CN111505500B - Intelligent motor fault detection method based on filtering in industrial field - Google Patents

Intelligent motor fault detection method based on filtering in industrial field Download PDF

Info

Publication number
CN111505500B
CN111505500B CN202010272259.7A CN202010272259A CN111505500B CN 111505500 B CN111505500 B CN 111505500B CN 202010272259 A CN202010272259 A CN 202010272259A CN 111505500 B CN111505500 B CN 111505500B
Authority
CN
China
Prior art keywords
interval
time
motor
representing
fault
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010272259.7A
Other languages
Chinese (zh)
Other versions
CN111505500A (en
Inventor
王子赟
张梦迪
王艳
纪志成
李南江
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangnan University
Original Assignee
Jiangnan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangnan University filed Critical Jiangnan University
Priority to CN202010272259.7A priority Critical patent/CN111505500B/en
Publication of CN111505500A publication Critical patent/CN111505500A/en
Application granted granted Critical
Publication of CN111505500B publication Critical patent/CN111505500B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Control Of Electric Motors In General (AREA)

Abstract

The invention discloses a filtering-based motor intelligent fault detection method in the industrial field, and belongs to the technical field of fault detection. The method uses the collective member estimation method to represent the state feasible set by the vector, does not need to know the prior knowledge of model disturbance and noise in advance, and increases the practicability and accuracy of the fault detection method; in the inverse filtering problem solving process, the interval box is represented by vectors, the interval box belonging to a feasible set is searched through Boolean operation of vector groups, the problems that the traditional interval filtering algorithm is large in calculation amount and calculation time is exponentially increased along with the increase of interval dimensions are solved, and the state interval is estimated more efficiently and accurately. The method is different from the traditional method that the upper and lower boundaries of the estimated residual error are utilized to realize fault detection, and the estimation of the fault range provides guarantee for the subsequent fault diagnosis of the motor.

