CN110988674A - Health state monitoring method and system of permanent magnet synchronous motor and mobile terminal - Google Patents

Health state monitoring method and system of permanent magnet synchronous motor and mobile terminal Download PDF

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CN110988674A
CN110988674A CN201911133949.8A CN201911133949A CN110988674A CN 110988674 A CN110988674 A CN 110988674A CN 201911133949 A CN201911133949 A CN 201911133949A CN 110988674 A CN110988674 A CN 110988674A
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permanent magnet
synchronous motor
magnet synchronous
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陈志文
魏焱烽
彭涛
阳春华
梁可天
彭霞
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Central South University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines
    • G01R31/343—Testing dynamo-electric machines in operation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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Abstract

The invention relates to the field of permanent magnet motor health state monitoring, and discloses a permanent magnet synchronous motor health state monitoring method and system based on multi-mode typical correlation analysis, which can find abnormal conditions of a permanent magnet motor during multi-working-condition operation, and is convenient to use and easy to implement; the method of the invention comprises the following steps: selecting normal operation data of the permanent magnet synchronous motor under more than two working conditions as a training data set, and inputting the training data set into an EM algorithm to calculate to obtain parameters required by modeling; acquiring online operation data of the permanent magnet synchronous motor, and establishing a multi-mode monitoring model according to the parameters and the operation data; and monitoring the health state of the permanent magnet synchronous motor by combining the evaluation index obtained by the model and a preset fault probability threshold value.

Description

Health state monitoring method and system of permanent magnet synchronous motor and mobile terminal
Technical Field
The invention relates to the field of health state monitoring of permanent magnet synchronous motors, in particular to a health state monitoring method and system of a permanent magnet synchronous motor and a mobile terminal.
Background
The permanent magnet synchronous motor has the advantages of compact structure, small volume, light weight, high efficiency, reliable work, low noise and the like, and is widely used in various industries, such as the fields of mechanical equipment, aerospace, elevators, household appliances, navigation and the like. In actual operation, a rotor permanent magnet of the permanent magnet synchronous motor is easily subjected to comprehensive influences of factors such as armature reaction, operation temperature rise and brittle characteristics of a sintered rare earth permanent magnet material, so that local or uniform demagnetization faults occur, output torque is reduced, torque pulsation is caused, torque control accuracy and operation reliability of a driving system are influenced, and operation faults occur. The most common fault is a fault of turn-to-turn short circuit of a stator winding in the fault of the motor, and the turn-to-turn short circuit of the stator winding of the motor can generate a large amount of eddy current in a short circuit loop, so that the temperature of the motor is increased continuously. If the faults cannot be diagnosed and discovered in time, the faults can be continuously worsened, and finally the whole system is out of work, so that huge losses are brought to industrial production.
Therefore, the problem of motor temperature increase caused by turn-to-turn short circuit of the stator winding of the permanent magnet synchronous motor is urgently needed to be solved.
Disclosure of Invention
The invention aims to provide a method, a system and a mobile terminal for monitoring the health state of a permanent magnet synchronous motor based on typical correlation analysis, so that the abnormal condition of the running of the permanent magnet synchronous motor can be found in time, and the method, the system and the mobile terminal are convenient to use and easy to implement.
In order to achieve the purpose, the invention provides a method for monitoring the health state of a permanent magnet synchronous motor, which comprises the following steps:
s1, selecting normal operation data of the permanent magnet synchronous motor under more than two working conditions as a training data set, and inputting the training data set into an EM algorithm to calculate to obtain parameters required by modeling;
s2, acquiring online operation data of the permanent magnet synchronous motor, and establishing a multi-mode monitoring model according to the parameters and the operation data;
s3: and monitoring the health state of the permanent magnet synchronous motor by combining the evaluation index obtained by the model and a preset fault probability threshold value.
