CN111678678B - Multi-sensor fusion-based fault diagnosis method and device for circulating dual-spectrum slicing shafting equipment - Google Patents
Multi-sensor fusion-based fault diagnosis method and device for circulating dual-spectrum slicing shafting equipment Download PDFInfo
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Abstract
The invention provides a method and a device for diagnosing faults of circulating bispectrum slicing shafting equipment based on multi-sensor fusion. The method comprises the following steps: acquiring shafting equipment data acquired by a plurality of sensors, and recording the shafting equipment data in a matrix form, wherein the measurement objects of the plurality of sensors are the same; dividing shafting equipment data according to a set sampling period to obtain a plurality of data sets, wherein each data set comprises data acquired by each sensor in a corresponding period; fusing the data in each data group by adopting a weighted fusion mode to obtain fused data; and performing circulating bispectrum slice analysis according to the fused data to obtain a fault diagnosis result of the shafting equipment. According to the invention, the original data are fused by using a multi-sensor fusion technology, and then the circulating bispectrum peak value slicing analysis is carried out based on the fused data, so that the interference of environmental noise can be avoided, the amplitude spectrum wave peak is obvious, and the slicing analysis is more convenient and faster.
Description
Technical Field
The invention relates to the technical field of shafting equipment fault diagnosis, in particular to a circulating bispectrum slicing shafting equipment fault diagnosis method and device based on multi-sensor fusion.
Background
With the production development and the modernization of science and technology, the structure of modern mechanical equipment is more complex, various functions are more comprehensive, the automation degree of the mechanical equipment is continuously improved, and shafting equipment is an important composition structure. Due to many factors, shafting equipment has a long service life and is prone to failure, which may result in reduced expected efficiency, shut down, etc., and even more serious catastrophic failure. The fault is found in time, the fault type is identified, the service life of the system is prolonged, and dangerous accidents can be effectively avoided.
Vibration signals measured by shafting equipment in the working process often show periodic stationarity. The low-order cycle statistics are adopted to diagnose the fault in the early stage, but for early-stage slight faults, the collected signals possibly contain a large amount of noise, so that the signal-to-noise ratio of the signals is low, and the data is difficult to analyze through the low-order statistics, so that the noise reduction of the signals is performed, and the main characteristic of the fault is extracted. The research in the literature, "Zhanggui, Sterling, Yang Tertiary" mechanical fault feature extraction method research [ J ]. university of Chinese science and technology, 1999(03):7-9 "finds that the high-order cycle statistics of signals can theoretically well inhibit noise, and monitors early bearing faults by utilizing the third-order cumulant spectrum, namely the cycle bispectrum, in the high-order cycle statistics. The application of the theory of high-order statistics and definition and theoretical basis of the high-order statistics and the high-order cycle statistics in mechanical fault diagnosis is described in detail in the literature [ J ] vibration engineering report, 2001(02):5-14 ], the theory is applied to the aspects of system identification, state monitoring and feature extraction of mechanical equipment fault diagnosis, the research result indicates that the high-order cycle spectrum has important practical significance in the field of shafting equipment fault diagnosis, and the aims of identifying and diagnosing different faults are achieved by obtaining fault feature frequency. The technical scheme includes that the method comprises the following steps of document ' summer, New year, Xiaoyunkui, and the like, ' piston pin vibration signal analysis [ J ] of a diesel engine based on high-order cyclostationarity, Chinese mechanical engineering, 2010,21(12):1410 + 1414 ' and ' Zhouyu, Chengxing, Dongbangming ', and the like, ' rolling bearing fault diagnosis [ J ] based on a cyclic bispectrum, vibration and impact, 2012,31(09):78-81 ', and further applies the cyclic bispectrum to the field of piston pin vibration signal analysis of the diesel engine and fault diagnosis of a rolling bearing, and indicates that the frequency component distribution condition of a vibration signal can be well reflected and the characteristic frequency value of equipment fault can be effectively extracted through the cyclic bispectrum.
