CN112270227A - Oil film whirl and friction concurrent fault analysis method and analysis system - Google Patents
Oil film whirl and friction concurrent fault analysis method and analysis system Download PDFInfo
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Abstract
The invention belongs to the technical field of oil film whirl and friction concurrent fault diagnosis, and discloses an oil film whirl and friction concurrent fault analysis method and an oil film whirl and friction concurrent fault analysis system, wherein a double probe is used for extracting a machine fault vibration signal; preprocessing the acquired data, and extracting nine dimensionless characteristics of the machine fault vibration signal; training by using the extracted feature vectors by using logistic regression classification; and predicting the unknown label vibration signal to obtain whether the signal has a fault and the type of the fault. The method can solve the problem of difficult feature extraction in the gear fault prediction process, has sufficient and comprehensive extracted features, can well predict the gear fault, and can effectively reduce the influence of noise on the result. The invention can find and diagnose oil film whirl fault and friction fault.
Description
Technical Field
The invention belongs to the technical field of oil film whirl and friction concurrent fault diagnosis, particularly relates to an oil film whirl and friction concurrent fault analysis method and an oil film whirl and friction concurrent fault analysis system, and particularly relates to an oil film whirl and friction concurrent fault analysis method based on nine dimensionless feature extraction and logistic regression classification.
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.
Many researches on oil film whirl and friction concurrent fault detection exist, such as wavelet transformation, empirical mode decomposition, minimum entropy deconvolution and other methods.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) in the prior art, only a limited number of local pulses can be extracted in the method for detecting the oil film whirl and friction concurrent faults, the method is still an iterative non-optimal solution, and the method has no immunity in a strong noise environment and is easy to have a misdiagnosis phenomenon.
(2) The existing common method has the problems of difficult feature extraction, incomplete feature extraction and the like.
The difficulty in solving the above problems and defects is:
and extracting useful features, and performing oil film whirl and friction concurrent fault analysis.
The significance of solving the problems and the defects is as follows:
aiming at the defects, the invention provides an oil film whirl and friction concurrent fault analysis method based on nine dimensionless feature extraction and logistic regression classification.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an oil film whirl and friction concurrent fault analysis method and an oil film whirl and friction concurrent fault analysis system.
The invention is realized in such a way that an oil film whirl and friction concurrent fault analysis method can diagnose oil film whirl and friction faults in a concurrent way, and the method comprises the following steps:
step 1, extracting a machine fault vibration signal by using a double probe;
step 2, preprocessing the data acquired in the step 1, and extracting nine dimensionless characteristics of the machine fault vibration signal;
step 3, training by using the characteristic vectors extracted in the step 2 and using logistic regression classification;
and 4, predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
Further, the step 2 is to extract the characteristics of the collected gear fault vibration signal, the gear fault vibration signal is often represented by a nonlinear non-stationary characteristic, and the early fault vibration signal often contains a strong background noise, which is not beneficial to extracting fault characteristics, and the nine dimensionless characteristic extractions performed on the machine fault vibration signal can effectively solve the problem, specifically including the following steps:
and 2.1, setting the size of a window and the sliding step length according to the data volume by a sliding window method, wherein each window is a sample, and extracting the characteristic value of each window.
Step 2.2, calculating dimensionless characteristic volatility index SfTaking the volatility index as one of the extracted features, the specific calculation formula is as follows:
wherein
Represents the root mean square value of the waveform data,
represents the absolute average of the waveform data;
step 2.3, 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;
step 2.4, calculating dimensionless characteristic pulse indexes, and taking the pulse indexes 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;
step 2.5, calculating a non-dimensional characteristic kurtosis index to show the height of the actual kurtosis relative to the normal kurtosis, wherein the kurtosis index reflects the impact characteristic in the vibration signal, and the non-dimensional characteristic kurtosis index is taken as one of the extracted characteristics, and a specific calculation formula is as follows:
wherein
And 2.6, 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:
wherein
Step 2.7, 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,
αtis the offset angle before and after time t.
And 2.8, calculating standard deviation, wherein the standard deviation is used for representing the discrete degree of the data, and is used for representing the variability of a single statistic in a plurality of 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:
step 2.9, calculating the standard deviation of the mean, which is a standard for measuring the degree of dispersion of the data distribution, and measuring the degree of deviation of the data values 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:
step 2.10, calculating a sample circle mean (circle _ mean) of the sample, and taking the sample circle mean as one of the extracted features, wherein a 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. Wherein
S＝∑i sin(angle)C＝∑i cos(angle)，res＝arctan2(S,C)。
Further, in the step 3, a logistic regression model is used for training, the extracted features are spliced by using the features extracted in the step 2, and the extracted features are brought into the logistic regression model for training to obtain a training model.
