CN115015756A - Fault diagnosis method for fine-crushing fused new material mixer - Google Patents

Fault diagnosis method for fine-crushing fused new material mixer Download PDF

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CN115015756A
CN115015756A CN202210827199.XA CN202210827199A CN115015756A CN 115015756 A CN115015756 A CN 115015756A CN 202210827199 A CN202210827199 A CN 202210827199A CN 115015756 A CN115015756 A CN 115015756A
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fault
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mixer
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赖玉娟
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Shenzhen Wenhao Technology Co ltd
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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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/028—Acoustic or vibration analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R15/00—Details of measuring arrangements of the types provided for in groups G01R17/00 - G01R29/00, G01R33/00 - G01R33/26 or G01R35/00
    • G01R15/14—Adaptations providing voltage or current isolation, e.g. for high-voltage or high-current networks
    • G01R15/20—Adaptations providing voltage or current isolation, e.g. for high-voltage or high-current networks using galvano-magnetic devices, e.g. Hall-effect devices, i.e. measuring a magnetic field via the interaction between a current and a magnetic field, e.g. magneto resistive or Hall effect devices
    • G01R15/202—Adaptations providing voltage or current isolation, e.g. for high-voltage or high-current networks using galvano-magnetic devices, e.g. Hall-effect devices, i.e. measuring a magnetic field via the interaction between a current and a magnetic field, e.g. magneto resistive or Hall effect devices using Hall-effect devices
    • 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
    • G01R31/346—Testing of armature or field windings

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Abstract

The invention discloses a fault diagnosis method for a finely-divided fused new material mixing machine, which relates to the technical field of fault diagnosis and solves the technical problem of fault diagnosis of the new material mixing machine; classifying the acquired data information, and classifying and screening the data information of the running state of the mixing machine; the method comprises the steps of extracting fault information, monitoring an electric signal of a generator of the material mixer bearing fault through a current sensor and a voltage sensor, denoising acquired data information through a signal based on the combination of a Hankel matrix and singular value decomposition, and detecting the mixer fault through a fault diagnosis module, wherein the fault diagnosis module is a VMD-LSTM algorithm model. The invention greatly improves the fault diagnosis capability of the new material mixer.

Description

Fault diagnosis method for fine-crushing fused new material mixer
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method for a finely-crushed fused new material mixer.
Background
The mixer is a mechanical device for uniformly mixing two or more materials by using mechanical force, gravity and the like. During the mixing process, the material contact surface area can be increased to promote chemical reaction; but also to accelerate physical changes. The novel material mixing machine that fuses in small, broken bits of axial motion is a novel machine, in the motion process, can realize axial motion, mixes the novel material that fuses in small, broken bits, and this mixes machine various faults very easily appear in the application, for example because most mix quick-witted product all adopts the vibration principle, generally adopts the motor direct connection reducing mechanism, this kind of connected mode is simple, easy maintenance. So in general, when the motor rotor becomes non-concentric with the mixer rotor, the concentricity of the two rotors will not be consistent.
The mixed quick-witted fault data information among the conventional art appears bearing overheated usually, electric motor rotor and mixed quick-witted rotor decentraction, the axle center is skew, motor fault, the rotor is unusual, lubricating oil lacks, the sealing strip is damaged, cylinder atmospheric pressure is not enough, multiple problems such as cylinder atmospheric pressure is not enough, how to realize that multiple new material mixes quick-witted fault diagnosis is the problem that awaits the solution urgently, conventional art passes through check out test set and detects mixed quick-witted trouble, this kind of detection mode is more single, detection efficiency is low.
Disclosure of Invention
Aiming at the defects of the technology, the invention discloses a fault diagnosis method for a finely-divided fused new material mixing machine, which improves the fault diagnosis capability of the new material mixing machine by an intelligent fault detection method.
In order to achieve the technical effects, the invention adopts the following technical scheme:
a fault diagnosis method for a finely-divided fused new material mixer comprises the following steps:
acquiring a mechanical rotor assembly and an axial motion bearing measurement electric signal and operation state data information of a new material mixing machine;
classifying the acquired data information, and classifying and screening the data information of the running state of the mixing machine;
in this step, the failure detection means generates two types of vibrations, i.e., torsional vibration and radial vibration, for the detection failure of the power train; the fault detection device is matched with the vibration sensor for detecting faults of the transmission system; the vibration sensor senses radial vibration and is used for monitoring the state of a mechanical part in the rotary machine, measuring the amplitude of a sideband harmonic frequency component of a vibration signal and comparing the amplitude with the amplitude of adjacent harmonic frequency;
step three: extracting fault information, namely monitoring an electric signal of a generator with a material mixer bearing fault through a current sensor and a voltage sensor;
step four: denoising the acquired data information through a signal based on the combination of a Hankel matrix and singular value decomposition;
step five: and realizing the fault detection of the mixer through a fault diagnosis module, wherein the fault diagnosis module is a VMD-LSTM algorithm model.
As a further technical scheme of the invention, the fault detection device comprises a main control module, a sensor group connected with the main control module, a power supply module, an external memory, a communication interface, a signal filtering module, a signal amplifying module, a clock module, a Hall effect current sensor and a configuration module.