Preferably, in S1, the normal operation data of the permanent magnet synchronous motor under different working conditions are different, and a gaussian mixture model is used to fit the normal operation data of the permanent magnet synchronous motor under different working conditions.
It should be noted that the set operation process in the invention can be represented by limited single working condition, each working point is linear, and the operation data of the single working condition meets the multivariate normal distribution. Therefore, a gaussian mixture model is used to model the operational process of the system.
Preferably, the S1 specifically includes the following steps:
s11: setting C different working conditions in the running state of the permanent magnet synchronous motor in an off-line state, and using MiI is 1, …, and C represents the i-th type of operating condition parameters under different operating conditions;
s12: assuming that the selected more than two working condition historical data D are N samples of normal operation data, each sample comprises process variables meeting the variable
Figure BDA0002279076530000021
Variables of
Figure BDA0002279076530000022
The historical data D is:
Figure BDA0002279076530000023
in the formula (d)kRepresents a random sample set under each working condition,
Figure BDA0002279076530000024
k is 1 and …, N represents a data set under different working conditions, l represents the number of variables of u, and m represents the number of variables of y;
s13: adopting Gaussian mixture model to represent random sample set d under various working conditionskThe probability density function of (a) is:
Figure BDA0002279076530000025
in the formula, ωiIs the ith MiWeight coefficient of (a), thetaiIs a parameter of the ith Gaussian component, where θiComprises the following steps:
θi＝{ωi,μu,i,μy,i,Σuu,i,Σuy,i,Σyy,i}
in the formula, g (d)k|θi) As a parameter M of the operating conditionsiThe calculation formula of the multivariate gaussian density function is as follows:
Figure BDA0002279076530000026
Figure BDA0002279076530000027
wherein, the mean value of u under more than two working conditions is established as follows:
Figure BDA0002279076530000031
the covariance matrix of u is:
Figure BDA0002279076530000032
the mean value of y is:
Figure BDA0002279076530000033
the covariance matrix of y is:
Figure BDA0002279076530000034
s14: let thetaiIs theta ═ theta1,θ2,…,θC-expressing a maximum likelihood ratio function of N different sample compositions as:
Figure BDA0002279076530000035
obtaining a distribution model according to the maximum likelihood ratio function as follows:
Figure BDA0002279076530000036
s15, assuming that Z is an acquired implicit variable of the N groups of sample sets under more than two working conditions, establishing a target expectation function as follows:
Figure BDA0002279076530000037
wherein Z is1,z2,…,zN，zi＝(zi1,zi2,…,ziC) Expressed as an implicit variable of the sample set under the i-th type of working condition, and under the condition that Z is unknown, Dr is { D, Z } for the complete data set;
and (3) calculating by M steps to obtain a maximization parameter:
Θ＝argmaxQ(Θ|Θold)
in the formula, thetaoldFor the desired derivation of the known parameters for the unknown parameters Θ, the training data D and the current estimate Θ are used0And updating through the M step and the E step to obtain the final parameters.
Preferably, the S2 specifically includes the following steps:
s21 two random variables for on-line operation under multiple working conditions of permanent magnet synchronous motor
Figure BDA0002279076530000038
And
Figure BDA0002279076530000041
wherein m and n are u and y variable numbers respectively, and online operation data is obtained according to the operation rule of the permanent magnet motor:
Figure BDA0002279076530000042
in the formula, it is assumed that u and y follow a Gaussian distribution, i.e., u — (μ)u,i,Σuu,i)，y～(μy,i,Σyy,i)，i＝1,…,N；
S22, after the two variables are normalized, recording as
Figure BDA0002279076530000043
And
Figure BDA0002279076530000044
constructing a typical correlation analysis matrix of the two variable data sets, and constructing the typical correlation analysis matrix y as follows:
Figure BDA0002279076530000045
Υ＝ΓΛΔT
wherein gamma-gamma (gamma-gamma)1，…，Υm)，Δ＝(δ1,…,δn)，
Figure BDA0002279076530000046
Ordered eigenvalues λ1≥λ2≥…≥λk(k min { m, n }), a feature vector γi,i∈[1,m]，δj,j∈[1,n](ii) a Two weighting matrices for a typical correlation analysis model are:
Figure BDA0002279076530000047
s23: it should be noted that the linear combination
Figure BDA0002279076530000048
And
Figure BDA0002279076530000049
with the largest correlation, in combination with the fact that noise is present in the process measurement, the correlation between them can be decomposed into the following equation:
Figure BDA00022790765300000410
wherein, Λk＝diag(λ1,…,λk) E is
Figure BDA00022790765300000411
E follows a multivariate normal distribution (consistent with the distribution of y and u).
