CN115795292A - Gear milling machine spindle box fault diagnosis system and method based on LabVIEW - Google Patents
Gear milling machine spindle box fault diagnosis system and method based on LabVIEW Download PDFInfo
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Abstract
The invention provides a LabVIEW-based gear milling machine spindle box fault diagnosis system and method, which comprises the following modules: the system comprises a signal acquisition module, a signal processing module and a fault diagnosis module; comprises the following steps: s1, establishing a standard vibration model according to the characteristic frequencies of a part of bearings and gears to be inspected; s2, carrying out envelope demodulation on the acquired vibration signals of the gear milling machine spindle box and the established standard vibration model to obtain demodulation signals of all the signals; s3, calculating a Pearson correlation coefficient between a demodulation signal of the acquired signal and a demodulation signal of the standard vibration model signal; s4, comparing the calculated Pearson correlation coefficients and judging whether a fault occurs; the invention can quickly and accurately diagnose and identify the fault of the gear milling machine spindle box, avoids the labor and time consumed by manual inspection, and can improve the safety and reliability of the gear milling machine spindle box and the gear milling processing efficiency.
Description
Technical Field
The invention relates to the field of fault diagnosis of gear milling machine spindle boxes, in particular to a LabVIEW-based fault diagnosis system and method for the gear milling machine spindle boxes.
Background
The gear milling machine spindle box is an important transmission part of a numerical control high-speed gear milling machine, adopts multi-stage gear transmission, runs under severe conditions such as high speed, heavy load and the like for a long time, and parts such as internal bearings, gears and the like are easy to break down. And the gear milling machine spindle box usually reaches several meters, and is troublesome and laborious to dismantle, therefore it wastes time and energy to carry out manual inspection when breaking down, has seriously influenced numerical control gear milling machine's operating efficiency. The common means for fault diagnosis generally comprises processing a signal, extracting a frequency related to a structural characteristic frequency, and judging whether a fault occurs according to the characteristic frequency, which requires a good noise reduction means and a signal decomposition algorithm, and the fault component in the spindle box can be accurately diagnosed by using the Pearson correlation coefficient of the signal after envelope demodulation and the established standard vibration model on the premise of not performing decomposition. Therefore, the fault diagnosis system of the gear milling machine spindle box is designed, and the method is embedded into the fault diagnosis system of the gear milling machine spindle box, so that labor and time consumed by manual inspection can be effectively avoided. The method has important significance for troubleshooting of the gear milling machine spindle box and gear milling efficiency.
Disclosure of Invention
The invention aims to provide a fault diagnosis system and method of a gear milling machine spindle box based on LabVIEW, which are used for collecting vibration signals of the gear milling machine spindle box in real time and carrying out fault diagnosis.
In order to achieve the purpose, the invention provides the following technical scheme:
a milling tooth main shaft box fault diagnosis system based on LabVIEW comprises the following modules: the system comprises a signal acquisition module, a signal processing module and a fault diagnosis module;
the signal acquisition module is used for acquiring a vibration signal of a gear milling machine spindle box;
the signal processing module is used for carrying out time-frequency domain analysis on the vibration signal;
the fault diagnosis module is used for carrying out fault diagnosis on the spindle box and comprises the following steps:
step S1: establishing a standard vibration model according to the characteristic frequencies of the bearing and the gear of the inspected part;
step S2: carrying out envelope demodulation on the acquired vibration signals of the gear milling machine spindle box and the established standard vibration model to obtain demodulation signals of all the signals;
and step S3: calculating a Pearson correlation coefficient between a demodulation signal of the acquired signal and a demodulation signal of the standard vibration model signal;
and step S4: and comparing the calculated Pearson correlation coefficients to judge whether a fault occurs.
The signal acquisition module is based on NI 9234 data acquisition card and the unidirectional vibration sensor of KISTLER and gathers milling teeth owner axle box vibration signal.
The NI 9234 data acquisition card is connected with an upper computer; the KISTLER one-way vibration sensor is arranged at the end cover of the main shaft box cutter of the gear milling machine.