Description

Intelligent motor fault detection method based on filtering in industrial field
Technical Field
The invention relates to a filtering-based motor intelligent fault detection method in the industrial field, and belongs to the technical field of fault detection.
Background
The motor converts electric energy into mechanical energy, so that mechanization of production and manufacturing is realized, and the motor is an essential part in current industrial production equipment. In order to meet the requirements of increasingly complex and integrated modern automatic control production equipment, the faults of the motor need to be accurately detected in real time, so that the loss is minimized.
When the traditional fault detection method is used for fault detection, noise and disturbance are assumed to be variables which are known or meet a certain probability distribution, and fault values are estimated on the basis to carry out fault detection.
In order to solve the problem that a detection result is inaccurate because disturbance and noise in an actual operation environment are not known variables or variables meeting certain probability distribution, a centralized member estimation method is used for fault detection in the conventional fault detection, measurement data, disturbance and noise are described by using space geometric bodies such as intervals, ellipsoids, multi-cell bodies and the like, and faults are judged by monitoring the consistency of the measurement output of a motor and the prediction output of a motor model system, but the method has the problems of large calculation amount, low accuracy, poor real-time performance and the like.
Disclosure of Invention
In order to solve the above problems, the present invention provides a filtering-based intelligent fault detection method for a motor in the industrial field, wherein the method comprises:
the method comprises the following steps: establishing a discrete model of the motor:
step two: constructing an augmentation system according to the motor discrete model, and acquiring an observer state estimation interval at the k moment;
step three: designing an inversion filtering problem according to input data and output data obtained under the actual operation condition of the motor at the time k and the subsequent time s;
step four: solving a feasible set of an inversion filtering problem, and obtaining a filtering state estimation interval at the k moment;
step five: calculating the intersection of the observer state estimation interval and the filtering state estimation interval at the moment k, and acquiring the state estimation interval and the fault estimation interval at the moment k;
step six: detecting whether the motor fails according to the failure estimation interval at the moment k;
and in the process of solving the feasible set of the inverse filtering problem, representing the interval boxes by using vectors, and searching the interval boxes belonging to the feasible set through Boolean operation of the vector group.
Optionally, the discrete model of the motor established in the first step is as follows:
Figure BDA0002443506030000021
wherein the content of the first and second substances,
Figure BDA0002443506030000022
represents the state vector of the motor at time k,
Figure BDA0002443506030000023
represents the input vector of the motor at time k,
Figure BDA0002443506030000024
representing the output vector of the motor at the moment k, A representing a state space matrix, B representing an input matrix, C representing an output matrix, E representing a disturbance action matrix, D representing a noise action matrix, F representing a fault action matrix,
Figure BDA0002443506030000025
representing an unknown but bounded perturbation vector,
Figure BDA0002443506030000026
representing an unknown but bounded noise vector,
Figure BDA0002443506030000027
indicating an additive failure.
Optionally, the second step: constructing an augmentation system according to a discrete model of the motor, and acquiring an observer state estimation interval at the k moment, wherein the method comprises the following steps:
2.1, constructing an augmentation system according to the discrete motor model, namely obtaining the following formula (5) according to the formula (4):
Figure BDA0002443506030000028
wherein the content of the first and second substances,
Figure BDA0002443506030000029
Δfk＝fk+1-fk；
2.2 constructing the state observer of the augmented system:
Figure BDA00024435060300000210
wherein the content of the first and second substances,
Figure BDA00024435060300000211
representing the observed state at time k, L representing the observer gain, and T and N representing constant matrices;
determining a state error interval [ e ] at time k according to equations (5) and (6)k]：
Figure BDA00024435060300000212
Wherein, [ Delta f [ ]k-1]Indicates the fault difference interval at time k-1, [ w ]k-1]Represents the disturbance interval at time k-1, [ v ]k-1]Representing the noise interval at time k-1;
the state observer is designed according to equation (7):
Figure BDA00024435060300000213
Figure BDA00024435060300000214
L＝P-1Z (10)
wherein the content of the first and second substances,
Figure BDA00024435060300000215
a pseudo-inverse of the matrix M is represented,
Figure BDA00024435060300000216
Figure BDA00024435060300000217
p, Y, Z denotes a matrix satisfying the constraint (11):
Figure BDA0002443506030000031
wherein γ represents a minimum positive scalar quantity satisfying equation (11);
Figure BDA0002443506030000032
Figure BDA0002443506030000033
Figure BDA0002443506030000034
Figure BDA0002443506030000035
Figure BDA0002443506030000036
2.3 determining the observer state estimation interval at the time k as follows:
Figure BDA0002443506030000037
ekindicating the state error at time k.
Optionally, in the third step, an inverse filtering problem is designed according to input data and output data obtained under the actual operation condition of the motor at the time k and at the time s after the time k, and the method includes:
Figure BDA0002443506030000038
where X represents a feasible set of inverse filtering problems,
Figure BDA0002443506030000039
represents the filter state estimation interval at time k, [ Y ]k]＝y(k:k+s)-Ou(k:k+s)u(k:k+s)-Of(k:k+s)[Δf(k:k+s)]-Ow(k:k+s)[w(k:k+s)]-Ov(k:k+s)[v(k:k+s)]，
Figure BDA00024435060300000310
Figure BDA00024435060300000311
y(k:k+s)，u(k:k+s)，[Δf(k:k+s)]，[w(k:k+s)]，[v(k:k+s)]And output data, input data, a fault difference interval, a disturbance interval and a noise interval at the time k and the time s after the time k are respectively represented.
