CN116304848B - Rolling bearing fault diagnosis system and method - Google Patents
Rolling bearing fault diagnosis system and method Download PDFInfo
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Abstract
The invention provides a rolling bearing fault diagnosis system and a rolling bearing fault diagnosis method, which are used for processing running parameter data of a rolling bearing to obtain an envelope spectrum, setting a fault window for theoretical fault characteristic frequency of a preset element, adjusting and calculating characteristic frequency amplitude of the envelope spectrum in the fault window to obtain fault characteristic frequency amplitude, and carrying out fault detection on the preset element, so that the fault occurrence position of the rolling bearing can be timely and accurately determined, and staff can take measures in time to avoid subsequent damage.
Description
Technical Field
The invention relates to the field of mechanical equipment, in particular to a rolling bearing fault diagnosis system and a rolling bearing fault diagnosis method.
Background
With the progress of scientific technology, the modern industrial productivity is greatly improved, mechanical equipment tends to be larger and more complex, and the rolling bearing is widely applied to the mechanical equipment in industrial production and plays a role in bearing load and transmitting load.
In the running process of mechanical equipment, the rolling shaft bears the influence of factors such as self process limit, working environment, overload running and the like, the risk of faults is gradually increased along with the running of the mechanical equipment, if the rolling bearing breaks down in the running process, a series of chain reactions are easily initiated, the mechanical equipment is additionally damaged, and even the personal safety of staff is threatened, so that the running condition of the rolling bearing needs to be monitored in real time and faults are timely found, and the staff can take corresponding measures to avoid subsequent damage.
In the prior art, a time domain analysis method or a frequency domain analysis method is generally adopted to monitor faults of the rolling bearing, and because the information provided by the time domain analysis is very limited, the time domain analysis method can only be used for judging whether the rolling bearing has faults or not and the severity of the faults, and the fault occurrence position is difficult to further determine. However, in the existing frequency domain analysis method, due to interference from external environment and coupling effect when different elements fail, a certain deviation exists between the failure characteristic frequency obtained through theoretical calculation and the actual failure characteristic frequency, so that a large error exists in failure judgment.
Disclosure of Invention
The embodiment of the invention provides a rolling bearing fault diagnosis system and a rolling bearing fault diagnosis method, which can be used for carrying out fault diagnosis on a preset element by converting running parameter data of the rolling bearing into an envelope spectrum, adjusting and calculating a characteristic frequency amplitude near a theoretical fault characteristic frequency of the preset element to obtain the fault characteristic frequency amplitude, can timely and accurately determine the fault occurrence position of the rolling bearing, and is beneficial to staff to timely take corresponding measures to avoid subsequent damage when a specific element breaks down.
To achieve the above object, an embodiment of the present invention provides a rolling bearing failure diagnosis system including:
the sensor is used for collecting the operation parameter data of the rolling bearing;
a controller for:
processing the operation parameter data acquired from the sensor to obtain an envelope spectrum;
setting a fault window for the envelope spectrum by taking the theoretical fault characteristic frequency of a preset element as the center;
scaling the characteristic frequency amplitude of the envelope spectrum in the fault window to expand the characteristic frequency amplitude with the value larger than the mean value by two times and reduce the characteristic frequency amplitude with the value smaller than the mean value by two times; the mean value is the mean value of the characteristic frequency amplitude values of the envelope spectrum in the fault window;
summing and averaging the characteristic frequency amplitude values subjected to scaling treatment to obtain a fault characteristic frequency amplitude value of the preset element;
and carrying out fault diagnosis on the preset element according to the fault characteristic frequency amplitude.
As an improvement of the above solution, the controller is further configured to:
and carrying out fault monitoring of the whole life cycle of the preset element to obtain a fault characteristic frequency amplitude of the whole life cycle, and drawing a graph according to the fault characteristic frequency amplitude of the whole life cycle.
As an improvement of the scheme, the sensor is a vibration sensor, a temperature sensor, an acceleration sensor or a displacement sensor;
the theoretical failure characteristic frequency includes at least one of an outer ring failure characteristic frequency, an inner ring failure characteristic frequency, a rolling body failure characteristic frequency, and a cage failure characteristic frequency.
As an improvement of the above-mentioned aspect, the rolling bearing includes an outer ring, an inner ring, rolling bodies, and a cage, and the controller is further configured to:
when the preset element is an outer ring, the theoretical fault characteristic frequency is the outer ring fault characteristic frequency, and the theoretical fault characteristic frequency is calculated by the following formula:
；
when the preset element is an inner ring, the theoretical fault characteristic frequency is the inner ring fault characteristic frequency, and the theoretical fault characteristic frequency is calculated by the following formula:
；
when the preset element is a rolling body, the theoretical failure characteristic frequency is the rolling body failure characteristic frequency, and the theoretical failure characteristic frequency is calculated by the following formula:
；
when the preset element is a retainer, the theoretical failure characteristic frequency is the retainer failure characteristic frequency, and the theoretical failure characteristic frequency is calculated by the following formula:
；
wherein ,representing the characteristic frequency of theoretical faults, +.>Indicates the number of rolling elements +.>Represents the pitch diameter of the rolling bearing->Indicating the diameter of the rolling element->Represents the contact angle of a rolling element rolling bearing, +.>Indicating the rolling bearing inner ring rotation frequency.