The signal processing module comprises three methods of Ensemble Empirical Mode Decomposition (EEMD), variational Mode Decomposition (VMD) and sine and cosine optimization resonance sparse decomposition (SCARSSD).
The fault diagnosis module specifically includes the following standard vibration models in step S1:
(1) The vibration signal of the bearing in normal operation can be approximated to be a sinusoidal signal with the shaft rotation frequency as the characteristic frequency, and the frequency spectrum of the sinusoidal signal contains the shaft rotation frequency and the frequency multiplication thereof, so that the standard vibration signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal and fn is the rotational frequency of the shaft;
(2) When the bearing has outer ring fault, the frequency spectrum of the bearing comprises the outer ring fault frequency and the frequency multiplication thereof, so the standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal, fo is the bearing outer ring fault characteristic frequency;
(3) When the bearing has inner ring fault, the frequency spectrum contains inner ring fault frequency and frequency multiplication thereof, and a sideband band cluster which takes shaft rotation frequency as interval exists at the characteristic frequency and frequency multiplication thereof, and two sideband frequencies on the left and the right are taken in the invention for simplifying calculation, so that a standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal, fi is the fault frequency of the inner ring of the bearing, and fn is the rotation frequency of the shaft where the bearing is located;
(4) Normal operation of gear
The vibration signal when the gears are normally engaged can be approximated as a sinusoidal signal with the gear engagement frequency as the characteristic frequency, and therefore the standard signal model thereof can be expressed as:
wherein A is the amplitude of the analysis signal, fm is the meshing frequency of the pair of gears;
(5) Local failure of gear
When a gear has a local fault, the frequency spectrum of the gear comprises the meshing frequency of the gear, the rotating frequency of a shaft where the fault gear is located and the frequency multiplication of the gear, and a sideband band cluster which takes the rotating frequency of the shaft as an interval exists at the characteristic frequency and the frequency multiplication position of the characteristic frequency, and two sideband frequencies are taken in the invention for simplifying calculation, so the standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal, fm is the meshing frequency of the pair of gears, and fn is the rotation frequency of the shaft on which the failed gear is located.
The envelope demodulation in step S2 of the fault diagnosis module specifically operates as follows: setting an original signal as x, performing hilbert transformation on the original signal and taking an absolute value x1 of the transformed signal; removing the direct current component of the signal x1 to obtain a signal x2; and performing fast Fourier transform on the signal x2, and taking an analysis signal x3 which is finally drawn into a frequency spectrum as a signal for calculating a correlation coefficient.
The pearson correlation coefficient and the pearson correlation coefficient in step S3 of the fault diagnosis module are applicable to the continuous variable in the normal distribution, and may be used to describe a linear correlation degree between two continuous variables, where a larger value indicates a larger correlation between the two continuous variables, and may be specifically represented as:
wherein X and Y respectively represent two continuous variables, cov (-) represents covariance, and Var [. Cndot. ] represents standard deviation.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a LabVIEW-based fault diagnosis system and method for a gear milling machine spindle box, which can be used for quickly and accurately diagnosing the fault of the gear milling machine spindle box, and diagnosing the fault by establishing a standard vibration model of a bearing and a gear and calculating a Pearson correlation coefficient between an analysis signal and the standard vibration model, thereby avoiding labor force consumed by manual inspection and improving the safety and reliability of the gear milling machine spindle box and the gear milling processing efficiency.