However, in practical applications, the above methods do not consider that when fault diagnosis is performed on large shafting equipment only by using single sensor data, the extracted features still have relatively high noise, and a situation that a spectral amplitude peak is not obvious may occur in the diagnosis process, and a peak frequency cannot be found, or when the noise peak frequency is used as the fault feature peak frequency, the result does not have accuracy, or even fault diagnosis cannot be completed.
Disclosure of Invention
The invention provides a method and a device for diagnosing faults of circulating bispectrum slicing shafting equipment based on multi-sensor fusion, and aims to solve the problems that in the existing shafting equipment fault diagnosis method, the diagnosis result is inaccurate and even the fault diagnosis cannot be completed due to the fact that the fault diagnosis is carried out only by means of single sensor data.
The invention provides a fault diagnosis method for circulating bispectrum slicing shafting equipment based on multi-sensor fusion, which comprises the following steps:
and 4, performing circulating bispectrum slice analysis according to the fused data to obtain a fault diagnosis result of the shafting equipment.
Further, in step 1, recording the shafting equipment data in a matrix form, specifically: the column elements of the matrix are adopted to represent the data collected by the same sensor at different moments; the row elements of the matrix are used to represent the data collected by different sensors at the same time.
Further, step 3 comprises:
step 3.1: acquiring the distance between each sensor and a fault point;
step 3.2: taking the distance between each sensor and the fault point as a fusion coefficient of each sensor;
step 3.3: and for each data group, fusing the data of all the sensors in the data group according to the fusion coefficient of each sensor to obtain fused data in a corresponding period.
Further, the sensors include a rotational speed sensor, a vibration displacement sensor and a vibration acceleration sensor.
The invention provides a circulating bispectrum slicing shafting equipment fault diagnosis device based on multisensor fusion, which comprises:
the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring shafting equipment data acquired by a plurality of sensors and recording the shafting equipment data in a matrix form, and measuring objects of the sensors are the same;
the data segmentation module is used for segmenting the shafting equipment data according to a set sampling period to obtain a plurality of data sets, and each data set comprises data acquired by each sensor in a corresponding period;
the data fusion module is used for fusing the data in each data group in a weighting fusion mode to obtain fused data;
and the fault diagnosis module is used for performing circulating bispectrum slice analysis according to the fused data to obtain a fault diagnosis result of the shafting equipment.
Further, the data acquisition module records the shafting equipment data in a matrix form, specifically: the column elements of the matrix are adopted to represent the data collected by the same sensor at different moments; the row elements of the matrix are used to represent the data collected by different sensors at the same time.
Further, the data fusion module is specifically configured to:
acquiring the distance between each sensor and a fault point; taking the distance between each sensor and the fault point as a fusion coefficient of each sensor; and for each data group, fusing the data of all the sensors in the data group according to the fusion coefficient of each sensor to obtain fused data in a corresponding period.
Further, the sensors include a rotational speed sensor, a vibration displacement sensor and a vibration acceleration sensor.
The invention has the beneficial effects that:
when shafting equipment runs, the acquired vibration signals often have the characteristic of stable circulation, and aiming at the characteristic, the high-order circulation spectrum can be utilized to analyze the vibration signals. Due to the fact that the operation environment of the shafting equipment is complex, the energy excited by faults is often submerged in background noise, and the acquired time domain data are difficult to further analyze. And the signal-to-noise ratio near the corresponding frequency at the peak of the frequency spectrum is always higher, and the characteristic is more obvious. However, for large shafting equipment, it is not practical to classify various faults only through data acquired by a single sensor, not only is the installation point of the sensor difficult to determine, but also the diagnosis effect can be influenced because of excessive environmental noise of some special parts.
Drawings
Fig. 1 is a schematic flowchart of a fault diagnosis method for a cyclic bispectrum slicing shafting device based on multi-sensor fusion according to an embodiment of the present invention;
FIG. 2 is a simulation diagram of a cyclic bispectrum peak value slice in a fan blade-breaking failure mode according to an embodiment of the present invention;
FIG. 3 is a simulation diagram of a cyclic bispectrum peak slice under a loose base failure mode according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a fault diagnosis device for a cyclic bispectrum slicing shafting device based on multi-sensor fusion, provided by the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described clearly below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for diagnosing faults of a cyclic bispectrum slicing shafting device based on multi-sensor fusion, including the following steps:
s101, obtaining shafting equipment data acquired by a plurality of sensors, and recording the shafting equipment data in a matrix form, wherein the measurement objects of the sensors are the same;
specifically, the shafting equipment data is recorded in a matrix form, specifically: the column elements of the matrix are adopted to represent the data collected by the same sensor at different moments; the row elements of the matrix are used to represent the data collected by different sensors at the same time.