As an improvement of the above solution, the processing the operation parameter data acquired from the sensor to obtain an envelope spectrum includes:
performing time domain analysis on the operation parameter data acquired from the sensor to obtain a preliminary diagnosis result of the rolling bearing;
and when the preliminary diagnosis result is abnormal rolling bearing, converting the operation parameter data into an envelope spectrum.
As an improvement of the above-described aspect, the converting the operating parameter data into an envelope spectrum when the preliminary diagnosis result is a rolling bearing abnormality includes:
when the preliminary diagnosis result is that the rolling bearing is abnormal, carrying out band-pass filtering on the operation parameter data;
performing Hilbert transform on the filtered operation parameter data to obtain an analysis signal;
performing modulo on the analysis signal to obtain an envelope signal;
and carrying out Fourier transform on the envelope signal to obtain an envelope spectrum.
As an improvement of the above solution, the performing fault diagnosis on the preset element according to the fault characteristic frequency amplitude includes:
comparing the fault characteristic frequency amplitude with a preset alarm threshold value or comparing the fault characteristic frequency amplitude at the current moment with a plurality of latest historical fault characteristic frequency amplitudes to obtain a comparison result;
and determining the fault level of the preset element according to the comparison result.
In order to achieve the above object, an embodiment of the present invention further provides a method for diagnosing a rolling bearing fault, including:
processing the acquired running parameter data of the rolling bearing to obtain an envelope spectrum;
setting a fault window for the envelope spectrum by taking the theoretical fault characteristic frequency of a preset element as the center;
scaling the characteristic frequency amplitude of the envelope spectrum in the fault window to expand the characteristic frequency amplitude with the value larger than the mean value by two times and reduce the characteristic frequency amplitude with the value smaller than the mean value by two times; the mean value is the mean value of the characteristic frequency amplitude values of the envelope spectrum in the fault window;
summing and averaging the characteristic frequency amplitude values subjected to scaling treatment to obtain a fault characteristic frequency amplitude value of the preset element;
and carrying out fault diagnosis on the preset element according to the fault characteristic frequency amplitude.
As an improvement of the above scheme, the method further comprises:
and carrying out fault monitoring of the whole life cycle of the preset element to obtain a fault characteristic frequency amplitude of the whole life cycle, and drawing a graph according to the fault characteristic frequency amplitude of the whole life cycle.
As an improvement of the above-described aspect, the theoretical failure characteristic frequency includes at least one of an outer ring failure characteristic frequency, an inner ring failure characteristic frequency, a rolling element failure characteristic frequency, and a cage failure characteristic frequency.
Compared with the prior art, the rolling bearing fault diagnosis system and the rolling bearing fault diagnosis method provided by the embodiment of the invention have the advantages that firstly, the operating parameter data acquired from the sensor is processed to obtain an envelope spectrum; setting a fault window for the envelope spectrum by taking the theoretical fault characteristic frequency of a preset element as a center; then, scaling the characteristic frequency amplitude of the envelope spectrum in the fault window to enable the characteristic frequency amplitude with the value larger than the mean value to be enlarged twice and the characteristic frequency amplitude with the value smaller than the mean value to be reduced twice; the mean value is the mean value of the characteristic frequency amplitude values of the envelope spectrum in the fault window; then, summing and averaging the characteristic frequency amplitude values subjected to scaling treatment to obtain a fault characteristic frequency amplitude value of the preset element; and finally, carrying out fault diagnosis on the preset element according to the fault characteristic frequency amplitude. According to the embodiment of the invention, the running parameter data of the rolling bearing is converted into the envelope spectrum, the characteristic frequency amplitude near the theoretical fault characteristic frequency of the preset element is adjusted, and the fault characteristic frequency amplitude is obtained through calculation, so that the fault diagnosis of the preset element can be carried out, the fault occurrence position of the rolling bearing can be timely and accurately determined, and the situation that a worker takes corresponding measures in time to avoid subsequent damage when a specific element breaks down is facilitated.
