CN112183344A - Large unit friction fault analysis method and system based on waveform and dimensionless learning - Google Patents
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Abstract
The invention belongs to the technical field of fault detection, and discloses a large unit friction fault analysis method and system based on waveform and dimensionless learning, wherein a double probe is used for extracting a machine fault vibration signal and preprocessing data; simultaneously, extracting friction fault characteristics, and establishing a fault prediction model by using a machine learning method; predicting whether the unknown tag signal has a fault or not, and determining the type of the fault. In the process of preprocessing a machine fault vibration signal and data, two probe points are installed, and a vibration double-view signal of the large-scale sliding unit is acquired through the two probe points; after the probe collects data, aligning and performing discrete Fourier transform, and modifying the transformed Fourier value; and setting an adaptive threshold according to the signal condition, reducing the signal storage amount and accelerating transmission. In the process of diagnosing the friction fault of the large unit, the problem of difficult feature extraction can be effectively solved; effective characteristics can be extracted, and the problem of failure prediction is solved.
Description
Technical Field
The invention belongs to the technical field of fault detection, and particularly relates to a large unit friction fault analysis method and system based on waveform and dimensionless learning.
Background
At present, large-scale mechanized equipment has complex structure, perfect functions and close connection among internal parts of the equipment, so that high speed and large-scale production are achieved in the production process, the large-scale mechanized equipment breaks down to cause huge loss, and the difficulty of fault diagnosis of the large-scale mechanized equipment is increased. When an object and another object move along the tangent direction of the contact surfaces or have a relative motion tendency, a force resisting the relative motion between the contact surfaces of the two objects exists, and the force is called friction force. This phenomenon or characteristic between the contact surfaces is called "friction", and therefore, there is a great significance in detecting a fault in a large mechanized apparatus based on a friction vibration signal. The existing common method has the problems of difficult feature extraction, incomplete feature extraction and the like.
Through the above analysis, the problems and defects of the prior art are as follows: the existing common method has the problems of difficult feature extraction, incomplete feature extraction and the like. Causing difficulty in fault diagnosis in the operation of the large computerized equipment.
The difficulty in solving the above problems and defects is:
the problems of friction failure, such as difficult feature extraction, incomplete feature extraction and the like.
The significance of solving the problems and the defects is as follows:
the problem about friction fault detection can be solved well, and the friction fault detection precision is improved.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a large unit friction fault analysis method and system based on waveform and dimensionless learning.
The invention is realized in such a way that a large unit friction fault analysis method based on waveform and dimensionless learning comprises the following steps:
extracting a machine fault vibration signal by using a double probe, and preprocessing data;
step two, extracting friction fault characteristics;
thirdly, establishing a fault prediction model by using a machine learning method;
and step four, predicting whether the unknown label signal has a fault or not, and determining the fault type.
Further, in the step one, the process of preprocessing the machine fault vibration signal and the data is as follows:
1) installing two probe points, acquiring a vibration double-view signal of the large-scale sliding unit through the acquisition of the two probe points, wherein the data acquisition is 32/rms, namely, 32 points are sampled every time the bearing rotates, and 32 circles of data are acquired;
2) after the probe collects data, performing discrete Fourier transform in an aligned mode, wherein the window size 32 x 32 is 1024 points, and modifying a Fourier value after the transform; and setting an adaptive threshold according to the signal condition, reducing the signal storage amount and accelerating transmission.
Further, the discrete fourier transform formula in 2) is specifically as follows:
where N is 0, …, N-1, N denotes the data length.
