CN111337250A - Machine tool state fault diagnosis system and method based on virtual instrument - Google Patents
Machine tool state fault diagnosis system and method based on virtual instrument Download PDFInfo
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Abstract
The invention discloses a machine tool state fault diagnosis system and method based on a virtual instrument. The invention adopts the piezoelectric acceleration sensor to be matched with the charge amplifier to realize the collection of the lathe vibration signal, then transmits the collected vibration signal to the computer through the data acquisition card, and realizes the analysis of the time domain and the frequency domain of the vibration signal by using LabVIEW software programming. The faults of the rolling bearing and the gear in the machine tool are identified by time domain and frequency domain analysis means and by integrating theoretical knowledge of fault diagnosis, corresponding solutions are provided according to the fault condition types, and the fault diagnosis accuracy is greatly improved.
Description
Technical Field
The invention belongs to the technical field of fault diagnosis, and particularly relates to a machine tool state fault diagnosis system and method based on a virtual instrument.
Background
The technology of mechanical fault diagnosis began in the last 70 th century, and the fault diagnosis work was conducted by the fault prevention group established by the united states space agency (NASA). Subsequently, other countries (english, french, germany, etc.) have continued to improve and explore the technology. Over the years of research, this technology has now penetrated from the original aerospace field to various manufacturing industries.
The research and development of the fault diagnosis technology in China are started in the last 90 th century, and although the time is more than 20 years after that of the developed countries, under the efforts of broad scholars and research and development teams, certain achievements are obtained, and the advanced technology of fault diagnosis is closed. The system comprises RTHTLENE, MMD-2 and MMD-3 systems developed by Harbin university of industry and Rb-20 system developed by the university of Western Ann transportation. Although more and more scholars in the country are involved in the development of the field of fault diagnosis, there is still a gap compared with the advanced level. The main reasons are that enterprises attach far less importance to the fault diagnosis of machine tools, pursue only short-term benefits, have low investment and dispersion on fault diagnosis technologies, and the universality and reliability of developed detection and diagnosis systems need to be improved, so that science and technology are disconnected from practical application.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings of the prior art, and provides a machine tool state fault diagnosis system and method based on a virtual instrument.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a machine tool state fault diagnosis system based on a virtual instrument, which comprises a signal acquisition module, a signal preprocessing module, a signal time domain analysis module and a signal frequency domain analysis module, wherein the signal acquisition module is connected with the signal preprocessing module;
the signal acquisition module is used for leading the data acquired by the data acquisition card into a LabVIEW program as an input module of the program,
the signal preprocessing module comprises a wavelet denoising module and a low-pass filtering module and is used for denoising and filtering the acquired data;
the signal time domain analysis module is used for analyzing a time domain signal and comprises a statistical characteristic parameter calculation module, a time sequence signal display module and an autocorrelation module;
the statistical characteristic parameter calculation module utilizes a statistical function of LabVIEW, and the statistical function is used for calculating the statistical characteristic parameter of the preprocessed vibration signal;
the time sequence signal display module is used for displaying the calculation result of the acquired vibration signal characteristic parameters;
the autocorrelation module is an average measurement of the characteristics of the signal in the time domain and is used for describing the degree of correlation between the signal x (t) at two different arbitrary moments s, t;
the signal frequency domain analysis module is used for reflecting fault characteristic frequency so as to effectively distinguish the type of the fault, and the signal time domain analysis module comprises a Fourier transform module, a self-power spectrum analysis module, a cepstrum analysis module and an envelope spectrum analysis module;
the Fourier transform module realizes the starting and closing of the functions of the autocorrelation module through the frequency domain analysis knob and the matching condition structure;
the self-power spectrum analysis module is used for obtaining the frequency structure characteristics of the vibration signal, and the vibration signal outputs a power spectrogram through a self-power spectrum function;
the cepstrum analysis module is used for carrying out logarithm operation on the Fourier transform spectrogram of the signal;
and the envelope spectrum analysis module is used for carrying out envelope analysis on the preprocessed vibration signals to obtain outer envelope information of the signals, and analyzing the outer envelope information by means of time domain and frequency domain.
As a preferred technical scheme, the signal acquisition module comprises a vibration sensor, a charge amplifier and a data acquisition card, wherein the vibration sensor is a piezoelectric acceleration sensor;
the piezoelectric acceleration sensor is used for collecting vibration signals and transmitting the signals to the charge amplifier;
the charge amplifier is used for receiving and amplifying charge signals of the piezoelectric acceleration sensor, and original weak signals can be transmitted to a computer after being amplified by the charge amplifier;
and the data acquisition card receives the signal amplified by the charge amplifier and is in data communication with a LabVIEW program in a computer.
As a preferred technical scheme, the data acquisition card acquires data of a vibration sensor, and the vibration sensor is a piezoelectric acceleration sensor; the data acquisition card is selected from USB-6008.
As a preferred technical solution, the wavelet denoising module is configured to perform wavelet transform on a signal, generate a wavelet coefficient including important information of the signal, where the wavelet coefficient of the signal itself is large, and the wavelet coefficient of the noise signal is small, and set a suitable threshold value to eliminate the noise signal and retain the original signal;
and the low-pass filtering module is used for allowing the signal below the low-pass cut-off frequency to pass and filtering the part of the signal exceeding the low-pass cut-off frequency.
As a preferred technical solution, the expression of the autocorrelation module is:
R(s,t)＝E[X(s)*X(t)]。
as a preferred technical scheme, in the fourier transform module, a vibration signal passes through a fast fourier transform function from left to right, and a double-side spectrogram is output, and in order to convert the double-side spectrogram into a single-side spectrogram, a half of the original array size is taken as a new array size, so that a single-side spectrogram can be obtained;
in the cepstrum analysis module, firstly, an input vibration signal is subjected to a self-power spectrum function to obtain a self-power spectrum, then, an abscissa is expressed as a logarithm with the base number of 10 by a logarithm function, and then, inverse Fourier transform is carried out to obtain a cepstrum of the signal;
in the envelope spectrum analysis module, the square of the vibration signal is added with the square of the vibration signal after Hilbert transformation, the arithmetic square root is taken as the obtained result, then Fourier transformation is carried out to obtain an envelope spectrum of the signal, the double-sided envelope spectrum is converted into a single-sided envelope spectrum for convenience of analysis, and half of the original array size is taken as a new array size to obtain the single-sided envelope spectrum.