Optionally, the fourth step: solving a feasible set of an inversion filtering problem, and obtaining a filtering state estimation interval at the k moment, wherein the method comprises the following steps:
setting an initial interval box;
expressing the set initial interval box by using a row vector, and searching the interval box belonging to a feasible set by using a test function [ t ] (. cndot.) shown in the following formula;
Figure BDA0002443506030000041
wherein
Figure BDA0002443506030000042
Vector groups composed of vector forms representing all interval boxes which are not searched, in, out and eps represent Boolean vectors,
Figure BDA0002443506030000043
representing a vector formed by the maximum interval widths of all interval boxes, wherein epsilon represents the precision of the obtained optimal feasible set;
Figure BDA0002443506030000044
vector groups in the form of vectors representing interval boxes belonging to a feasible set are to be formed
Figure BDA0002443506030000045
All the represented interval boxes are put into a solution set;
Figure BDA0002443506030000046
vector groups composed of vector forms representing interval boxes not belonging to the feasible sets;
Figure BDA0002443506030000047
vector group composed of vector form of interval box with maximum interval width smaller than precision epsilon;
Figure BDA0002443506030000048
vector groups in the form of vectors representing only a part of interval boxes belonging to a feasible set but having a maximum interval width greater than the precision epsilon will be represented
Figure BDA0002443506030000049
Each interval box represented is divided into two new interval boxes along the dimension of the maximum interval width, and the vector forms of all the new interval boxes form a new vector group
Figure BDA00024435060300000410
The search process is cycled until
Figure BDA00024435060300000411
When the time is space, all interval boxes in the solution set form an optimal feasible set of the inverse filtering problem, namely a filtering state estimation interval at the moment of k
Figure BDA00024435060300000412
Optionally, in the fifth step, calculating an intersection of the observer state estimation interval and the filter state estimation interval at the time k, and acquiring the state estimation interval and the fault estimation interval at the time k, includes:
determining a state estimation interval at time k according to equation (15):
Figure BDA00024435060300000413
wherein the content of the first and second substances,
Figure BDA00024435060300000414
a state estimation interval representing time k;
determining a fault estimation interval at time k according to equation (16):
Figure BDA00024435060300000415
wherein, [ f ]k]Indicating the fault estimation interval at time k, ImAn m-dimensional identity matrix is represented.
Optionally, the step six of detecting whether the motor fails according to the failure estimation interval at the time k includes:
if the upper and lower boundaries of the fault estimation interval obtained in the step five are positioned at two sides of 0, the motor has no fault;
if the upper and lower boundaries of the fault estimation interval are at one side of 0 at the same time, the motor is indicated to be in fault, and the fault value is in the fault estimation range.
The invention also provides an intelligent fault detection system which adopts the intelligent fault detection method to detect faults.
Optionally, when the intelligent fault detection system detects a fault of the motor, it is necessary to obtain input data and output data obtained under the actual operation condition of the motor at the time k and the time s after the time k.
Optionally, the input data obtained under the actual operation condition of the motor represents motor armature voltage, and the output data represents motor armature current and motor rotation speed.
The invention has the beneficial effects that:
according to the method, the state feasible set is represented by the vector by using the collective member estimation method, prior knowledge of model disturbance and noise is not required to be known in advance, and the practicability and accuracy of the fault detection method are improved; in the inverse filtering problem solving process, the interval box is represented by vectors, the interval box belonging to a feasible set is searched through Boolean operation of vector groups, the problems that the traditional interval filtering algorithm is large in calculation amount and calculation time is exponentially increased along with the increase of interval dimensions are solved, and the state interval is estimated more efficiently and accurately. The method is different from the traditional method that the upper and lower boundaries of the estimated residual error are utilized to realize fault detection, and the estimation of the fault range provides guarantee for the subsequent fault diagnosis of the motor.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced 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 to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of an industrial field filtering-based intelligent motor fault detection method disclosed in an embodiment of the invention;
fig. 2 is a diagram showing a relationship between a fault estimation section (solid line) of the present invention, a fault estimation section (dot-dash line) of a conventional fault estimation method, and an applied fault value (broken line) after a fault signal is applied to a motor according to an embodiment of the present invention.
Fig. 3 is a comparison of the number of times functions are called by the vector interval filter algorithm of the present invention and the conventional interval filter algorithm in the motor fault estimation process disclosed in one embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The first embodiment is as follows:
the embodiment provides an intelligent motor fault detection method based on filtering in the industrial field, and with reference to fig. 1, the method includes:
the method comprises the following steps: and establishing a discrete model of the permanent magnet direct current motor.
According to the working principle of the permanent magnet direct current motor, acquiring a continuous time nonlinear dynamic model:
Figure BDA0002443506030000061
wherein u represents the armature voltage, KeRepresenting the back emf coefficient, n representing the motor speed, RaRepresenting resistance, i representing current, L1Represents the inductance, KtRepresenting the torque coefficient, T0And T2Respectively, an idling torque and a load torque, J a moment of inertia of the rotor and the load, and Ω an angular velocity.
No-load torque T generated by losses of the motor0Producing a friction torque equivalent to friction in the bearing or between the brush and the commutator