Further, in the second step, the friction fault feature extraction process is as follows:
(1) carrying out wavelet packet 2-layer decomposition transformation on the fault signal, wherein the wavelet packet is to analyze the detail part of an input signal by utilizing multi-iteration wavelet transformation to obtain wavelet coefficients under different scales, and setting the scale coefficient of an HH layer of the signal to be zero;
(2) calculating dimensionless characteristic waviness index SfTaking the volatility index as one of the extracted features, the specific calculation formula is as follows:
whereinRepresents the root mean square value of the waveform data,represents the absolute average of the waveform data;
(3) calculating a dimensionless characteristic peak index, and taking the peak index as one of the extracted characteristics, wherein a specific calculation formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,the representation represents a root mean square value;
(4) calculating a dimensionless characteristic pulse index, and taking the pulse index as one of the extracted characteristics, wherein a specific calculation formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,represents the absolute average of the waveform data;
(5) calculating a non-dimensional feature kurtosis index to represent the height of the actual kurtosis relative to the normal kurtosis, wherein the kurtosis index reflects the impact feature in the vibration signal, and the non-dimensional feature kurtosis index is taken as one of the extracted features, and a specific calculation formula is as follows:
(6) Calculating a dimensionless characteristic margin index, which is generally used for detecting the abrasion condition of mechanical equipment; if the distortion index changes little, the ratio of the effective value to the average value is increased, which shows that the clearance is increased due to abrasion, so that the effective value of the energy index of vibration is increased faster than the average value, the margin index of the energy index of vibration is also increased, and the dimensionless feature margin index is taken as one of the extracted features, and the specific calculation formula is as follows:
(7) Calculating a dimensionless characteristic Teager energy operator, wherein the Teager energy operator is one of the extracted characteristics, and the specific calculation formula is as follows:
wherein t represents the data acquisition time,alpha t is an offset angle before and after the time t;
(8) calculating a standard deviation characterizing the degree of dispersion of the data and the variability exhibited by a single statistic over multiple samples; it can be understood that the former is the variability of the data itself, and the latter characterizes the variability of the sampling behavior, and the specific calculation formula is as follows:
(9) calculating the standard deviation of the average, wherein the standard deviation of the average is a standard for measuring the dispersion degree of data distribution and is used for measuring the degree of the data value deviating from the arithmetic average; the smaller the standard deviation, the less the values deviate from the mean and vice versa; the magnitude of the standard deviation can be measured by the multiplying power relationship between the standard deviation and the average value, and the specific calculation formula is as follows:
(10) calculating a sample circle mean (circle _ mean) of the sample, wherein the sample circle mean is taken as one of the extracted features, and the specific calculation formula is as follows:
wherein X is a sample, sin is a sine function, cos is a cosine function, arctan2 is a tangent function, and pi is a circumferential ratio;
Further, cca dimension reduction is performed by using the two view features extracted in the second step, the features of the two views after dimension reduction are spliced and used as input vectors, and a machine learning model is used for training.
Another object of the present invention is to provide a waveform and dimensionless learning-based mainframe friction fault analysis system for implementing the waveform and dimensionless learning-based mainframe friction fault analysis method, which includes:
the data acquisition module extracts a machine fault vibration signal by using the double probes and preprocesses data;
the characteristic extraction module is used for extracting the characteristics of the friction fault signal;
the prediction model building module is used for building a fault prediction model by utilizing a machine learning method;
and the fault prediction module predicts whether the unknown label signal has a fault or not and determines the type of the fault.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
extracting a machine fault vibration signal by using a double probe, and preprocessing data;
extracting the characteristics of the friction fault signal;
establishing a fault prediction model by using a machine learning method;
predicting whether the unknown tag signal has a fault or not, and determining the type of the fault.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
extracting a machine fault vibration signal by using a double probe, and preprocessing data;
extracting the characteristics of the friction fault signal;
establishing a fault prediction model by using a machine learning method;
predicting whether the unknown tag signal has a fault or not, and determining the type of the fault.
The invention also aims to provide a large mechanized device for implementing the large-unit friction fault analysis method based on waveform and dimensionless learning.