The invention also provides a fault diagnosis method of the machine tool state fault diagnosis system based on the virtual instrument, which comprises the following steps:
arranging a measuring point of a vibration signal near a spindle box to obtain the working frequency and fault characteristics of a bearing and a gear in the spindle box, arranging two vibration sensors on the main box, wherein one sensor is responsible for collecting axial vibration signals of the spindle, and the other sensor is responsible for collecting radial vibration signals of the spindle;
different matching modes are selected according to the gear pair in the main spindle box and the adjustment of the rotating speed gear,
calculating the characteristic frequency of the rolling bearing and the gear;
a method for diagnosing a failure of a rolling bearing,
for the rolling bearing, the fault characteristic frequency calculation formula is shown in formulas (2) to (4);
bearing outer ring fault frequency BPFO ═ (N/2) z [1- (D/D) cos α ] (2)
Bearing inner race failure frequency BPFI ═ (N/2) z [1+ (D/D) cos α ] (3)
Holder failure frequency FTF ═ N/2) [1- (D/D) cos α ] (4)
Wherein:
d is the diameter of the rolling body;
d is the average diameter of the rolling bearing;
α ═ radial direction contact angle;
z is the number of rolling elements;
n is the rotational speed of the shaft;
in actual calculation, calculating the fault characteristic frequency of the movable bearing by using the formulas (5) to (7);
bearing outer ring failure frequency:
bearing inner race failure frequency:
cage failure frequency:
wherein:
z is the number of rolling elements;
n is the rotational speed of the shaft;
the failure analysis of the gear is as follows:
the gear teeth are compared with the spring, the mass of the gear body is used as the mass of the spring body, and under the condition of neglecting the friction force of the gear teeth, the vibration equation of the gear pair is shown as the formula (8).
MrX＝CX+k(t)X＝k(t)E1+k(t)E2(t) (8)
M in the formularX＝(m1m2)/(m1+m2),m1Mass of the capstan, m2The mass of the driven wheel, C, the damping of the gear engagement, X, the relative displacement on the action line of the gear, and k (t), the meshing stiffness of the gear, which changes with time, i.e. k (t) denotes the dynamic process of the gear meshing; e1Expressed is the average value of the static elastic deformation of the gear wheel after being subjected to a load, E2(t) is the relative displacement between gears due to gear failure and error, i.e., the failure function.
As a preferred technical solution, the four stages of bearing failure are:
(1) the first stage is as follows: at the initial stage of bearing failure, the bearing firstly forms micro cracks or dislocation of crystal lattices on the secondary surface, cracks or micro peeling cannot be seen on the surface of the bearing, and no obvious impact signal can be formed in the low-frequency stage of a vibration signal, so that a vibration fault signal cannot be picked up by using a traditional acceleration sensor, but an acoustic emission signal or a stress wave signal can be generated in the micro cracks or dislocation of the crystal lattices on the secondary surface, so that the characteristic of the bearing fault is reflected in the ultrasonic frequency stage at the stage, and the peak value or the energy value of a measured signal is increased by using the acoustic emission sensor or the acceleration sensor based on resonance to obtain the signal;
(2) and a second stage: a bearing failure development period, in which the microscopic deterioration of the bearing begins to spread from the sub-surface to the surface, and a damage point such as a crack or a micro-peeling is generated on the contact surface of the bearing; when the surface of an element of the bearing is in contact with the damaged points, impact pulses with a certain frequency are formed, according to Fourier transform, a signal of short-time impact is a broadband signal in a frequency domain, so that the impact signal can excite the high-frequency natural frequency of a bearing part to resonate, the vibration of the bearing part is strengthened, the signal can be obtained through an acceleration sensor, the fault characteristic frequency of the bearing can be observed by utilizing an envelope demodulation technology, and the frequency multiplication of the fault characteristic frequency can be observed at the end of the second stage; at this stage, the temperature of the bearing is normal, the noise is slightly increased, the total vibration speed is slightly increased, the vibration frequency spectrum change is not obvious, but the peak energy is increased, and the frequency spectrum is more prominent; the fault frequency of the bearing at this moment appears in the range of about 500hz-2khz, and the fault characteristic frequency of the bearing is temporarily buried in high noise of a low frequency band, so that clear fault characteristic frequency cannot be observed in the fault characteristic frequency band;
(3) and a third stage: in the bearing failure rapid development period, along with the accelerated development of bearing damage, the impact of a damage point on a bearing contact surface is stronger and stronger, the frequency multiplication of the characteristic frequency of the bearing fault demodulated in the resonance frequency section is more and more, the magnitude of the periodic impact energy can be directly observed through a vibration signal power spectrum, at the moment, the characteristic frequency of the bearing fault is directly and clearly seen on the vibration signal power spectrum, and the frequency multiplication also has a more and more trend; at the moment, the fault frequency of the bearing is in the range of 0-1 khz approximately; the bearing is recommended to be replaced at the later stage of the third stage, and the fault characteristics of the rolling bearing such as abrasion and the like can be easily seen;
(4) a fourth stage: at the end of the bearing failure, the rolling bearing reaches the end of the service life quickly at this stage, damage points can be obtained by visual observation, the bearing movement noise becomes extremely large, and the temperature rises rapidly; at the moment, not only can the bearing fault characteristic frequency and frequency multiplication be clearly seen on the power spectrum, but also a very obvious modulation side frequency can be seen beside the fault characteristic frequency if the damage points alternately enter the load area; at the end of the fourth phase, the spectral lines of the signal become less clear and form a convex "mound" in the power spectrum, at which point the energy of the dither is likely not to rise or fall, and once the high frequency monitoring begins to fall, it is not necessarily the case that the bearing surface condition is good, but rather it is said that the bearing has reached the end of its life.
As a preferable technical solution, the step of diagnosing the failure of the gear comprises:
the expression of the change rate of the meshing rigidity is shown as a formula (9);
n in the formula1And n2The rotational speeds of the primary and secondary gears are indicated, respectively, in r/mi and z1And z2Respectively showing the tooth number of the driving wheel and the driven wheel; and k (t) E2(t) is an excitation source causing gear vibration abnormality, which is affected by both meshing stiffness and a failure function;
as a preferred technical solution, the step of diagnosing the fault of the gear includes calculating the meshing frequency of the gear, specifically:
for the gear pair, the calculation formula of the gear meshing frequency is shown as the formula (10);
f＝Nz (10)
wherein:
z is the gear tooth number;
n is the rotational speed of the shaft.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention adopts the piezoelectric acceleration sensor to be matched with the charge amplifier to realize the collection of the lathe vibration signal, then transmits the collected vibration signal to the computer through the data acquisition card, and realizes the analysis of the time domain and the frequency domain of the vibration signal by using LabVIEW software programming. The faults of the rolling bearing and the gear in the machine tool are identified by time domain and frequency domain analysis means and by integrating theoretical knowledge of fault diagnosis, corresponding solutions are provided according to the fault condition types, and finally fault diagnosis of the machine tool is realized.
