, a ₂..., a _2p] ^T, S=[S _m(k), S _m(k+1) ..., S _m(N)] ^T, wherein

Then formula (7) can be expressed as:

$S=\mathrm{Ax}+\stackrel{\‾}{v}---\left(10\right)$

In the formula, v=[v (k), v (k+1) ... V (N)]

$A=\left[\begin{array}{cccc}S(k-1)& S(k-2)& \·\·\·& S(k-2p)\\ S\left(k\right)& S(k-1)& \·\·\·& S(k+1-2p)\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ S(N-1)& S(N-2)& \·\·\·& S(N-2p)\end{array}\right]$

So, wait to ask the minimum norm least square solution of parameter x to be:

x＝A ⁺S (11)

In the formula, A ⁺Generalized inverse matrix for matrix A.Be that the AR parameter is confirmed.

(f) utilize method of Harmonic to calculate the AC portion frequency

Separate the proper polynomial of the AR part of formula (7) arma modeling:

$1+\underset{i=1}{\overset{2p}{\mathrm{\Σ}}}{a}_i{z}^{-i}=0---\left(12\right)$

Conjugate root to i=1 wherein; 2 ... P

Then the frequency of each harmonic wave of AC compounent is:

f _i＝arctan[Im(z _i)/Re(z _i)]/2π，i＝1，2，…p (13)

So far, we have discerned the AC compounent of year machine magnetic environment.

It is following that the beneficial effect of recognition methods of machine magnetic environment is carried in the present invention:

1, owing to need not flight, just can discern, greatly reduce the expense that identification carrier aircraft magnetic environment is consumed, more help in engineering, implementing and large-scale promotion the magnetic environment of carrier aircraft by the ground survey data.

2, can discern the three-component that carries the machine magnetic environment, design compensation carries the three-component wave filter of machine magnetic environment according to this, makes at a year airborne measurements earth magnetism three-component to be achieved.

3, adopt the arma modeling of setting up High-Resolution Spectral Estimation, can accurately estimate to carry the frequency spectrum of machine magnetic environment AC compounent, can design high-precision wave filter according to this, make airborne Magnetic Sensor energy measurement arrive magnetic environment more accurately.

Description of drawings

Fig. 1 is that machine magnetic environment recognition principle process flow diagram is carried in the present invention.

Fig. 2 be the present invention when carrying the data acquisition of machine magnetic environment aircraft park synoptic diagram;

Embodiment

Below in conjunction with accompanying drawing 1 and accompanying drawing 2, further specify the present invention and how to realize.

Embodiment

A kind of to the total amount of year machine magnetic environment and the recognition methods of component.Fig. 1 has provided the principle flow chart of year machine magnetic environment identification.Aircraft was parked synoptic diagram when Fig. 2 had provided year machine magnetic environment data acquisition; Carry out the large sample collection to carrying a machine magnetic environment, its fixedly the DC component on the course be a determined value, can try to achieve by sample average.Come survey aircraft magnetic environment data through two vectors, purpose is in order to resolve the parameters C of direct current component ₁And C ₂

The AC portion that carries the machine magnetic environment can be regarded as a plurality of harmonic signal sums.The identification of AC portion is promptly identified the frequency spectrum (frequency of each harmonic signal) of AC signal.Make up the arma modeling of the measured value of band additive white noise, confirm the exponent number of this model, promptly confirm to carry the number that machine magnetic environment AC portion contains harmonic signal through the mode of separating singular value.Try to achieve the parameter of arma modeling AR part again through minimum norm least square method, utilize this parameter, adopt the harmonic wave restoring method to identify the frequency of harmonic signal.