As a further technical scheme, the Hall-effect current sensor comprises an input end, a Hall current driving module, an analog switch, a dynamic compensation module, a sensing fine adjustment module, a signal recovery module and a current offset adjustment module, wherein the input end is connected with the analog switch, the analog switch is connected with the dynamic compensation module, the dynamic compensation module is connected with the signal recovery module, and the signal recovery module is further connected with an operational amplifier circuit.
As a further technical scheme of the invention, the current sensor is an ACS712 type current sensor, the nominal current of 0A to 5A is measured, and the sensitivity is 185 mV/A.
As a further technical scheme of the invention, the signal denoising method of the improved singular value decomposition comprises the following steps:
step (1): inputting new materials in the time domain analysis processHankel matrix of material mixer fault discrete signal, new material mixer fault signal
Figure 759335DEST_PATH_IMAGE001
Sliding length by corresponding vector is
Figure 942054DEST_PATH_IMAGE002
Window (2)
Figure 784108DEST_PATH_IMAGE003
The metering formula is as follows:
Figure 11608DEST_PATH_IMAGE004
（1）
in the formula (1), the first and second groups of the compound,
Figure 91560DEST_PATH_IMAGE005
and
Figure 445181DEST_PATH_IMAGE006
all are the fault signal ordinal numbers of the new material mixing machine,
Figure 40110DEST_PATH_IMAGE007
is the total number of samples and is,
Figure 770169DEST_PATH_IMAGE008
the Hankel matrix in the formula (1) is used for singular value decomposition, and the method for quantizing the signal pulse characteristics is kurtosis which is expressed as:
Figure 704627DEST_PATH_IMAGE009
（2）
in the formula (2), the first and second groups,
Figure 730614DEST_PATH_IMAGE010
、
Figure 16102DEST_PATH_IMAGE011
、
Figure 81010DEST_PATH_IMAGE012
and
Figure 135553DEST_PATH_IMAGE013
respectively the kurtosis value, the probability density function, the standard deviation and the average value of the time domain signal of the new material mixer fault signal; obtaining a matrix related to the first singular value
Figure 830977DEST_PATH_IMAGE014
Then, by performing an arithmetic mean calculation along the anti-diagonals of the matrix, the calculation function is expressed as:
Figure 603761DEST_PATH_IMAGE015
（3）
in the formula (3), the first and second groups,
Figure 410043DEST_PATH_IMAGE016
represent
Figure 879945DEST_PATH_IMAGE017
A maximum value parameter of the interval;
Figure 746270DEST_PATH_IMAGE018
to represent
Figure 740771DEST_PATH_IMAGE019
A minimum value parameter of the interval; vector quantity
Figure 350744DEST_PATH_IMAGE020
Representing a denoised new material mixer fault time domain signal;
step (2): in the frequency domain analysis process, when a bearing of the new material mixer has a local fault, generating pulses at a basic fault frequency by an axial movement rotor assembly through the local fault, wherein the frequency spectrum of a fault signal comprises the fault frequency and harmonic waves of the mixer, and when local fault information exists on the rotor assembly, the local fault influences inner and outer axial roller paths;
using discrete Fourier transform to obtainThe obtained frequency spectrum removes noise and disperses Fourier series components of fault signalsy i Expressed as:
Figure 176617DEST_PATH_IMAGE021
（4）
hankel matrix of discrete fault signal input similarly to the formula (1) to obtain matrix [ 2 ]A]Expressed as:
Figure 479422DEST_PATH_IMAGE022
（5）
similar to equation (5), the spectrum vector of the discrete fault signal reconstruction process is:
Figure 462684DEST_PATH_IMAGE023
（6）
the reconstructed spectrum vector in equation (6) is a denoised spectrum vector.
As a further technical scheme of the invention, the fault diagnosis model comprises a variational modal decomposition VMD model and a long-short term memory neural network LSTM model.