2. The invention learns to use related instruments for vibration signal analysis, such as a charge amplifier, a piezoelectric acceleration sensor, a data acquisition card and the like, in a hardware part, and can accurately acquire the vibration signals of the machine tool by using the instruments for software analysis.
3. The invention learns LabVIEW programming in a software part to complete the compiling of a vibration signal analysis system, and can well analyze the vibration signal transmitted by hardware.
Drawings
FIG. 1 is a block diagram of a fault diagnosis system of the present invention;
fig. 2 is a schematic diagram of the configuration of the failure diagnosis system of the present invention.
FIG. 3 is a schematic diagram of a signal preprocessing module according to the present invention;
FIG. 4 is a schematic illustration of a statistical function of the present invention;
FIG. 5 is a schematic of the autocorrelation function of the present invention;
FIG. 6 is a schematic diagram of a Fourier transform module of the present invention;
FIG. 7 is a schematic diagram of the structure of the self-power spectrum of the present invention;
FIG. 8 is a schematic diagram of the structure of the envelope spectrum of the present invention;
FIG. 9 is a schematic structural diagram of a cepstral analysis of the present invention;
FIG. 10 is a time domain spectrum of a vibration signal at a rotational speed of 105r/min according to the present invention;
FIG. 11 is a frequency domain spectrogram of a vibration signal at a rotation speed of 105r/min according to the present invention;
FIG. 12 is a time domain spectrum of a vibration signal at a rotational speed of 180r/min according to the present invention;
FIG. 13 is a frequency domain spectrum plot of a vibration signal at a rotational speed of 180r/min in accordance with the present invention;
FIG. 14 is a time domain spectrogram of a vibration signal at a rotation speed of 560r/min in accordance with the present invention;
FIG. 15 is a frequency domain spectrogram of a vibration signal at a rotation speed of 560r/min in accordance with the present invention;
FIG. 16 is a time domain spectrum plot of a vibration signal at a rotation speed of 800r/min in accordance with the present invention;
FIG. 17 is a frequency domain spectrum plot of a vibration signal at a rotation speed of 800r/min in accordance with the present invention;
FIG. 18 is the envelope spectrum of the vibration signal at 800r/min according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Examples
As shown in fig. 1 and fig. 2, the machine tool state fault diagnosis system based on the virtual instrument includes a signal acquisition module, a signal preprocessing module, a signal time domain analysis module, and a signal frequency domain analysis module, where the signal acquisition module is connected to the signal preprocessing module, and the signal time domain analysis module and the signal frequency domain analysis module are respectively connected to the signal preprocessing module.
The signal acquisition module is used for importing data acquired by the data acquisition card into a LabVIEW program as an input module of a program. The sampling rate and the sampling number of the acquisition module can be respectively controlled through the two input controls to achieve the optimal signal acquisition effect, and a basis is provided for subsequent signal conditioning and signal analysis.
Based on above-mentioned signal acquisition module, the vibration test platform of this embodiment includes: 2 piezoelectric acceleration sensors, 1 charge amplifier, a USB-6008 data acquisition card, a vibration exciter, a TD1630A function generator, an eccentric wheel and a simply supported beam.
Ensuring the mutual cooperation of the hardware is the first step of realizing signal acquisition, and the functions of each hardware are respectively as follows:
(1) piezoelectric acceleration sensor: collecting vibration signals and transmitting the signals to a charge amplifier;
(2) a charge amplifier: the charge signal of the piezoelectric acceleration sensor is received and amplified, and the originally weak signal can be transmitted with a computer before being amplified by the charge amplifier;
(3) USB-6008: receiving the amplified signal from the charge amplifier, and carrying out data communication with a LabVIEW program in a computer;
(4) TD1630A function generator and vibration exciter: the function generator is responsible for generating excitation signals, different excitation effects are realized by selecting different parameters (signal types, initial phases, output amplitudes and the like), and the vibration exciter is an excitation tool of the function generator.
(5) Eccentric wheel: the rotation of the eccentric wheel is driven by the rotation of the motor, so that the sinusoidal excitation of the simply supported beam is realized.
The signal preprocessing module comprises a wavelet denoising module and a low-pass filtering module and is used for denoising and filtering the acquired data; the signal preprocessing module is shown in fig. 3, and includes two signal conditioning functions: wavelet de-noising and low pass filters. The following is a basic introduction and programming idea of the related functions:
(1) the basic idea of wavelet denoising is that after wavelet transformation is performed on a signal, a generated wavelet coefficient contains important information of the signal, wherein the wavelet coefficient of the signal is large, the wavelet coefficient of a noise signal is small, and the noise signal is eliminated and the original signal is kept by setting a proper threshold value.
(2) The low-pass filter has the functions of: the part of the signal which is lower than the low-pass cut-off frequency and exceeds the low-pass cut-off frequency is allowed to pass through and is subjected to filtering processing. The magnitude of the low-pass cutoff frequency can be controlled in the program by an input control.
The programming idea is as follows: the left side of fig. 3 is an input port of a vibration signal, wavelet denoising and enabling and disabling of a low-pass filter are respectively realized through a boolean button matching condition structure, and the right side is an output port of the vibration signal after preprocessing.
Further, the signal time domain analysis module is configured to analyze a time domain signal, and the signal time domain analysis module includes a statistical characteristic parameter calculation module, a time sequence signal display module, and an autocorrelation module.
The program block diagram for realizing each function is as follows:
(1) the calculation of the statistical characteristic parameters was carried out using a statistical function of LabVIEW as shown in fig. 4. The function can realize the calculation of the statistical characteristic parameters of the preprocessed vibration signals. Directly embodies the intelligent advantage of LabVIEW.
The programming idea is as follows: the left side is a signal input port, and a plurality of time domain statistical characteristic parameters are output through a statistical function.
(2) A block diagram of a process for performing autocorrelation of a signal is shown in fig. 5. The autocorrelation function is an average measure of the characteristics of the signal in the time domain, and is used to describe the degree of correlation between the signal x (t) at two different arbitrary times s, t, and is expressed by formula (1).
