Bearing fault detection method and device, equipment and medium

Abstract

The invention relates to the technical field of rotary mechanical equipment vibration monitoring and fault diagnosis, and discloses a bearing fault detection method, device and equipment and a medium, and the method comprises the steps: obtaining a vibration spectrum of a subcomponent of a to-be-detected bearing; the vibration spectrum is scanned in a first sub-interval of a frequency interval, a first frequency is obtained, and the frequency interval is composed of the frequency of the vibration spectrum; obtaining a second subinterval based on the first frequency, and scanning the vibration spectrum in the second subinterval to obtain a second frequency; obtaining an inclusion relation between a frequency value corresponding to a peak value in the third sub-interval and a fourth sub-interval, and adjusting a counter to obtain a count value; and obtaining a target value based on the counting value, and determining whether the bearing has a fault based on the target value. According to the invention, nested cyclic scanning is carried out on the vibration spectrum in different sub-intervals of the frequency interval, so that the actual fault characteristic frequency can be accurately obtained, whether the bearing has a fault is determined, and the accuracy of bearing fault detection is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vibration monitoring and fault diagnosis of rotating mechanical equipment, and particularly relates to a method, device, equipment and medium for detecting bearing faults. Background Art

[0002] As one of the most critical components of industrial rotating mechanical equipment, the operating health status of rolling bearings is directly related to the equipment performance and production efficiency. Implementing online condition monitoring for rolling bearings, especially vibration signal monitoring and analysis, has become increasingly popular and has become one of the most important monitoring means to promote the transformation of industrial equipment from passive maintenance to predictive maintenance.

[0003] Traditional technical solutions use the theoretical characteristic frequency of bearing faults as the fundamental frequency, search for the peak spectral lines of integer multiples of the frequency and their side frequencies, and continuously approximate the actual characteristic frequency by scanning near the theoretical characteristic frequency. However, this method has the following deficiencies: due to the error between the actual characteristic frequency and the theoretical characteristic frequency, the accuracy of bearing fault detection is low and the difficulty of fault identification is high. Summary of the Invention

[0004] In view of this, the present disclosure provides a method, device, equipment and medium for detecting bearing faults to solve the problem of low accuracy in bearing fault detection.

[0005] In a first aspect, the present disclosure provides a method for detecting bearing faults, the method comprising:

[0006] Obtaining the vibration spectrum of the sub-components of the bearing to be detected, wherein the number of sub-components is at least one;

[0007] Scanning the vibration spectrum within a first sub-interval of a frequency interval, obtaining a first frequency, wherein the frequency interval is composed of the frequencies of the vibration spectrum, the first sub-interval is used to characterize that the probability of the vibration spectrum having a homologous frequency of a fault characteristic frequency is greater than a first preset threshold, and the number of the first frequencies is at least one;

[0008] Obtaining a second sub-interval based on the first frequency, scanning the vibration spectrum within the second sub-interval, obtaining a second frequency, wherein the second sub-interval is used to characterize that the probability of the vibration spectrum having a bias frequency of a fault characteristic frequency is greater than the first preset threshold;

[0009] Obtaining the inclusion relationship between the frequency value corresponding to the peak within a third sub-interval and a fourth sub-interval, adjusting a counter to obtain a count value, wherein the third sub-interval and the fourth sub-interval are obtained based on the first frequency, the second frequency and an arithmetic frequency ambiguity factor;

[0010] Obtain a target value based on the count value, and determine whether the bearing fails based on the target value, where the target value is used to represent the ratio of the maximum value in the count value to the number of corresponding fourth sub-intervals.

[0011] In an embodiment of the present disclosure, by obtaining the vibration spectrum of the sub-components of the bearing to be detected; scanning the vibration spectrum in the first sub-interval of the frequency interval to obtain the first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum; obtaining the second sub-interval based on the first frequency, and scanning the vibration spectrum in the second sub-interval to obtain the second frequency; obtaining the inclusion relationship between the frequency value corresponding to the peak in the third sub-interval and the fourth sub-interval, adjusting the counter to obtain the count value; obtaining the target value based on the count value, and determining whether the bearing fails based on the target value. Since in the embodiment of the present disclosure, by performing nested loop scanning on the vibration spectrum in different sub-intervals of the frequency interval, the actual fault characteristic frequency can be accurately obtained to determine whether the bearing fails, thereby improving the accuracy of bearing fault detection.

[0012] In an alternative embodiment, before scanning the vibration spectrum in the first sub-interval of the frequency interval to obtain the first frequency, the method further includes:

[0013] Obtain the fault characteristic frequency order of the sub-components and the rotational frequency of the equipment where the bearing is located;

[0014] Fuse the fault characteristic frequency order and the rotational frequency to obtain the third frequency;

[0015] Obtain the first sub-interval based on the third frequency and the frequency scanning range factor.

[0016] In the embodiment of the present disclosure, by fusing the fault characteristic frequency order and the rotational frequency to obtain the third frequency, the theoretical fault characteristic frequency of the bearing sub-components can be obtained. Furthermore, by obtaining the first sub-interval based on the third frequency and the frequency scanning range factor, the scanning range of the possible fault characteristic frequencies of the bearing sub-components can be obtained, improving the efficiency and accuracy of bearing fault detection.

[0017] In an alternative embodiment, obtaining the inclusion relationship between the frequency value corresponding to the peak in the third sub-interval and the fourth sub-interval includes:

[0018] Obtain the third sub-interval and the fourth sub-interval based on the first frequency, the second frequency, and the arithmetic frequency ambiguity factor;

[0019] Scan the vibration spectrum in the third sub-interval to obtain the peak in the third sub-interval;

[0020] Based on the peak in the third sub-interval, obtain the frequency value corresponding to the peak in the third sub-interval;

[0021] Compare the frequency value with the fourth sub - interval to obtain the inclusion relationship between the frequency value and the fourth sub - interval.

[0022] In the embodiments of the present disclosure, by scanning the vibration spectrum within the third sub - interval, obtaining the peak value within the third sub - interval and the frequency value corresponding to the peak value, and obtaining the inclusion relationship between the frequency value and the fourth sub - interval, it is possible to determine whether the frequency value is a fault characteristic frequency, providing a reliable basis for subsequent adjustment of the counter and determination of bearing faults.