The residual signal can be constructed as:
Figure BDA00022790765300000412
wherein, r (t),
Figure BDA00022790765300000413
and
Figure BDA00022790765300000414
data that are time series, respectively;
s24: from the residual signal, the Q statistic can be constructed as:
Qcca＝rT(t)r(t)。
preferably, said calculating said preset evaluation index Tg(k) The formula of (1) is as follows:
Figure BDA0002279076530000051
Figure BDA0002279076530000052
where f denotes a fault, p (d (k) e Ci) Denotes the genus d (k)Under the i-th working condition CiThe probability of (d); p (d (k) e f | d (k) e Ci) Indicating class i operating conditions Ci(k) probability of belonging to a failed sample;
the i-th working condition CiThe probability of (d), (k) belonging to a fault sample can be calculated by:
p(d(k)∈f|d(k)∈Ci)＝p(Qcca(d,i)＜Qcca(d(k),i))
in the formula, Qcca(d,i)＜Qcca(d (k), i) represents the i-th operating mode CiOn-line sample Q under the same conditionsccaThe statistic probability is larger than that in an off-line state;
and S33, the preset failure probability threshold is 1- α, wherein α is the acceptable false alarm rate, and the judgment standard for monitoring the health state is determined according to the evaluation index as follows:
Figure BDA0002279076530000053
as a general technical concept, the present invention also provides a health state monitoring system of a permanent magnet synchronous motor, including:
the device comprises a first unit, a second unit and a third unit, wherein the first unit is used for selecting normal operation data of the permanent magnet synchronous motor under more than two working conditions as a training data set, and inputting the training data set into an EM (effective magnetic field) algorithm to calculate to obtain parameters required by modeling;
the second unit is used for acquiring online operation data of the permanent magnet synchronous motor and establishing a multi-mode monitoring model according to the parameters and the operation data;
and the third unit is used for monitoring the health state of the permanent magnet synchronous motor by combining the evaluation index obtained by the model and a preset fault probability threshold value.
As a general technical concept, the present invention also provides a mobile terminal including: a processor and a memory; the memory is used for storing a computer program, and the processor runs the computer program to enable the mobile terminal to execute the permanent magnet synchronous motor health state monitoring method.
The invention has the following beneficial effects:
the invention provides a method, a system and a mobile terminal for monitoring the health state of a permanent magnet synchronous motor, which comprises the steps of selecting normal operation data of the permanent magnet synchronous motor under more than two working conditions as a training data set, and inputting the training data set into an EM algorithm to calculate to obtain parameters required by modeling; acquiring online operation data of the permanent magnet synchronous motor, and establishing a multi-mode monitoring model according to the parameters and the operation data; and optimizing the multi-mode monitoring model by adopting a preset fault probability index, and monitoring the health state of the permanent magnet synchronous motor according to the optimized multi-mode monitoring model. The monitoring method can find the abnormal operation condition of the permanent magnet synchronous motor in time, and has the advantages of wide applicability, high accuracy, convenient use and easy implementation; the maintenance work can be conveniently and timely arranged, and the safety maintenance device has important significance for improving the safe operation of the permanent magnet synchronous motor.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for monitoring the health status of a permanent magnet synchronous motor according to an embodiment of the present invention;
FIG. 2 is a diagram of normal multi-condition operating data of a permanent magnet synchronous motor in accordance with an embodiment of the present invention;
FIG. 3 is a diagram of multi-condition operating data for a fault in a PMSM according to an embodiment of the present invention;
FIG. 4 is a diagram of the result of monitoring the health status of the PMSM under multiple operating conditions according to the embodiment of the present invention;
fig. 5 is a schematic structural diagram of a system for monitoring the health status of a permanent magnet synchronous motor according to an embodiment of the present invention.