For example, data collected by a plurality of sensors is recorded in the form of:
wherein, y(j)(k) The k-th data collected by the J-th sensor is shown, L represents the data length, and J represents the total number of the sensors.
It should be noted that the measurement objects of the plurality of sensors need to be the same, that is: the vibration acceleration sensor may be a plurality of vibration acceleration sensors.
S102, segmenting shafting equipment data according to a set sampling period to obtain a plurality of data sets, wherein each data set comprises data acquired by each sensor in a corresponding period;
for example, the data set Y shown in equation (1) may be divided into N data groups by dividing the data set Y into sampling periods R, and the data set Y may be rewritten as follows:
Y＝[Y(1),Y(2),…,Y(N)]L×J (2)
wherein, Y(i)The data group representing the ith cycle when the data set Y is divided according to the cycle R specifically includes:
in the formula (3), the reaction mixture is,
the "k" is the kth data acquired by the jth sensor in the data group corresponding to the ith cycle after the data set Y is divided by the cycle R, and k is 1,2, …, R.
S103, fusing the data in each data group by adopting a weighted fusion mode to obtain fused data, and the method comprises the following substeps:
s1031: acquiring the distance between each sensor and a fault point;
s1032: taking the distance between each sensor and the fault point as a fusion coefficient of each sensor;
s1033: and for each data group, fusing the data of all the sensors in the data group according to the fusion coefficient of each sensor to obtain fused data in a corresponding period.
Specifically, as the shafting devices of the same type are almost the same in prone failure, the failure occurrence position (namely, the position of the failure point) needs to be determined empirically in the early stage of failure diagnosis, and then the distance from each sensor to the failure point can be determined.
For example, the distance A between the sensor 1 and the fault point1The distance A between the sensor 2 and the fault point is used as the fusion coefficient of the sensor 12As the fusion coefficient for this sensor 2, … … and so on, each sensor has its fusion coefficient AjWith Y(1)For example, the data fusion process is as follows:
wherein x is(1)I.e. the fused data.
And S104, performing circulating bispectrum slice analysis according to the fused data to obtain a fault diagnosis result of the shafting equipment.
Specifically, the process of performing cyclic bispectrum slice analysis from the fused data is as follows:
first, based on the fused data (in order to)X in the above(1)For example) directly using fast fourier transform to obtain a signal amplitude spectrum
Then, after finding a peak on the amplitude spectrum, performing cyclic bispectrum peak slice analysis:
assuming x (t) is a k-order cyclostationary signal, the k-order cyclic moment
Is defined as follows:
wherein, tau1,τ2,…,τk-1Is the lag time; α is the cycle frequency.
When k is 3, a third-order cyclic moment of the signal x (t) can be obtained, and the mean value of the cyclostationary signal x (t) is considered to be approximate to zero, then the third-order cyclic moment of the signal can be equivalent to a third-order cyclic accumulation quantity
For third order cyclic accumulation
And (3) performing two-dimensional Fourier transform to obtain a third-order cyclic spectrum, wherein the third-order cyclic spectrum of the signal is a cyclic bispectrum:
and (3) acquiring fault characteristic frequency by performing cyclic bispectrum slice analysis, and corresponding the fault characteristic frequency to the fault one by one to achieve the effect of fault diagnosis.
For large-scale shafting equipment, classification of various faults is unrealistic only through data acquired by a single sensor, the installation point of the sensor is difficult to determine, and the diagnosis effect can be influenced due to excessive environmental noise of some special parts.