Figure BDA0002443506030000062
Torque T affected by bearing lubrication conditionsrTorque T generated aerodynamically, e.g. by resistance of fanpA sum of where Tr＝frn，Tp＝fpn2，frAnd fpRespectively representing the viscous friction coefficient and the air friction coefficient of the motor bearing, and n represents the rotating speed.
The air resistance has very little influence on the operation of the motor, and can be ignored, namely f is ignoredpThe motor model is linearized according to equation (1):
Figure BDA0002443506030000063
wherein n is 60 omega/2 pi, J1＝(2π/60)J。
Converting the dynamic linear model of the motor into a state space expression:
Figure BDA0002443506030000064
obtaining a discrete model of the permanent magnet direct current motor by using a forward Euler method discretization formula (3):
Figure BDA0002443506030000065
wherein the content of the first and second substances,
Figure BDA0002443506030000071
represents the state vector of the motor at time k,
Figure BDA0002443506030000072
represents the input vector of the motor at time k,
Figure BDA0002443506030000073
representing the output vector of the motor at the moment k, A representing a state space matrix, B representing an input matrix, C representing an output matrix, E representing a disturbance action matrix, D representing a noise action matrix, F representing a fault action matrix,
Figure BDA0002443506030000074
representing an unknown but bounded perturbation vector,
Figure BDA0002443506030000075
representing an unknown but bounded noise vector,
Figure BDA0002443506030000076
indicating an additive fault;
step two: constructing an augmentation system according to the discrete model of the permanent magnet direct current motor established in the first step:
Figure BDA0002443506030000077
wherein the content of the first and second substances,
Figure BDA0002443506030000078
Δfk＝fk+1-fk；
a state observer for constructing the augmentation system (5):
Figure BDA0002443506030000079
wherein the content of the first and second substances,
Figure BDA00024435060300000710
representing the observed state at time k, L representing the observer gain, and T and N representing constant matrices;
from (5) and (6), the state error interval [ e ] at time k can be determinedk]：
Figure BDA00024435060300000711
Wherein, [ Delta f [ ]k-1]Indicates the fault difference interval at time k-1, [ w ]k-1]Represents the disturbance interval at time k-1, [ v ]k-1]Representing the noise interval at time k-1;
designing the state observer according to (7):
Figure BDA00024435060300000712
Figure BDA00024435060300000713
Figure BDA00024435060300000714
wherein the content of the first and second substances,
Figure BDA00024435060300000715
a pseudo-inverse of the matrix M is represented,
Figure BDA00024435060300000716
Figure BDA00024435060300000717
p, Y, Z denotes a matrix satisfying the constraint (11):
Figure BDA00024435060300000718
wherein γ represents a minimum positive scalar quantity satisfying the expression (11),
Figure BDA0002443506030000081
Figure BDA0002443506030000082
Figure BDA0002443506030000083
Figure BDA0002443506030000084
Figure BDA0002443506030000085
the observer state estimation interval at time k is:
Figure BDA0002443506030000086
ekindicating the state error at time k;
step three: designing an inversion filtering problem according to input data and output data obtained under the actual operation condition of the motor at the time k and the time s later:
Figure BDA0002443506030000087
where X represents a feasible set of inverse filtering problems,
Figure BDA0002443506030000088
represents the filter state estimation interval at time k, [ Y ]k]＝y(k:k+s)-Ou(k:k+s)u(k:k+s)-Of(k:k+s)[Δf(k:k+s)]-Ow(k:k+s)[w(k:k+s)]-Ov(k:k+s)[v(k:k+s)]，
Figure BDA0002443506030000089
Figure BDA00024435060300000810
u(k:k+s)，[Δf(k:k+s)]，[w(k:k+s)]，[v(k:k+s)]Respectively representing input data, a fault difference interval, a disturbance interval and a noise interval at the moment k and the moment s after the moment k;
step four: solving a feasible set of the inverse filtering problem in step three:
initially, a very large interval box including a feasible set is given, the interval box is represented by a row vector, and a test function is used
Figure BDA00024435060300000811
Searching an interval box belonging to a feasible set in the initial interval box;
wherein the content of the first and second substances,
Figure BDA0002443506030000091
vector groups composed of vector forms representing all interval boxes to be searched, in, out and eps represent Boolean vectors,
Figure BDA0002443506030000092
representing a vector formed by the maximum interval widths of all interval boxes, wherein epsilon represents the precision of the obtained optimal feasible set;
Figure BDA0002443506030000093
representing a set of vectors
Figure BDA0002443506030000094
Row i of (1);
if it is
Figure BDA0002443506030000095
in (i) ═ 1, otherwise, in (i) ═ 0;
Figure BDA0002443506030000096
vector groups in the form of vectors representing interval boxes belonging to a feasible set are to be formed
Figure BDA0002443506030000097
All the represented interval boxes are put into a solution set;
if it is
Figure BDA0002443506030000098
out (i) ═ 1, otherwise out (i) ═ 0;
Figure BDA0002443506030000099
vector groups composed of vector forms representing interval boxes belonging to the infeasible sets;
Figure BDA00024435060300000910
to represent
Figure BDA00024435060300000911
The rest of (1)Vector group, the interval box represented does not belong to the feasible set, not belong to the infeasible set;
if it is
Figure BDA00024435060300000912
Otherwise, eps (i) is 0;
Figure BDA00024435060300000913
vector group composed of vector form of interval box with maximum interval width smaller than precision epsilon;
Figure BDA00024435060300000914
vector group formed by vector form of interval box only a part of which belongs to feasible set but maximum interval width is greater than precision epsilon
Figure BDA00024435060300000915
The interval box represented by each vector is divided into two new interval boxes along the dimension of the maximum interval width, and the vector forms of all the new interval boxes form a new vector group
Figure BDA00024435060300000916
The search process is cycled until
Figure BDA00024435060300000917
When the time is empty, all interval boxes in the solution set form an optimal feasible set of the inverse filtering problem, namely a filtering state estimation interval at the moment k;
step five: calculating the intersection of the observer state estimation interval and the filtering state estimation interval at the moment k:
Figure BDA00024435060300000918
wherein the content of the first and second substances,