Detailed Description
Example 1
As shown in fig. 1, the present embodiment provides a method for monitoring a health status of a permanent magnet synchronous motor, including the following steps:
s1, selecting normal operation data of the permanent magnet synchronous motor under more than two working conditions as a training data set, and inputting the training data set into an EM algorithm to calculate to obtain parameters required by modeling;
s2, acquiring online operation data of the permanent magnet synchronous motor, and establishing a multi-mode monitoring model according to the parameters and the operation data;
s3: and monitoring the health state of the permanent magnet synchronous motor by combining the evaluation index obtained by the model and a preset fault probability threshold value.
The method for monitoring the health state of the permanent magnet synchronous motor can find the abnormal operation condition of the permanent magnet synchronous motor in time, and has the advantages of wide applicability, high accuracy, convenient use and easy implementation; the maintenance work can be conveniently and timely arranged, and the safety maintenance device has important significance for improving the safe operation of the permanent magnet synchronous motor.
Specifically, the present embodiment is described by taking a common failure of the permanent magnet synchronous motor as an example.
In the embodiment, the three-phase current of the permanent magnet synchronous motor is derived from the phase a, the phase b and the phase c of the rotor; in the off-line stage, selecting running samples under different working conditions to form a training data set; fitting training samples under different working conditions by using a Gaussian mixture model, and calculating a parameter with the maximum possibility of the Gaussian distribution model which can be generated under corresponding multiple working conditions by using an EM (effective electromagnetic) algorithm; the parameters will be applied to construct a typical correlation analysis model for health status monitoring. And in the online stage, selecting the running data of the permanent magnet synchronous motor, performing typical correlation analysis by using parameters obtained offline, and monitoring the health state by using a fault probability index based on a Bayesian inference strategy. The effectiveness of the invention is illustrated herein with respect to turn-to-turn short circuit faults of permanent magnet synchronous machines.
Firstly, in an off-line stage, three operating conditions of the permanent magnet synchronous motor are as follows: the three-phase current data and a-phase voltage data of three working conditions are respectively collected at the rotating speeds of 500r/min, 800r/min and 1100r/min, the corresponding time sequence of each group of samples is 2s as shown in FIG. 2, and a training data set is formed:
Figure BDA0002279076530000071
Figure BDA0002279076530000072
which represents the current of the three phases,
Figure BDA0002279076530000073
represents the a-phase voltage; corresponding to y1,y2,y3And u1,u2,u3The working conditions are 500r/min, 800r/min and 1100 r/min;
Figure BDA0002279076530000074
k-1, …,3 denotes the training data set for the k-th class of operating conditions. Wherein u and y are variables, and can be current, voltage or other variables capable of influencing the running state of the motor. The specific type of which may be selected by the worker based on field experience.
Further, the gaussian mixture model for the three conditions can be expressed as:
Figure BDA0002279076530000075
suppose MiWhere i is 1, …,3 denotes the i-th type of operating condition parameter, ω, for different operating conditionsiIs the ith MiWeight coefficient of (a), thetaiIs a parameter of the ith Gaussian component, θiComprises the following steps:
θi＝{ωi,μu,i,μy,i,Σuu,i,Σuy,i,Σyy,i} (3)
g(dk|θi) As a parameter M of the operating conditionsiMultiple gaussian density function of (1):
Figure BDA0002279076530000081
Figure BDA0002279076530000082
further, in order to make the formula(4) The probability of the Gaussian distribution model is maximum, let θiIs theta ═ theta1,θ2,θ3And the maximum likelihood ratio function formed under 3 groups of working conditions can be expressed as:
Figure BDA0002279076530000083
further, after iterative updating calculation according to the EM algorithm (11) (12), parameters required by the maximum joint probability density of the sample set are obtained.