R(s,t)＝E[X(s)*X(t)](1)
The programming idea is as follows: the self-correlation function is enabled and closed through the knob matched with the condition structure, the vibration signal input port is arranged on the left side, and the self-correlation waveform is output to the time domain analysis oscillogram through the self-correlation function.
The frequency domain analysis of the signal can effectively reflect the fault characteristic frequency, so that the type of the fault can be effectively distinguished. In this embodiment, the signal frequency domain analysis module is configured to reflect a fault characteristic frequency, so as to effectively distinguish a type of a fault, and the signal time domain analysis module includes a fourier transform module, a self-power spectrum analysis module, a cepstrum analysis module, and an envelope spectrum analysis module;
the Fourier transform module realizes the starting and closing of the functions of the autocorrelation module through the frequency domain analysis knob and the matching condition structure; the Fourier transform is formed on the basis of an orthogonal function expansion of a Fourier series, the Fourier series form of the periodic signal expansion is called frequency domain analysis, the frequencies of all the components of the signal can be clearly observed through the Fourier transform so as to identify the fault characteristic frequency, and a program block diagram for realizing the Fourier transform of the signal is shown in FIG. 6.
The programming idea is as follows: the method is characterized in that the self-correlation function is enabled and closed through a frequency domain analysis knob matching condition structure, from left to right, a vibration signal passes through a fast Fourier transform function and outputs a double-side spectrogram, the double-side spectrogram is converted into a single-side spectrogram for analysis, and the single-side spectrogram can be obtained by taking half of the original array size as a new array size.
The self-power spectrum analysis module is used for obtaining the frequency structure characteristics of the vibration signal, and the vibration signal outputs a power spectrogram through a self-power spectrum function; the purpose of performing the self-power spectrum analysis is mainly to obtain the frequency structure characteristics of the vibration signal, and the power spectrum actually reflects the square of the amplitude of the vibration signal, so that the self-power spectrum analysis has an advantage in reflecting the frequency structure of the vibration signal. A block diagram of a process for performing signal self-power spectral analysis is shown in fig. 7.
The programming idea is as follows: and the vibration signal passes through the self-power spectrum function to output a power spectrum.
And the cepstrum analysis module is used for carrying out logarithm operation on the Fourier transform spectrogram of the signal.
Cepstrum analysis is an information analysis method in which a fourier transform spectrogram of a signal is subjected to a logarithm operation and then subjected to an inverse fourier transform. For a large-sized machine running at a high speed, the motion state of the machine is very complicated, and especially when the phenomena of shafting misalignment, gear or rolling bearing defect, oil film damage and the like occur in the equipment, the obtained vibration signal is more complicated. The general frequency domain analysis method is not enough to identify the fault characteristic frequency, but the cepstrum analysis is adopted to enhance the capability of identifying the fault characteristic frequency, and a program block diagram for realizing the cepstrum analysis is shown in fig. 9.
The programming idea is as follows: firstly, obtaining a self-power spectrum from an input vibration signal through a self-power spectrum function, then expressing an abscissa as a logarithm with the base number of 10 through a logarithm function, and performing inverse Fourier transform to obtain a cepstrum of the signal.
And the envelope spectrum analysis module is used for carrying out envelope analysis on the preprocessed vibration signals to obtain outer envelope information of the signals, and analyzing the outer envelope information by means of time domain and frequency domain. After the envelope analysis is performed on the preprocessed vibration signal, the outer envelope information of the signal can be obtained, and the outer envelope information can be analyzed by means of time domain and frequency domain, for example, the characteristic parameters of the vibration signal are extracted. Certain time-domain characteristics of the vibration signal are helpful in describing the performance condition of the bearing. However, in special cases, such as the case where the time domain characteristic parameters cannot be used for accurately diagnosing the bearing fault position, the envelope signal itself is often subjected to frequency domain analysis. In the method for diagnosing the faults of the rolling bearing, the edge frequency can be effectively identified by envelope spectrum analysis, and then the characteristics of a modulation signal are found out and the faults of the bearing are diagnosed; a block diagram of a process for performing spectral analysis of a signal envelope is shown in fig. 8.
The programming idea is as follows: the square of the vibration signal is added with the square of the vibration signal after Hilbert transformation, the arithmetic square root is taken as an obtained result, then Fourier transformation is carried out to obtain an envelope spectrogram of the signal, similarly, the double-edge envelope spectrogram is converted into a single-edge envelope spectrogram for convenience of analysis, and the specific idea is that half of the original array size is taken as a new array size, so that the single-edge envelope spectrogram can be obtained.
In another embodiment of the present invention, a fault diagnosis method of a virtual instrument-based machine tool state fault diagnosis system includes the steps of:
s1, arranging a measuring point of a vibration signal near a spindle box to obtain the working frequency and fault characteristics of a bearing and a gear in the spindle box, arranging two vibration sensors on the main box, wherein one sensor is responsible for collecting axial vibration signals of the spindle, and the other sensor is responsible for collecting radial vibration signals of the spindle;
s2, selecting different matching modes according to the gear pair in the main spindle box according to the adjustment of the rotating speed and the gear,
s3, calculating the characteristic frequency of the rolling bearing and the gear;
s4, a fault diagnosis method for a rolling bearing,
for the rolling bearing, the fault characteristic frequency calculation formula is shown in formulas (2) to (4);
bearing outer ring fault frequency BPFO ═ (N/2) z [1- (D/D) cos α ] (2)
Bearing inner race failure frequency BPFI ═ (N/2) z [1+ (D/D) cos α ] (3)
Holder failure frequency FTF ═ N/2) [1- (D/D) cos α ] (4)
Wherein:
d is the diameter of the rolling body;
d is the average diameter of the rolling bearing;
α ═ radial direction contact angle;
z is the number of rolling elements;
n is the rotational speed of the shaft;
in actual calculation, calculating the fault characteristic frequency of the movable bearing by using the formulas (5) to (7);
bearing outer ring failure frequency:
bearing inner race failure frequency:
cage failure frequency:
wherein:
z is the number of rolling elements;
n is the rotational speed of the shaft;
s5, analyzing the fault of the gear as follows:
the gear teeth are compared with the spring, the mass of the gear body is used as the mass of the spring body, and under the condition of neglecting the friction force of the gear teeth, the vibration equation of the gear pair is shown as the formula (8).