Claims (5)

1. a recognition methods of carrying the machine magnetic environment comprises the steps:

(a), carry the collection of machine magnetic environment data:

The machine of carrying is parked on the runway, and vector is that

aircraft engine and various electronic equipment are all in running order; Synchronization, the difference of the geomagnetic data that the geomagnetic survey system that loads on geomagnetic data that magnetic survey station, ground is measured and the aircraft is measured are the measured value that carries the machine magnetic environment; Carrying out N time according to this measures; The total amount of vector when

measure its three axial three-components of sequence

and geographic coordinate system lower edge measure sequences

change vectors record to

under this course

wherein; I=1; 2 ... N;

(b), set up additive white noise observation data model, the foundation of model is following:

Its total amount B _HWith component B _x, B _y, B _zModel all can be expressed as:

B(i)＝C+S(i)+v(i) (1)

In the formula, i=1,2 ... N, N be for measuring sample size, and C is for carrying the DC component of machine magnetic environment, and S (i) is for carrying the AC compounent of machine magnetic environment, and v (i) is that zero, mean square deviation is σ for average ²The measurement noise;

(c), to carry machine magnetic environment direct current component: a DC component C be a constant relevant with course angle cosine only in identification because sample size N is very big, carrying a machine magnetic environment is that large sample is sampled, so the size of its DC component is sample average;

(d), set up and carry machine magnetic environment AC portion model: measurement data is deducted the direct current component of having discerned, promptly obtain measurement sequence, that is: S with the AC portion of measuring noise _m(n)=and B (n)-C, n=1,2 ..., N, wherein S _mFor carrying the AC portion of machine magnetic environment, B (n) is shown in (1) formula.

(e), identification exchanges the exponent number and the parameter of model: set up the arma modeling of the measurement sequence of AC portion, confirm the AR exponent number of this model through singular value decomposition method; Utilize minimum norm least square method to calculate the parameter of AR model again;

(f) utilize method of Harmonic to calculate the frequency of AC portion: to utilize the parameter of AR model to set up the proper polynomial of AC harmonic signal, separate the frequency that this proper polynomial can obtain each harmonic signal of AC compounent, realize identification to AC portion.

2. the recognition methods of a kind of year according to claim 1 machine magnetic environment is characterized in that, said step (c) specifically adopts following mode that direct current component is discerned:

Because setting the machine of carrying is that the flat of vectoring flies, DC component C is a constant relevant with course angle cosine, so C can be expressed as:

In the formula, C ₁, C ₂Be undetermined parameter;

Because sample size N is very big; Carrying the machine magnetic environment is the large sample sampling; So the size of its DC component is sample average; The sample

that is obtained with the x direction is an example, and DC component can be expressed as:

Can obtain the DC component parameters C of x direction through separating this equation ₁, C ₂, in like manner, all available this equation of the parameter of the DC component of y and z direction and total amount obtains.

3. the recognition methods of a kind of year according to claim 1 machine magnetic environment is characterized in that, the concrete steps that said step (d) is set up the AC portion model are following:

DC component by under the definite course of step (c) knowledge is discerned order:

S _m(n)＝B(n)-C，n＝1，2，…，N (4)

The conclusion that adopts the signal Processing field to assert, the difference equation of S (n) is:

$S\left(n\right)+\underset{i=1}{\overset{2p}{\mathrm{\Σ}}}{a}_{i}x(n-i)=0---\left(5\right)$

Get by (1) and (4) formula:

S _m(n)＝S(n)+v(n) (6)

Be the measurement sequence S of AC compounent S (n) at additive white noise v (n) _m(n) observed in;

(6) substitution (5) is got:

$S_m+\underset{i=1}{\overset{2p}{\mathrm{\Σ}}}{a}_i{S}_m(n-i)=v\left(n\right)+\underset{i=1}{\overset{2p}{\mathrm{\Σ}}}{a}_{i}v(n-i)---\left(7\right)$

Formula (7) is measures sequence S _m(n) (exponent number of its AR model is all consistent with the exponent number and the parameter of MA model with parameter for 2p, 2p) model for ARMA.