Collecting real-time operation data of the permanent magnet synchronous motor; setting the normal operation working condition as 500r/min of rotation speed; the working condition of turn-to-turn short circuit is set as the rotating speed of 1200r/min and the rotating speed of 1300 r/min; the three-phase current data and a-phase voltage data of the permanent magnet synchronous motor under three working conditions are respectively collected and shown in figure 3, the time sequence of each group of samples is 2000 and is 2s, the turn-to-turn short circuit fault injection position is 3000 and 5000, and a test data set is formed:
Figure BDA0002279076530000084
Figure BDA0002279076530000085
which represents the current of the three phases,
Figure BDA0002279076530000086
represents the a-phase voltage; corresponding to y1,y2,y3And u1,u2,u3The working conditions are represented as 500r/min, 1200r/min and 1300r/min of rotating speed; suppose u to (mu)u,i,Σuu,i)，y～(μy,i,Σyy,i) The u and y mean matrices are calculated according to equation (5): mu.su,iAnd muy,iThe covariance matrix: sigmauu,i，Σyy,iSum-sigmauy,i。
Further, after the two variables are normalized, they are still recorded as
Figure BDA0002279076530000087
And
Figure BDA0002279076530000088
constructing a typical correlation analysis matrix y according to the parameters obtained by the EM algorithm formula (12) in the off-line state:
Figure BDA0002279076530000089
Υ＝ΓΛΔT(10)
further, two weighting matrices of the typical correlation analysis model are obtained according to equation (16):
Figure BDA0002279076530000091
constructing a residual signal:
Figure BDA0002279076530000092
r(t)，
Figure BDA0002279076530000093
and
Figure BDA0002279076530000094
data that is a time series; from the residual signal, the Q statistic can be constructed as:
Qcca＝rT(t)r(t) (13)
further, the preset evaluation index T is calculatedg(k)：
Figure BDA0002279076530000095
Figure BDA0002279076530000096
Where f denotes a fault, p (d (k) e Ci) Indicating d (k) belongs to the i-th operating condition CiThe probability of (d); p (d (k) e f | d (k) e Ci) To representClass i operating mode Ci(k) probability of belonging to a failed sample;
the i-th working condition CiThe probability of (d), (k) belonging to a fault sample can be calculated by:
p(d(k)∈f|d(k)∈Ci)＝p(Qcca(d,i)＜Qcca(d(k),i)) (14)
wherein Q iscca(d,i)＜Qcca(d (k), i) represents the i-th operating mode CiOn-line sample Q under the same conditionsccaThe statistical probability is greater than in the offline state.
Further, since 0 ≦ Tg(k) Confidence intervals (1- α) of ≦ 1 may be used for health status monitoring according to the following criteria:
Figure BDA0002279076530000101
referring specifically to fig. 4, fault occurrences can be monitored at sample locations 3000 and 5000, and no fault occurrences below the threshold.
Example 2
As shown in fig. 5, this embodiment provides a health state monitoring system of a permanent magnet synchronous motor corresponding to embodiment 1, including:
the device comprises a first unit, a second unit and a third unit, wherein the first unit is used for selecting normal operation data of the permanent magnet synchronous motor under more than two working conditions as a training data set, and inputting the training data set into an EM (effective magnetic field) algorithm to calculate to obtain parameters required by modeling;
the second unit is used for acquiring online operation data of the permanent magnet synchronous motor and establishing a multi-mode monitoring model according to the parameters and the operation data;
and the third unit is used for optimizing the multi-mode monitoring model by adopting a preset fault probability index and monitoring the health state of the permanent magnet synchronous motor according to the optimized multi-mode monitoring model.