[0023] In an alternative embodiment, obtaining the third sub - interval and the fourth sub - interval based on the first frequency, the second frequency, and the equal - difference frequency fuzzy factor includes:

[0024] Fuse the first frequency and the equal - difference frequency fuzzy factor to obtain the width of the fourth sub - interval;

[0025] Based on the first frequency, the second frequency, and the width of the fourth sub - interval, obtain the third sub - interval and the fourth sub - interval.

[0026] In the embodiments of the present disclosure, by fusing the first frequency and the equal - difference frequency fuzzy factor to obtain the width of the fourth sub - interval, and obtaining the third sub - interval and the fourth sub - interval based on the first frequency, the second frequency, and the width of the fourth sub - interval, it is possible to obtain the scanning range of the possible fault offset frequency of the bearing sub - component, improving the efficiency and accuracy of bearing fault detection.

[0027] In an alternative embodiment, obtaining the target value based on the count value includes:

[0028] Based on the first frequency and the highest analysis frequency of the vibration spectrum, obtain the number of the fourth sub - intervals;

[0029] Obtain the ratio of the maximum value of the count value to the number of the corresponding fourth sub - intervals to obtain the target value.

[0030] In the embodiments of the present disclosure, by obtaining the target value based on the ratio of the maximum value of the count value to the number of the corresponding fourth sub - intervals, it is possible to obtain the maximum value of the density of the fault characteristic frequency, providing a judgment basis for subsequent determination of bearing faults.

[0031] In an alternative embodiment, determining whether the bearing fails based on the target value includes:

[0032] Compare the target value with the second preset threshold to determine whether the bearing fails, where the second preset threshold is used to determine bearing failure;

[0033] In the case where the target value of any sub - component is greater than or equal to the corresponding second preset threshold, determine that the bearing fails;

[0034] When the target values of all sub-components are less than the corresponding second preset threshold, it is determined that the bearing has not failed.

[0035] In the embodiments of the present disclosure, by comparing the target value with the second preset threshold to determine whether a sub-component has failed, and then determining whether the bearing has failed, the accuracy and reliability of bearing fault detection can be improved.

[0036] In an alternative embodiment, after determining whether the bearing has failed based on the target value, the method further includes:

[0037] When it is determined that the bearing has failed, obtain a fifth frequency and a sixth frequency corresponding to the target value, where the fifth frequency is included in the first sub-interval and the sixth frequency is included in the second sub-interval;

[0038] Based on the fifth frequency and the sixth frequency, obtain a fifth sub-interval, where the fifth sub-interval is used to represent the fault frequency interval in the vibration spectrum;

[0039] Obtain the effective value of the physical quantity corresponding to the vibration spectrum within the fifth sub-interval;

[0040] Compare the effective value with a third preset threshold to determine the fault level of the sub-component, where the third preset threshold is used to determine the fault level of the sub-component;

[0041] Based on the fault level of the sub-component, determine the fault level of the bearing.

[0042] In the embodiments of the present disclosure, by comparing the effective value of the physical quantity corresponding to the vibration spectrum within the fifth sub-interval with the third preset threshold, the fault level of the sub-component can be determined, and then the fault level of the bearing can be determined, realizing a quantitative evaluation of the bearing fault degree.

[0043] In a second aspect, the present disclosure provides a detection device for bearing faults, the device includes:

[0044] A first acquisition module, configured to acquire the vibration spectrum of the sub-components of the bearing to be detected, where the number of sub-components is at least one;

[0045] A first obtaining module, configured to scan the vibration spectrum within the first sub-interval of the frequency interval to obtain a first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum, and the first sub-interval is used to represent that the probability of the homologous frequencies of the fault characteristic frequencies appearing in the vibration spectrum is greater than the first preset threshold, and the number of the first frequencies is at least one;

[0046] A second obtaining module, configured to obtain a second sub-interval based on a first frequency, scan a vibration spectrum within the second sub-interval, and obtain a second frequency, where the second sub-interval is used to represent that the probability of a bias frequency of a fault characteristic frequency appearing in the vibration spectrum is greater than a first preset threshold;

[0047] A third obtaining module, configured to obtain an inclusion relationship between a frequency value corresponding to a peak within a third sub-interval and a fourth sub-interval, and adjust a counter to obtain a count value, where the third sub-interval and the fourth sub-interval are obtained based on the first frequency, the second frequency, and an arithmetic frequency ambiguity factor;

[0048] A first determination module, configured to obtain a target value based on the count value, and determine whether a bearing fails based on the target value, where the target value is used to represent the proportion of the maximum value in the count value to the number of corresponding fourth sub-intervals.

[0049] In a third aspect, the present disclosure provides a computer device, including: a memory and a processor, which are communicatively connected to each other, where the memory stores computer instructions, and the processor executes the computer instructions to execute the bearing fault detection method according to the first aspect or any corresponding embodiment thereof.

[0050] In a fourth aspect, the present disclosure provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the bearing fault detection method according to the first aspect or any corresponding embodiment thereof.

[0051] In a fifth aspect, the present disclosure provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the bearing fault detection method according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0052] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Figure 1 is a flowchart of a bearing fault detection method according to an embodiment of the present disclosure;

[0054] Figure 2 is a flowchart of another bearing fault detection method according to an embodiment of the present disclosure;

[0055] Figure 3It is a schematic flowchart of another method for detecting bearing faults according to an embodiment of the present disclosure;

[0056] Figure 4 It is a structural block diagram of a device for detecting bearing faults according to an embodiment of the present disclosure;

[0057] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present disclosure. Detailed implementation manners

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0059] As one of the most critical components of industrial rotating mechanical equipment, the operating health status of rolling bearings is directly related to the equipment performance and production efficiency. Implementing online condition monitoring for rolling bearings, especially vibration signal monitoring and analysis, has become increasingly popular and has become one of the most important monitoring means to promote the transformation of industrial equipment from passive maintenance to predictive maintenance.