As a further technical scheme of the invention, the working method of the fault diagnosis model comprises the following steps:
the variable mode decomposition VMD model decomposes fault characteristic signals of the mixing machine, wavelet half-soft threshold denoising obtains denoising signals through wavelet coefficients subjected to reconstruction processing, the long and short term memory neural network LSTM model is responsible for extracting long mode information hidden in a mixing machine fault characteristic sequence, high-order information in a sequence mixing machine sample is mined, and the collected mixing machine fault characteristic signals are represented as follows:
Figure 610769DEST_PATH_IMAGE024
（7）
in the formula (7), the first and second groups,
Figure 291149DEST_PATH_IMAGE025
showing mixing machinesThe dominant component of the fault signature information is,
Figure 764856DEST_PATH_IMAGE026
representing the interference-dominant component in the signal,
Figure 733949DEST_PATH_IMAGE027
a sequence number of the fault signal is indicated,
Figure 685724DEST_PATH_IMAGE028
which is indicative of the time of the fault sequence,
Figure 423873DEST_PATH_IMAGE029
representing the residual component of the fault, equation (7) represents the component of the mixer fault signal, the variational modal decomposition VMD model decomposes the fault signal into sub-signals and shifts the spectrum of the solid state mode to baseband, decomposing into:
Figure 561157DEST_PATH_IMAGE030
（8）
in the formula (8), the first and second groups,
Figure 814283DEST_PATH_IMAGE031
a signal representative of a change in resolution of the mixer fault,
Figure 569750DEST_PATH_IMAGE032
representing a fourier transform of the mixer data information,
Figure 162405DEST_PATH_IMAGE033
representing the number of solid state modes of the signal resolved by the mixer,
Figure 712335DEST_PATH_IMAGE034
a signal indicative of a fault mode in operation of the mixer,
Figure 656020DEST_PATH_IMAGE035
representing the second penalty term coefficient in the mixer run,
Figure 215178DEST_PATH_IMAGE036
representing Lagrange operators in the calculation process of the state of the mixer, i representing the serial number of the decomposed sub-signals of the mixer, decomposing the original signals in the running state of the mixer into a plurality of eigenmode signals through a formula (8), and representing the function as:
Figure 226121DEST_PATH_IMAGE037
（9）
in the formula (9), the reaction mixture,
Figure 946953DEST_PATH_IMAGE038
a wavelet based signal representative of the fault characteristics of the mixer,
Figure 112355DEST_PATH_IMAGE039
representing the wavelet coefficients after the processing,
Figure 209624DEST_PATH_IMAGE040
which is indicative of an initial fault sub-signal,
Figure 839188DEST_PATH_IMAGE041
representing the second penalty term coefficient in the mixer run,
Figure 730921DEST_PATH_IMAGE042
and (3) expressing the translation amount of the wavelet transform, completing the wavelet transform of the fault characteristic signal by a formula (9), wherein a wavelet half-soft threshold function can be expressed as follows:
Figure 882154DEST_PATH_IMAGE043
（10）
in the formula (10), the first and second groups,
Figure 783114DEST_PATH_IMAGE044
representing the wavelet coefficients after thresholding,
Figure 204868DEST_PATH_IMAGE045
is shown smallThe threshold value of the wave is set to be,
Figure 533081DEST_PATH_IMAGE046
representing the second penalty term coefficient in the mixer run,
inputting the fault characteristic signal into the LSTM for convolution, and carrying out batch standardization processing and activating functions after a convolution module, wherein the function expression is as follows:
Figure 469813DEST_PATH_IMAGE047
（11）
in the formula (11), the reaction mixture,
Figure 908885DEST_PATH_IMAGE048
a convolution module representing a fault identification model,
Figure 450725DEST_PATH_IMAGE049
a signal representative of the characteristic of the fault input,
Figure 451304DEST_PATH_IMAGE050
which represents the offset of the convolution module(s),
Figure 875332DEST_PATH_IMAGE051
which represents a process of standardization of a batch,
Figure 852515DEST_PATH_IMAGE052
representing the activation function, introducing non-linear features through equation (11) can represent more complex cases, and the new state of the LSTM module is represented as:
Figure 248862DEST_PATH_IMAGE053
（12）
in the formula (12), the first and second groups,
Figure 918877DEST_PATH_IMAGE054
the output of the forgetting gate is represented,
Figure 830202DEST_PATH_IMAGE055
the status information of the last moment in time is represented,
Figure 115470DEST_PATH_IMAGE056
the output of the memory gate is represented,
Figure 631902DEST_PATH_IMAGE057
a new candidate vector is represented that is,
integrating the state information and the output signal, and the fault identification result output by the output gate of the LSTM module is expressed as:
Figure 207240DEST_PATH_IMAGE058
（13）
in the formula (13), the first and second groups,
Figure 809123DEST_PATH_IMAGE059
an output vector representing the identification of the fault,
Figure 393688DEST_PATH_IMAGE060
a matrix of weights is represented by a matrix of weights,
Figure 561364DEST_PATH_IMAGE061
the output vector of the previous layer is represented,
Figure 573182DEST_PATH_IMAGE062
indicating the current output fault signature.
The invention has the following positive beneficial effects:
the invention obtains the measured electrical signal and the running state data information of the mechanical rotor component and the axial moving bearing of the new material mixing machine; classifying the acquired data information, and classifying and screening the data information of the running state of the mixing machine; the method comprises the steps of extracting fault information, monitoring an electric signal of a generator of the material mixer bearing fault through a current sensor and a voltage sensor, denoising acquired data information through a signal based on the combination of a Hankel matrix and singular value decomposition, and detecting the mixer fault through a fault diagnosis module, wherein the fault diagnosis module is a VMD-LSTM algorithm model.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive exercise, wherein:
FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a circuit diagram of a Hall effect based current sensor according to the present invention;
fig. 3 is a schematic diagram of the fault detection apparatus of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are merely for the purpose of illustrating and explaining the present invention and are not intended to limit the present invention.