MrX＝CX+k(t)X＝k(t)E1+k(t)E2(t) (8)
M in the formularX＝(m1m2)/(m1+m2),m1Mass of the capstan, m2The mass of the driven wheel, C, the damping of the gear engagement, X, the relative displacement on the action line of the gear, and k (t), the meshing stiffness of the gear, which changes with time, i.e. k (t) denotes the dynamic process of the gear meshing; e1Expressed is the average value of the static elastic deformation of the gear wheel after being subjected to a load, E2(t) is the relative displacement between gears due to gear failure and error, i.e., the failure function.
Further, the four stages of bearing failure are:
(1) the first stage is as follows: at the initial stage of bearing failure, the bearing firstly forms micro cracks or dislocation of crystal lattices on the secondary surface, cracks or micro peeling cannot be seen on the surface of the bearing, and no obvious impact signal can be formed in the low-frequency stage of a vibration signal, so that a vibration fault signal cannot be picked up by using a traditional acceleration sensor, but an acoustic emission signal or a stress wave signal can be generated in the micro cracks or dislocation of the crystal lattices on the secondary surface, so that the characteristic of the bearing fault is reflected in the ultrasonic frequency stage at the stage, and the peak value or the energy value of a measured signal is increased by using the acoustic emission sensor or the acceleration sensor based on resonance to obtain the signal;
(2) and a second stage: a bearing failure development period, in which the microscopic deterioration of the bearing begins to spread from the sub-surface to the surface, and a damage point such as a crack or a micro-peeling is generated on the contact surface of the bearing; when the surface of an element of the bearing is in contact with the damaged points, impact pulses with a certain frequency are formed, according to Fourier transform, a signal of short-time impact is a broadband signal in a frequency domain, so that the impact signal can excite the high-frequency natural frequency of a bearing part to resonate, the vibration of the bearing part is strengthened, the signal can be obtained through an acceleration sensor, the fault characteristic frequency of the bearing can be observed by utilizing an envelope demodulation technology, and the frequency multiplication of the fault characteristic frequency can be observed at the end of the second stage; at this stage, the temperature of the bearing is normal, the noise is slightly increased, the total vibration speed is slightly increased, the vibration frequency spectrum change is not obvious, but the peak energy is increased, and the frequency spectrum is more prominent; the fault frequency of the bearing at this moment appears in the range of about 500hz-2khz, and the fault characteristic frequency of the bearing is temporarily buried in high noise of a low frequency band, so that clear fault characteristic frequency cannot be observed in the fault characteristic frequency band;
(3) and a third stage: in the bearing failure rapid development period, along with the accelerated development of bearing damage, the impact of a damage point on a bearing contact surface is stronger and stronger, the frequency multiplication of the characteristic frequency of the bearing fault demodulated in the resonance frequency section is more and more, the magnitude of the periodic impact energy can be directly observed through a vibration signal power spectrum, at the moment, the characteristic frequency of the bearing fault is directly and clearly seen on the vibration signal power spectrum, and the frequency multiplication also has a more and more trend; at the moment, the fault frequency of the bearing is in the range of 0-1 khz approximately; the bearing is recommended to be replaced at the later stage of the third stage, and the fault characteristics of the rolling bearing such as abrasion and the like can be easily seen;
(4) a fourth stage: at the end of the bearing failure, the rolling bearing reaches the end of the service life quickly at this stage, damage points can be obtained by visual observation, the bearing movement noise becomes extremely large, and the temperature rises rapidly; at the moment, not only can the bearing fault characteristic frequency and frequency multiplication be clearly seen on the power spectrum, but also a very obvious modulation side frequency can be seen beside the fault characteristic frequency if the damage points alternately enter the load area; at the end of the fourth phase, the spectral lines of the signal become less clear and form a convex "mound" in the power spectrum, at which point the energy of the dither is likely not to rise or fall, and once the high frequency monitoring begins to fall, it is not necessarily the case that the bearing surface condition is good, but rather it is said that the bearing has reached the end of its life.
Further, the step of diagnosing the gear failure comprises:
the expression of the change rate of the meshing rigidity is shown as a formula (9);
n in the formula1And n2The rotational speeds of the primary and secondary gears are indicated, respectively, in r/mi and z1And z2Respectively showing the tooth number of the driving wheel and the driven wheel; and k (t) E2(t) is an excitation source causing gear vibration abnormality, which is affected by both meshing stiffness and a failure function;
the step of fault diagnosis of the gear comprises the calculation of the meshing frequency of the gear, which specifically comprises the following steps:
for the gear pair, the calculation formula of the gear meshing frequency is shown as the formula (10);
f＝Nz (10)
wherein:
z is the gear tooth number;
n is the rotational speed of the shaft.
In order to better verify the testing effect of the application, the C6132a1 machine tool is taken as an example to further illustrate the invention, the transmission structure of the C6132a1 machine tool is concentrated above the machine tool body (also called a headstock), and the power input is concentrated at the foot part of the machine tool. The driving motor transmits energy from the bed legs to the spindle box by belt transmission, and the transmission ratio is adjusted by adjusting the matching of each gear set in the spindle box, so that the purpose of adjusting the rotating speed of the spindle is achieved.
(1) Selecting a test point:
the vibration signal acquisition measuring point is based on the reality, and the main shaft which can reflect the precision of the processed part is the optimal measuring point. Therefore, the measuring points are arranged near the spindle box, and the working frequency and the fault characteristics of a bearing and a gear in the spindle box can be acquired more conveniently. One sensor is responsible for the collection of the axial vibration signal of the main shaft, and the other sensor is responsible for the collection of the radial vibration signal of the main shaft.
(2) The construction of a vibration signal analysis system in a gold workshop:
because the workshop environment of the gold is more complicated, the hardware that chooses for use is mated with lightly small and exquisite as the main, so the hardware of choosing has: piezoelectric acceleration sensor, charge amplifier, USB-6008 data acquisition card and notebook computer.
(3-1) fault diagnosis of the C6132A1 lathe;
(3-1) selecting rotating speed gears, wherein 4 groups of gear pairs are shared in the main spindle box, different matching modes can be selected according to adjustment of the rotating speed gears, and all the gear pairs can be detected and subjected to fault diagnosis by selecting proper rotating speed gears.
Through analysis, the final speed gear selection and the corresponding enabled gear pair are shown in table 1. And the failure diagnosis of all gear pairs in the main spindle box can be completed by analyzing the vibration signal frequency spectrums under the four groups of rotating speeds.