[0060] The traditional technical solution uses the theoretical characteristic frequency of bearing faults as the fundamental frequency, searches for the peak spectral lines of integer multiples of the frequency and their side frequencies, and continuously approaches the actual characteristic frequency by scanning near the theoretical characteristic frequency. However, this method has the following deficiencies: due to the error between the actual characteristic frequency and the theoretical characteristic frequency, the accuracy of bearing fault detection is low and the difficulty of fault identification is great.

[0061] To solve the above problems, according to an embodiment of the present disclosure, an embodiment of a method for detecting bearing faults is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0062] In this embodiment, a method for detecting bearing faults is provided, as Figure 1 shown, Figure 1 It is a schematic flowchart of a method for detecting bearing faults according to an embodiment of the present disclosure. This process can be applied to a server and includes the following steps:

[0063] Step S101, obtain the vibration spectrum of the sub-components of the bearing to be detected, where the number of sub-components is at least one.

[0064] Optionally, in the embodiments of the present disclosure, the sub-components within the bearing include an inner ring, an outer ring, rolling elements, a cage, etc. The vibration spectrum is an image obtained by converting the vibration signal in the time domain to the frequency domain through mathematical means such as Fourier transform. The abscissa is the frequency, the ordinate is the amplitude, and the magnitude of the amplitude reflects the energy of the corresponding frequency component in the original vibration signal.

[0065] Specifically, the server can use a vibration sensor (such as an acceleration sensor, etc.) installed on the bearing to measure the vibration signal of the sub-components. Since the vibration signal collected by the sensor is an analog signal, the server converts the vibration signal into a digital signal through a data acquisition system and transmits the digital signal to a computer or other analysis device, and uses signal processing methods (such as Fourier transform, etc.) to convert the time-domain signal into a frequency-domain signal to obtain the vibration spectrum of the sub-components of the bearing to be detected.

[0066] In addition, after obtaining the vibration spectrum, the server can preprocess it, only retain the frequency components with peak attributes (that is, the central amplitude is higher than the adjacent spectral lines on both sides), and then use methods such as Gaussian smoothing to perform noise reduction processing on the vibration spectrum to obtain the preprocessed vibration spectrum.

[0067] Step S102, scan the vibration spectrum within the first sub-interval of the frequency interval to obtain the first frequency. The frequency interval is composed of the frequencies of the vibration spectrum, and the first sub-interval is used to characterize that the probability of the homologous frequencies of the fault characteristic frequencies appearing in the vibration spectrum is greater than the first preset threshold, and the number of the first frequencies is at least one.

[0068] It should be noted that when the bearing fails, generally two possible frequency distribution characteristics will appear in the vibration spectrum:

[0069] (1) Taking the fault characteristic frequency of the sub-component as the fundamental frequency, multiple-frequency components that are integer multiples of the fundamental frequency are generated intermittently or continuously within the entire vibration spectrum range. The highest order of the multiple frequencies is related to the highest analysis frequency of the vibration spectrum. Different sub-component damages (such as the inner ring, rolling elements) will also have side-frequency components distributed intermittently or continuously around each multiple frequency, and the adjacent side-frequency interval is the rotor rotation frequency or its fractional multiple frequency (such as 0.4 times the frequency, that is, the cage rotation frequency);

[0070] (2) Within the local high-frequency narrowband range of the vibration spectrum, a vibration peak group with equal intervals based on the fault characteristic frequency of a certain sub-component is randomly generated, and the components of such a peak group are not integer multiples of the fault characteristic frequency of the sub-component.

[0071] Optionally, in the embodiments of the present disclosure, the first preset threshold refers to the probability value that a sub-component may fail. When the probability of the homologous frequency of the fault characteristic frequency appearing in the vibration spectrum is greater than the first preset threshold, it indicates that the sub-component may have a fault. The first sub-interval is included in the frequency interval of the vibration spectrum, representing the interval where the possible fault characteristic frequency exists. The first frequency is included in the first sub-interval, representing the possible fault characteristic frequency.

[0072] Specifically, the server first obtains the first sub-interval, that is, the interval where the possible fault characteristic frequency exists. Then, the server scans the vibration spectrum within the first sub-interval of the frequency interval at a certain step size (such as the frequency resolution) to obtain the first frequency, that is, the possible fault characteristic frequency.

[0073] Step S103: Obtain a second sub-interval based on the first frequency, and scan the vibration spectrum within the second sub-interval to obtain a second frequency, where the second sub-interval is used to characterize that the probability of the offset frequency of the fault characteristic frequency appearing in the vibration spectrum is greater than the first preset threshold.

[0074] Optionally, in the embodiments of the present disclosure, the second sub-interval is included in the frequency interval of the vibration spectrum, representing the interval where the possible fault offset frequency exists. The second frequency is included in the second sub-interval, representing the possible fault offset frequency.

[0075] Specifically, the server first obtains the second sub-interval (such as [0, the first frequency]) based on the first frequency, that is, the interval where the possible fault offset frequency exists. Then, the server scans the vibration spectrum within the second sub-interval of the frequency interval at a certain step size (such as the frequency resolution) to obtain the second frequency, that is, the possible fault offset frequency.

[0076] Step S104: Obtain the inclusion relationship between the frequency value corresponding to the peak value within the third sub-interval and the fourth sub-interval, and adjust the counter to obtain a count value, where the third sub-interval and the fourth sub-interval are obtained based on the first frequency, the second frequency, and the equal-difference frequency ambiguity factor.

[0077] Optionally, in the embodiments of the present disclosure, the third sub-interval is included in the frequency interval of the vibration spectrum, representing the first scanning range of the possible fault characteristic frequency. The fourth sub-interval is included in the frequency interval of the vibration spectrum, representing the second scanning range of the possible fault characteristic frequency. The equal-difference frequency ambiguity factor is used to determine the width of the fourth sub-interval, and its value range can be [0.05, 0.5].

[0078] Specifically, the server first obtains the width of the fourth sub-interval based on the first frequency and the arithmetic frequency fuzzy factor, and then obtains the third sub-interval, that is, the first scanning range of possible fault characteristic frequencies, based on the first frequency, the second frequency, and the width of the fourth sub-interval, and obtains the fourth sub-interval, that is, the second scanning range of possible fault characteristic frequencies, based on the first frequency, the second frequency, and the width of the fourth sub-interval.