Figure BDA00024435060300000919
a state estimation interval representing time k;
determining a fault estimation interval at time k according to equation (16):
Figure BDA00024435060300000920
step six: and detecting whether the motor fails according to the failure estimation interval at the moment k.
If the upper and lower boundaries of the fault estimation interval are positioned at two sides of 0, the motor has no fault;
if the upper and lower boundaries of the fault estimation interval are at one side of 0 at the same time, the motor is indicated to be in fault, and the fault value is in the fault estimation range.
Example two
The embodiment provides an intelligent motor fault detection system based on filtering in the industrial field, and the intelligent fault detection method of the embodiment is adopted to detect faults of a motor, specifically:
the MAX472 current sensing amplifier is used for measuring the armature current i when the permanent magnet direct current motor runs, and the photoelectric encoder is used for measuring the rotating speed n of the motor.
Applying DC voltage u to motor and measuring no-load speed n0No-load current I0Dead time constant TaAnd load speed n2Load current I2The time t required when the armature current reaches 0.95 times of the current when the motor stably operatessThe following parameters of the motor are obtained:
armature resistance RaComprises the following steps:
Figure BDA0002443506030000101
inductor L1Comprises the following steps:
Figure BDA0002443506030000102
coefficient of back electromotive force KeComprises the following steps:
Figure BDA0002443506030000103
viscous friction coefficient f of motor bearingrComprises the following steps:
Figure BDA0002443506030000104
moment of inertia J1Is composed of
Figure BDA0002443506030000105
Coefficient of torque KtAnd KeApproximately equal.
And constructing a motor model according to the parameters, and performing fault detection on the motor by adopting the intelligent fault detection method in the embodiment one.
In this embodiment, in a predetermined time range, after the step one to the step six are executed, a fault estimation interval of the motor at each time in the predetermined time range is obtained, and whether the motor fails or not is detected. As can be seen from fig. 2, when both the conventional fault estimation method and the method proposed by the present invention can implement a fault-free signal, the upper and lower boundaries of the fault estimation interval are located at both sides of 0; after the fault signal is applied, the fault estimation interval is positioned at two sides of the applied fault signal. But compared with the fault estimation interval obtained by the existing fault estimation method, the fault estimation interval obtained by the method has smaller width and is more accurate.
FIG. 3 is a comparison of the number of times that a function is called by using the method of the present invention and the conventional interval filtering algorithm, and it can be seen from FIG. 3 that the function is called many times by the conventional interval filtering algorithm at each time, whereas the function is called only once at each time by the vector interval filtering algorithm of the present invention, because the conventional interval filtering algorithm is a recursive algorithm, the function is called many times to solve the filtering problem, and the number of calling layers is too many, which results in large calculation amount and long consumed time; in the method provided by the invention, all the interval boxes are represented as a vector group, the vector group is continuously updated in the solving process, and only one solving function needs to be called, so that the calculated amount and the calculating time are reduced.
Some steps in the embodiments of the present invention may be implemented by software, and the corresponding software program may be stored in a readable storage medium, such as an optical disc or a hard disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. An intelligent fault detection method, characterized in that the method comprises:
the method comprises the following steps: establishing a discrete model of the motor:
step two: constructing an augmentation system according to the motor discrete model, and acquiring an observer state estimation interval at the k moment;
step three: designing an inversion filtering problem according to input data and output data obtained under the actual operation condition of the motor at the time k and the subsequent time s;
step four: solving a feasible set of an inversion filtering problem, and obtaining a filtering state estimation interval at the k moment;
step five: calculating the intersection of the observer state estimation interval and the filtering state estimation interval at the moment k, and acquiring the state estimation interval and the fault estimation interval at the moment k;
step six: detecting whether the motor fails according to the failure estimation interval at the moment k;
and in the process of solving the feasible set of the inverse filtering problem, representing the interval boxes by using vectors, and searching the interval boxes belonging to the feasible set through Boolean operation of the vector group.
2. The method of claim 1, wherein the discrete model of the electric machine created in step one is:
Figure FDA0002836396320000011
wherein the content of the first and second substances,
Figure FDA0002836396320000012
represents the state vector of the motor at time k,
Figure FDA0002836396320000013
represents the input vector of the motor at time k,
Figure FDA0002836396320000014
representing the output vector of the motor at the moment k, A representing a state space matrix, B representing an input matrix, C representing an output matrix, E representing a disturbance action matrix, D representing a noise action matrix, F representing a fault action matrix,
Figure FDA0002836396320000015
representing an unknown but bounded perturbation vector,
Figure FDA0002836396320000016
representing an unknown but bounded noise vector,
Figure FDA0002836396320000017
indicating an additive failure.
3. The method of claim 2, wherein the second step: constructing an augmentation system according to a discrete model of the motor, and acquiring an observer state estimation interval at the k moment, wherein the method comprises the following steps:
2.1, constructing an augmentation system according to the discrete motor model, namely obtaining the following formula (5) according to the formula (4):
Figure FDA0002836396320000018
wherein the content of the first and second substances,
Figure FDA0002836396320000019
Δfk＝fk+1-fk；