A fault diagnosis method for a finely-divided fused new material mixer comprises the following steps:
acquiring a mechanical rotor assembly and an axial motion bearing measurement electric signal and operation state data information of a new material mixing machine;
and a fault detection device is applied to the axial motion bearing to acquire bearing overheating, the motor rotor is not concentric with the mixer rotor, the axis deviates, the motor fault, the rotor is abnormal, lubricating oil is lacked, a sealing strip is damaged, air pressure of an air cylinder is insufficient or insufficient, a gear fault, a high-speed shaft gear fault, a high-speed intermediate gear fault, a planetary gear fault, a gear ring fault or a sun gear fault;
in this step, the rotor assembly is coupled to the generator through the axial motion bearing of the drive train, and the material-based mixer main controller receives the electrical signals from the current sensor and the voltage sensor to generate an electrical signal signature; receiving a vibration signal measured by a vibration sensor to generate a vibration signal signature; the vibration signal indicia and the electrical signal indicia are used to determine a material mixing machine fault detection signal, which may be representative of one or more diagnostic parameters;
classifying the obtained data information, and classifying and screening the mixer operation state data information;
in this step, the failure detection means generates two types of vibrations, i.e., torsional vibration and radial vibration, for the detection failure of the power train; the fault detection device is matched with the vibration sensor for detecting faults of the transmission system; the vibration sensor senses radial vibration and is used for monitoring the state of a mechanical part in the rotary machine, measuring the amplitude of a sideband harmonic frequency component of a vibration signal and comparing the amplitude with the amplitude of adjacent harmonic frequency;
step three: extracting fault information, namely monitoring an electric signal of a generator with a material mixer bearing fault through a current sensor and a voltage sensor; the electric signal is measured and sent to the controller by the electric sensor; the electrical signal is a voltage signal, the electrical signal is a current signal, the current signal is converted into the voltage signal through a Hall circuit to improve the signal analysis efficiency, the electrical characteristic code analysis of the electrical signal is formed by equipment based on a main controller of the material mixing machine, and one or more fault signals are generated through a computing unit;
step four: denoising the acquired data information through a signal based on the combination of a Hankel matrix and singular value decomposition; the algorithm is applied to the new material mixer fault signal and the frequency spectrum thereof, background noise is eliminated from two aspects of time domain and frequency domain, the reliability of the new material mixer fault diagnosis process is improved, and finally the fault diagnosis result display is realized through the server.
Step five: and realizing the fault detection of the mixer through a fault diagnosis module, wherein the fault diagnosis module is a VMD-LSTM algorithm model.
In the above embodiment, the fault detection apparatus includes a main control module, and a sensor group, a power module, an external memory, a communication interface, a signal filtering module, a signal amplifying module, a clock module, and a configuration module connected to the main control module.
In a specific embodiment, the sensor group includes a vibration sensor, a displacement sensor, a flow sensor, a speed sensor, a temperature sensor, a flame sensor, and the like, so as to obtain a plurality of fault data information. The main control module of the fault detection device uses an ARM Cortex-M332-bit STM32F103RBT6 chip, a built-in 20K RAM and an 8M crystal oscillator, a FLASH with 12-bit AD and 128KB, and resources including 4 16-bit timers, a 3-path serial communication interface and the like. The data acquisition module finishes acquisition of various electrical parameters such as voltage, electric power, frequency, power and the like by using a special metering chip ATT7022, has a specific data verification function to ensure acquisition accuracy, and is internally integrated with a high-precision ADC structure and data signal processing capacity. The ATT7022 special chip processor is used for measuring various information such as voltage effective value, current effective value, frequency measurement value, active power, reactive power, power factor and the like of the mixer motor. The signal filtering module can realize clutter data information interference in the circuit, the signal amplifying module can realize data information amplification, the clock module can output clock data information, and the data information is passed through
In the above embodiment, the current sensor with hall effect includes an input terminal, a hall current driving module, an analog switch, a dynamic compensation module, a sensing fine tuning module, a signal recovery module, and a current offset adjustment module, where the input terminal is connected to the analog switch, the analog switch is connected to the dynamic compensation module, the dynamic compensation module is connected to the signal recovery module, and the signal recovery module is further connected to an operational amplifier circuit.
In the above embodiment, the current sensor was an ACS712 type current sensor measuring a nominal current of 0A to 5A with a sensitivity of 185 mV/A.
In a specific embodiment, the Hall effect based current sensor circuit employs an ACS712 model current sensor, designed by the Worcester electronics plant, Mass., as a low cost alternative with off-the-shelf components that can monitor well the current encountered in most motor control circuits; the current sensor can measure nominal current from 0A to 5A, has the sensitivity of 185 mV/A, works under the power supply voltage of 5V, and has the bandwidth reaching 80 kHz; the generator is connected with the current sensor by adopting a copper conducting wire, and the path of the copper conducting wire is positioned near the surface of the current sensor; the current sensor does not support sleep mode and therefore includes an analog switch to control its turn off.