Another object of the present invention is to provide an oil film whirl and friction concurrent fault analysis system, including:
the double probes are used for extracting machine fault vibration signals;
the nine dimensionless feature extraction modules are used for preprocessing the acquired data and extracting nine dimensionless features of the machine fault vibration signal;
the training module is used for utilizing the extracted feature vectors to train by using logistic regression classification;
and the prediction module is used for predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
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:
step 1, extracting a machine fault vibration signal by using a double probe;
step 2, preprocessing the data acquired in the step 1, and extracting nine dimensionless characteristics of the machine fault vibration signal;
step 3, training by using the characteristic vectors extracted in the step 2 and using logistic regression classification;
and 4, predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
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:
step 1, extracting a machine fault vibration signal by using a double probe;
step 2, preprocessing the data acquired in the step 1, and extracting nine dimensionless characteristics of the machine fault vibration signal;
step 3, training by using the characteristic vectors extracted in the step 2 and using logistic regression classification;
and 4, predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
The invention also aims to provide large mechanized equipment for implementing the oil film whirl and friction concurrent fault analysis method.
The invention also aims to provide an information data processing terminal, which is used for realizing the oil film whirl and friction concurrent fault analysis method.
By combining all the technical schemes, the invention has the advantages and positive effects that:
the invention uses double probes to extract the machine fault vibration signal; nine dimensionless feature extractions are carried out on the machine fault vibration signal; training by logistic regression classification; predicting whether the unknown tag signal has a fault or not, and determining the type of the fault. The invention relates to a fault analysis method for oil film whirl and friction concurrence, which can effectively extract fault characteristic information.
The method can solve the problem of difficult feature extraction in the gear fault prediction process, has sufficient and comprehensive extracted features, can well predict the gear fault, and can effectively reduce the influence of noise on the result.
Oil film whirl faults and friction faults can be found and diagnosed.
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 an oil whirl and friction concurrent fault analysis method provided by an embodiment of the present invention.
Fig. 2 is a schematic diagram of an oil film whirl and friction concurrent fault analysis method provided by an embodiment of the 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 an oil film whirl and friction concurrent fault analysis method and an analysis system, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides an oil whirl and friction concurrent fault analysis method, which can diagnose oil whirl and friction faults concurrently, and the method includes the following steps:
and S101, extracting a machine fault vibration signal by using a double probe.
And S102, preprocessing the data acquired in the step S101, and extracting nine dimensionless features of the machine fault vibration signal.
And S103, training by using the characteristic vectors extracted in the step S102 and using logistic regression classification.
S104, predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
The invention provides an oil film whirl and friction concurrent fault analysis system, which comprises:
the double probes are used for extracting machine fault vibration signals;
the nine dimensionless feature extraction modules are used for preprocessing the acquired data and extracting nine dimensionless features of the machine fault vibration signal;
the training module is used for utilizing the extracted feature vectors to train by using logistic regression classification;
and the prediction module is used for predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
The invention is further described with reference to specific examples.
Examples
As shown in fig. 2, the invention provides an oil whirl and friction concurrent fault analysis method based on nine dimensionless feature extractions and logistic regression classification, which comprises the following steps:
step 1, extracting a machine fault vibration signal by using a double probe;
step 2, preprocessing the data acquired in the step 1, and extracting nine dimensionless characteristics of the machine fault vibration signal;
and 2.1, setting the size of a window and the sliding step length according to the data volume by a sliding window method, wherein each window is a sample, and extracting the characteristic value of each window.
Step 2.2, calculating dimensionless characteristic volatility index SfTaking the volatility index as one of the extracted features, the specific calculation formula is as follows:
wherein
Represents the root mean square value of the waveform data,
represents the absolute average of the waveform data;
step 2.3, 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;
step 2.4, calculating dimensionless characteristic pulse indexes, and taking the pulse indexes 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;
step 2.5, calculating a non-dimensional characteristic kurtosis index to show the height of the actual kurtosis relative to the normal kurtosis, wherein the kurtosis index reflects the impact characteristic in the vibration signal, and the non-dimensional characteristic kurtosis index is taken as one of the extracted characteristics, and a specific calculation formula is as follows:
wherein
And 2.6, 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:
wherein
Step 2.7, 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,
αtis the offset angle before and after time t.
And 2.8, calculating standard deviation, wherein the standard deviation is used for representing the discrete degree of the data, and is used for representing the variability of a single statistic in a plurality of 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:
step 2.9, calculating the standard deviation of the mean, which is a standard for measuring the degree of dispersion of the data distribution, and measuring the degree of deviation of the data values 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:
step 2.10, calculating a sample circle mean (circle _ mean) of the sample, and taking the sample circle mean as one of the extracted features, wherein a 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. Wherein
S＝∑i sin(angle)C＝∑i cos(angle)，res＝arctan2(S,C)。
And 3, training by using a logistic regression model, splicing the extracted features by using the features extracted in the step 2, and carrying out training by bringing the extracted features into the logistic regression model to obtain a training model.