2.2 constructing the state observer of the augmented system:
Figure FDA0002836396320000021
wherein the content of the first and second substances,
Figure FDA0002836396320000022
representing the observed state at time k, L representing the observer gain, and T and N representing constant matrices;
determining a state error interval [ e ] at time k according to equations (5) and (6)k]：
Figure FDA0002836396320000023
Wherein, [ Delta f [ ]k-1]Indicates the fault difference interval at time k-1, [ w ]k-1]Represents the disturbance interval at time k-1, [ v ]k-1]Representing the noise interval at time k-1;
the state observer is designed according to equation (7):
Figure FDA0002836396320000024
Figure FDA0002836396320000025
L＝P-1Z (10)
wherein the content of the first and second substances,
Figure FDA0002836396320000026
a pseudo-inverse of the matrix M is represented,
Figure FDA0002836396320000027
p, Y, Z denotes a matrix satisfying the constraint (11):
Figure FDA0002836396320000028
wherein γ represents a minimum positive scalar quantity satisfying equation (11); i ismAn identity matrix representing m dimensions;
Figure FDA0002836396320000029
Figure FDA00028363963200000210
Figure FDA00028363963200000211
Figure FDA00028363963200000212
Figure FDA00028363963200000213
2.3 determining observer State estimation interval at time k
Figure FDA00028363963200000214
Comprises the following steps:
Figure FDA0002836396320000031
ekindicating the state error at time k.
4. The method according to claim 3, wherein in step three, an inverse filter problem is designed according to input data and output data obtained under the actual operation condition of the motor at the time k and the time s later, and the method comprises the following steps:
Figure FDA0002836396320000032
where X represents a feasible set of inverse filtering problems,
Figure FDA0002836396320000033
represents the filter state estimation interval at time k, [ Y ]k]＝y(k:k+s)-Ou(k:k+s)u(k:k+s)-Of(k:k+s)[Δf(k:k+s)]-Ow(k:k+s)[w(k:k+s)]-Ov(k:k+s)[v(k:k+s)]，
Figure FDA0002836396320000034
Figure FDA0002836396320000035
y(k:k+s)，u(k:k+s)，[Δf(k:k+s)]，[w(k:k+s)]，[v(k:k+s)]And output data, input data, a fault difference interval, a disturbance interval and a noise interval at the time k and the time s after the time k are respectively represented.
5. The method of claim 4, wherein the fourth step: solving a feasible set of an inversion filtering problem, and obtaining a filtering state estimation interval at the k moment, wherein the method comprises the following steps:
setting an initial interval box;
expressing the set initial interval box by using a row vector, and searching the interval box belonging to a feasible set by using a test function [ t ] (. cndot.) shown in the following formula;
Figure FDA0002836396320000036
wherein
Figure FDA0002836396320000037
Vector groups composed of vector forms representing all interval boxes which are not searched, in, out and eps represent Boolean vectors,
Figure FDA0002836396320000038
representing a vector formed by the maximum interval widths of all interval boxes, wherein epsilon represents the precision of the obtained optimal feasible set;
Figure FDA0002836396320000039
vector groups in the form of vectors representing interval boxes belonging to a feasible set are to be formed
Figure FDA00028363963200000310
All the represented interval boxes are put into a solution set;
Figure FDA0002836396320000041
vector groups composed of vector forms representing interval boxes not belonging to the feasible sets;
Figure FDA0002836396320000042
vector group composed of vector form of interval box with maximum interval width smaller than precision epsilon;
Figure FDA0002836396320000043
meaning that only a part of the data belongs to the feasible set but the maximum interval width is larger thanVector group consisting of interval boxes of precision epsilon in vector form
Figure FDA0002836396320000044
Each interval box represented is divided into two new interval boxes along the dimension of the maximum interval width, and the vector forms of all the new interval boxes form a new vector group
Figure FDA0002836396320000045
The search process is cycled through
Figure FDA0002836396320000046
When the time is space, all interval boxes in the solution set form an optimal feasible set of the inverse filtering problem, namely a filtering state estimation interval at the moment of k
Figure FDA0002836396320000047
6. The method according to claim 5, wherein the fifth step of calculating the intersection of the observer state estimation interval and the filter state estimation interval at the time k and obtaining the state estimation interval and the fault estimation interval at the time k comprises:
determining a state estimation interval at time k according to equation (15):
Figure FDA0002836396320000048
wherein the content of the first and second substances,
Figure FDA0002836396320000049
a state estimation interval representing time k;
determining a fault estimation interval at time k according to equation (16):
Figure FDA00028363963200000410
wherein, [ f ]k]Indicating the fault estimation interval at time k, ImAn m-dimensional identity matrix is represented.
7. The method according to claim 6, wherein the step six of detecting whether the motor has a fault according to the fault estimation interval at the time k comprises:
if the upper and lower boundaries of the fault estimation interval obtained in the step five are positioned at two sides of 0, the motor has no fault;
if the upper and lower boundaries of the fault estimation interval are at one side of 0 at the same time, the motor is indicated to be in fault, and the fault value is in the fault estimation range.
8. An intelligent fault detection system, characterized in that the intelligent fault detection system adopts the intelligent fault detection method of any one of claims 1-7 to perform fault detection.
9. The intelligent fault detection system according to claim 8, wherein when detecting a fault of the motor, the intelligent fault detection system needs to obtain input data and output data obtained under the actual operation condition of the motor at the time k and at the time s later.
10. The intelligent fault detection system of claim 8, wherein the input data obtained under actual operating conditions of the motor represents motor armature voltage, and the output data represents motor armature current and motor speed.
CN202010272259.7A 2020-04-09 2020-04-09 Intelligent motor fault detection method based on filtering in industrial field Active CN111505500B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010272259.7A CN111505500B (en) 2020-04-09 2020-04-09 Intelligent motor fault detection method based on filtering in industrial field