By combining all the technical schemes, the invention has the advantages and positive effects that:
in the process of diagnosing the friction fault of the large unit, the problem of difficult feature extraction can be effectively solved; meanwhile, effective features can be extracted, the problem of failure prediction is solved, and a new feature extraction method is provided. The invention obtains good results on the diagnosis problem of the friction fault of the large unit.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of a large-scale unit friction fault analysis method based on waveform and dimensionless learning according to an embodiment of the present invention.
FIG. 2 is a schematic structural diagram of a large-scale unit friction fault analysis system based on waveform and dimensionless learning according to an embodiment of the present invention;
in the figure: 1. a data acquisition module; 2. a feature extraction module; 3. a prediction model construction module; 4. and a failure prediction module.
Fig. 3 is a schematic diagram of a wavelet packet 2 layer decomposition transformation structure according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a large-unit friction fault analysis method and system based on waveform and dimensionless learning, and the invention is described in detail with reference to the attached drawings.
As shown in fig. 1, a large-unit friction fault analysis method based on waveform and dimensionless learning according to an embodiment of the present invention includes:
s101: and extracting a machine fault vibration signal by using the double probes, and preprocessing data.
S102: and (5) extracting the friction fault characteristics.
S103: and establishing a fault prediction model by using a machine learning method.
S104: predicting whether the unknown tag signal has a fault or not, and determining the type of the fault.
In S101 provided by the embodiment of the present invention, a process of preprocessing a machine fault vibration signal and data includes:
1) two probe points are installed, vibration double-view signals of the large-scale sliding unit are acquired through the two probe points, data acquisition is 32/rms, namely 32 points are sampled every time the bearing rotates one circle, and 32 circles of data are acquired.
2) After the probe collects data, performing discrete Fourier transform in an aligned mode, wherein the window size 32 x 32 is 1024 points, and modifying a Fourier value after the transform; and setting an adaptive threshold according to the signal condition, reducing the signal storage amount and accelerating transmission.
The discrete fourier transform formula is specifically as follows:
where N is 0, …, N-1, N denotes the data length.
In S102 provided by the embodiment of the present invention, the process of extracting the friction fault feature is as follows:
(1) the fault signal is processed by wavelet packet 2 layer decomposition transformation, and the wavelet packet is a detailed part of the input signal analyzed by multi-iteration wavelet transform, and its specific structure diagram is shown in fig. 2. Wavelet coefficients under different scales are obtained, and the scale coefficient of the HH layer of the signal is set to be zero.
(2) Calculating dimensionless characteristic waviness index SfWill waveThe index is taken as one of the extracted features, and the specific calculation formula is as follows:
whereinRepresents the root mean square value of the waveform data,representing the absolute average of the waveform data.
(3) Calculating a dimensionless characteristic peak index, and taking the peak index as one of the extracted characteristics, wherein a specific calculation formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,the representation represents the root mean square value.
(4) Calculating a dimensionless characteristic pulse index, and taking the pulse index as one of the extracted characteristics, wherein a specific calculation formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,representing the absolute average of the waveform data.
(5) Calculating a non-dimensional feature kurtosis index to represent the height of the actual kurtosis relative to the normal kurtosis, wherein the kurtosis index reflects the impact feature in the vibration signal, and the non-dimensional feature kurtosis index is taken as one of the extracted features, and a specific calculation formula is as follows:
(6) And calculating a dimensionless characteristic margin index, which is generally used for detecting the abrasion condition of the mechanical equipment. If the distortion index changes little, the ratio of the effective value to the average value is increased, which shows that the clearance is increased due to abrasion, so that the effective value of the energy index of vibration is increased faster than the average value, the margin index of the energy index of vibration is also increased, and the dimensionless feature margin index is taken as one of the extracted features, and the specific calculation formula is as follows:
(7) Calculating a dimensionless characteristic Teager energy operator, wherein the Teager energy operator is one of the extracted characteristics, and the specific calculation formula is as follows:
(8) The standard deviation, which characterizes the degree of dispersion of the data, is calculated, and characterizes the variability exhibited by individual statistics over multiple samples. It can be understood that the former is the variability of the data itself, and the latter characterizes the variability of the sampling behavior, and the specific calculation formula is as follows:
(9) the standard deviation of the mean, which is a measure of the degree of dispersion of the data distribution, is calculated as the degree to which the data values deviate from the arithmetic mean. The smaller the standard deviation, the less the values deviate from the mean and vice versa. The magnitude of the standard deviation can be measured by the multiplying power relationship between the standard deviation and the average value, and the specific calculation formula is as follows:
(10) calculating a sample circle mean (circle _ mean) of the sample, wherein the sample circle mean is taken as one of the extracted features, and the specific calculation formula is as follows:
where X is the sample, sin is the sine function, cos is the cosine function, arctan2 is the tangent function, and π is the circumferential ratio.