TABLE 1
(3-2) calculating the characteristic frequency of the rolling bearing and the gear; before collecting and analyzing the vibration signal, certain preliminary work needs to be completed: calculating characteristic frequency and analyzing related fault theory. The final fault diagnosis result can be obtained only by comparing and analyzing the data result in the early stage and the acquired vibration signal.
(3-2-1) at a rotation speed of 105 r/min:
calculating the fault characteristic frequency of the rolling bearing: for the rolling bearing, its failure characteristic frequency can be obtained from equations (5) to (7).
The radical B is Bu N105/60 Hz 1.75Hz, the number of the bearing rolling elements z 28;
outer ring failure frequency:
inner ring failure frequency:
cage failure frequency:
calculating the meshing frequency of the gear: for the gear pair, the gear mesh frequency is obtained from equation (10). At this speed, N1105/60 Hz, gear tooth number Z1＝81；
f81＝f20＝N1z1＝(105/60)×81＝148Hz
Therefore, the meshing frequency of the gears and the frequency of bearing failure at a rotational speed of 105r/min are shown in tables 2 and 3.
(3-3-2) at a rotation speed of 180 r/min:
similarly, at this rotational speed;
ben pi is 180/60 Hz, the number of rolling elements in bearing is 28, and the number of teeth in gear is Z1＝65；
Outer ring failure frequency:
inner ring failure frequency:
cage failure frequency:
meshing frequency of gears: f. of27＝f65＝N1z1＝(180/60)×65＝199.8Hz
Therefore, the meshing frequency of the gears and the frequency of bearing failure at a rotational speed of 180r/min are shown in tables 4 and 5.
(3-2-3)560 r/min:
in the same way, at this rotation speed,
ben pi is 560/60 Hz, number of bearing rolling elements Z is 28, number of gear teeth Z is 9.33Hz1＝44；
Outer ring failure frequency:
inner ring failure frequency:
cage failure frequency:
meshing frequency of gears: f. of44＝f54＝N1z1＝(560/60)×44＝410.67Hz
Therefore, the meshing frequency of the gears and the frequency of bearing failure at a rotational speed of 560r/mim are shown in tables 6 and 7
(3-2-4) at the rotating speed of 800 r/min:
in the same way, at this rotation speed,
ben pi is 13.33Hz (800/60 Hz), number of rolling elements (Z is 28), and number of teeth (Z) of gear1＝36；
Outer ring failure frequency:
inner ring failure frequency:
cage failure frequency:
meshing frequency of gears: f. of36＝f65＝N1z1＝(800/60)×36＝480Hz
Therefore, the meshing frequency of the gears and the frequency of bearing failure at a rotation speed of 800r/mim are shown in tables 8 and 9.
(4) Vibration monitoring and fault diagnosis of the C6132A1 lathe;
(4-1) the frequency spectrum of the vibration signal at a rotational speed of 105r/min is shown in FIGS. 10 and 11.
As can be seen from the power spectrum, the spectrograms corresponding to the two sensors mainly differ: the first sensor spectrum contains bearing outer race fault frequencies, while the second sensor spectrum contains shaft frequencies, higher amplitudes and less noise. The low frequency component of the vibration signal contains the meshing frequency of the gears, namely 148Hz and the frequency doubling component thereof, and has the side frequency band phenomenon, wherein the amplitude of 148Hz is very high. Comparing the gear frequency spectrum diagrams under different states can find that the phenomenon corresponds to the gear overload fault. Namely, the gear pair formed by the main spindle box and the gear of the machine tool can have overload faults. The spectrum has fewer side bands and lower amplitudes, so the fault is slight.
Meanwhile, a peak value is also generated near 30Hz, the fault frequency of the bearing inner ring can be found by comparing tables 2 and 3, the fault frequency is the third stage of bearing failure according to the analysis conclusion of the bearing vibration signal, and the fault frequency of the bearing is found to have no side frequency band phenomenon and no frequency doubling component by further analysis. This is the initial third stage of bearing failure, indicating that this fault has little effect on the machine tool and does not require replacement of the bearings.
(2) The frequency spectrum of the vibration signal at a rotation speed of 180r/min is shown in fig. 12 and 13.
As can be seen from the power spectrogram, comparing the spectrogram at the rotating speed of 105r/min can find that more side bands appear near the meshing frequency of the gear, the triple frequency of 600Hz does not decrease and reversely increases, and the natural frequency of about 120Hz appears, and comparing the spectrogram of the gear in different states can find that the phenomenon corresponds to the gear abrasion fault. Namely, the gear pair formed by the main spindle box and the gear of the machine tool can have abrasion faults. The spectrum has fewer side bands and lower amplitudes, so the fault is slight.
Similarly, a peak value occurs in the vicinity of 50Hz, and it can be found from comparison of tables 2 and 3 that this is the failure frequency of the inner race of the bearing, and because the bearing of the machine tool is not changed with the change of the gear position of the rotating speed, the bearing failure diagnosis is consistent with that at the rotating speed of 105 r/min. Namely, the bearing failure frequency is the third stage of bearing failure, and no side band phenomenon exists in the bearing failure frequency, and no frequency doubling component exists. This is the initial third stage of bearing failure, indicating that this fault has little effect on the machine tool and does not require replacement of the bearings.
(3)560r/min as shown in FIGS. 14 and 15,
from the power spectrogram, the comparison of the spectrogram at the rotating speed of 180r/min can find that the side frequency band near the gear meshing frequency is reduced, the frequency multiplication of the side frequency band is in a gradually decreasing trend, and the frequency multiplication of the side frequency band 1230Hz is buried by the noise signal. The gear spectrograms in different states are compared to find that the phenomenon corresponds to the gear of the graph normally, namely, the gear pair consisting of the gear and the main spindle box of the machine tool works normally.
Similarly, a peak value occurs in the vicinity of 156Hz, and it can be found from comparison of tables 2 and 3 that this is the failure frequency of the inner race of the bearing, and because the bearing of the machine tool does not change with the change of the gear position of the rotating speed, the bearing failure diagnosis is consistent with that at the rotating speed of 105 r/min. Namely, the bearing failure frequency is the third stage of bearing failure, and no side band phenomenon exists in the bearing failure frequency, and no frequency doubling component exists. This is the initial third stage of bearing failure, indicating that this fault has little effect on the machine tool and does not require replacement of the bearings.
(4) The frequency spectrum of the vibration signal at the rotation speed of 800r/min is shown in fig. 16 and 17.