Drawings
FIG. 1 is a structural block diagram of a LabVIEW-based gear milling machine spindle box fault diagnosis system provided by the invention;
FIG. 2 is a flow chart of a LabVIEW-based fault diagnosis method for a gear milling machine spindle box provided by the invention;
FIG. 3 is a view of a failed component in the spindle head;
FIG. 4 is an original time domain plot and spectrogram of the acquired signal of the present invention;
FIG. 5 is a signal acquisition module interface of the LabVIEW-based gear milling spindle box fault diagnosis system provided by the invention;
FIG. 6 is a signal processing module interface of a LabVIEW-based gear milling machine spindle box fault diagnosis system provided by the invention;
fig. 7 is a fault diagnosis module interface of the LabVIEW-based gear milling spindle box fault diagnosis system provided by the invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to clarify technical problems, technical solutions, implementation processes and performance displays. It should be understood that the specific embodiments described herein are for illustrative purposes only. The present invention is not limited to the above embodiments. Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Example 1
The data adopted in this embodiment is actually measured vibration data of a gap-eliminating spindle box of a high-speed numerical control gear milling machine of south-Beijing industry and large numerical control science and technology limited, the model of a bearing used in the spindle box is NN3016 of NSK company, the number of rolling elements is 26, the diameter of the rolling element is 10mm, the inner ring of a spindle bearing fails, the bearing load under an idling condition is 0, and a tooth surface peeling failure occurs on a spindle gear in a box body, as shown in FIG. 3. The method comprises the steps of selecting the milling rotation speed under the actual working condition, wherein the rotation speed of a cutter main shaft is 90r/min, the sampling frequency is 800Hz, taking the first 4096 data points, and obtaining the fault characteristic frequency of a main shaft bearing inner ring by theoretical calculation to be 21.39Hz, the main shaft rotation frequency to be 1.5Hz, and the meshing frequency of the main shaft and an idler shaft gear to be 34.5Hz.
The invention will be described with reference to fig. 1 to 7, and in order to achieve the above object, the technical solution adopted by the invention is as follows:
a milling tooth main shaft box fault diagnosis system based on LabVIEW comprises the following modules: the system comprises a signal acquisition module, a signal processing module and a fault diagnosis module;
as shown in fig. 5, the signal acquisition module is configured to acquire a vibration signal of a spindle housing of the gear milling machine;
as a possible implementation manner, the signal acquisition module acquires a vibration signal of a main shaft box of the gear milling machine based on an NI 9234 data acquisition card and a KISTLER unidirectional vibration sensor; the NI 9234 data acquisition card is connected with the upper computer, and the KISTLER unidirectional vibration sensor is installed at the end cover of the main shaft of the gear milling machine spindle box cutter.
As shown in fig. 6, the signal processing module is configured to perform time-frequency domain analysis on the vibration signal;
as a possible implementation, the signal processing module includes three methods of Ensemble Empirical Mode Decomposition (EEMD), variational Mode Decomposition (VMD), and sine-cosine optimized resonance sparse decomposition (scarsd).
As shown in fig. 7, the fault diagnosis module is used for performing fault diagnosis on the spindle box, and includes the following steps:
step S1: establishing a standard vibration model according to the characteristic frequencies of the bearing and the gear of the inspected part;
step S2: carrying out envelope demodulation on the acquired vibration signals of the gear milling machine spindle box and the established standard vibration model to obtain demodulation signals of all the signals;
and step S3: calculating a Pearson correlation coefficient between a demodulation signal of the acquired signal and a demodulation signal of the standard vibration model signal;
and step S4: and comparing the calculated Pearson correlation coefficients to judge whether a fault occurs.
Further, in step S1: the model of the vibration sensor is selected from a PCB SN40166 one-way acceleration sensor, and data are collected through an LMS test.