In order to verify the effectiveness of the method for diagnosing the faults of the circulating bispectrum slicing shafting equipment based on the multi-sensor fusion, the invention also provides the following verification tests:
the test device adopts a ZHS-2 type multifunctional motor flexible rotor test bed; 8 vibration acceleration sensors are installed in the horizontal direction of the rotor supporting seat, each vibration acceleration sensor is used for collecting a time domain vibration signal of the shafting equipment rotor, and the signal is transmitted to an upper computer through an HG8902 collecting box.
This test bench can simulate the multiple operational mode of shafting equipment, including rotor unbalance fault mode, ball fault mode, fan broken blade fault mode, the not hard up fault mode of base, gear broken tooth fault mode and normal operating mode etc. adopted two kinds of operational mode to carry out the experiment emulation in this experiment, do respectively: a base loosening fault mode and a fan blade breakage fault mode. Wherein, the base loosening failure mode: namely, the base loosening fault is manufactured in an artificial screw loosening mode; fan broken blade failure mode: namely, the fan broken blade is manufactured artificially.
In the motor rotor system, a vibration signal acquired by a sensor can reflect abnormal vibration caused by an artificially set operation mode, and the vibration amplitude of equipment can be changed to a certain extent in different operation modes. The rotating speed of the motor rotor is set to be 1500r/m, and the system error of the sensor is set to be +/-1% according to the precision provided by a manufacturer. In each mode, a total of 3072000 data points were acquired continuously for 240s, i.e., each sensor measured one data point at approximately 0.0008 second intervals, i.e., J-8 and L-3072000 in equation (1); then, the sampling period R is 10240 and N is 300. The data collected from the base loosening fault mode and the fan blade breakage fault mode are simulated according to the fault diagnosis method provided by the invention, and the results are shown in fig. 2 and fig. 3.
As can be seen from fig. 2 and 3, the frequencies at which the peaks are found at the maximum peaks of the spectrograms of the vibration signals in fig. 2(b) and 3(b) are 66Hz and 107 Hz. After the cyclic bispectrum estimation, secondary slicing is carried out at the peak frequency, so that slicing graphs as shown in fig. 2(c) and fig. 3(c) can be obtained, and the fault characteristic frequency under the fan blade failure mode and the base loosening failure mode can be obviously seen from the slicing graphs. Different modulation frequencies, namely characteristic frequencies, are set in different operation modes, and the fault type can be accurately judged by analyzing the frequency components of the slice images. Therefore, the fault characteristic frequency can be obtained by performing peak value slice spectrum analysis on the cyclic bispectrum of the experimental signals of the shafting equipment, and faults can be identified and classified more clearly and intuitively, so that a good diagnosis effect is achieved.
Corresponding to the above method, as shown in fig. 4, an embodiment of the present invention further provides a device for diagnosing faults of a cyclic bispectrum slicing shafting device based on multi-sensor fusion, including: the system comprises a data acquisition module 401, a data segmentation module 402, a data fusion module 403 and a fault diagnosis module 404; wherein:
the data acquisition module 401 is configured to acquire shafting equipment data acquired by multiple sensors and record the shafting equipment data in a matrix form, where measurement objects of the multiple sensors are the same; the data segmentation module 402 is configured to segment the shafting equipment data according to a set sampling period to obtain a plurality of data sets, where each data set includes data acquired by each sensor in a corresponding period; the data fusion module 403 is configured to fuse the data in each data group in a weighted fusion manner to obtain fused data; the fault diagnosis module 404 is configured to perform cyclic bispectrum slice analysis according to the fused data to obtain a fault diagnosis result of the shafting device.
Specifically, the data acquisition module 401 records the shafting device data in a matrix form, specifically: the column elements of the matrix are adopted to represent the data collected by the same sensor at different moments; the row elements of the matrix are used to represent the data collected by different sensors at the same time.
The data fusion module 403 is specifically configured to: acquiring the distance between each sensor and a fault point; taking the distance between each sensor and the fault point as a fusion coefficient of each sensor; and for each data group, fusing the data of all the sensors in the data group according to the fusion coefficient of each sensor to obtain fused data in a corresponding period.
The sensor comprises a rotating speed sensor, a vibration displacement sensor and a vibration acceleration sensor.