In the embodiment of the invention, cca dimension reduction is performed by using the two view features extracted in the step S102 in the step S103, the features of the two views after dimension reduction are spliced to be used as an input vector, and a machine learning model is used for training.
As shown in fig. 2, the large-unit friction fault analysis system based on waveform and dimensionless learning according to the embodiment of the present invention includes:
and the data acquisition module 1 is used for extracting a machine fault vibration signal by using the double probes and preprocessing data.
And the characteristic extraction module 2 is used for extracting the characteristics of the friction fault signal.
The prediction model construction module 3 builds a fault prediction model by using a machine learning method.
And the fault prediction module 4 predicts whether the unknown label signal has a fault or not and determines the type of the fault.
The technical scheme of the invention is further described by taking the sliding mechanical data of a large unit as an example.
The large unit provided by the invention is provided with two probes for data acquisition, wherein the data is acquired at 32 points every time the mechanical bearing rotates for one circle, and then the period is 32 revolutions. One set of data is 1024 waveform points, which translates to a waveform length of 1024. Extracting a frequency spectrum vector and other dimensionless vectors from the data, and splicing the frequency spectrum vector and the other dimensionless vectors to form the characteristics of a view; performing feature dimension reduction on a single view through cca, splicing the features subjected to dimension reduction, and then bringing the features into a machine learning model for training; extracting the characteristics of the fault signal of the unknown label, and obtaining a fault prediction result through a trained model.
In the description of the present invention, "a plurality" means two or more unless otherwise specified; the terms "upper", "lower", "left", "right", "inner", "outer", "front", "rear", "head", "tail", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A large unit friction fault analysis method based on waveform and dimensionless learning is characterized in that the large unit friction fault analysis method based on waveform and dimensionless learning comprises the following steps:
extracting a machine fault vibration signal by using a double probe, and preprocessing data;
extracting friction fault characteristics;
establishing a fault prediction model by using a machine learning method;
predicting whether the unknown tag signal has a fault or not, and determining the type of the fault.
2. The analysis method for large unit friction fault based on waveform and dimensionless learning of claim 1, wherein the pre-processing of the vibration signals and data of machine fault is as follows:
1) installing two probe points, acquiring a vibration double-view signal of the large-scale sliding unit through the acquisition of the two probe points, wherein the data acquisition is 32/rms, namely, 32 points are sampled every time the bearing rotates, and 32 circles of data are acquired;
2) after the probe collects data, performing discrete Fourier transform in an aligned mode, wherein the window size 32 x 32 is 1024 points, and modifying a Fourier value after the transform; and setting an adaptive threshold according to the signal condition, reducing the signal storage amount and accelerating transmission.