[0079] Then, the server scans the vibration spectrum within the third sub-interval of the frequency interval, obtains the peak value within the third sub-interval and the frequency value corresponding to the peak value, determines the inclusion relationship between the frequency value and the fourth sub-interval, and adjusts the count value of the counter according to the inclusion relationship: if the frequency value is included in the fourth sub-interval, it indicates that the frequency value may be a fault characteristic frequency, and the count value of the counter is incremented by one; if the frequency value is not included in the fourth sub-interval, it indicates that the frequency value is not a fault characteristic frequency, and the count value of the counter remains unchanged. After the server finishes the scan, it obtains the count value of the counter and clears the counter.

[0080] Step S105, obtain a target value based on the count value, and determine whether the bearing has a fault based on the target value, where the target value is used to represent the proportion of the maximum value in the count value to the number of corresponding fourth sub-intervals.

[0081] Optionally, in the embodiments of the present disclosure, the target value is the proportion of the maximum value in the count value to the number of corresponding fourth sub-intervals, which reflects the maximum value of the density of fault characteristic frequencies.

[0082] Specifically, the server first obtains the maximum value of the count value, obtains the number of corresponding fourth sub-intervals of the count value, then calculates the proportion of the count value to the number of corresponding fourth sub-intervals to obtain the target value, and finally compares the target value with the corresponding preset threshold to determine whether the bearing has a fault.

[0083] It should be noted that the server can perform a two-layer nested loop scan on the vibration spectrum by constructing a fuzzy arithmetic frequency searcher. The searcher consists of a bracket and several identical subnets evenly distributed on the bracket. The length of the bracket is the highest analysis frequency of the vibration spectrum. The center of the first subnet is located at the left end of the searcher bracket. The subnet distribution interval is the first frequency. The number of subnets is determined by the length of the bracket and the subnet distribution interval. The width of the subnet is determined by the first frequency and the arithmetic frequency fuzzy factor. The scanning range of the outer loop is the first sub-interval, the moving range of the searcher is the second sub-interval, and the scanning ranges of the inner loop are the third sub-interval and the fourth sub-interval.

[0084] The server step - by - step scans the first sub - interval through an outer loop to obtain the first frequency, and step - by - step searches for a searcher within the second sub - interval through an inner loop, and step - by - step scans the third sub - interval and the fourth sub - interval. After each inner - loop scan, the count value of the counter is obtained and the counter is cleared. After the outer - loop scan is completed, the maximum value of the count value is obtained, and a target value is obtained based on this count value. Finally, it is determined whether the bearing fails based on the target value.

[0085] In the embodiment of the present disclosure, the vibration spectrum of the sub - components of the bearing to be detected is obtained; the vibration spectrum is scanned within the first sub - interval of the frequency interval to obtain the first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum; a second sub - interval is obtained based on the first frequency, and the vibration spectrum is scanned within the second sub - interval to obtain the second frequency; the inclusion relationship between the frequency value corresponding to the peak within the third sub - interval and the fourth sub - interval is obtained, the counter is adjusted to obtain the count value; a target value is obtained based on the count value, and it is determined whether the bearing fails based on the target value. Since the embodiment of the present disclosure performs nested - loop scanning on the vibration spectrum within different sub - intervals of the frequency interval, the actual fault characteristic frequency can be accurately obtained to determine whether the bearing fails, thereby improving the accuracy of bearing fault detection.

[0086] In this embodiment, a method for detecting bearing faults is provided, as Figure 2 shown Figure 2 is a schematic flowchart of another method for detecting bearing faults according to the embodiment of the present disclosure. This process can be applied to a server and includes the following steps:

[0087] Step S201, obtain the vibration spectrum of the sub - components of the bearing to be detected, where the number of sub - components is at least one. For details, please refer to Figure 1 step S101 of the embodiment shown here, which will not be elaborated here.

[0088] Step S202, obtain the fault characteristic frequency order of the sub - components and the rotation frequency of the equipment where the bearing is located.

[0089] Optionally, in the embodiment of the present disclosure, the server queries the bearing database according to the model, serial number, etc. of the bearing to obtain the fault characteristic frequency order of the sub - components included in the bearing, and obtains the rotation frequency of the equipment where the bearing is located through an external tachometer, or calculates the rotation frequency of the equipment where the bearing is located by using the real - time vibration signal collected by the vibration sensor.

[0090] Step S203, fuse the fault characteristic frequency order and the rotation frequency to obtain the third frequency.

[0091] Optionally, in the embodiment of the present disclosure, the third frequency represents the theoretical fault characteristic frequency of the sub - components.

[0092] Specifically, the server multiplies the fault characteristic frequency order of the sub-component by the rotation frequency of the device to calculate a third frequency, that is, the theoretical fault characteristic frequency of the sub-component.

[0093] Step S204: Based on the third frequency and the frequency scanning range factor, obtain the first sub-interval.

[0094] Optionally, in the embodiments of the present disclosure, the third frequency is the center of the first sub-interval, and the frequency scanning range factor is used to determine the width of the first sub-interval.

[0095] Specifically, the server multiplies the third frequency by the frequency scanning range factor to calculate the width of the first sub-interval, and based on this width and the third frequency, calculates the first sub-interval. For example: if the third frequency is 100 Hz and the frequency scanning range factor is 0.2, then the center of the first sub-interval is 100 Hz, the interval width is 20 Hz, and the first sub-interval is [90 Hz, 110 Hz].

[0096] Step S205: Scan the vibration spectrum within the first sub-interval of the frequency interval to obtain a first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum, and the first sub-interval is used to represent that the probability of the vibration spectrum having a homologous frequency of the fault characteristic frequency is greater than the first preset threshold, and the number of the first frequencies is at least one. For details, please refer to Figure 1 Step S102 of the illustrated embodiment, which will not be elaborated here.

[0097] Step S206: Based on the first frequency, obtain a second sub-interval, and scan the vibration spectrum within the second sub-interval to obtain a second frequency, where the second sub-interval is used to represent that the probability of the vibration spectrum having a bias frequency of the fault characteristic frequency is greater than the first preset threshold. For details, please refer to Figure 1 Step S103 of the illustrated embodiment, which will not be elaborated here.