Example 3
The present embodiment provides a mobile terminal, including: a processor and a memory; the memory is used for storing a computer program, and the processor runs the computer program to enable the mobile terminal to execute the health state monitoring method of the permanent magnet synchronous motor.
In the several embodiments provided in the present application, it should be understood that the disclosed method and system may be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
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 (7)

1. A health state monitoring method of a permanent magnet synchronous motor is characterized by comprising the following steps:
s1, selecting normal operation data of the permanent magnet synchronous motor under more than two working conditions as a training data set, and inputting the training data set into an EM algorithm to calculate to obtain parameters required by modeling;
s2, acquiring online operation data of the permanent magnet synchronous motor, and establishing a multi-mode monitoring model according to the parameters and the operation data;
s3: and monitoring the health state of the permanent magnet synchronous motor by combining the evaluation index obtained by the model and a preset fault probability threshold value.
2. The method for monitoring the health state of the permanent magnet synchronous motor according to claim 1, wherein in the step S1, the normal operation data of the permanent magnet synchronous motor under different working conditions are different, and a gaussian mixture model is adopted to fit the normal operation data of the permanent magnet synchronous motor under different working conditions.
3. The method for monitoring the state of health of a permanent magnet synchronous motor according to claim 1, wherein the step S1 specifically comprises the steps of:
s11: setting C different working conditions in the running state of the permanent magnet synchronous motor in an off-line state, and using MiI is 1, …, and C represents the i-th type of operating condition parameters under different operating conditions;
s12: assuming that the selected more than two working condition historical data D are N samples of normal operation data, each sample comprises process variables meeting the variable
Figure FDA0002279076520000011
Variables of
Figure FDA0002279076520000012
The history data D is:
Figure FDA0002279076520000013
in the formula (d)kRepresents a random sample set under each working condition,
Figure FDA0002279076520000014
representing data sets under different working conditions, wherein l represents the variable number of u, and m represents the variable number of y; (ii) a
S13: adopting Gaussian mixture model to represent random sample set d under various working conditionskThe probability density function p of (a) is:
Figure FDA0002279076520000015
in the formula, ωiIs the ith MiWeight coefficient of (a), thetaiIs a parameter of the ith Gaussian component, C represents the total number of operating conditions, where θiComprises the following steps:
θi＝{ωi,μu,i,μy,i,Σuu,i,Σuy,i,Σyy,i}
in the formula, g (d)k|θi) As a parameter M of the operating conditionsiThe calculation formula of the multivariate gaussian density function is as follows:
Figure FDA0002279076520000021
Figure FDA0002279076520000022
wherein, the mean value of u under more than two working conditions is established as follows:
Figure FDA0002279076520000023
the covariance matrix of u is:
Figure FDA0002279076520000024
the mean value of y is:
Figure FDA0002279076520000025
the covariance matrix of y is:
Figure FDA0002279076520000026
s14: let thetaiIs theta ═ theta1,θ2,…,θC-expressing a maximum likelihood ratio function of N different sample compositions as:
Figure FDA0002279076520000027
obtaining a distribution model according to the maximum likelihood ratio function as follows:
Figure FDA0002279076520000028
s15, assuming that Z is an acquired implicit variable of the N groups of sample sets under more than two working conditions, establishing a target expectation function as follows:
Figure FDA0002279076520000029
wherein Z is1,z2,…,zN，zi＝(zi1,zi2,…,ziC) Expressed as an implicit variable of the sample set under the i-th type of working condition, and under the condition that Z is unknown, Dr is { D, Z } for the complete data set;
and (3) calculating by M steps to obtain a maximization parameter:
Θ＝arg maxQ(Θ|Θold)
in the formula, thetaoldFor the desired derivation of the known parameters for the unknown parameters Θ, the training data D and the current estimate Θ are used0And updating through the M step and the E step to obtain the final parameters.