The current sensor consists of an accurate and low-offset linear Hall circuit, the current sensor is provided with four current signal input ports and is driven by Hall current after being input, and the Hall current drives an external open-circuit voltage VCC; the input current signal generates a magnetic field by dynamically compensating the current flowing through the Cooper conducting path, and the Hall circuit converts the magnetic field into proportional voltage; the current sensor measures the current flowing through the primary winding by using a Hall element, and the current in the secondary winding of the Hall circuit and the current in the primary winding are changed linearly; finally, the inverter amplifier with single-ended output converts the sensed current into a voltage signal; again, this output voltage is read by the internal ADC of the material mixer main controller; the offset of the current sensor is 2.5V, and the current for each phase is calculated as:I n =（analogread-compensation)/sensitivity, where analoglead is the input voltage of the material mixer master controller port;
the signal denoising method of the improved singular value decomposition comprises the following steps:
in a specific embodiment, the invention provides a signal denoising process based on a combination of a Hankel (Hankel) matrix and singular value decomposition, the algorithm is applied to a new material mixer fault signal and a frequency spectrum thereof, background noise is eliminated from two aspects of a time domain and a frequency domain, and the reliability of the new material mixer fault diagnosis process is improved, and the algorithm comprises the following steps:
step (1): in the time domain analysis process, a Hankel matrix of a fault discrete signal of the new material mixer is input, and a fault signal of the new material mixer is input
Figure 632668DEST_PATH_IMAGE063
Sliding length by corresponding vector is
Figure 20924DEST_PATH_IMAGE064
Of (2)
Figure 246369DEST_PATH_IMAGE065
Formula of measurement：
Figure 225826DEST_PATH_IMAGE066
（1）
In the formula (1), the first and second groups,
Figure 802301DEST_PATH_IMAGE005
and
Figure 463089DEST_PATH_IMAGE006
all are the fault signal ordinal numbers of the new material mixing machine,
Figure 543041DEST_PATH_IMAGE067
is the total number of samples and is,
Figure 457514DEST_PATH_IMAGE068
the Hankel matrix in the formula (1) is used for singular value decomposition, and the method for quantizing the signal pulse characteristics is kurtosis, which is expressed as:
Figure 255705DEST_PATH_IMAGE069
（2）
in the formula (2), the first and second groups,
Figure 985764DEST_PATH_IMAGE070
、
Figure 654643DEST_PATH_IMAGE071
、
Figure 444744DEST_PATH_IMAGE072
and
Figure 526970DEST_PATH_IMAGE073
respectively the kurtosis value, the probability density function, the standard deviation and the average value of the time domain signal of the new material mixer fault signal; obtaining a matrix related to the first singular value
Figure 296605DEST_PATH_IMAGE074
After that, by following the matrixThe inverse diagonals are calculated as the arithmetic mean, and the calculated function is expressed as:
Figure 351149DEST_PATH_IMAGE075
（3）
in the formula (3), the first and second groups,
Figure 46572DEST_PATH_IMAGE016
to represent
Figure 350514DEST_PATH_IMAGE076
A maximum value parameter of the interval;
Figure 422376DEST_PATH_IMAGE077
represent
Figure 597005DEST_PATH_IMAGE078
A minimum value parameter of the interval; vector quantity
Figure 463330DEST_PATH_IMAGE079
Representing a denoised new material mixer fault time domain signal;
step (2): in the frequency domain analysis process, when a bearing of the new material mixer has a local fault, generating pulses at a basic fault frequency by an axial moving rotor assembly through the local fault, wherein the frequency spectrum of a fault signal comprises the fault frequency and harmonic waves of the mixer, and when local fault information exists on the rotor assembly, the local fault influences inner and outer axial roller paths;
denoising frequency spectrum obtained by using discrete Fourier transform, and Fourier series component of discrete fault signaly i Expressed as:
Figure 973944DEST_PATH_IMAGE080
（4）
hankel matrix of discrete fault signal input similar to the formula (1) is obtained to obtain matrix 2A]Expressed as:
Figure 583917DEST_PATH_IMAGE081
（5）
similar to equation (5), the spectrum vector of the discrete fault signal reconstruction process is:
Figure 409790DEST_PATH_IMAGE082
（6）
the reconstructed spectrum vector in equation (6) is a denoised spectrum vector.
The fault diagnosis model comprises a variational modal decomposition VMD model and a long-short term memory neural network LSTM model.