As can be seen from the power spectrogram, the comparison of the spectrograms at the rotating speed of 560r/min can find that the side frequency bands near the gear meshing frequency are increased, the peak value corresponding to the meshing frequency is also very high, the tripled frequency of 1440Hz does not decrease or increase reversely, and the comparison of the spectrograms of the gears at different states can find that the phenomenon corresponds to the gear wear fault of the graph, namely, the gear formed by the gear and the main shaft box of the machine tool has the wear fault. The spectrum has fewer side bands and lower amplitudes, so the fault is slight.
On the aspect of the bearing, no matter the frequency spectrums of the first sensor and the second sensor have no fault frequency of the bearing, comparing the frequency spectrums of the first three rotating speeds can find that the noise of the vibration signal frequency spectrums at the rotating speed of 800 is obviously increased, and the fault signal of the bearing is preliminarily deduced to be possibly buried in the noise signal. To verify this inference, the envelope spectrum of this vibration signal is analyzed.
The envelope spectrum is shown in fig. 18, the peak value near 224Hz can be clearly seen in the envelope spectrum, and the fault frequency of the bearing inner ring can be found by comparing tables 2 and 3, because the bearing of the machine tool cannot change along with the change of the gear of the rotating speed, the fault diagnosis of the bearing is consistent with the fault diagnosis at the rotating speed of 105 r/min. Namely, the bearing failure frequency is the third stage of bearing failure, and no side band phenomenon exists in the bearing failure frequency, and no frequency doubling component exists. This is the initial third stage of bearing failure, indicating that this fault has little effect on the machine tool and does not require replacement of the bearings.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A machine tool state fault diagnosis system based on a virtual instrument is characterized by comprising a signal acquisition module, a signal preprocessing module, a signal time domain analysis module and a signal frequency domain analysis module, wherein the signal acquisition module is connected with the signal preprocessing module;
the signal acquisition module is used for leading the data acquired by the data acquisition card into a LabVIEW program as an input module of the program,
the signal preprocessing module comprises a wavelet denoising module and a low-pass filtering module and is used for denoising and filtering the acquired data;
the signal time domain analysis module is used for analyzing a time domain signal and comprises a statistical characteristic parameter calculation module, a time sequence signal display module and an autocorrelation module;
the statistical characteristic parameter calculation module utilizes a statistical function of LabVIEW, and the statistical function is used for calculating the statistical characteristic parameter of the preprocessed vibration signal;
the time sequence signal display module is used for displaying the calculation result of the acquired vibration signal characteristic parameters;
the autocorrelation module is an average measurement of the characteristics of the signal in the time domain and is used for describing the degree of correlation between the signal x (t) at two different arbitrary moments s, t;
the signal frequency domain analysis module is used for reflecting fault characteristic frequency so as to effectively distinguish the type of the fault, and the signal time domain analysis module comprises a Fourier transform module, a self-power spectrum analysis module, a cepstrum analysis module and an envelope spectrum analysis module;
the Fourier transform module realizes the starting and closing of the functions of the autocorrelation module through the frequency domain analysis knob and the matching condition structure;
the self-power spectrum analysis module is used for obtaining the frequency structure characteristics of the vibration signal, and the vibration signal outputs a power spectrogram through a self-power spectrum function;
the cepstrum analysis module is used for carrying out logarithm operation on the Fourier transform spectrogram of the signal;
and the envelope spectrum analysis module is used for carrying out envelope analysis on the preprocessed vibration signals to obtain outer envelope information of the signals, and analyzing the outer envelope information by means of time domain and frequency domain.
2. The virtual instrument based machine tool state fault diagnosis system of claim 1, wherein the signal acquisition module comprises a vibration sensor, a charge amplifier and a data acquisition card, and the vibration sensor is a piezoelectric acceleration sensor;
the piezoelectric acceleration sensor is used for collecting vibration signals and transmitting the signals to the charge amplifier;
the charge amplifier is used for receiving and amplifying charge signals of the piezoelectric acceleration sensor, and original weak signals can be transmitted to a computer after being amplified by the charge amplifier;
and the data acquisition card receives the signal amplified by the charge amplifier and is in data communication with a LabVIEW program in a computer.
3. The virtual instrument based machine tool state fault diagnosis system of claim 2, wherein the data acquisition card collects data of a vibration sensor, and the vibration sensor is a piezoelectric acceleration sensor; the data acquisition card is selected from USB-6008.
4. The system for diagnosing machine tool state faults based on the virtual instrument according to claim 1, wherein the wavelet denoising module is configured to perform wavelet transformation on the signals to generate wavelet coefficients including important information of the signals, wherein the wavelet coefficients of the signals are larger, and the wavelet coefficients of the noise signals are smaller, and by setting a suitable threshold, the noise signals are eliminated and the original signals are kept;
and the low-pass filtering module is used for allowing the signal below the low-pass cut-off frequency to pass and filtering the part of the signal exceeding the low-pass cut-off frequency.
5. The virtual machine based machine state fault diagnosis system according to claim 1, wherein the expression of the autocorrelation module is:
R(s,t)＝E[X(s)*X(t)]。
6. the system for diagnosing the fault of the state of the machine tool based on the virtual instrument as claimed in claim 1, wherein in the fourier transform module, the vibration signal passes through a fast fourier transform function from left to right, and a double-side spectrogram is output, and in order to convert the double-side spectrogram into a single-side spectrogram, a half of the original array size is taken as a new array size, so that the single-side spectrogram can be obtained;
in the cepstrum analysis module, firstly, an input vibration signal is subjected to a self-power spectrum function to obtain a self-power spectrum, then, an abscissa is expressed as a logarithm with the base number of 10 by a logarithm function, and then, inverse Fourier transform is carried out to obtain a cepstrum of the signal;
in the envelope spectrum analysis module, the square of the vibration signal is added with the square of the vibration signal after Hilbert transformation, the arithmetic square root is taken as the obtained result, then Fourier transformation is carried out to obtain an envelope spectrum of the signal, the double-sided envelope spectrum is converted into a single-sided envelope spectrum for convenience of analysis, and half of the original array size is taken as a new array size to obtain the single-sided envelope spectrum.