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010272259.7A CN111505500B (en) 2020-04-09 2020-04-09 Intelligent motor fault detection method based on filtering in industrial field

Publications (2)

Publication Number Publication Date
CN111505500A CN111505500A (en) 2020-08-07
CN111505500B true CN111505500B (en) 2021-01-29

Family

ID=71872716

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010272259.7A Active CN111505500B (en) 2020-04-09 2020-04-09 Intelligent motor fault detection method based on filtering in industrial field

Country Status (1)

Country Link
CN (1) CN111505500B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112149886B (en) * 2020-09-07 2023-10-31 江南大学 Four-capacity water tank system state estimation method based on multidimensional spatial filtering
CN112305418B (en) * 2020-10-13 2021-09-28 江南大学 Motor system fault diagnosis method based on mixed noise double filtering
CN112883508B (en) * 2021-01-22 2024-03-08 江南大学 Parallel spatial filtering-based spring damping system state estimation method
CN113050608A (en) * 2021-03-30 2021-06-29 上海海事大学 Automatic control system fault detection method based on collective estimation

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107272660A (en) * 2017-07-26 2017-10-20 江南大学 A kind of random fault detection method of the network control system with packet loss
CN108196532A (en) * 2018-03-07 2018-06-22 山东科技大学 A kind of unmanned plane longitudinal flight control system failure detection and separation method based on nonlinear adaptive observer
CN108445759A (en) * 2018-03-13 2018-08-24 江南大学 A kind of random fault detection method of sensor constraint of saturation lower network system
KR20180096075A (en) * 2017-02-20 2018-08-29 재단법인대구경북과학기술원 Apparatus for detecting fault of sensor using EMB system and method using the same
CN108845495A (en) * 2018-04-03 2018-11-20 南通大学 Intermittent fault diagnosis and Active Fault-tolerant Control Method based on the double-deck Kalman filter
CN109474472A (en) * 2018-12-03 2019-03-15 江南大学 A kind of fault detection method based on the more cell space filtering of holohedral symmetry
CN110209148A (en) * 2019-06-18 2019-09-06 江南大学 A kind of Fault Estimation method of the networked system based on description systematic observation device