4. The analysis method for the large unit friction fault based on the waveform and the dimensionless learning of claim 1, wherein the process of extracting the friction fault features is as follows:
(1) carrying out wavelet packet 2-layer decomposition transformation on the fault signal, wherein the wavelet packet is to analyze the detail part of an input signal by utilizing multi-iteration wavelet transformation to obtain wavelet coefficients under different scales, and setting the scale coefficient of an HH layer of the signal to be zero;
(2) calculating dimensionless characteristic waviness index SfTaking the volatility index as one of the extracted features, the specific calculation formula is as follows:
whereinRepresents the root mean square value of the waveform data,represents the absolute average of the waveform data;
(3) calculating a dimensionless characteristic peak index, and taking the peak index as one of the extracted characteristics, wherein a specific calculation formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,the representation represents a root mean square value;
(4) calculating a dimensionless characteristic pulse index, and taking the pulse index as one of the extracted characteristics, wherein a specific calculation formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,represents the absolute average of the waveform data;
(5) calculating a non-dimensional feature kurtosis index to represent the height of the actual kurtosis relative to the normal kurtosis, wherein the kurtosis index reflects the impact feature in the vibration signal, and the non-dimensional feature kurtosis index is taken as one of the extracted features, and a specific calculation formula is as follows:
(6) Calculating a dimensionless characteristic margin index, which is generally used for detecting the abrasion condition of mechanical equipment; if the distortion index changes little, the ratio of the effective value to the average value is increased, which shows that the clearance is increased due to abrasion, so that the effective value of the energy index of vibration is increased faster than the average value, the margin index of the energy index of vibration is also increased, and the dimensionless feature margin index is taken as one of the extracted features, and the specific calculation formula is as follows:
(7) Calculating a dimensionless characteristic Teager energy operator, wherein the Teager energy operator is one of the extracted characteristics, and the specific calculation formula is as follows:
(8) calculating a standard deviation characterizing the degree of dispersion of the data and the variability exhibited by a single statistic over multiple samples; it can be understood that the former is the variability of the data itself, and the latter characterizes the variability of the sampling behavior, and the specific calculation formula is as follows:
(9) calculating the standard deviation of the average, wherein the standard deviation of the average is a standard for measuring the dispersion degree of data distribution and is used for measuring the degree of the data value deviating from the arithmetic average; the smaller the standard deviation, the less the values deviate from the mean and vice versa; the magnitude of the standard deviation can be measured by the multiplying power relationship between the standard deviation and the average value, and the specific calculation formula is as follows:
(10) calculating a sample circle mean (circle _ mean) of the sample, wherein the sample circle mean is taken as one of the extracted features, and the specific calculation formula is as follows:
wherein X is a sample, sin is a sine function, cos is a cosine function, arctan2 is a tangent function, and pi is a circumferential ratio;
5. The method for analyzing large unit friction fault based on waveform and dimensionless learning of claim 1, wherein the cca dimension reduction is performed by using the extracted features of the two views, and the features of the two views after dimension reduction are spliced to be used as input vectors and trained by using a machine learning model.
6. A waveform and dimensionless learning-based mainframe friction fault analysis system implementing the waveform and dimensionless learning-based mainframe friction fault analysis method according to any of claims 1-5, wherein the waveform and dimensionless learning-based mainframe friction fault analysis system comprises:
the data acquisition module extracts a machine fault vibration signal by using the double probes and preprocesses data;
the characteristic extraction module is used for extracting the characteristics of the friction fault signal;
the prediction model building module is used for building a fault prediction model by utilizing a machine learning method;
and the fault prediction module predicts whether the unknown label signal has a fault or not and determines the type of the fault.
7. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of:
extracting a machine fault vibration signal by using a double probe, and preprocessing data;
extracting the characteristics of the friction fault signal;
establishing a fault prediction model by using a machine learning method;
predicting whether the unknown tag signal has a fault or not, and determining the type of the fault.
8. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
extracting a machine fault vibration signal by using a double probe, and preprocessing data;
extracting the characteristics of the friction fault signal;
establishing a fault prediction model by using a machine learning method;
predicting whether the unknown tag signal has a fault or not, and determining the type of the fault.
9. A large mechanized apparatus for implementing a large unit friction fault analysis method based on waveform and dimensionless learning according to any of claims 1-5.
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