The working method of the fault diagnosis model comprises the following steps:
the variable mode decomposition VMD model decomposes fault characteristic signals of the mixing machine, wavelet half-soft threshold denoising obtains denoising signals through wavelet coefficients subjected to reconstruction processing, the long and short term memory neural network LSTM model is responsible for extracting long mode information hidden in a mixing machine fault characteristic sequence, high-order information in a sequence mixing machine sample is mined, and the collected mixing machine fault characteristic signals are represented as follows:
Figure 447017DEST_PATH_IMAGE083
（7）
in the formula (7), the first and second groups,
Figure 928814DEST_PATH_IMAGE084
a dominant component of fault characteristic information representing the mixing machine,
Figure 342477DEST_PATH_IMAGE085
representing the interference-dominant component in the signal,
Figure 226120DEST_PATH_IMAGE027
a sequence number of the fault signal is indicated,
Figure 201291DEST_PATH_IMAGE086
which is indicative of the time of the fault sequence,
Figure 967122DEST_PATH_IMAGE087
representing the residual component of the fault, equation (7) represents the component of the mixer fault signal, the variational modal decomposition VMD model decomposes the fault signal into sub-signals and shifts the spectrum of the solid state mode to baseband, decomposing into:
Figure 918897DEST_PATH_IMAGE088
（8）
in the formula (8), the first and second groups,
Figure 657046DEST_PATH_IMAGE089
a signal representative of a change in resolution of the mixer fault,
Figure 36075DEST_PATH_IMAGE032
representing a fourier transform of the mixer data information,
Figure 289202DEST_PATH_IMAGE033
representing the number of solid state modes of the signal resolved by the mixer,
Figure 543203DEST_PATH_IMAGE090
a signal indicative of a fault mode in operation of the mixer,
Figure 135859DEST_PATH_IMAGE091
representing the second penalty term coefficient in the mixer run,
Figure 951368DEST_PATH_IMAGE036
representing Lagrange operators in the calculation process of the state of the mixer, i representing the serial number of the decomposed sub-signals of the mixer, decomposing the original signals in the running state of the mixer into a plurality of eigenmode signals through a formula (8), and representing the function as:
Figure 895053DEST_PATH_IMAGE092
（9）
in the formula (9), the reaction mixture,
Figure 985369DEST_PATH_IMAGE093
a wavelet based signal representative of the fault characteristics of the mixer,
Figure 698110DEST_PATH_IMAGE094
representing the wavelet coefficients after the processing,
Figure 418941DEST_PATH_IMAGE095
which is indicative of an initial fault sub-signal,
Figure 351388DEST_PATH_IMAGE096
representing the second penalty term coefficient in the mixer run,
Figure 448657DEST_PATH_IMAGE097
the translation amount of wavelet transform is expressed, the wavelet transform of the fault characteristic signal is completed through a formula (9), and the wavelet half-soft threshold function can be expressed as:
Figure 812642DEST_PATH_IMAGE098
（10）
in the formula (10), the first and second groups,
Figure 969954DEST_PATH_IMAGE099
representing the wavelet coefficients after thresholding,
Figure 888231DEST_PATH_IMAGE100
which represents a wavelet threshold value, is a function of,
Figure 523612DEST_PATH_IMAGE101
representing the second penalty term coefficient in the mixer run,
inputting the fault characteristic signal into the LSTM for convolution, and carrying out batch standardization processing and activating functions after a convolution module, wherein the function expression is as follows:
Figure 945366DEST_PATH_IMAGE102
（11）
in the formula (11), the reaction mixture,
Figure 777974DEST_PATH_IMAGE103
a convolution module representing a fault identification model,
Figure 714706DEST_PATH_IMAGE049
a signal representative of the characteristic of the fault input,
Figure 419356DEST_PATH_IMAGE104
which represents the offset of the convolution module(s),
Figure 961196DEST_PATH_IMAGE105
which represents a batch normalization process, is shown,
Figure 194731DEST_PATH_IMAGE106
representing the activation function, introducing non-linear features through equation (11) can represent more complex cases, and the new state of the LSTM module is represented as:
Figure 822022DEST_PATH_IMAGE107
（12）
in the formula (12), the first and second groups,
Figure 97408DEST_PATH_IMAGE108
the output of the forgetting gate is represented,
Figure 759333DEST_PATH_IMAGE109
the status information of the last moment in time is represented,
Figure 429349DEST_PATH_IMAGE110
the output of the memory gate is represented,
Figure 278356DEST_PATH_IMAGE111
a new candidate vector is represented that is,
integrating the state information and the output signal, and the fault identification result output by the output gate of the LSTM module is expressed as:
Figure 59230DEST_PATH_IMAGE112
（13）
in the formula (13), the first and second groups,
Figure 372400DEST_PATH_IMAGE113
an output vector representing the identification of the fault,
Figure 213317DEST_PATH_IMAGE114
a matrix of weights is represented by a matrix of weights,
Figure 313735DEST_PATH_IMAGE115
the output vector of the previous layer is represented,
Figure 632721DEST_PATH_IMAGE116
indicating the current output fault signature.
And (4) finishing outputting the fault identification result of the LSTM neural network through a formula (13). In summary, the VMD decomposes the original mixer fault signature signal, transforms each modal signal to construct an analysis signal, removes noise components by wavelet transform, and inputs the analysis signal into the LSTM network for fault identification. The LSTM is provided with a plurality of equal-gate control units, so that the feature information and the state vector selectively pass through, the backward propagation of the fault feature can be carried out, and the long mode information hidden in the data can be better mined.
Although specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are merely illustrative and that various omissions, substitutions and changes in the form of the detail of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the steps of the methods described above to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is to be limited only by the following claims.