7. The fault diagnosis method of the virtual machine-based machine tool state fault diagnosis system according to any one of claims 1 to 6, comprising the steps of:
arranging a measuring point of a vibration signal near a spindle box to obtain the working frequency and fault characteristics of a bearing and a gear in the spindle box, arranging two vibration sensors on the main box, wherein one sensor is responsible for collecting axial vibration signals of the spindle, and the other sensor is responsible for collecting radial vibration signals of the spindle;
different matching modes are selected according to the gear pair in the main spindle box and the adjustment of the rotating speed gear,
calculating the characteristic frequency of the rolling bearing and the gear;
a method for diagnosing a failure of a rolling bearing,
for the rolling bearing, the fault characteristic frequency calculation formula is shown in formulas (2) to (4);
bearing outer ring fault frequency BPFO ═ (N/2) z [1- (D/D) cos α ] (2)
Bearing inner race failure frequency BPFI ═ (N/2) z [1+ (D/D) cos α ] (3)
Holder failure frequency FTF ═ N/2) [1- (D/D) cos α ] (4)
Wherein:
d is the diameter of the rolling body;
d is the average diameter of the rolling bearing;
α ═ radial direction contact angle;
z is the number of rolling elements;
n is the rotational speed of the shaft;
in actual calculation, calculating the fault characteristic frequency of the movable bearing by using the formulas (5) to (7);
bearing outer ring failure frequency:
bearing inner race failure frequency:
cage failure frequency:
wherein:
z is the number of rolling elements;
n is the rotational speed of the shaft;
the failure analysis of the gear is as follows:
the gear teeth are compared with the spring, the mass of the gear body is used as the mass of the spring body, and under the condition of neglecting the friction force of the gear teeth, the vibration equation of the gear pair is shown as the formula (8).
MrX＝CX+k(t)X＝k(t)E1+k(t)E2(t) (8)
M in the formularX＝(m1m2)/(m1+m2),m1Mass of the capstan, m2The mass of the driven wheel, C, the damping of the gear engagement, X, the relative displacement on the action line of the gear, and k (t), the meshing stiffness of the gear, which changes with time, i.e. k (t) denotes the dynamic process of the gear meshing; e1Expressed is the average value of the static elastic deformation of the gear wheel after being subjected to a load, E2(t) is the relative displacement between gears due to gear failure and error, i.e., the failure function.
8. The fault diagnosis method of the virtual machine-based machine tool state fault diagnosis system according to claim 6, wherein the four stages of the bearing failure are:
(1) the first stage is as follows: at the initial stage of bearing failure, the bearing firstly forms micro cracks or dislocation of crystal lattices on the secondary surface, cracks or micro peeling cannot be seen on the surface of the bearing, and no obvious impact signal can be formed in the low-frequency stage of a vibration signal, so that a vibration fault signal cannot be picked up by using a traditional acceleration sensor, but an acoustic emission signal or a stress wave signal can be generated in the micro cracks or dislocation of the crystal lattices on the secondary surface, so that the characteristic of the bearing fault is reflected in the ultrasonic frequency stage at the stage, and the peak value or the energy value of a measured signal is increased by using the acoustic emission sensor or the acceleration sensor based on resonance to obtain the signal;
(2) and a second stage: a bearing failure development period, in which the microscopic deterioration of the bearing begins to spread from the sub-surface to the surface, and a damage point such as a crack or a micro-peeling is generated on the contact surface of the bearing; when the surface of an element of the bearing is in contact with the damaged points, impact pulses with a certain frequency are formed, according to Fourier transform, a signal of short-time impact is a broadband signal in a frequency domain, so that the impact signal can excite the high-frequency natural frequency of a bearing part to resonate, the vibration of the bearing part is strengthened, the signal can be obtained through an acceleration sensor, the fault characteristic frequency of the bearing can be observed by utilizing an envelope demodulation technology, and the frequency multiplication of the fault characteristic frequency can be observed at the end of the second stage; at this stage, the temperature of the bearing is normal, the noise is slightly increased, the total vibration speed is slightly increased, the vibration frequency spectrum change is not obvious, but the peak energy is increased, and the frequency spectrum is more prominent; the fault frequency of the bearing at this moment appears in the range of about 500hz-2khz, and the fault characteristic frequency of the bearing is temporarily buried in high noise of a low frequency band, so that clear fault characteristic frequency cannot be observed in the fault characteristic frequency band;
(3) and a third stage: in the bearing failure rapid development period, along with the accelerated development of bearing damage, the impact of a damage point on a bearing contact surface is stronger and stronger, the frequency multiplication of the characteristic frequency of the bearing fault demodulated in the resonance frequency section is more and more, the magnitude of the periodic impact energy can be directly observed through a vibration signal power spectrum, at the moment, the characteristic frequency of the bearing fault is directly and clearly seen on the vibration signal power spectrum, and the frequency multiplication also has a more and more trend; at the moment, the fault frequency of the bearing is in the range of 0-1 khz approximately; the bearing is recommended to be replaced at the later stage of the third stage, and the fault characteristics of the rolling bearing such as abrasion and the like can be easily seen;
(4) a fourth stage: at the end of the bearing failure, the rolling bearing reaches the end of the service life quickly at this stage, damage points can be obtained by visual observation, the bearing movement noise becomes extremely large, and the temperature rises rapidly; at the moment, not only can the bearing fault characteristic frequency and frequency multiplication be clearly seen on the power spectrum, but also a very obvious modulation side frequency can be seen beside the fault characteristic frequency if the damage points alternately enter the load area; at the end of the fourth phase, the spectral lines of the signal become less clear and form a convex "mound" in the power spectrum, at which point the energy of the dither is likely not to rise or fall, and once the high frequency monitoring begins to fall, it is not necessarily the case that the bearing surface condition is good, but rather it is said that the bearing has reached the end of its life.
9. The method for diagnosing the failure of the virtual-instrument-based machine tool state failure diagnosis system according to claim 6, wherein the step of diagnosing the failure of the gear includes:
the expression of the change rate of the meshing rigidity is shown as a formula (9);
n in the formula1And n2The rotational speeds of the primary and secondary gears are indicated, respectively, in r/mi and z1And z2Respectively showing the tooth number of the driving wheel and the driven wheel; and k (t) E2(t) is the source of the excitation causing the gear vibration anomaly, which is affected by both the mesh stiffness and the fault function.
10. The method for diagnosing the fault of the virtual machine-based machine tool state fault diagnosis system according to claim 6, wherein the step of diagnosing the fault of the gear comprises calculating the meshing frequency of the gear, and specifically comprises the following steps:
for the gear pair, the calculation formula of the gear meshing frequency is shown as the formula (10);
f＝Nz (10)
wherein:
z is the gear tooth number;
n is the rotational speed of the shaft.
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