4. The method for monitoring the state of health of a permanent magnet synchronous motor according to claim 1, wherein the step S2 specifically comprises the steps of:
s21 variable of online operation under multiple working conditions of assumed permanent magnet synchronous motor
Figure FDA0002279076520000031
And variables
Figure FDA0002279076520000032
Wherein m and n are u and y variable numbers respectively, and online operation data is obtained according to the operation rule of the permanent magnet motor:
Figure FDA0002279076520000033
in the formula, it is assumed that the variable u and the variable y follow a Gaussian distribution, i.e., u — (μ)u,i,Σuu,i)，y～(μy,i,Σyy,i)，i＝1,…,N；
S22, after the two variables are normalized, recording as
Figure FDA0002279076520000034
And
Figure FDA0002279076520000035
constructing a typical correlation analysis matrix of the two variable data sets, and constructing the typical correlation analysis matrix y as follows:
Figure FDA0002279076520000036
Υ＝ΓΛΔT
wherein gamma-gamma (gamma-gamma)1，…，Υm)，Δ＝(δ1,…,δn)，
Figure FDA0002279076520000037
Ordered eigenvalues λ1≥λ2≥…≥λk(k min { m, n }), a feature vector γi,i∈[1,m]，δj,j∈[1,n](ii) a Two weighting matrices for a typical correlation analysis model are:
Figure FDA0002279076520000038
s23: the residual signal is constructed as:
Figure FDA0002279076520000039
wherein, r (t),
Figure FDA00022790765200000310
and
Figure FDA00022790765200000311
data that are time series, respectively;
s24: from the residual signal, the Q statistic can be constructed as:
Qcca＝rT(t)r(t)。
5. the method according to claim 1, wherein the predetermined evaluation index T is calculatedg(k) The formula of (1) is as follows:
Figure FDA0002279076520000041
Figure FDA0002279076520000042
where f denotes a fault, p (d (k) e Ci) Indicating d (k) belongs to the i-th operating condition CiThe probability of (d); p (d (k) e f | d (k) e Ci) Indicating class i operating conditions Ci(k) probability of belonging to a failed sample;
the i-th working condition CiThe probability of (d), (k) belonging to a fault sample can be calculated by:
p(d(k)∈f|d(k)∈Ci)＝p(Qcca(d,i)＜Qcca(d(k),i))
in the formula, Qcca(d,i)＜Qcca(d (k), i) represents the i-th operating mode CiOn-line sample Q under the same conditionsccaStatisticsThe probability is greater than that in an off-line state;
s33, the preset failure probability threshold is 1- α, wherein α is the acceptable false alarm rate, and the judgment standard for monitoring the health state is determined according to the evaluation index as follows:
Figure FDA0002279076520000043
6. a health status monitoring system of a permanent magnet synchronous motor, comprising:
the device comprises a first unit, a second unit and a third unit, wherein the first unit is used for selecting normal operation data of the permanent magnet synchronous motor under more than two working conditions as a training data set, and inputting the training data set into an EM (effective magnetic field) algorithm to calculate to obtain parameters required by modeling;
the second unit is used for acquiring online operation data of the permanent magnet synchronous motor and establishing a multi-mode monitoring model according to the parameters and the operation data;
and the third unit is used for monitoring the health state of the permanent magnet synchronous motor by combining the evaluation index obtained by the model and a preset fault probability threshold value.
7. A mobile terminal, comprising: a processor and a memory; the memory is used for storing a computer program, and the processor runs the computer program to make the mobile terminal execute the health status monitoring method of the permanent magnet synchronous motor according to any one of claims 1 to 5.
CN201911133949.8A 2019-11-19 2019-11-19 Health state monitoring method and system of permanent magnet synchronous motor and mobile terminal Pending CN110988674A (en)

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