As a possible implementation manner, the standard vibration model in step S1 of the fault diagnosis module is specifically as follows:
(1) The vibration signal of the bearing during normal operation can be approximated to a sinusoidal signal with the shaft rotation frequency as the characteristic frequency, and the frequency spectrum of the sinusoidal signal contains the shaft rotation frequency and the frequency multiplication thereof, so that the standard vibration signal model can be expressed as follows:
wherein A is the peak value of 0.0132m/s of the analysis signal 2 Fn is the rotation frequency of the shaft 1.5Hz, and for simplifying the calculation, N is taken as 2;
(2) When the bearing has outer ring fault, the frequency spectrum of the bearing comprises the outer ring fault frequency and the frequency multiplication thereof, so the standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal of 0.132m/s 2 The fo is the bearing outer ring fault characteristic frequency of 17.61Hz, and for simplifying calculation, N is taken as 2;
(3) When the bearing has inner ring fault, the frequency spectrum contains inner ring fault frequency and frequency multiplication thereof, and a sideband band cluster which takes shaft rotation frequency as interval exists at the characteristic frequency and frequency multiplication thereof, and two sideband frequencies on the left and the right are taken in the invention for simplifying calculation, so that a standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal of 0.132m/s 2 Fi is the fault frequency of the bearing inner ring 21.39Hz, fn is the rotation frequency of the shaft 1.5Hz, andtaking N as 3;
(4) Normal operation of gear
The vibration signal when the gears are normally engaged can be approximated as a sinusoidal signal with the gear engagement frequency as a characteristic frequency, and therefore its standard signal model can be expressed as:
wherein A is the amplitude of the analysis signal of 0.132m/s 2 Fm is the meshing frequency of the pair of gears 34.5Hz, and for simplifying calculation, N is taken as 2;
(5) Local failure of gear
When a gear has a local fault, the frequency spectrum of the gear comprises the meshing frequency of the gear, the rotating frequency of a shaft where the fault gear is located and the frequency multiplication of the gear, and a sideband band cluster which takes the rotating frequency of the shaft as an interval exists at the characteristic frequency and the frequency multiplication position of the characteristic frequency, and two sideband frequencies are taken in the invention for simplifying calculation, so the standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal of 0.132m/s 2 Fm is the meshing frequency of the pair of gears 34.5Hz, fn is the rotating frequency of the shaft of the fault gear 1.5Hz.
As a possible implementation manner, the envelope demodulation in step S2 of the fault diagnosis module specifically operates as follows: setting an original signal as x, performing hilbert transformation on the original signal and taking an absolute value x1 of the transformed signal; removing the direct current component of the signal x1 to obtain a signal x2; and performing fast Fourier transform on the signal x2, and taking an analysis signal x3 of a finally drawn frequency spectrum as a signal for calculating a correlation coefficient.
The pearson correlation coefficient and the pearson correlation coefficient in step S3 of the fault diagnosis module are applicable to the continuous variable in the normal distribution, and may be used to describe a linear correlation degree between two continuous variables, where a larger value indicates a larger correlation between the two continuous variables, and may be specifically represented as:
wherein X and Y respectively represent two continuous variables, cov (-) represents covariance, and Var [. Cndot. ] represents standard deviation. In this embodiment, the pearson correlation coefficients of the gear milling spindle housing signal and the standard vibration model are respectively: 1) The standard vibration model for normal operation of the bearing is 0.1257; the standard vibration model of the bearing outer ring fault is 0.1152; the standard vibration model of the bearing inner ring fault is 0.1318; 2) The standard vibration model for normal operation of the gear is 0.0745; the standard vibration model of the local fault of the gear is 0.1660; the fault of the inner ring of the main shaft bearing and the local fault of the main shaft gear of the gear milling machine main shaft box can be diagnosed through comparison of the Pearson correlation coefficients, and the fault diagnosis is correct when the fault diagnosis is consistent with the data used in the embodiment, so that the provided fault diagnosis system and method for the gear milling machine main shaft box based on LabVIEW are effective, labor consumed by manual inspection is avoided, and the safety and reliability of the gear milling machine main shaft box and the gear milling processing efficiency are improved.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention, and that various changes and modifications may be made without departing from the spirit and scope of the present invention, which fall within the scope of the claimed invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The utility model provides a mill tooth owner axle box fault diagnosis system based on LabVIEW which characterized in that includes following module: the system comprises a signal acquisition module, a signal processing module and a fault diagnosis module;
the signal acquisition module is used for acquiring a vibration signal of a gear milling machine spindle box;
the signal processing module is used for carrying out time-frequency domain analysis on the vibration signal;
the fault diagnosis module is used for carrying out fault diagnosis on the spindle box and comprises the following steps:
step S1: establishing a standard vibration model according to the characteristic frequencies of the bearing and the gear of the inspected part;
step S2: carrying out envelope demodulation on the acquired vibration signals of the gear milling machine spindle box and the established standard vibration model to obtain demodulation signals of all the signals;
and step S3: calculating a Pearson correlation coefficient between a demodulation signal of the acquired signal and a demodulation signal of the standard vibration model signal;
and step S4: and comparing the calculated Pearson correlation coefficients and judging whether a fault occurs.