Drawings
FIG. 1 is a first workflow diagram of a controller provided by an embodiment of the present invention;
FIG. 2 is a second workflow diagram of a controller provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a horizontal vibration signal time domain of an outer ring fault condition provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of a horizontal vibration signal time domain for a cage failure condition provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a horizontal vibration signal time domain for an inner ring failure condition provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of an envelope spectrum of an outer ring fault condition provided by an embodiment of the present invention;
FIG. 7 is a schematic illustration of envelope spectrum analysis provided by an embodiment of the present invention;
fig. 8 is a schematic diagram of envelope spectrum comparison before and after a scaling process according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a rolling bearing fault diagnosis system, which comprises: a sensor and a controller. The sensor is used for collecting the operation parameter data of the rolling bearing; referring to fig. 1, fig. 1 is a first workflow diagram of a controller according to an embodiment of the present invention, where the controller is configured to execute steps S1 to S5:
s1, processing the operation parameter data acquired from the sensor to obtain an envelope spectrum;
s2, setting a fault window for the envelope spectrum by taking the theoretical fault characteristic frequency of a preset element as a center;
s3, scaling the characteristic frequency amplitude of the envelope spectrum in the fault window to enable the characteristic frequency amplitude with the value larger than the mean value to be enlarged twice and the characteristic frequency amplitude with the value smaller than the mean value to be reduced twice; the mean value is the mean value of the characteristic frequency amplitude values of the envelope spectrum in the fault window;
s4, summing and averaging the characteristic frequency amplitude values subjected to scaling treatment to obtain the fault characteristic frequency amplitude value of the preset element;
s5, performing fault diagnosis on the preset element according to the fault characteristic frequency amplitude.
It is noted that a rolling bearing is a precise mechanical element that changes sliding friction between an operating shaft and a shaft seat into rolling friction, thereby reducing friction loss. The rolling bearing generally consists of four parts, namely an inner ring, an outer ring, rolling bodies and a retainer, wherein the inner ring is matched with the shaft and rotates together with the shaft; the outer ring is matched with the bearing seat to play a supporting role; the rolling bodies are uniformly distributed between the inner ring and the outer ring by means of the retainer, and the shape, the size and the number of the rolling bodies directly influence the service performance and the service life of the rolling bearing; the retainer can uniformly distribute the rolling bodies and guide the rolling bodies to rotate for lubrication. The cause of failure (failure) of the rolling bearing can be roughly classified into internal factors such as its own process design, quality, external factors such as working environment and overload operation, etc., and can be summarized as fatigue pitting, plastic deformation, abrasive wear, adhesive wear, etc.
The sensor is an important means for acquiring the state of the rolling bearing, and various operation parameter data of the equipment are acquired in real time through the sensor arranged on the equipment, and common sensors include a vibration sensor, a temperature sensor, an acceleration sensor, a displacement sensor and the like. The rolling bearing fault diagnosis system can be divided into two types, namely offline type and online type. Most of off-line type portable instruments are mainly used for monitoring and simply diagnosing the state of the rolling bearing by equipment maintenance and management personnel periodically or irregularly. The online rolling bearing monitoring system is mainly used for providing continuous state monitoring functions for rolling bearings in important occasions, and generally has the functions of real-time state early warning, historical data query, precise diagnosis and the like.
For example, in the case of a rolling bearing, when a localized defect is encountered at the contact of the rolling element with the raceway, an impact signal is generated, the defect occurs on a different element, and the frequency at which the contact point passes the defect is different, which is called the characteristic frequency. In general, when different elements in the rolling bearing fail, the theoretical failure characteristic frequency can be calculated by a related formula. However, due to the complex operating conditions and external disturbances, the characteristic frequency of the fault is only a theoretical value, and there is usually a certain error compared with the actual characteristic frequency of the fault. If the bearing fault type is judged by directly utilizing the theoretical fault characteristic frequency, an erroneous conclusion is likely to occur. In order to eliminate related errors and improve fault diagnosis capability, the embodiment of the invention provides the following processing method:
processing the operation parameter data acquired by the sensor to obtain an envelope spectrum; selecting a corresponding theoretical fault characteristic frequency according to a preset element, taking the theoretical fault characteristic frequency as a window center, setting a fault characteristic frequency range window (fault window) of +/-5% in an envelope spectrum corresponding to each moment, carrying out summation and averaging on characteristic frequency amplitude values in the fault window, comparing an obtained average value with the characteristic frequency amplitude values in the fault window, expanding the characteristic frequency amplitude value which is larger than the average value by two times, shrinking the characteristic frequency amplitude value which is smaller than the average value by two times, and carrying out summation and averaging on the characteristic frequency amplitude values which are scaled in the fault window to obtain a new fault characteristic frequency amplitude value; and finally, carrying out fault diagnosis on the preset element according to the calculated fault characteristic frequency amplitude. In this embodiment, according to the result of comparing the characteristic frequency amplitude near the selected theoretical fault characteristic frequency with the average value thereof, the nearby amplitude is scaled, so that the part with larger influence on the fault characteristic in the selected range is amplified, and the part with smaller influence on the fault characteristic in the selected range is reduced, so that the new fault characteristic frequency amplitude index constructed through subsequent summation and average value extraction processing effectively retains the useful information representing the fault characteristic, can effectively avoid the interference of external or accidental impact, eliminates the error possibly caused by judging the bearing fault type by directly utilizing the theoretical fault characteristic frequency, and can effectively judge the fault part.