[0098] Step S207: Obtain the inclusion relationship between the frequency value corresponding to the peak within the third sub-interval and the fourth sub-interval, and adjust the counter to obtain a count value, where the third sub-interval and the fourth sub-interval are obtained based on the first frequency, the second frequency, and the equal-difference frequency ambiguity factor.

[0099] Specifically, the obtaining of the inclusion relationship between the frequency value corresponding to the peak within the third sub-interval and the fourth sub-interval in step S207 above includes:

[0100] Step S2071: Based on the first frequency, the second frequency, and the equal-difference frequency ambiguity factor, obtain the third sub-interval and the fourth sub-interval.

[0101] Specifically, the server first obtains the width of the fourth sub-interval based on the first frequency and the equal-difference frequency fuzzy factor, and then obtains at least one third sub-interval and at least one fourth sub-interval based on the first frequency, the second frequency, and the width of the fourth sub-interval.

[0102] In some alternative embodiments, the above step S2071 includes:

[0103] Step a1: Fuse the first frequency and the equal-difference frequency fuzzy factor to obtain the width of the fourth sub-interval.

[0104] Step a2: Obtain the third sub-interval and the fourth sub-interval based on the first frequency, the second frequency, and the width of the fourth sub-interval.

[0105] Specifically, the server first multiplies the first frequency and the equal-difference frequency fuzzy factor to calculate the width value of the fourth sub-interval. For example, if the first frequency is 100 Hz and the equal-difference frequency fuzzy factor is 0.2, then the interval width of the fourth sub-interval is 20 Hz.

[0106] Then, the server calculates the interval width and the interval center of each third sub-interval based on the first frequency, the second frequency, and the width of the fourth sub-interval, so as to calculate each third sub-interval. Among them, the interval width of each third sub-interval is the same, which is the difference between twice the first frequency and the interval width of the corresponding fourth sub-interval. The interval center of the first third sub-interval is the second frequency, and the distance between the interval centers of each third sub-interval is the same, which is the first frequency.

[0107] It should be noted that the lower limit of the interval of each third sub-interval is greater than or equal to 0 Hz, and the upper limit of the interval is less than or equal to the maximum value of the abscissa of the vibration spectrum.

[0108] For example, if the first frequency is 100 Hz, the second frequency is 90 Hz, and the width of the fourth sub-interval is 20 Hz, then the interval width of each third sub-interval is 180 Hz, and the interval centers of the third sub-intervals are 90 Hz, 190 Hz, 290 Hz,... in sequence. By analogy, the third sub-intervals are [0 Hz, 180 Hz], [100 Hz, 280 Hz], [200 Hz, 380 Hz],... in sequence.

[0109] After that, the server calculates the interval center of each fourth sub-interval based on the first frequency, the second frequency, and the width of the fourth sub-interval, and calculates each fourth sub-interval in combination with the interval width of the fourth sub-interval. Among them, the interval center of the first fourth sub-interval is the second frequency, and the distance between the interval centers of each fourth sub-interval is the same, which is the first frequency.

[0110] It should be noted that the lower limit of the fourth sub-interval is greater than or equal to 0 Hz, and the upper limit is less than or equal to the maximum value of the abscissa of the vibration spectrum.

[0111] For example: If the first frequency is 100 Hz, the second frequency is 90 Hz, and the width of the fourth sub-interval is 20 Hz, then the center frequencies of the fourth sub-intervals are 90 Hz, 190 Hz, 290 Hz, ……, and so on. Accordingly, the fourth sub-intervals are [80 Hz, 100 Hz], [180 Hz, 200 Hz], [280 Hz, 300 Hz], ……, and so on.

[0112] In the above embodiments, the width of the fourth sub-interval is obtained by fusing the first frequency and the arithmetic frequency fuzzy factor, and the third and fourth sub-intervals are obtained based on the first frequency, the second frequency, and the width of the fourth sub-interval, so as to obtain the scanning range of the possible fault offset frequencies of the bearing sub-components, improving the efficiency and accuracy of bearing fault detection.

[0113] Step S2072: Scan the vibration spectrum within the third sub-interval to obtain the peak value within the third sub-interval.

[0114] Specifically, the server scans the vibration spectrum within the third sub-interval of the frequency interval to obtain the maximum value of the ordinate (amplitude) within the third sub-interval, that is, the peak value.

[0115] Step S2073: Based on the peak value within the third sub-interval, obtain the frequency value corresponding to the peak value within the third sub-interval.

[0116] Specifically, the server obtains the value of the abscissa (i.e., the frequency value) corresponding to the maximum value of the ordinate (i.e., the peak value) within the third sub-interval based on the peak value within the third sub-interval.

[0117] Step S2074: Compare the frequency value with the fourth sub-interval to obtain the inclusion relationship between the frequency value and the fourth sub-interval.

[0118] Specifically, the server compares the frequency value with the fourth sub-interval to determine the inclusion relationship between the frequency value and the fourth sub-interval, that is, whether the frequency value is included in the fourth sub-interval or not.

[0119] Step S208: Obtain a target value based on the count value, and determine whether the bearing has a fault based on the target value, where the target value is used to represent the proportion of the maximum value in the count value to the number of corresponding fourth sub-intervals. For details, please refer to Figure 1 Step S105 of the illustrated embodiment, which will not be elaborated here.

[0120] In the embodiments of the present disclosure, by fusing the fault characteristic frequency order and the rotation frequency to obtain a third frequency, the theoretical fault characteristic frequency of the bearing sub-component can be obtained. Furthermore, based on the third frequency and the frequency scanning range factor, a first sub-interval can be obtained, and the scanning range of the possible fault characteristic frequencies of the bearing sub-component can be obtained, improving the efficiency and accuracy of bearing fault detection. By scanning the vibration spectrum within the third sub-interval, the peak value within the third sub-interval and the frequency value corresponding to the peak value are obtained, and the inclusion relationship between the frequency value and the fourth sub-interval is obtained, so as to determine whether the frequency value is a fault characteristic frequency, providing a reliable basis for subsequent adjustment of the counter and determination of bearing faults.