2. The LabVIEW-based gear milling machine spindle box fault diagnosis system as claimed in claim 1, wherein the signal acquisition module acquires a gear milling machine spindle box vibration signal based on an NI 9234 data acquisition card and a KISTLER unidirectional vibration sensor.
3. The LabVIEW-based gear milling machine spindle box fault diagnosis system as claimed in claim 1, wherein the NI 9234 data acquisition card is connected with an upper computer; the KISTLER one-way vibration sensor is arranged at the end cover of the main shaft box cutter of the gear milling machine.
4. The LabVIEW-based gear milling spindle box fault diagnosis system as claimed in claim 1, wherein the signal processing module comprises three methods of Ensemble Empirical Mode Decomposition (EEMD), variational Mode Decomposition (VMD) and sine and cosine optimized resonance sparse decomposition (SCARSSD).
5. The LabVIEW-based gear milling machine spindle box fault diagnosis system as claimed in claim 1, wherein the fault diagnosis module specifies the following standard vibration model in step S1:
(1) The vibration signal of the bearing in normal operation can be approximated to be a sinusoidal signal with the shaft rotation frequency as the characteristic frequency, and the frequency spectrum of the sinusoidal signal contains the shaft rotation frequency and the frequency multiplication thereof, so that the standard vibration signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal and fn is the rotational frequency of the shaft;
(2) When the bearing has outer ring fault, the frequency spectrum of the bearing comprises the outer ring fault frequency and the frequency multiplication thereof, so the standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal, fo is the bearing outer ring fault characteristic frequency;
(3) When the bearing has inner ring fault, the frequency spectrum contains inner ring fault frequency and frequency multiplication thereof, and a sideband band cluster which takes shaft rotation frequency as interval exists at the characteristic frequency and frequency multiplication thereof, and two sideband frequencies on the left and the right are taken in the invention for simplifying calculation, so that a standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal, fi is the fault frequency of the inner ring of the bearing, and fn is the rotation frequency of the shaft where the bearing is located;
(4) Normal operation of gear
The vibration signal when the gears are normally engaged can be approximated as a sinusoidal signal with the gear engagement frequency as the characteristic frequency, and therefore the standard signal model thereof can be expressed as:
wherein A is the amplitude of the analysis signal, fm is the meshing frequency of the pair of gears;
(5) Local failure of gear
When a gear has a local fault, the frequency spectrum of the gear comprises the meshing frequency of the gear, the rotating frequency of a shaft where the fault gear is located and the frequency multiplication of the gear, and a sideband band cluster which takes the rotating frequency of the shaft as an interval exists at the characteristic frequency and the frequency multiplication position of the characteristic frequency, and two sideband frequencies are taken in the invention for simplifying calculation, so the standard signal model can be expressed as follows:
wherein A is the amplitude of the analysis signal, fm is the meshing frequency of the pair of gears, and fn is the rotation frequency of the shaft on which the fault gear is positioned.
6. A LabVIEW-based gear milling machine spindle box fault diagnosis system according to claim 1, wherein the fault diagnosis module performs the envelope demodulation in step S2, and specifically operates to: setting an original signal as x, performing hilbert transformation on the original signal and taking an absolute value x1 of the transformed signal; removing the direct current component of the signal x1 to obtain a signal x2; and performing fast Fourier transform on the signal x2, and taking an analysis signal x3 of a finally drawn frequency spectrum as a signal for calculating a correlation coefficient.
7. A LabVIEW-based gear milling machine spindle box fault diagnosis system according to claim 1, wherein the fault diagnosis module in step S3, the pearson correlation coefficient, which is applied to a continuous variable in a normal distribution, can be used to describe a linear correlation degree between two continuous variables, and a larger value thereof indicates a larger correlation therebetween, which can be specifically expressed as:
wherein X and Y respectively represent two continuous variables, cov (-) represents covariance, and Var [. Cndot. ] represents standard deviation.
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