Claims (7)

1. A fault diagnosis method for a finely-divided fused new material mixer is characterized by comprising the following steps: the method comprises the following steps:
acquiring a mechanical rotor assembly and an axial motion bearing measurement electric signal and operation state data information of a new material mixing machine;
classifying the acquired data information, and classifying and screening the data information of the running state of the mixing machine;
in this step, the failure detection means generates two types of vibrations, i.e., torsional vibration and radial vibration, for the detection failure of the power train; the fault detection device is matched with the vibration sensor for detecting faults of the transmission system; the vibration sensor senses radial vibration and is used for monitoring the state of a mechanical part in the rotary machine, measuring the amplitude of a sideband harmonic frequency component of a vibration signal and comparing the amplitude with the amplitude of adjacent harmonic frequency;
step three: extracting fault information, namely monitoring an electric signal of a generator with a material mixer bearing fault through a current sensor and a voltage sensor;
step four: denoising the acquired data information through a signal based on the combination of a Hankel matrix and singular value decomposition;
step five: and realizing the fault detection of the mixer through a fault diagnosis module, wherein the fault diagnosis module is a VMD-LSTM algorithm model.
2. The method of claim 1, wherein the method comprises the steps of:
the fault detection device comprises a main control module, a sensor group, a power supply module, an external memory, a communication interface, a signal filtering module, a signal amplifying module, a clock module, a Hall-effect current sensor and a configuration module, wherein the sensor group, the power supply module, the external memory, the communication interface, the signal filtering module, the signal amplifying module, the clock module, the Hall-effect current sensor and the configuration module are connected with the main control module.
3. The method of claim 1, wherein the method comprises the steps of:
the current sensor with the Hall effect comprises an input end, a Hall current driving module, an analog switch, a dynamic compensation module, a sensing fine adjustment module, a signal recovery module and a current offset adjustment module, wherein the input end is connected with the analog switch, the analog switch is connected with the dynamic compensation module, the dynamic compensation module is connected with the signal recovery module, and the signal recovery module is further connected with an operational amplifier amplification circuit.
4. The method of claim 3, wherein the method comprises the steps of:
the current sensor was an ACS712 model current sensor measuring nominal currents of 0A to 5A with a sensitivity of 185 mV/A.
5. The method of claim 4, wherein the method comprises the steps of: the signal denoising method of the improved singular value decomposition comprises the following steps:
step (1): in the time domain analysis process, a Hankel matrix of a new material mixer fault discrete signal and a new material mixer fault signal are input
Figure 11746DEST_PATH_IMAGE001
Sliding length by corresponding vector is
Figure 312978DEST_PATH_IMAGE002
Of (2)
Figure 870998DEST_PATH_IMAGE003
The metering formula is as follows:
Figure 259254DEST_PATH_IMAGE004
（1）
in the formula (1), the first and second groups,
Figure 484699DEST_PATH_IMAGE005
and
Figure 464157DEST_PATH_IMAGE006
all are the fault signal ordinal numbers of the new material mixing machine,
Figure 40632DEST_PATH_IMAGE007
is the total number of samples and is,
Figure 202885DEST_PATH_IMAGE008
the Hankel matrix in the formula (1) is used for singular value decomposition, and the method for quantizing the signal pulse characteristics is kurtosis, which is expressed as:
Figure 282836DEST_PATH_IMAGE009
（2）
in the formula (2), the first and second groups,
Figure 698774DEST_PATH_IMAGE010
、
Figure 496966DEST_PATH_IMAGE011
、
Figure 961445DEST_PATH_IMAGE012
and
Figure 895903DEST_PATH_IMAGE013
respectively the kurtosis value, the probability density function, the standard deviation and the average value of the time domain signal of the new material mixer fault signal; in obtaining a matrix associated with the first singular value
Figure 686005DEST_PATH_IMAGE014
Then, by performing an arithmetic mean calculation along the anti-diagonals of the matrix, the calculation function is expressed as:
Figure 204449DEST_PATH_IMAGE015
（3）
in the formula (3), the first and second groups,
Figure 534936DEST_PATH_IMAGE016
to represent
Figure 589479DEST_PATH_IMAGE017
A maximum value parameter of the interval;
Figure 19324DEST_PATH_IMAGE018
to represent
Figure 792108DEST_PATH_IMAGE019
A minimum value parameter of the interval; vector quantity
Figure 660707DEST_PATH_IMAGE020
Representing a denoised new material mixer fault time domain signal;
step (2): in the frequency domain analysis process, when a bearing of the new material mixer has a local fault, generating pulses at a basic fault frequency by an axial moving rotor assembly through the local fault, wherein the frequency spectrum of a fault signal comprises the fault frequency and harmonic waves of the mixer, and when local fault information exists on the rotor assembly, the local fault influences inner and outer axial roller paths;
denoising frequency spectrum obtained by using discrete Fourier transform, and Fourier series component of discrete fault signaly i Expressed as:
Figure 71222DEST_PATH_IMAGE021
（4）
hankel matrix of discrete fault signal input similar to the formula (1) is obtained to obtain matrix 2A]Expressed as:
Figure 203126DEST_PATH_IMAGE022
（5）
similar to equation (5), the spectrum vector of the discrete fault signal reconstruction process is:
Figure 932047DEST_PATH_IMAGE023
（6）
the reconstructed spectrum vector in equation (6) is a denoised spectrum vector.
6. The method of claim 4, wherein the method comprises the steps of: the fault diagnosis model comprises a variational modal decomposition VMD model and a long-short term memory neural network LSTM model.