The invention relates to an oil film whirl and friction concurrent fault analysis method based on nine dimensionless feature extraction and logistic regression classification, which takes oil film whirl and friction concurrent fault analysis methods of certain petrochemical equipment gear vibration mechanical data as an example, and extracts a feature value of each window by a sliding window method, namely the window size is 2048, the sliding step length is 512, each window is a sample, the feature values of the windows are spliced after the features are extracted, a logistic regression model is brought into the splicing for training, when whether faults exist in unknown labels or not is predicted, the features of signals of the unknown labels are extracted, and then fault prediction results are obtained through the trained model. The invention carries out relevant tests to obtain test results, as shown in the following tables 1 and 2.
TABLE 1 number of data strips
TABLE 2 evaluation of the results
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.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
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 (10)
1. An oil film whirl and friction concurrent fault analysis method is characterized by comprising the following steps:
and preprocessing the acquired data, and extracting nine dimensionless characteristics of the machine fault vibration signal.
2. The oil film whirl and friction concurrent fault analysis method according to claim 1, wherein the method for nine dimensionless feature extractions of the machine fault vibration signal comprises:
step 1, setting window size and sliding step length according to data quantity by a sliding window method, wherein each window is a sample, and extracting a characteristic value of each window;
step 2, calculating dimensionless characteristic volatility index SfTaking the volatility index as one of the extracted features, the specific calculation formula is as follows:
wherein
Represents the root mean square value of the waveform data,
represents the absolute average of the waveform data;
step 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;
step 4, calculating dimensionless characteristic pulse indexes, and taking the pulse indexes 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;
step 5, calculating a non-dimensional characteristic kurtosis index to show the height of the actual kurtosis relative to the normal kurtosis, wherein the kurtosis index reflects the impact characteristic in the vibration signal, and the non-dimensional characteristic kurtosis index is taken as one of the extracted characteristics, and the specific calculation formula is as follows:
wherein
Step 6, calculating the dimensionless characteristic margin index, wherein the specific calculation formula is as follows:
wherein
Step 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,
αtis the offset angle before and after time t.
And 8, calculating the standard deviation, wherein the specific calculation formula is as follows:
and 9, calculating the standard deviation of the average value, wherein the specific calculation formula is as follows:
step 10, calculating a sample circle average value of the sample, and taking the sample circle average value as one of the extracted features, wherein a 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; wherein
S＝∑isin(angle)C＝∑icos(angle)，res＝arctan2(S,C)。
3. The oil film whirl and friction concurrent fault analysis method as claimed in claim 1, wherein the collected data is preprocessed, and before nine dimensionless features of the machine fault vibration signal are extracted, the following steps are carried out: and extracting a machine fault vibration signal by using the double probes.
4. The oil film whirl and friction concurrent fault analysis method as claimed in claim 1, wherein the collected data is preprocessed, and after nine dimensionless features of the machine fault vibration signal are extracted, the following steps are carried out:
training by using the extracted feature vectors by using logistic regression classification;
and predicting the unknown label vibration signal to obtain whether the signal has a fault and the type of the fault.
5. The oil film whirl and friction concurrent fault analysis method of claim 4, wherein the training is performed by using a logistic regression model, the extracted features are spliced by using the extracted features, and the extracted features are brought into the logistic regression model to be trained to obtain the training model.
6. An oil whirl and friction concurrent fault analysis system, comprising:
the double probes are used for extracting machine fault vibration signals;
the nine dimensionless feature extraction modules are used for preprocessing the acquired data and extracting nine dimensionless features of the machine fault vibration signal;
the training module is used for utilizing the extracted feature vectors to train by using logistic regression classification;
and the prediction module is used for predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
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:
step 1, extracting a machine fault vibration signal by using a double probe;
step 2, preprocessing the data acquired in the step 1, and extracting nine dimensionless characteristics of the machine fault vibration signal;
step 3, training by using the characteristic vectors extracted in the step 2 and using logistic regression classification;
and 4, predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
8. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
step 1, extracting a machine fault vibration signal by using a double probe;
step 2, preprocessing the data acquired in the step 1, and extracting nine dimensionless characteristics of the machine fault vibration signal;
step 3, training by using the characteristic vectors extracted in the step 2 and using logistic regression classification;
and 4, predicting the unknown label vibration signal to obtain whether the signal has faults or not and the type of the faults.
9. A large mechanized apparatus for implementing the oil film whirl and friction concurrent fault analysis method according to any one of claims 1 to 5.
10. An information data processing terminal, characterized in that the information data processing terminal is used for realizing the oil film whirl and friction concurrent fault analysis method of any one of claims 1 to 5.
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