In one embodiment, the controller is further configured to:
and carrying out fault monitoring of the whole life cycle of the preset element to obtain a fault characteristic frequency amplitude of the whole life cycle, and drawing a graph according to the fault characteristic frequency amplitude of the whole life cycle.
Specifically, the rolling bearing fault diagnosis method described in the above embodiment is used to perform spectrum analysis of the whole life cycle of the rolling bearing, so as to obtain the amplitude change of the fault characteristic frequency under the working conditions of different fault characteristics of the whole life cycle. In this embodiment, the frequency domain analysis of a single time originally is used for fault monitoring of the whole life cycle in the mode of calculating the amplitude of the fault characteristic frequency by using the above embodiment, and the fault severity of the fault changing with time can be intuitively reflected in the whole life cycle.
Further, the fault signature frequency magnitudes obtained from fault monitoring of different components over the full life cycle are compared to determine an early fault type. This way, a change in early minor faults, as well as a distinction between different fault types, can be effectively observed in a visual way.
In one embodiment, the sensor is a vibration sensor, a temperature sensor, an acceleration sensor, or a displacement sensor;
the theoretical failure characteristic frequency includes at least one of an outer ring failure characteristic frequency, an inner ring failure characteristic frequency, a rolling body failure characteristic frequency, and a cage failure characteristic frequency.
In one embodiment, the rolling bearing comprises an outer ring, an inner ring, rolling bodies and a cage, the controller further being configured to:
when the preset element is an outer ring, the theoretical fault characteristic frequency is the outer ring fault characteristic frequency, and the theoretical fault characteristic frequency is calculated by the following formula:
；
when the preset element is an inner ring, the theoretical fault characteristic frequency is the inner ring fault characteristic frequency, and the theoretical fault characteristic frequency is calculated by the following formula:
；
when the preset element is a rolling body, the theoretical failure characteristic frequency is the rolling body failure characteristic frequency, and the theoretical failure characteristic frequency is calculated by the following formula:
；
when the preset element is a retainer, the theoretical failure characteristic frequency is the retainer failure characteristic frequency, and the theoretical failure characteristic frequency is calculated by the following formula:
；
wherein ,representing the characteristic frequency of theoretical faults, +.>Indicates the number of rolling elements +.>Represents the pitch diameter of the rolling bearing->Indicating the diameter of the rolling element->Represents the contact angle of a rolling element rolling bearing, +.>Indicating the rolling bearing inner ring rotation frequency.
It can be understood that when different elements of the rolling bearing fail, the corresponding theoretical failure characteristic frequencies are different, and the rolling bearing can be obtained through calculation according to a theoretical calculation formula.
In one embodiment, the processing the operating parameter data acquired from the sensor to obtain an envelope spectrum includes:
performing time domain analysis on the operation parameter data acquired from the sensor to obtain a preliminary diagnosis result of the rolling bearing;
and when the preliminary diagnosis result is abnormal rolling bearing, converting the operation parameter data into an envelope spectrum.
Illustratively, a time domain graph is obtained according to the operation parameter data preprocessing stage, and a time period with a large amplitude change (such as an amplitude change rate greater than a preset threshold value) is selected for frequency domain analysis, so that the operation parameter data is converted into an envelope spectrum. Compared with the mode of carrying out frequency domain analysis on all data in all time periods, the mode of carrying out the frequency domain analysis on all data in all time periods is carried out, and then partial data is selected for carrying out the frequency domain analysis, so that the data processing amount can be effectively reduced, and the data processing pressure of a controller is reduced.
It should be noted that, the time domain eigenvalue is an important index for measuring signal characteristics, and the time domain eigenvalue is generally divided into a dimensionality parameter and a dimensionless parameter. The feature values with dimensions often have visual physical meanings, and are the most commonly used feature indexes. Common dimensional eigenvalues include peak-to-peak, mean, variance, standard deviation, root mean square value, and the like. Although the dimension index is sensitive to signal characteristics, the dimension index also changes due to the change of working conditions such as load, is extremely easily influenced by environmental interference, and has the defect of unstable performance. In contrast, dimensionless indicators can exclude the influence of these disturbance factors, and are thus widely used in the field of feature extraction. The peak value factor, the pulse factor, the margin factor, the kurtosis factor, the waveform factor, the kurtosis factor, the skewness factor and the like are common. In practical application, the time domain characteristic value can be selected according to the requirement to perform time domain analysis on the data.