[0121] In this embodiment, a method for detecting bearing faults is provided, as Figure 3 shown Figure 3 is a schematic flowchart of another method for detecting bearing faults according to the embodiments of the present disclosure. This process can be applied to a server and includes the following steps:

[0122] Step S301, obtain the vibration spectrum of the sub-components of the bearing to be detected, where the number of sub-components is at least one. For details, please refer to Figure 2 Step S201 of the embodiment shown, which will not be elaborated here.

[0123] Step S302, scan the vibration spectrum within the first sub-interval of the frequency interval to obtain a first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum, and the first sub-interval is used to represent that the probability of the homologous frequency of the fault characteristic frequency appearing in the vibration spectrum is greater than a first preset threshold, and the number of the first frequencies is at least one. For details, please refer to Figure 2 Step S205 of the embodiment shown, which will not be elaborated here.

[0124] Step S303, obtain a second sub-interval based on the first frequency, and scan the vibration spectrum within the second sub-interval to obtain a second frequency, where the second sub-interval is used to represent that the probability of the offset frequency of the fault characteristic frequency appearing in the vibration spectrum is greater than the first preset threshold. For details, please refer to Figure 2 Step S206 of the embodiment shown, which will not be elaborated here.

[0125] Step S304, obtain the inclusion relationship between the frequency value corresponding to the peak value within the third sub-interval and the fourth sub-interval, and adjust the counter to obtain a count value, where the third sub-interval and the fourth sub-interval are obtained based on the first frequency, the second frequency, and the equal-difference frequency ambiguity factor. For details, please refer to Figure 2 Step S207 of the embodiment shown, which will not be elaborated here.

[0126] Step S305: Obtain a target value based on the count value, and determine whether the bearing has a fault based on the target value, where the target value is used to represent the proportion of the maximum value in the count value to the number of corresponding fourth sub-intervals.

[0127] Specifically, the above step S305 includes:

[0128] Step S3051: Obtain the number of fourth sub-intervals based on the first frequency and the highest analysis frequency of the vibration spectrum.

[0129] Specifically, the server first obtains the highest analysis frequency of the vibration spectrum, and then calculates the number of fourth sub-intervals based on the first frequency and the highest analysis frequency of the vibration spectrum. For example: The server can use the floor function to calculate the number of fourth sub-intervals, that is, the number of fourth sub-intervals = floor(highest analysis frequency of the vibration spectrum / first frequency) + 1.

[0130] Step S3052: Obtain the proportion of the maximum value of the count value to the number of corresponding fourth sub-intervals to get the target value.

[0131] Specifically, the server first obtains the maximum value of the count value, and then calculates the proportion of the count value to the number of corresponding fourth sub-intervals to get the target value.

[0132] Step S3053: Compare the target value with a second preset threshold to determine whether the bearing has a fault, where the second preset threshold is used to determine that the bearing has a fault.

[0133] Specifically, the server compares the target value with the second preset threshold to obtain a comparison result, and determines whether the bearing has a fault according to the comparison result.

[0134] Step S3054: Determine that the bearing has a fault when the target value of any sub-component is greater than or equal to the corresponding second preset threshold.

[0135] Specifically, if the target value of any sub-component is greater than or equal to the corresponding second preset threshold, it means that there is a fault in the sub-component inside the bearing, then the server determines that the bearing has a fault.

[0136] Step S3055: Determine that the bearing has no fault when the target values of all sub-components are less than the corresponding second preset thresholds.

[0137] Specifically, if the target values of all sub-components are less than the corresponding second preset thresholds, it means that all sub-components inside the bearing have no faults, then the server determines that the bearing has no fault.

[0138] Step S306, in the case of determining that the bearing fails, obtain the fifth frequency and the sixth frequency corresponding to the target value, where the fifth frequency is included in the first sub-interval and the sixth frequency is included in the second sub-interval.

[0139] Optionally, in the embodiments of the present disclosure, the fifth frequency being included in the first sub-interval represents the fault characteristic frequency of the sub-component. The sixth frequency being included in the second sub-interval represents the fault offset frequency of the sub-component.

[0140] Specifically, if the bearing fails, the server obtains the fifth frequency corresponding to the target value, that is, the fault characteristic frequency, and obtains the sixth frequency corresponding to the target value, that is, the fault offset frequency.

[0141] Step S307, based on the fifth frequency and the sixth frequency, obtain a fifth sub-interval, where the fifth sub-interval is used to characterize the fault frequency interval in the vibration spectrum.

[0142] Specifically, the server calculates at least one fifth sub-interval based on the fifth frequency and the sixth frequency. The center of the first fifth sub-interval is the sixth frequency, and the distance between the centers of each fifth sub-interval is the same, which is the fifth frequency.

[0143] Step S308, obtain the effective value of the physical quantity corresponding to the vibration spectrum within the fifth sub-interval.

[0144] Optionally, in the embodiments of the present disclosure, the physical quantity corresponding to the vibration spectrum includes but is not limited to vibration acceleration, vibration velocity, etc.

[0145] Specifically, the server calculates the amplitude of the vibration spectrum within the fifth sub-interval using the root mean square formula, that is, the effective value of the physical quantity corresponding to the vibration spectrum within the fifth sub-interval.

[0146] Step S309, compare the effective value with a third preset threshold to determine the fault level of the sub-component, where the third preset threshold is used to determine the fault level of the sub-component.

[0147] Optionally, in the embodiments of the present disclosure, the number of the third preset thresholds is at least one, and the number of the fault levels of the sub-component is at least two.

[0148] Specifically, the server compares the effective value and the third preset threshold. If the effective value is greater than or equal to the third preset threshold, the fault level of the sub-component is determined to be a higher level; if the effective value is less than the third preset threshold, the fault level of the sub-component is determined to be a lower level.

[0149] Step S3010, based on the fault level of the sub-component, determine the fault level of the bearing.

[0150] Specifically, the server obtains the maximum value, minimum value, average value, etc. of the fault levels of all sub-components contained in the bearing, and determines it as the fault level of the bearing.