4. the recognition methods of a kind of year according to claim 1 machine magnetic environment is characterized in that, said step (e) specifically adopts following mode to confirm to exchange the exponent number and the parameter of model:

Know S by step (d) _m(n) observation sample is: [S _m(1), S _m(2) ... S _m(N)]

The autocorrelation function of definition sample $\widehat{R_S}\left(k\right)=S_m\left(n\right)\·S_m(n-k),$ K＞2p

Structure expansion N-p _e* p _e/ 2 dimension autocorrelation matrixes:

$R_e=\left[\begin{array}{cccc}\widehat{R_S}(p_e+1)& \widehat{R_S}(p_e-1)& \·\·\·& \widehat{R_S}\left(1\right)\\ \widehat{R_S}(p_e+2)& \widehat{R_S}\left(p_e\right)& \·\·\·& \widehat{R_S}\left(2\right)\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \widehat{R_S}\left(N\right)& \widehat{R_S}(N-2)& \·\·\·& \widehat{R_S}(N-p_e)\end{array}\right]---\left(8\right)$

In the formula, p _eGet certain value greater than 2p, and N-p _e＞＞p _e

To R _eDo svd: R _e=U ∑ V ^H

∑ is N-P in the formula _e* P _e/ 2 dimension diagonal matrixs, the element on its principal diagonal is non-negative, and presses following order and arrange:

${\mathrm{\σ}}_{11}\≥{\mathrm{\σ}}_{22}\≥\·\·\·\≥{\mathrm{\σ}}_{\frac{p_e}{2}\frac{p_e}{2}}\≥0---\left(9\right)$

Order $\mathrm{\α}\left(k\right)={\left[\frac{{\mathrm{\σ}}_{11}^2+\·\·\·+{\mathrm{\σ}}_{\mathrm{Kk}}^2}{{\mathrm{\σ}}_{11}^2+\·\·\·+{\mathrm{\σ}}_{\frac{p_e}{2}\frac{p_e}{2}}^2}\right]}^{1/2},$ $1\≤k\≤\frac{p_e}{2}$

And confirm one in advance and be in close proximity to 1 threshold value, constantly approach with k

As p when being α (k), p is confirmed as matrix R more than or equal to the smallest positive integral of this threshold value _eEffective order, the AR exponent number that can obtain arma modeling is p;

After the AR of arma modeling exponent number p confirms, for formula (7):

Order $\stackrel{\‾}{v}\left(n\right)=(v\left(n\right)-a_{1}v(n-1)-..-a_{2p}v(n-2p)),$ Because v (n) is for having variances sigma ²The zero-mean white noise, so

Also be the zero-mean white noise, have unknown variance $(1+a_1^2+\·\·\·a_{2p}^2){\mathrm{\σ}}^2;$

Make x=[a ₁, a ₂..., a _2p] ^T, S=[S _m(k), S _m(k+1) ..., S _m(N)] ^T, wherein

Then formula (7) can be expressed as:

$S=\mathrm{Ax}+\stackrel{\‾}{v}---\left(10\right)$

In the formula, v=[v (k), v (k+1) ... V (N)]

$A=\left[\begin{array}{cccc}S(k-1)& S(k-2)& \·\·\·& S(k-2p)\\ S\left(k\right)& S(k-1)& \·\·\·& S(k+1-2p)\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ S(N-1)& S(N-2)& \·\·\·& S(N-2p)\end{array}\right])$

So, wait to ask the minimum norm least square solution of parameter x to be:

x＝A ⁺S (11)

In the formula, A ⁺Be the generalized inverse matrix of matrix A, promptly the AR parameter is confirmed.

5. the recognition methods of a kind of year according to claim 1 machine magnetic environment is characterized in that, said step (f) specifically adopts following mode to calculate the AC portion frequency:

Exponent number and the parameter of being known arma modeling by step (e) are all definite, separate the proper polynomial of the AR part of arma modeling:

$1+\underset{i=1}{\overset{2p}{\mathrm{\Σ}}}{a}_i{z}^{-i}=0---\left(12\right)$

Conjugate root to

i=1 wherein; 2 ... P

Then the frequency of each harmonic wave of AC compounent is:

f _i＝arctan[Im(z _i)/Re(z _i)]/2π，i＝1，2，…p (13)

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