In one embodiment, said converting said operating parameter data into an envelope spectrum when said preliminary diagnosis is that the rolling bearing is abnormal, comprises:
when the preliminary diagnosis result is that the rolling bearing is abnormal, carrying out band-pass filtering on the operation parameter data;
performing Hilbert transform on the filtered operation parameter data to obtain an analysis signal;
performing modulo on the analysis signal to obtain an envelope signal;
and carrying out Fourier transform on the envelope signal to obtain an envelope spectrum.
Specifically, the envelope spectrum method is a demodulation method for signal analysis, and is commonly used for analyzing vibration and pulse signals of mechanical products, particularly in the aspect of bearings, the signals are modulated with natural vibration, frequency components of high-frequency damping vibration are removed through an envelope detector, and a low-frequency envelope signal only containing fault characteristic information is obtained for fault detection and diagnosis.
The basic steps of envelope spectrum analysis are as follows:
step one: the original signal (operating parameter data) is first bandpass filtered to limit the frequency range of the signal to a narrower bandwidth, which reduces the effects of high frequency noise while preserving the main frequency content of the signal.
Step two: and performing Hilbert transformation on the filtered signal to obtain an analysis signal, wherein the real part is an original signal, the imaginary part is a Hilbert transformation pair, and the envelope signal can be obtained by performing modulo on the obtained analysis signal. The formula used for the hilbert transform is as follows:
wherein: x (t) is the original signal, x is the convolution,hilbert transform of the original signal, z (t) is the resolved signal, +.>。
And thirdly, carrying out Fourier transformation on the extracted envelope signal to obtain the spectrum amplitude (envelope spectrum) of the signal.
The envelope spectrum reflects the energy distribution condition and the frequency characteristic of the signal, and compared with the amplitude spectrum and the power spectrum, the envelope spectrum has good low-frequency vibration interference resistance and high signal to noise ratio, can highlight the fault characteristic frequency, is more beneficial to judging the fault type of the rolling bearing and positioning the specific position of the bearing fault. The basic principle of the envelope spectrum analysis is that the high-frequency oscillation component and noise in the original signal are removed by utilizing the signal filtering and Hilbert transform technology, envelope information in the signal is extracted, and then the frequency spectrum amplitude of the envelope signal is calculated through Fourier transform, so that the frequency characteristic and the variation trend of the signal are obtained.
In one embodiment, the performing fault diagnosis on the preset element according to the fault characteristic frequency amplitude includes:
comparing the fault characteristic frequency amplitude with a preset alarm threshold value or comparing the fault characteristic frequency amplitude at the current moment with a plurality of latest historical fault characteristic frequency amplitudes to obtain a comparison result;
and determining the fault level of the preset element according to the comparison result.
The method includes the steps that three alarm thresholds are set, wherein the three alarm thresholds comprise a light alarm threshold, a moderate alarm threshold and a heavy alarm threshold, specific data corresponding to the alarm thresholds are gradually increased, the fault characteristic frequency amplitude is compared with the alarm thresholds, when the fault characteristic frequency amplitude is smaller than the light alarm threshold, the fact that an element corresponding to the fault characteristic frequency amplitude does not have faults is indicated, when the fault characteristic frequency amplitude is larger than or equal to the light alarm threshold and smaller than the moderate alarm threshold, the fact that the element corresponding to the fault characteristic frequency amplitude has slight faults is indicated, and the like, so that the specific element with faults and the severity of the faults can be determined. In addition, the amplitude of the fault characteristic frequency at the current moment can be compared with the amplitude of a plurality of latest historical fault characteristic frequencies, the current amplitude change rate is calculated, and when the current amplitude change rate is larger than a set value, the element is indicated to have faults, and the severity of the faults is in direct proportion to the amplitude change rate.
It should be noted that, the workflow of the controller according to the embodiment of the present invention may also be shown in fig. 2.
For a clearer and more visual explanation of the workflow of the controller according to the embodiments of the present invention, see the following specific examples, the feasibility of the present invention is illustrated by verifying using the disclosed XJTU-SY rolling bearing accelerated life test dataset, as follows:
1) Data acquisition and time domain analysis detection
The XJTU-SY rolling bearing accelerated life test data set is test data obtained by combining Lei ya national teaching team of the university of Western An traffic mechanical engineering with Zhejiang Changxing Yang-raising technology Co., ltd, selecting a typical key component rolling bearing in an industrial scene as a test object, developing a rolling bearing accelerated life test for two years, and obtaining test data.
The test designs 3 kinds of working conditions, wherein the rotating speeds of motors driving the bearings to rotate are 2100r/min, 2250r/min and 2400r/min respectively. Vibration signals in the horizontal direction and the vertical direction of the bearing are respectively acquired through two unidirectional acceleration sensors. The sampling frequency is 25.6kHz, the sampling interval is 1min, and the sampling time length is 1.28s each time. In each sampling, the obtained vibration signals are stored in a csv file, wherein the first column is the vibration signals in the horizontal direction, and the second column is the vibration signals in the vertical direction. The bearing failure conditions are four, and failure positions are respectively an outer ring, an inner ring, a retainer and rolling bodies, wherein the outer ring, the inner ring and the retainer all have independent failure conditions. Because of the numerous types of faults, three data sets Bearing1_1, bearing1_4 and Bearing2_1, which individually fail under different conditions, are selected for analysis.