[0151] In the embodiments of the present disclosure, by obtaining the target value based on the ratio of the maximum value of the count value to the number of the corresponding fourth sub-intervals, the maximum value of the density of the fault characteristic frequencies can be obtained, providing a basis for determining the bearing fault subsequently. By comparing the target value with the second preset threshold, it is determined whether the sub-component fails, and further whether the bearing fails, which can improve the accuracy and reliability of the bearing fault detection. By comparing the effective value of the physical quantity corresponding to the vibration spectrum within the fifth sub-interval with the third preset threshold, the fault level of the sub-component can be determined, and further the fault level of the bearing can be determined, realizing the quantitative evaluation of the bearing fault degree.

[0152] In this embodiment, a bearing fault detection device is also provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can implement a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0153] This embodiment provides a bearing fault detection device, as Figure 4 shown, including:

[0154] A first acquisition module 401, configured to acquire the vibration spectrum of the sub-components of the bearing to be detected, where the number of sub-components is at least one;

[0155] A first obtaining module 402, configured to scan the vibration spectrum within the first sub-interval of the frequency interval to obtain a first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum, and the first sub-interval is used to represent that the probability of the vibration spectrum having the homologous frequencies of the fault characteristic frequencies is greater than the first preset threshold, and the number of the first frequencies is at least one;

[0156] A second obtaining module 403, configured to obtain a second sub-interval based on the first frequency, and scan the vibration spectrum within the second sub-interval to obtain a second frequency, where the second sub-interval is used to represent that the probability of the vibration spectrum having the offset frequencies of the fault characteristic frequencies is greater than the first preset threshold;

[0157] A third obtaining module 404, configured to obtain the inclusion relationship between the frequency value corresponding to the peak within the third sub-interval and the fourth sub-interval, and adjust the counter to obtain a count value, where the third sub-interval and the fourth sub-interval are obtained based on the first frequency, the second frequency, and the equal-difference frequency fuzzy factor;

[0158] The first determination module 405 is configured to obtain a target value based on a count value, and determine whether a bearing fails based on the target value, where the target value is used to represent the proportion of the maximum value in the count value to the number of corresponding fourth sub-intervals.

[0159] In an embodiment of the present disclosure, by acquiring the vibration spectrum of a sub-component of a bearing to be detected; scanning the vibration spectrum within a first sub-interval of a frequency interval to obtain a first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum; obtaining a second sub-interval based on the first frequency, and scanning the vibration spectrum within the second sub-interval to obtain a second frequency; acquiring the inclusion relationship between the frequency value corresponding to the peak within a third sub-interval and a fourth sub-interval, adjusting a counter to obtain a count value; obtaining a target value based on the count value, and determining whether the bearing fails based on the target value. Since, in the embodiment of the present disclosure, the vibration spectrum is scanned in a nested loop manner within different sub-intervals of the frequency interval, the actual fault characteristic frequency can be accurately obtained to determine whether the bearing fails, thereby improving the accuracy of bearing fault detection.

[0160] In some alternative embodiments, the apparatus further includes:

[0161] A second acquisition module, configured to acquire the fault characteristic frequency order of the sub-component and the rotation frequency of the equipment where the bearing is located;

[0162] A fourth obtaining module, configured to fuse the fault characteristic frequency order and the rotation frequency to obtain a third frequency;

[0163] A fifth obtaining module, configured to obtain a first sub-interval based on the third frequency and a frequency scanning range factor.

[0164] In some alternative embodiments, the third obtaining module 404 includes:

[0165] A first obtaining sub-module, configured to obtain a third sub-interval and a fourth sub-interval based on the first frequency, the second frequency, and an arithmetic frequency ambiguity factor;

[0166] A second obtaining sub-module, configured to scan the vibration spectrum within the third sub-interval to obtain the peak within the third sub-interval;

[0167] A third obtaining sub-module, configured to obtain the frequency value corresponding to the peak within the third sub-interval based on the peak within the third sub-interval;

[0168] A fourth obtaining sub-module, configured to compare the frequency value with the fourth sub-interval to obtain the inclusion relationship between the frequency value and the fourth sub-interval.

[0169] In some alternative embodiments, the first obtaining sub-module includes:

[0170] A first obtaining unit, configured to fuse a first frequency and an arithmetic frequency ambiguity factor to obtain the width of a fourth sub-interval;

[0171] A second obtaining unit, configured to obtain a third sub-interval and a fourth sub-interval based on the first frequency, the second frequency, and the width of the fourth sub-interval.

[0172] In some alternative embodiments, the determining module 405 includes:

[0173] A fifth obtaining sub-module, configured to obtain the number of fourth sub-intervals based on the first frequency and the highest analysis frequency of the vibration spectrum;

[0174] A sixth obtaining sub-module, configured to obtain a ratio of the maximum value of the count value to the number of corresponding fourth sub-intervals to obtain a target value.

[0175] In some alternative embodiments, the determining module 405 includes:

[0176] A first determining sub-module, configured to compare the target value with a second preset threshold to determine whether the bearing fails, where the second preset threshold is used to determine the bearing failure;

[0177] A second determining sub-module, configured to determine that the bearing fails when the target value of any sub-component is greater than or equal to the corresponding second preset threshold;

[0178] A third determining sub-module, configured to determine that the bearing does not fail when the target values of all sub-components are less than the corresponding second preset thresholds.

[0179] In some alternative embodiments, the apparatus further includes:

[0180] A third obtaining module, configured to obtain a fifth frequency and a sixth frequency corresponding to the target value when it is determined that the bearing fails, where the fifth frequency is included in the first sub-interval and the sixth frequency is included in the second sub-interval;

[0181] A sixth obtaining module, configured to obtain a fifth sub-interval based on the fifth frequency and the sixth frequency, where the fifth sub-interval is used to characterize the fault frequency interval in the vibration spectrum;

[0182] A fourth obtaining module, configured to obtain the effective value of the physical quantity corresponding to the vibration spectrum within the fifth sub-interval;

[0183] A second determining module, configured to compare the effective value with a third preset threshold to determine the fault level of the sub-component, where the third preset threshold is used to determine the fault level of the sub-component;

[0184] A third determining module, configured to determine the fault level of the bearing based on the fault level of the sub-component.

[0185] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0186] The bearing fault detection device in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0187] This disclosure embodiment also provides a computer device having the above Figure 4 shown bearing fault detection device.