7. The method of claim 6, wherein the method comprises the steps of: the working method of the fault diagnosis model comprises the following steps:
the variable mode decomposition VMD model decomposes fault characteristic signals of the mixing machine, wavelet half-soft threshold denoising obtains denoising signals through wavelet coefficients subjected to reconstruction processing, the long and short term memory neural network LSTM model is responsible for extracting long mode information hidden in a mixing machine fault characteristic sequence, high-order information in a sequence mixing machine sample is mined, and the collected mixing machine fault characteristic signals are represented as follows:
Figure 604337DEST_PATH_IMAGE024
（7）
in the formula (7), the first and second groups,
Figure 633473DEST_PATH_IMAGE025
representing the dominant component of the fault characteristic information of the mixing machine,
Figure 670699DEST_PATH_IMAGE026
representing the dominant component of interference in the signal,
Figure 152496DEST_PATH_IMAGE027
a sequence number of the fault signal is indicated,
Figure 70554DEST_PATH_IMAGE028
which is indicative of the time of the fault sequence,
Figure 750934DEST_PATH_IMAGE029
representing the residual component of the fault, equation (7) represents the component of the mixer fault signal, the variational modal decomposition VMD model decomposes the fault signal into sub-signals and shifts the spectrum of the solid state mode to baseband, decomposing into:
Figure 959062DEST_PATH_IMAGE030
（8）
in the formula (8), the first and second groups,
Figure 928155DEST_PATH_IMAGE031
a translation resolution signal indicative of a mixer fault,
Figure 879930DEST_PATH_IMAGE032
representing a fourier transform of the mixer data information,
Figure 414817DEST_PATH_IMAGE033
representing the number of solid state modes of the signal resolved by the mixer,
Figure 560890DEST_PATH_IMAGE034
a signal indicative of a fault mode in operation of the mixer,
Figure 17279DEST_PATH_IMAGE035
representing the second penalty term coefficient in the mixer run,
Figure 507166DEST_PATH_IMAGE036
representing Lagrange operators in the calculation process of the state of the mixer, i representing the serial number of the decomposed sub-signals of the mixer, decomposing the original signals in the running state of the mixer into a plurality of eigenmode signals through a formula (8), and representing the function as:
Figure 365400DEST_PATH_IMAGE037
（9）
in the formula (9), the reaction mixture,
Figure 977647DEST_PATH_IMAGE038
a wavelet based signal representative of the fault characteristics of the mixer,
Figure 655753DEST_PATH_IMAGE039
representing the wavelet coefficients after the processing,
Figure 214911DEST_PATH_IMAGE040
which is indicative of an initial fault sub-signal,
Figure 160608DEST_PATH_IMAGE041
representing the second order penalty term coefficient in the mixer operation,
Figure 147018DEST_PATH_IMAGE042
the translation amount of wavelet transform is expressed, the wavelet transform of the fault characteristic signal is completed through a formula (9), and the wavelet half-soft threshold function can be expressed as:
Figure 374737DEST_PATH_IMAGE043
（10）
in the formula (10), the first and second groups,
Figure 472006DEST_PATH_IMAGE044
representing the wavelet coefficients after thresholding,
Figure 39254DEST_PATH_IMAGE045
which represents a wavelet threshold value, is a value,
Figure 930987DEST_PATH_IMAGE046
representing the second order penalty term coefficient in the mixer operation,
inputting the fault characteristic signal into the LSTM for convolution, and carrying out batch standardization processing and activating functions after a convolution module, wherein the function expression is as follows:
Figure 583685DEST_PATH_IMAGE047
（11）
in the formula (11), the reaction mixture is,
Figure 782847DEST_PATH_IMAGE048
a convolution module representing a fault identification model,
Figure 470180DEST_PATH_IMAGE049
a signal representative of the characteristic of the fault input,
Figure 329552DEST_PATH_IMAGE050
which represents the offset of the convolution module(s),
Figure 469546DEST_PATH_IMAGE051
which represents a batch normalization process, is shown,
Figure 643039DEST_PATH_IMAGE052
representing the activation function, introducing non-linear features through equation (11) can represent more complex cases, and the new state of the LSTM module is represented as:
Figure 184879DEST_PATH_IMAGE053
（12）
in the formula (12), the first and second groups,
Figure 996844DEST_PATH_IMAGE054
the output of the forgetting gate is represented,
Figure 624134DEST_PATH_IMAGE055
the status information of the last moment in time is represented,
Figure 601318DEST_PATH_IMAGE056
the output of the memory gate is represented,
Figure 263243DEST_PATH_IMAGE057
a new candidate vector is represented that is,
integrating the state information and the output signal, and the fault identification result output by the output gate of the LSTM module is expressed as:
Figure 293778DEST_PATH_IMAGE058
（13）
in the formula (13), the first and second groups,
Figure 673944DEST_PATH_IMAGE059
an output vector representing the identification of the fault,
Figure 454818DEST_PATH_IMAGE060
a matrix of weights is represented by a matrix of weights,
Figure 971250DEST_PATH_IMAGE061
the output vector of the upper layer is represented,
Figure 608905DEST_PATH_IMAGE062
indicating the current output fault signature.
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