Respectively drawing time domain diagrams according to vibration signals in the horizontal directions selected by the data sets of bearing1_1, bearing1_4 and bearing2_1, wherein the time domain diagrams are shown in fig. 3, 4 and 5, the time domain diagrams of the horizontal vibration signals of the outer ring fault working condition are shown in fig. 3, the time domain diagrams of the horizontal vibration signals of the retainer fault working condition are shown in fig. 4, the time domain diagrams of the horizontal vibration signals of the inner ring fault working condition are shown in fig. 5,
selecting working conditions of Bearing outer ring failure of Bearing1_1 as an example, respectively carrying out frequency domain analysis at the time of 20s normal working conditions and 90s abnormal working conditions of vibration signals in the horizontal direction to obtain corresponding envelope spectra, as shown in fig. 6, wherein fig. 6 is an envelope spectrum schematic diagram of outer ring failure working conditions, wherein the left frequency spectrum is an envelope spectrum of 20s normal working conditions, and the right frequency spectrum is an envelope spectrum of 90s abnormal working conditions;
the data sets bearing1_1, bearing1_4 and bearing2_1 are respectively subjected to data preprocessing to obtain time domain diagrams of three Bearing failure working conditions of the outer ring, the inner ring and the retainer, but the information provided by time domain analysis can be only used as a means for primarily judging whether the Bearing fails or not, and meanwhile, early failure characteristics are not obvious enough, whether the failure occurs or not can not be accurately identified, and the failure occurrence part can not be further judged. Then, an envelope spectrum analysis is performed, the calculated theoretical fault characteristic frequency is compared with the envelope spectrum to determine the type position of the fault, and the severity of the fault is further analyzed, but the corresponding theoretical fault characteristic frequency amplitude is deviated from the actual situation due to the external condition or the self fault, as shown in fig. 7, fig. 7 is an envelope spectrum analysis schematic diagram, in the figure, the broken line is an envelope diagram of the vibration signal, the frequency corresponding to the vertical line perpendicular to the abscissa is the theoretical fault characteristic frequency of the outer ring, and the theoretical fault characteristic frequency amplitude obtained by intersecting the two cannot truly feed back the current situation of the bearing. And the envelope spectrum can be analyzed in the frequency domain only by analyzing signals in a certain time period, so that fault monitoring of the whole life cycle is designed, and outer ring analysis is taken as an example:
step one: firstly, carrying out envelope spectrum analysis on vibration signals in a csv file obtained by sampling a data set Bearing1_1 each time to obtain a corresponding envelope spectrum.
Step two: the method comprises the steps of obtaining theoretical fault characteristic frequency of an outer ring, setting a fault characteristic frequency range window (fault window) of +/-5% of the theoretical fault characteristic frequency in order to reduce amplitude deviation, carrying out mean comparison scaling on characteristic amplitude in the range, expanding the characteristic amplitude which is larger than the mean by two times, contracting the characteristic amplitude which is smaller than the mean by two times, and finally summing all the characteristic amplitude in the fault characteristic range to average to obtain the new fault characteristic frequency amplitude of the outer ring, wherein a specific calculation formula is as follows:
in the formula ：for the theoretical failure characteristic frequency of the outer ring, +.>For frequencies within the failure window, +.>Is thatA is the average value of the characteristic frequency amplitude of the envelope spectrum before being scaled in the fault window, N is the data length in the fault window, < + >>For scaling processed +.>Frequency amplitude of>And the characteristic frequency amplitude value is the fault of the outer ring. The amplitude pairs before and after the scaling process are as shown in fig. 8, and the thicker lines in fig. 8 are envelope spectra after the scaling process and the thinner lines are envelope spectra before the scaling process.
Step three: repeating the first and second steps for all csv files in the data set Bearing1_1 to obtain new outer ring fault characteristic frequency amplitude values sampled each time, and drawing the fault characteristic amplitude value change of the whole life cycle.
Aiming at different rolling Bearing fault characteristic working conditions, carrying out data analysis on the outer ring faults, the retainer faults and the inner ring faults according to the data sets Bearin1_1, bearin1_4 and Bearin2_1 to obtain a full life cycle fault diagnosis result of the rolling Bearing.
The embodiment of the invention also provides a rolling bearing fault diagnosis method, which comprises the following steps:
processing the acquired running parameter data of the rolling bearing to obtain an envelope spectrum;
setting a fault window for the envelope spectrum by taking the theoretical fault characteristic frequency of a preset element as the center;
scaling the characteristic frequency amplitude of the envelope spectrum in the fault window to expand the characteristic frequency amplitude with the value larger than the mean value by two times and reduce the characteristic frequency amplitude with the value smaller than the mean value by two times; the mean value is the mean value of the characteristic frequency amplitude values of the envelope spectrum in the fault window;
summing and averaging the characteristic frequency amplitude values subjected to scaling treatment to obtain a fault characteristic frequency amplitude value of the preset element;
and carrying out fault diagnosis on the preset element according to the fault characteristic frequency amplitude.
In one embodiment, the method further comprises:
and carrying out fault monitoring of the whole life cycle of the preset element to obtain a fault characteristic frequency amplitude of the whole life cycle, and drawing a graph according to the fault characteristic frequency amplitude of the whole life cycle.
In one embodiment, the theoretical failure characteristic frequency includes at least one of an outer race failure characteristic frequency, an inner race failure characteristic frequency, a rolling element failure characteristic frequency, and a cage failure characteristic frequency.
Compared with the prior art, the rolling bearing fault diagnosis system and the rolling bearing fault diagnosis method provided by the embodiment of the invention have the advantages that firstly, the operating parameter data acquired from the sensor is processed to obtain an envelope spectrum; setting a fault window for the envelope spectrum by taking the theoretical fault characteristic frequency of a preset element as a center; then, scaling the characteristic frequency amplitude of the envelope spectrum in the fault window to enable the characteristic frequency amplitude with the value larger than the mean value to be enlarged twice and the characteristic frequency amplitude with the value smaller than the mean value to be reduced twice; the mean value is the mean value of the characteristic frequency amplitude values of the envelope spectrum in the fault window; then, summing and averaging the characteristic frequency amplitude values subjected to scaling treatment to obtain a fault characteristic frequency amplitude value of the preset element; and finally, carrying out fault diagnosis on the preset element according to the fault characteristic frequency amplitude. According to the embodiment of the invention, the running parameter data of the rolling bearing is converted into the envelope spectrum, the characteristic frequency amplitude near the theoretical fault characteristic frequency of the preset element is adjusted, and the fault characteristic frequency amplitude is obtained through calculation, so that the fault diagnosis of the preset element can be carried out, the fault occurrence position of the rolling bearing can be timely and accurately determined, and the situation that a worker takes corresponding measures in time to avoid subsequent damage when a specific element breaks down is facilitated.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (7)
1. A rolling bearing failure diagnosis system, characterized by comprising:
the sensor is used for collecting the operation parameter data of the rolling bearing;
a controller for:
processing the operation parameter data acquired from the sensor to obtain an envelope spectrum;
setting a fault window for the envelope spectrum by taking the theoretical fault characteristic frequency of a preset element as the center;
scaling the characteristic frequency amplitude of the envelope spectrum in the fault window to expand the characteristic frequency amplitude with the value larger than the mean value by two times and reduce the characteristic frequency amplitude with the value smaller than the mean value by two times; the mean value is the mean value of the characteristic frequency amplitude values of the envelope spectrum in the fault window;
summing and averaging the characteristic frequency amplitude values subjected to scaling treatment to obtain a fault characteristic frequency amplitude value of the preset element;
performing fault diagnosis on the preset element according to the fault characteristic frequency amplitude;
the rolling bearing comprises an outer ring, an inner ring, rolling bodies and a retainer, and the controller is further used for:
when the preset element is an outer ring, the theoretical fault characteristic frequency is the outer ring fault characteristic frequency, and the theoretical fault characteristic frequency is calculated by the following formula:
；
when the preset element is an inner ring, the theoretical fault characteristic frequency is the inner ring fault characteristic frequency, and the theoretical fault characteristic frequency is calculated by the following formula:
；
when the preset element is a rolling body, the theoretical failure characteristic frequency is the rolling body failure characteristic frequency, and the theoretical failure characteristic frequency is calculated by the following formula:
；
when the preset element is a retainer, the theoretical failure characteristic frequency is the retainer failure characteristic frequency, and the theoretical failure characteristic frequency is calculated by the following formula:
；
wherein ,representing the characteristic frequency of theoretical faults, +.>Indicates the number of rolling elements +.>Represents the pitch diameter of the rolling bearing->Indicating the diameter of the rolling element->Represents the contact angle of a rolling element rolling bearing, +.>Indicating the rolling bearing inner ring rotation frequency;
the processing the operation parameter data acquired from the sensor to obtain an envelope spectrum includes:
performing time domain analysis on the operation parameter data acquired from the sensor to obtain a preliminary diagnosis result of the rolling bearing;
when the preliminary diagnosis result is that the rolling bearing is abnormal, converting the operation parameter data into an envelope spectrum;
said converting said operating parameter data into an envelope spectrum when said preliminary diagnosis is that the rolling bearing is abnormal, comprising:
when the preliminary diagnosis result is that the rolling bearing is abnormal, carrying out band-pass filtering on the operation parameter data;
performing Hilbert transform on the filtered operation parameter data to obtain an analysis signal;
performing modulo on the analysis signal to obtain an envelope signal;
and carrying out Fourier transform on the envelope signal to obtain an envelope spectrum.
2. The rolling bearing fault diagnosis system of claim 1, wherein the controller is further configured to:
and carrying out fault monitoring of the whole life cycle of the preset element to obtain a fault characteristic frequency amplitude of the whole life cycle, and drawing a graph according to the fault characteristic frequency amplitude of the whole life cycle.
3. The rolling bearing fault diagnosis system according to claim 1, wherein the sensor is a vibration sensor, a temperature sensor, an acceleration sensor, or a displacement sensor;
the theoretical failure characteristic frequency includes at least one of an outer ring failure characteristic frequency, an inner ring failure characteristic frequency, a rolling body failure characteristic frequency, and a cage failure characteristic frequency.
4. A rolling bearing fault diagnosis system according to any one of claims 1 to 3, wherein the fault diagnosis of the preset element according to the fault characteristic frequency amplitude comprises:
comparing the fault characteristic frequency amplitude with a preset alarm threshold value or comparing the fault characteristic frequency amplitude at the current moment with a plurality of latest historical fault characteristic frequency amplitudes to obtain a comparison result;
and determining the fault level of the preset element according to the comparison result.
5. A rolling bearing failure diagnosis method, characterized by comprising:
processing the acquired running parameter data of the rolling bearing to obtain an envelope spectrum;
setting a fault window for the envelope spectrum by taking the theoretical fault characteristic frequency of a preset element as the center;
scaling the characteristic frequency amplitude of the envelope spectrum in the fault window to expand the characteristic frequency amplitude with the value larger than the mean value by two times and reduce the characteristic frequency amplitude with the value smaller than the mean value by two times; the mean value is the mean value of the characteristic frequency amplitude values of the envelope spectrum in the fault window;
summing and averaging the characteristic frequency amplitude values subjected to scaling treatment to obtain a fault characteristic frequency amplitude value of the preset element;
performing fault diagnosis on the preset element according to the fault characteristic frequency amplitude;
the rolling bearing comprises an outer ring, an inner ring, rolling bodies and a retainer;
when the preset element is an outer ring, the theoretical fault characteristic frequency is the outer ring fault characteristic frequency, and the theoretical fault characteristic frequency is calculated by the following formula:
；
when the preset element is an inner ring, the theoretical fault characteristic frequency is the inner ring fault characteristic frequency, and the theoretical fault characteristic frequency is calculated by the following formula:
；
when the preset element is a rolling body, the theoretical failure characteristic frequency is the rolling body failure characteristic frequency, and the theoretical failure characteristic frequency is calculated by the following formula:
；
when the preset element is a retainer, the theoretical failure characteristic frequency is the retainer failure characteristic frequency, and the theoretical failure characteristic frequency is calculated by the following formula:
；
wherein ,representing the characteristic frequency of theoretical faults, +.>Indicates the number of rolling elements +.>Represents the pitch diameter of the rolling bearing->Indicating the diameter of the rolling element->Represents the contact angle of a rolling element rolling bearing, +.>Representation ofThe rolling bearing inner ring rotates in frequency;
the processing the acquired operating parameter data of the rolling bearing to obtain an envelope spectrum comprises the following steps:
performing time domain analysis on the acquired running parameter data of the rolling bearing to obtain a preliminary diagnosis result of the rolling bearing;
when the preliminary diagnosis result is that the rolling bearing is abnormal, converting the operation parameter data into an envelope spectrum;
said converting said operating parameter data into an envelope spectrum when said preliminary diagnosis is that the rolling bearing is abnormal, comprising:
when the preliminary diagnosis result is that the rolling bearing is abnormal, carrying out band-pass filtering on the operation parameter data;
performing Hilbert transform on the filtered operation parameter data to obtain an analysis signal;
performing modulo on the analysis signal to obtain an envelope signal;
and carrying out Fourier transform on the envelope signal to obtain an envelope spectrum.
6. The rolling bearing failure diagnosis method according to claim 5, further comprising:
and carrying out fault monitoring of the whole life cycle of the preset element to obtain a fault characteristic frequency amplitude of the whole life cycle, and drawing a graph according to the fault characteristic frequency amplitude of the whole life cycle.
7. The rolling bearing failure diagnosis method according to claim 5, wherein the theoretical failure characteristic frequency includes at least one of an outer ring failure characteristic frequency, an inner ring failure characteristic frequency, a rolling element failure characteristic frequency, and a cage failure characteristic frequency.
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