It should be noted that the fault diagnosis device for the cyclic bispectrum slicing shafting equipment based on multi-sensor fusion provided in the embodiment of the present invention is for implementing the above method embodiment, and the functions thereof may specifically refer to the above method embodiment, and are not described herein again.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (4)
1. A fault diagnosis method for circulating dual-spectrum slicing shafting equipment based on multi-sensor fusion is characterized by comprising the following steps:
step 1, acquiring shafting equipment data acquired by a plurality of sensors, and recording the shafting equipment data in a matrix form shown in a formula (1), wherein the plurality of sensors have the same measurement object;
wherein, y(j)(k) The data of the kth acquired by the jth sensor is represented, L represents the data length, J represents the total number of sensors, and k is 1,2, … and L; step 2, segmenting the shafting equipment data according to a set sampling period to obtain a plurality of data sets, wherein each data set comprises data acquired by each sensor in a corresponding period; the method specifically comprises the following steps:
the data set Y shown in equation (1) is divided into N data groups, L/R, by the sampling period R, to rewrite the data set Y into the form of equation (2):
Y＝[Y(1),Y(2),…,Y(N)]L×J (2)
wherein, Y(i)The data group representing the ith cycle when the data set Y is divided according to the cycle R specifically includes:
in the formula (3), the reaction mixture is,
represents Y(i)The kth data collected by the jth sensor, k being 1,2, …, R;
step 3, fusing the data in each data group by adopting a weighted fusion mode to obtain fused data; the method specifically comprises the following steps:
step 3.1: acquiring the distance between each sensor and a fault point;
step 3.2: taking the distance between each sensor and the fault point as a fusion coefficient of each sensor;
step 3.3: for each data group, fusing the data of all the sensors in the data group according to the fusion coefficient of each sensor to obtain fused data in a corresponding period; the method specifically comprises the following steps:
for data set Y(i)Calculating fused data according to formula (4):
wherein x is(i)I.e. fused data, A1、A2…AJIndicating the distance of each sensor from the fault point;
and 4, performing circulating bispectrum slice analysis according to the fused data to obtain a fault diagnosis result of the shafting equipment.
2. The method of claim 1, wherein the sensors comprise a rotational speed sensor, a vibratory displacement sensor, and a vibratory acceleration sensor.
3. Two spectrum section shafting equipment failure diagnosis device of circulation based on multisensor fuses, its characterized in that includes:
the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring shafting equipment data acquired by a plurality of sensors and recording the shafting equipment data in a matrix form shown in a formula (1), and measuring objects of the sensors are the same;
wherein, y(j)(k) The data of the kth acquired by the jth sensor is represented, L represents the data length, J represents the total number of sensors, and k is 1,2, … and L;
the data segmentation module is used for segmenting the shafting equipment data according to a set sampling period to obtain a plurality of data sets, and each data set comprises data acquired by each sensor in a corresponding period; the method is specifically used for:
the data set Y shown in equation (1) is divided into N data groups, L/R, by the sampling period R, to rewrite the data set Y into the form of equation (2):
Y＝[Y(1),Y(2),…,Y(N)]L×J (2)
wherein, Y(i)The data group representing the ith cycle when the data set Y is divided according to the cycle R specifically includes:
in the formula (3), the reaction mixture is,
represents Y(i)The kth data collected by the jth sensor, k being 1,2, …, R;
the data fusion module is used for fusing the data in each data group in a weighting fusion mode to obtain fused data;
the method is specifically used for: acquiring the distance between each sensor and a fault point; taking the distance between each sensor and the fault point as a fusion coefficient of each sensor; for each data group, fusing the data of all the sensors in the data group according to the fusion coefficient of each sensor to obtain fused data in a corresponding period; the method is specifically used for:
for data set Y(i)Calculating fused data according to formula (4):
wherein x is(i)I.e. fused data, A1、A2…AJIndicating the distance of each sensor from the fault point;
and the fault diagnosis module is used for performing circulating bispectrum slice analysis according to the fused data to obtain a fault diagnosis result of the shafting equipment.
4. The apparatus of claim 3, wherein the sensors comprise a rotational speed sensor, a vibratory displacement sensor, and a vibratory acceleration sensor.
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