[0188] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a computer device provided by an optional embodiment of this disclosure. As Figure 5 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional implementation manners, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 5 In

[0189] FIG. 404, one processor 10 is taken as an example.

[0190] The processor 10 may be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 may further include a hardware chip. The above hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device may be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0191] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0192] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memory.

[0193] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0194] The embodiments of the present disclosure also provide a computer-readable storage medium. The methods according to the embodiments of the present disclosure can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the methods described herein can be stored in such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0195] A part of the present disclosure can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can call or provide the methods and / or technical solutions according to the present disclosure through the operations of the computer. Those skilled in the art should understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways for a computer to execute computer program instructions include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0196] Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for detecting bearing faults, characterized in that, The method includes: Obtaining the vibration spectrum of the sub-components of the bearing to be detected, where the number of the sub-components is at least one; Scanning the vibration spectrum within a first sub-interval of a frequency interval to obtain a first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum, and the first sub-interval is used to characterize that the probability of the vibration spectrum having a homologous frequency of a fault characteristic frequency is greater than a first preset threshold, and the number of the first frequencies is at least one; Obtaining a second sub-interval based on the first frequency, and scanning the vibration spectrum within the second sub-interval to obtain a second frequency, where the second sub-interval is used to characterize that the probability of the vibration spectrum having a bias frequency of a fault characteristic frequency is greater than the first preset threshold; Obtaining the inclusion relationship between the frequency value corresponding to the peak within a third sub-interval and a fourth sub-interval, and adjusting a counter to obtain a count value, where the third sub-interval and the fourth sub-interval are obtained based on the first frequency, the second frequency, and an equal-difference frequency fuzzy factor; Obtaining a target value based on the count value, and determining whether the bearing has a fault based on the target value, where the target value is used to characterize the proportion of the maximum value in the count value to the number of corresponding fourth sub-intervals.

2. The method according to claim 1, wherein Before scanning the vibration spectrum within the first sub-interval of the frequency interval to obtain the first frequency, the method further includes: Obtaining the fault characteristic frequency order of the sub-component and the rotational frequency of the equipment where the bearing is located; Fusing the fault characteristic frequency order and the rotational frequency to obtain a third frequency; Obtaining the first sub-interval based on the third frequency and a frequency scanning range factor.

3. The method according to claim 1, wherein The obtaining the inclusion relationship between the frequency value corresponding to the peak within the third sub-interval and the fourth sub-interval includes: Obtaining the third sub-interval and the fourth sub-interval based on the first frequency, the second frequency, and the equal-difference frequency fuzzy factor; Scanning the vibration spectrum within the third sub-interval to obtain the peak within the third sub-interval; Obtaining the frequency value corresponding to the peak within the third sub-interval based on the peak within the third sub-interval; Comparing the frequency value with the fourth sub-interval to obtain the inclusion relationship between the frequency value and the fourth sub-interval.

4. The method according to claim 3, wherein The obtaining the third sub-interval and the fourth sub-interval based on the first frequency, the second frequency, and the equal-difference frequency fuzzy factor includes: Fusing the first frequency and the equal-difference frequency fuzzy factor to obtain the width of the fourth sub-interval; Obtaining the third sub-interval and the fourth sub-interval based on the first frequency, the second frequency, and the width of the fourth sub-interval.

5. The method according to claim 1, wherein The obtaining the target value based on the count value includes: Obtaining the number of the fourth sub-intervals based on the first frequency and the highest analysis frequency of the vibration spectrum; Obtaining the proportion of the maximum value in the count value to the number of corresponding fourth sub-intervals to obtain the target value.

6. The method according to claim 1, characterized in that, The determining whether the bearing has a fault based on the target value includes: Compare the target value with a second preset threshold to determine whether the bearing fails, where the second preset threshold is used to determine that the bearing fails; When the target value of any of the sub-components is greater than or equal to the corresponding second preset threshold, it is determined that the bearing fails; When the target values of all the sub-components are less than the corresponding second preset thresholds, it is determined that the bearing does not fail.

7. The method according to claim 1, characterized in that, After determining whether the bearing fails based on the target value, the method further includes: When it is determined that the bearing fails, obtain a fifth frequency and a sixth frequency corresponding to the target value, where the fifth frequency is included in the first sub-interval and the sixth frequency is included in the second sub-interval; Based on the fifth frequency and the sixth frequency, obtain a fifth sub-interval, where the fifth sub-interval is used to characterize the fault frequency interval in the vibration spectrum; Obtain the effective value of the physical quantity corresponding to the vibration spectrum within the fifth sub-interval; Compare the effective value with a third preset threshold to determine the fault level of the sub-component, where the third preset threshold is used to determine the fault level of the sub-component; Based on the fault level of the sub-component, determine the fault level of the bearing.

8. A detection device for bearing faults, characterized in that, The device includes: A first acquisition module, configured to acquire the vibration spectrum of the sub-components of the bearing to be detected, where the number of the sub-components is at least one; A first obtaining module, configured to scan the vibration spectrum within a first sub-interval of a frequency interval to obtain a first frequency, where the frequency interval is composed of the frequencies of the vibration spectrum, and the first sub-interval is used to characterize that the probability of the homologous frequencies of the fault characteristic frequencies appearing in the vibration spectrum is greater than a first preset threshold, and the number of the first frequencies is at least one; A second obtaining module, configured to obtain a second sub-interval based on the first frequency, and scan the vibration spectrum within the second sub-interval to obtain a second frequency, where the second sub-interval is used to characterize that the probability of the offset frequency of the fault characteristic frequencies appearing in the vibration spectrum is greater than the first preset threshold; A third obtaining module, configured to obtain the inclusion relationship between the frequency value corresponding to the peak value within a third sub-interval and a fourth sub-interval, adjust a counter, and obtain a count value, where the third sub-interval and the fourth sub-interval are obtained based on the first frequency, the second frequency, and an equal-difference frequency fuzzy factor; A first determination module, configured to obtain a target value based on the count value, and determine whether the bearing fails based on the target value, where the target value is used to characterize the proportion of the maximum value in the count value to the number of the corresponding fourth sub-intervals.

9. A computer device, characterized in that, Including: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method for detecting bearing faults according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the method for detecting a bearing fault according to any one of claims 1 to 7.

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