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109241639B (en) * 2018-09-17 2022-10-18 合肥工业大学 Electromechanical system residual life prediction method based on double-time-scale particle filtering
CN110441643B (en) * 2019-08-07 2020-11-06 北京航空航天大学 Inverter power tube open circuit fault diagnosis method in permanent magnet synchronous motor control system
CN110634198B (en) * 2019-09-24 2020-12-01 江南大学 Industrial system layered fault diagnosis method based on regular polycell filtering
CN110789350A (en) * 2019-11-20 2020-02-14 电子科技大学 Fault-tolerant control method for four-wheel drive electric vehicle
CN110854912B (en) * 2019-11-27 2023-05-26 中国石油大学(华东) Current instruction control method for doubly-fed fan during fault ride-through in weak network environment

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20180096075A (en) * 2017-02-20 2018-08-29 재단법인대구경북과학기술원 Apparatus for detecting fault of sensor using EMB system and method using the same
CN107272660A (en) * 2017-07-26 2017-10-20 江南大学 A kind of random fault detection method of the network control system with packet loss
CN108196532A (en) * 2018-03-07 2018-06-22 山东科技大学 A kind of unmanned plane longitudinal flight control system failure detection and separation method based on nonlinear adaptive observer
CN108445759A (en) * 2018-03-13 2018-08-24 江南大学 A kind of random fault detection method of sensor constraint of saturation lower network system
CN108845495A (en) * 2018-04-03 2018-11-20 南通大学 Intermittent fault diagnosis and Active Fault-tolerant Control Method based on the double-deck Kalman filter
CN109474472A (en) * 2018-12-03 2019-03-15 江南大学 A kind of fault detection method based on the more cell space filtering of holohedral symmetry
CN110209148A (en) * 2019-06-18 2019-09-06 江南大学 A kind of Fault Estimation method of the networked system based on description systematic observation device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
非线性系统的滤波辨识方法及其应用研究;王子赟;《中国博士学位论文全文数据库 基础科学辑（月刊）》;20151130(第11期);全文 *

Also Published As

Publication number Publication date
CN111505500A (en) 2020-08-07

Similar Documents

Publication Publication Date Title
CN111505500B (en) Intelligent motor fault detection method based on filtering in industrial field
Nguyen et al. Model-based diagnosis and RUL estimation of induction machines under interturn fault
Cheng et al. Overview of fault diagnosis theory and method for permanent magnet machine
CN108092567B (en) Permanent magnet synchronous motor rotating speed control system and method
Husain et al. A sliding mode observer based controller for switched reluctance motor drives
Campbell et al. Practical sensorless induction motor drive employing an artificial neural network for online parameter adaptation
Ondel et al. Coupling pattern recognition with state estimation using Kalman filter for fault diagnosis
CN114004164A (en) Motor rotor temperature prediction method and system for control
CN102779238A (en) Brushless DC (Direct Current) motor system identification method on basis of adaptive Kalman filter
Akar et al. Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model
Ben Regaya et al. Electric drive control with rotor resistance and rotor speed observers based on fuzzy logic
JP6850458B1 (en) AI-equipped motor state estimation system and machine learning method for motor models
CN111740683A (en) Fault diagnosis method for position sensor of permanent magnet fault-tolerant motor
CN111881587A (en) Filtering-based permanent magnet direct current motor fault detection method
Yao et al. Data fusion methods for convolutional neural network based on self-sensing motor drive system
CN112083349B (en) Method for diagnosing turn-to-turn short circuit fault of stator winding of permanent magnet synchronous motor
Lee et al. Performance estimation of induction motor using artificial neural network
CN112039387A (en) Fault diagnosis method for permanent magnet synchronous motor position sensor
CN112149953A (en) Electromechanical equipment operation safety assessment method based on multi-mode linkage and multi-stage cooperation
CN112149274A (en) Online modeling method for multi-axis engraving machine system with dead zone input nonlinearity
Joshi et al. Error compensation in initial temperature estimation of electric motors using a kalman filter
Fezzani et al. Robust control of permanent magnet synchronous motor
Jiang et al. On gradation and classification of faults for permanent magnet synchronous motor systems based on v-gap metric
Meng et al. A non-invasive dual-EKF-based rotor temperature estimation technique for permanent magnet machines
CN107547024B (en) No speed sensor of no bearing PMSM

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant