A bearing fault detection method, system and device

Abstract

The present invention discloses a bearing fault detection method, system and device, which relates to the field of fault detection technology, obtains impact data that characterizes the impact information of each bearing component in the bearing in the time domain within the current sampling period, processes the impact data to obtain an impact spectrum, and then determines the bearing component that has failed in the bearing, and further determines the fault level of the bearing component that has failed based on the impact data. It can be seen that according to the method in the present application, although the temperature change is not obvious when a minor fault occurs in the early stage of the bearing component, it can still be discovered and determined in time, so as to subsequently alarm, which is convenient for developers to discover and handle, and avoid the fault from further affecting the work of other components in the rotating mechanical equipment including the bearing, thereby ensuring the reliability and timeliness of the solution; and compared with the relatively lagging early warning method in the prior art, it ensures the normal operation of other components, saves maintenance costs, and ensures the reliable and safe operation of the rotating mechanical equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to a bearing fault detection method, system and device. Background Art

[0002] The main fan, also known as the axial flow fan, is one of the important equipment in the metro electromechanical environment control system. It plays an important role in air supply, exhaust, heat removal and accident smoke exhaust, and maintains the operation safety of the metro. However, the main fan is bound to fail during operation, and the failure that most affects the reliable operation of the main fan is the bearing failure.

[0003] Therefore, in order to detect bearing failures, the prior art will install a temperature sensor inside the main fan when it leaves the factory, and monitor the bearing temperature through the temperature sensor. However, when a minor failure occurs in the early stage of the bearing, the temperature change of the bearing is very small, so the temperature sensor naturally cannot reach the temperature alarm threshold and will not alarm; but when the temperature sensor alarms for excessively high temperature of the bearing, the bearing has been running at an extremely high temperature for a period of time and has been seriously damaged. In addition, due to the extremely high temperature operation of the bearing, other components in the main fan are likely to be damaged, so other components in the main fan need to be screened to further clarify the problem.

[0004] It can be seen that the reliability of the detection method in the prior art is low, and it is essentially an untimely, post-warning method. Therefore, how to find an effective method for bearing fault detection is a problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to provide a bearing fault detection method, system and device, which can realize early and accurate detection of bearing faults and corresponding fault levels, facilitate developers to discover and deal with them in time, ensure the normal operation of other components in rotating mechanical equipment including bearings, save maintenance costs, and ensure the reliable and safe operation of rotating mechanical equipment.

[0006] In order to solve the above technical problems, the present invention provides a bearing fault detection method, comprising:

[0007] Acquire impact data representing impact information of each bearing component in the bearing in the time domain during the current sampling period;

[0008] Processing the impact data to obtain an impact spectrum representing the impact information of each bearing component in the frequency domain;

[0009] Determine the bearing component that has failed in the bearing within the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault position correspondence relationship;

[0010] A fault level of the failed bearing component in the current sampling period is determined based on the impact data.

[0011] Preferably, determining the bearing component having a fault in the bearing within the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault position correspondence relationship includes:

[0012] For each fault frequency in the pre-stored fault frequency-fault position correspondence relationship, determining whether there is a peak in the impulse spectrum within a preset error frequency range corresponding to the fault frequency;

[0013] If so, the fault position corresponding to the fault frequency is determined based on the fault frequency-fault position correspondence relationship to determine the bearing component in which the fault occurs in the bearing during the current sampling period.

[0014] Preferably, determining the fault level of the faulty bearing component in the current sampling period based on the impact data includes:

[0015] Determining an impact amplitude representing the impact intensity of the failed bearing component based on the impact data;

[0016] The fault level of the bearing component that fails in the current sampling period is determined according to the impact amplitude and the preset graded alarm threshold.

[0017] Preferably, determining the impact amplitude characterizing the impact strength of the failed bearing component based on the impact data comprises:

[0018] Determine, based on the impact data, an impact characteristic value corresponding to the impact data and characterizing an impact condition of the failed bearing component;

[0019] An impact amplitude representing the impact intensity of the failed bearing component is determined based on the impact characteristic value.

[0020] Preferably, determining an impact characteristic value corresponding to the impact data and characterizing an impact condition of the failed bearing component based on the impact data includes:

[0021] Determine, based on the impact data and a preset data-characteristic value relationship, an impact characteristic value corresponding to the impact data and characterizing an impact condition of the failed bearing component;

[0022] The preset data-characteristic value relationship is:

[0023]

[0024] Wherein, SV is the impact characteristic value, N is the total number of revolutions of the rotor in the rotating mechanical equipment including the bearing in the current sampling period, and A n is the maximum value of the impact data corresponding to the nth rotation of the rotor, wherein 1≤n≤N is an integer, and N is an integer not less than 1.

[0025] Preferably, determining the impact amplitude representing the impact strength of the failed bearing component based on the impact characteristic value includes:

[0026] Determining an impact amplitude representing the impact strength of the failed bearing component based on the impact characteristic value and a preset characteristic value-strength relationship;

[0027] The preset characteristic value-intensity relationship is:

[0028]

[0029] Wherein, SV is the impact characteristic value, dB is the impact amplitude, E is the rotation speed of the rotor in the rotating mechanical equipment including the bearing, and D is the shaft diameter of the bearing.

[0030] Preferably, the preset graded alarm thresholds include a warning threshold, a first-level alarm threshold and a second-level alarm threshold, wherein the warning threshold < the first-level alarm threshold < the second-level alarm threshold;

[0031] Determining the fault level of the bearing component that has failed in the current sampling period according to the impact amplitude and the preset graded alarm threshold includes:

[0032] When the warning threshold value ≤ the impact amplitude value < the first-level alarm threshold value, determining that the fault level of the bearing component having the fault is a warning level;

[0033] When the first-level alarm threshold value ≤ the impact amplitude value < the second-level alarm threshold value, determining that the fault level of the faulty bearing component is level one;

[0034] When the impact amplitude is greater than or equal to the level 2 alarm threshold value, it is determined that the fault level of the faulty bearing component is level 2.

[0035] Preferably, a sensor is provided on the bearing;

[0036] Obtain the impact data representing the impact information of each bearing component in the bearing in the time domain during the current sampling period, including:

[0037] The sensor is used to obtain impact data representing the impact information of each bearing component in the bearing in the time domain during the current sampling period.

[0038] Preferably, after determining the fault level of the faulty bearing component in the current sampling period based on the impact data, the method further comprises:

[0039] S21: Determine whether the number of impact sampling times i for acquiring the impact data in the current sampling period is not less than N; if so, proceed to S22; if not, proceed to S29; wherein 1≤i≤N and i is an integer, and N is an integer not less than 1;

[0040] S22: Counting the number of faults corresponding to each bearing component that has a fault within all the impact sampling times in the current sampling period;

[0041] S23: Counting the number of occurrences of the fault level corresponding to each of the bearing components that have failed within all the impact sampling times in the current sampling period;

[0042] S24: taking the highest fault level among all fault levels in the current sampling period as the current alarm level determination mark;

[0043] S25: Determine whether the number of occurrences corresponding to the current alarm level determination mark is greater than the comprehensive decision-making graded impact alarm threshold; if so, proceed to S26; if not, proceed to S28;

[0044] S26: determining the bearing component corresponding to the maximum value of the number of occurrences of each fault as an alarm bearing component, and using the current alarm level determination mark as the alarm level of the alarm bearing component;

[0045] S27: outputting bearing component failure alarm information corresponding to the alarm bearing component and its alarm level;

[0046] S28: taking the highest fault level after the current alarm level determination mark as the current alarm level determination mark, and returning to S25;

[0047] S29: Let i=i+1, and return to the step of obtaining impact data representing the impact information of each bearing component in the bearing in the time domain within the current sampling period.

[0048] Preferably, after outputting the bearing component failure alarm information corresponding to the alarm bearing component and its alarm level, the method further includes:

[0049] Clear all impact data, impact sampling times, occurrence times, fault occurrence times, faulty bearing components and corresponding fault levels recorded in the current sampling period.

[0050] In order to solve the above technical problems, the present invention also provides a bearing fault detection system, comprising:

[0051] An acquisition unit, used to acquire impact data representing impact information of each bearing component in the bearing in the time domain within a current sampling period;

[0052] A processing unit, used for processing the impact data to obtain an impact spectrum representing the impact information of each bearing component in the frequency domain;

[0053] A bearing fault determination unit, configured to determine a bearing component that has failed in the bearing within the current sampling period according to the impact spectrum and a pre-stored fault frequency-fault position correspondence relationship;

[0054] A fault level determination unit is used to determine the fault level of the bearing component that has failed in the current sampling period based on the impact data.

[0055] In order to solve the above technical problems, the present invention also provides a bearing fault detection device, comprising:

[0056] Memory for storing computer programs;

[0057] A processor is used to execute the steps of the bearing fault detection method as described above.

[0058] The present invention provides a bearing fault detection method, system and device, which obtains impact data that characterizes the impact information of each bearing component in the bearing in the time domain within the current sampling period, obtains the impact spectrum by processing the impact data, and then determines the bearing component that has failed in the bearing and further determines the fault level of the bearing component that has failed based on the impact data. It can be seen that according to the method in the present application, although the temperature change is not obvious when a minor fault occurs in the early stage of the bearing component, it can still be discovered and determined in time, so as to subsequently alarm, which is convenient for developers to discover and handle, and avoid the fault from further affecting the work of other components in the mechanical equipment including the bearing, thereby ensuring the reliability and timeliness of the solution; and compared with the relatively lagging early warning method in the prior art, it ensures the normal operation of other components, saves maintenance costs, and ensures the reliable and safe operation of mechanical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the prior art and the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 A flow chart of a bearing fault detection method provided by the present invention;

[0061] Figure 2 A structural schematic diagram of a bearing fault detection system provided by the present invention;

[0062] Figure 3 A structural schematic diagram of a bearing fault detection device provided by the present invention. DETAILED DESCRIPTION

[0063] The core of the present invention is to provide a bearing fault detection method, system and device, which can realize early and accurate detection of bearing faults and corresponding fault levels, facilitate developers to discover and deal with them in time, ensure the normal operation of other components in rotating mechanical equipment including bearings, save maintenance costs, and ensure the reliable and safe operation of rotating mechanical equipment.

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Please refer to Figure 1 , Figure 1 The present invention provides a flow chart of a fault detection method.

[0066] The bearing fault detection method comprises:

[0067] S11: Acquire impact data representing impact information of each bearing component in the bearing in the time domain within the current sampling period;

[0068] S12: Processing the impact data to obtain an impact spectrum representing the impact information of each bearing component in the frequency domain;

[0069] S13: determining the bearing component that has failed in the bearing within the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault position correspondence relationship;

[0070] S14: Determine the fault level of the bearing component that fails in the current sampling period based on the impact data.

[0071] In this embodiment, in order to detect the bearing fault, the conventional method is to install a temperature sensor inside, and monitor the bearing temperature based on the temperature sensor and the preset temperature alarm threshold. However, when a bearing has a minor fault, the change in its temperature is very small, so that the temperature sensor will not detect the fault and alarm; and by the time the temperature sensor alarms, the bearing and other components in the main fan including the bearing have usually been damaged due to the temperature increase caused by the bearing fault. In order to solve the above technical problems, the present application provides a bearing fault detection method, which can reliably and timely detect bearing faults.

[0072] First of all, the bearing here can specifically be a bearing in a rotating mechanical device, and the rotating mechanical device includes but is not limited to an axial flow fan (main fan), which is not specifically limited here; taking the bearing here as an example, the bearing in the fan, the bearing fault detection method can be specifically applied to the control device in the main fan, such as a diagnostic instrument in the fan, which is not specifically limited here.

[0073] Obtain impact data that characterizes the impact information of each bearing component in the bearing in the time domain within the current sampling period. It should be noted that, based on the fact that when a minor fault occurs in the early stage of the bearing, its temperature change is not obvious, the duration of the sampling period can be set to be less than the minimum value of the time required for each bearing component from the occurrence of the fault to its temperature reaching the temperature alarm threshold. It can be understood that if there is only a temperature alarm threshold for the entire bearing, it is only necessary to adaptively set the duration of the sampling period to be less than the time required for the entire bearing from the occurrence of the fault to its temperature reaching the temperature alarm threshold, and no special limitation is made here.

[0074] It should also be noted that the various bearing components here include but are not limited to an inner ring, an outer ring, rollers and a retaining frame.

[0075] A sensor can be installed on the bearing end cover. Therefore, for step S11, the speed tracking sampling technology can be used to collect the impact data in the time domain through the sensor, and further considering that the signal when the bearing component fails is usually a high-frequency fault signal, the resonance demodulation technology can be used to filter the impact data collected by the above-mentioned sensor to filter out the low-frequency vibration signal, and the filtered impact data is used as the impact data in the current sampling period. No special limitation is made here, and it is sufficient to implement the execution logic in this application.

[0076] Subsequently, the impact data is processed to obtain an impact spectrum representing the impact information of each bearing component in the frequency domain. Specifically, the impact data may be subjected to a fast Fourier transform to obtain the impact spectrum in step S12.

[0077] Afterwards, the bearing component in which the fault occurs in the bearing can be determined according to the impact spectrum and the pre-stored fault frequency-fault position correspondence. Specifically, the fault position in the correspondence here includes each of the bearing components, and the corresponding fault frequency refers to the fault frequency value corresponding to each of the bearing components in the frequency domain when the fault occurs. Furthermore, the determination mechanism of the fault frequency-fault position correspondence here can be to predetermine the fault frequency-fault position correspondence when each bearing component fails according to the bearing component parameters of the bearing, and store it, wherein the bearing component parameters here can include the middle diameter of the bearing, the shaft diameter of the bearing, the number of rolling elements, the diameter of the rolling element, and the contact angle of the rolling element. In order to facilitate the distinction of the various bearing components that have failed, the fault number corresponding to each bearing component can be set. Taking the bearing components including the inner ring, the outer ring, the roller and the cage as an example, the outer ring is numbered 1 when it fails, the inner ring is numbered 2 when it fails, the roller is numbered 3 when it fails, and the cage is numbered 4 when it fails, so if none of the bearing components fails, it can be numbered 0.

[0078] Finally, the fault level of the bearing component that fails in the current sampling period is determined based on the impact data. It is understandable that after the bearing component that fails in the bearing and the corresponding fault level are determined, corresponding alarm information can be further output to prompt maintenance personnel to perform maintenance.

[0079] In summary, the present application provides a bearing fault detection method. Compared with the prior art, the scheme has higher reliability and timeliness, can achieve early detection of bearing faults, and can further determine which specific bearing component has failed, i.e., accurately detect, and the corresponding fault level, i.e., graded alarm, so that developers can discover and perform repairs in time. Compared with the relatively lagging early warning method in the prior art, this early warning method ensures the normal operation of other components in the rotating mechanical equipment including bearings, saves maintenance costs, and ensures the reliable and safe operation of the rotating mechanical equipment.

[0080] Based on the above embodiments:

[0081] As a preferred embodiment, determining the bearing component having a fault in the bearing within the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault position correspondence relationship includes:

[0082] For each fault frequency in the pre-stored fault frequency-fault position correspondence relationship, determine whether there is a peak in the preset error frequency range corresponding to the fault frequency in the impulse spectrum;

[0083] If so, the fault position corresponding to the fault frequency is determined based on the fault frequency-fault position correspondence relationship to determine the bearing component that has failed in the bearing within the current sampling period.

[0084] In this embodiment, in order to determine the bearing component that has a fault in the bearing, based on each fault frequency stored in the pre-stored fault frequency-fault position correspondence, that is, taking each fault frequency as a reference, it is determined whether there is a peak in the preset error frequency range corresponding to the fault frequency in the impact spectrum. If so, it means that there is a faulty bearing component at this time. Therefore, based on the fault frequency, the fault position corresponding to the fault frequency can be determined in the fault frequency-fault position correspondence, so that the faulty bearing component in the bearing corresponding to the fault frequency can be determined. It should be noted that the preset error frequency range here can be set according to actual needs, and the preset error frequency range corresponding to each fault frequency can be different or the same, and is not particularly limited here.

[0085] Specifically, as an example, the impact spectrum is essentially a multi-order spectrum line, whose horizontal axis is frequency and the vertical axis is peak value. Here, an outer loop fault is taken as an example to illustrate. It is assumed that in the pre-stored fault frequency-fault position correspondence, the outer loop fault frequency corresponding to the outer loop fault (fault position) is 100, and the preset error frequency range corresponding to the outer loop fault is 97-103. Therefore, based on the outer loop fault frequency as the reference, it is determined whether there is a peak within the preset error frequency range of 97-103 in the impact spectrum. If so, based on the above fault frequency-fault position correspondence, it can be determined that the location of the fault is the outer loop.

[0086] It can be seen that, through the above execution logic, the determination of the faulty bearing component can be realized simply and reliably, which is easy to implement.

[0087] As a preferred embodiment, determining the fault level of a bearing component that fails in a current sampling period based on the impact data includes:

[0088] determining an impact amplitude representing the impact intensity of the failed bearing component based on the impact data;

[0089] The fault level of the bearing component that fails in the current sampling period is determined according to the impact amplitude and the preset classification alarm threshold.

[0090] In this embodiment, a method for determining the fault level of a faulty bearing component is provided, wherein the impact amplitude describes the magnitude of the impact intensity to which the faulty bearing component is subjected.

[0091] Specifically, the preset graded alarm thresholds here can be set according to actual needs, and can be one or more, and the specific preset graded alarm thresholds can be the same or different, and are not particularly limited here.

[0092] It can be seen that by setting the above-mentioned graded alarm execution logic, quantitative and graded alarms can be implemented for faulty bearing components, which is convenient for subsequent maintenance personnel to handle in a targeted manner, and realizes early alarm and graded alarm for faulty bearing components.

[0093] As a preferred embodiment, determining the impact amplitude representing the impact strength of the failed bearing component based on the impact data includes:

[0094] Determine, based on the impact data, an impact characteristic value corresponding to the impact data and characterizing an impact condition of the failed bearing component;

[0095] An impact amplitude representing the impact intensity of the failed bearing component is determined based on the impact characteristic value.

[0096] In this embodiment, a method for determining the impact amplitude of a failed bearing component is provided, which is specifically described above and will not be repeated here.

[0097] As a preferred embodiment, determining an impact characteristic value corresponding to the impact data and characterizing an impact condition of a failed bearing component based on the impact data includes:

[0098] Determine, based on the impact data and a preset data-characteristic value relationship, an impact characteristic value corresponding to the impact data and representing an impact condition of a failed bearing component;

[0099] The default data-eigenvalue relationship is:

[0100]

[0101] Where SV is the impact characteristic value, N is the total number of revolutions of the rotor in the rotating mechanical equipment including the bearing in the current sampling period, and A n It is the maximum value among the impact data corresponding to the nth rotation of the rotor, wherein 1≤n≤N is an integer, and N is an integer not less than 1.

[0102] In this embodiment, a specific method for determining the impact characteristic value is provided, as described above, and the implementation method is simple and reliable. It should be noted that the impact data is the impact data obtained by rotating the rotor in the rotating mechanical equipment including the bearing for a total of N turns, and one or more corresponding impact data can be obtained for each turn, so the impact characteristic value can be obtained according to the above-mentioned preset data-characteristic value relationship.

[0103] As a preferred embodiment, determining the impact amplitude representing the impact strength of the failed bearing component based on the impact characteristic value includes:

[0104] Determine an impact amplitude representing the impact strength of the failed bearing component based on the impact characteristic value and a preset characteristic value-strength relationship;

[0105] The default eigenvalue-intensity relationship is:

[0106]

[0107] Wherein, SV is the impact characteristic value, dB is the impact amplitude, E is the rotation speed of the rotor in the rotating mechanical equipment including the bearing, and D is the shaft diameter of the bearing.

[0108] In this embodiment, a specific method for determining the impact amplitude is provided. As described above, the implementation method is simple and reliable. It should be noted that the impact amplitude represents the magnitude of the impact strength of the failed bearing component. Specifically, in actual application, the unit of the rotor speed E in the preset characteristic value-strength relationship can be revolutions per minute; the unit of the shaft diameter D of the bearing here can be millimeters.

[0109] As a preferred embodiment, the preset graded alarm thresholds include a warning threshold, a first-level alarm threshold and a second-level alarm threshold, wherein the warning threshold < the first-level alarm threshold < the second-level alarm threshold;

[0110] The fault level of the bearing component that fails in the current sampling period is determined based on the impact amplitude and the preset graded alarm threshold, including:

[0111] When the warning threshold value ≤ the impact amplitude value < the first-level alarm threshold value, it is determined that the fault level of the faulty bearing component is the warning level;

[0112] When the first-level alarm threshold value ≤ impact amplitude < second-level alarm threshold value, it is determined that the fault level of the faulty bearing component is level one;

[0113] When the impact amplitude is ≥ the second-level alarm threshold value, it is determined that the fault level of the faulty bearing component is second level.

[0114] In this embodiment, the preset graded alarm thresholds here may include a warning threshold value, a first-level alarm threshold value and a second-level alarm threshold value. The fault level of the second-level alarm is higher than the fault level of the first-level alarm, and the fault level of the first-level alarm is higher than the fault level of the warning level. Therefore, the warning threshold value < the first-level alarm threshold value < the second-level alarm threshold value.

[0115] When the impact amplitude is less than the warning threshold value, it means that although the bearing component that has failed at this time has some minor faults, it is not necessary to issue an alarm temporarily, and subsequent continuous monitoring is sufficient; it should be noted that, in order to facilitate the distinction of different fault levels, a binary marking method can be used here, such as marking the fault level of the bearing fault component corresponding to the embodiment here as 0000, or a decimal marking method can be directly used, such as marking the fault level of the bearing fault component corresponding to the embodiment here as 0;

[0116] When the warning threshold value ≤ impact amplitude < first-level alarm threshold value, it means that the fault level of the faulty bearing component has reached the warning level, so the fault level of the faulty bearing component is determined to be the warning level. It should be noted that, in order to distinguish different fault levels, a binary marking method can be used here, such as marking the fault level of the bearing fault component corresponding to the embodiment here as 0001, or a decimal marking method can be directly used, such as marking the fault level of the bearing fault component corresponding to the embodiment here as 1;

[0117] When the first-level alarm threshold value ≤ impact amplitude < second-level alarm threshold value, it means that the fault level of the faulty bearing component has reached the fault level of the first-level alarm, so the fault level of the faulty bearing component is determined to be level 1. It should be noted that, in order to distinguish different fault levels, a binary marking method can be used here, such as marking the fault level of the bearing fault component corresponding to the embodiment here as 0010, or a decimal marking method can be directly used, such as marking the fault level of the bearing fault component corresponding to the embodiment here as 2;

[0118] When the impact amplitude is ≥ the level 2 alarm threshold, it indicates that the fault level of the bearing component that has failed is very high and has reached the level 2 alarm fault level, so the fault level of the bearing component that has failed is determined to be level 2. It should be noted that, in order to facilitate the distinction of different fault levels, a binary marking method may be used here, such as marking the fault level of the bearing fault component corresponding to the embodiment here as 0011, or a decimal marking method may be directly used, such as marking the fault level of the bearing fault component corresponding to the embodiment here as 3, which is not particularly limited here.

[0119] It should also be noted that here, the example of 3 preset graded alarm thresholds is used for explanation. In actual applications, of course, more or fewer preset graded alarm thresholds can be set according to actual needs, and no special limitation is made here.

[0120] It can be seen that by setting the above preset graded alarm thresholds and the corresponding execution logic, the graded alarm for the failed bearing components can be reliably implemented, which is convenient for subsequent maintenance personnel to handle it in a targeted and timely manner.

[0121] As a preferred embodiment, a sensor is provided on the bearing;

[0122] Obtain the impact data representing the impact information of each bearing component in the bearing in the time domain during the current sampling period, including:

[0123] The impact data representing the impact information of each bearing component in the bearing in the time domain within the current sampling period is obtained through the sensor.

[0124] In this embodiment, in order to obtain the impact data within the current sampling period, a sensor can be set on the bearing, such as on the end cover of the bearing. The sensor can reliably obtain impact data representing the impact information of each bearing component in the bearing in the time domain within a first preset time length.

[0125] As a preferred embodiment, after determining the fault level of the bearing component that fails in the current sampling period based on the impact data, the method further includes:

[0126] S21: Determine whether the number of impact sampling times i for acquiring impact data in the current sampling period is not less than N; if so, proceed to S22; if not, proceed to S29; wherein 1≤i≤N and i is an integer, and N is an integer not less than 1;

[0127] S22: Counting the number of faults corresponding to each bearing component that has failed within all the impact sampling times in the current sampling period;

[0128] S23: Counting the number of occurrences of the fault level corresponding to each bearing component that has a fault within all the impact sampling times in the current sampling period;

[0129] S24: taking the highest fault level among all fault levels in the current sampling period as the current alarm level determination mark;

[0130] S25: Determine whether the number of occurrences corresponding to the current alarm level determination mark is greater than the comprehensive decision-making graded impact alarm threshold; if so, proceed to S26; if not, proceed to S28;

[0131] S26: Determine the bearing component corresponding to the maximum value of the number of occurrences of each fault as the alarm bearing component, and use the current alarm level determination mark as the alarm level of the alarm bearing component;

[0132] S27: outputting bearing component fault alarm information corresponding to the alarm bearing component and its alarm level;

[0133] S28: taking the highest fault level after the current alarm level determination mark as the current alarm level determination mark, and returning to S25;

[0134] S29: Let i=i+1, and return to the step of obtaining impact data representing the impact information of each bearing component in the bearing in the time domain within the current sampling period.

[0135] In this embodiment, the inventors consider that in order to further ensure the practicability of the method, when the number of impact sampling reaches a preset impact sampling threshold N, the corresponding bearing component fault alarm information can be output.

[0136] First, at each impact sampling number, the faulty bearing component and the corresponding fault level are stored. Then, after determining the fault level of the bearing component that has failed in the current sampling period based on the impact data, it is determined whether the impact sampling number i of the impact data in the current sampling period is not less than N;

[0137] If not, it means that the impact sampling number i has not reached the preset impact sampling threshold N at this time, and storage can continue without temporarily alarming. Then the impact sampling number i is accumulated, and the step of obtaining the impact data of the impact information of each bearing component in the bearing of the motor in the time domain in the current sampling period is returned to continue sampling.

[0138] If yes, it means that the impact sampling times i has reached the preset impact sampling threshold value N, and it is necessary to count the stored information and output the corresponding bearing component fault alarm information so as to carry out maintenance as soon as possible. Therefore, firstly, count the number of faults corresponding to each bearing component that fails within all the impact sampling times in the current sampling period (of course, it is also possible to count the percentage of the number of faults corresponding to each bearing component that fails in the total impact sampling times N), and count all the impact sampling times in the current sampling period, that is, the number of occurrences of each fault level within N (of course, it is also possible to count the percentage of the number of occurrences of the fault level in the total impact sampling times N).

[0139] Then, considering that the higher the level of the fault, the higher its danger, starting from the highest level of the fault in the current sampling period, the highest level of all fault levels is used as the current alarm level judgment mark, and it is judged whether the number of occurrences corresponding to the current alarm level judgment mark is greater than the comprehensive decision-making graded impact alarm threshold;

[0140] If so, the current alarm level determination mark is used as the alarm level of the alarm bearing component, and based on the fact that the bearing component with the most fault occurrences usually has the same corresponding fault level, the bearing component corresponding to the maximum number of fault occurrences is determined as the alarm bearing component; finally, the bearing component fault alarm information corresponding to the alarm bearing component and its alarm level is output;

[0141] If not, it means that the alarm condition is not met under the current alarm level determination mark. Based on the fault level order from high to low, the highest fault level after the current alarm level determination mark is used as the current alarm level determination mark, and the determination logic of S25 is returned again. It can be understood that if there is no fault level that can be used as a new current alarm level determination mark after the current alarm level determination mark, the cycle can be stopped, and the information of the faulty bearing component and the corresponding fault level corresponding to the current N times of impact sampling can be directly cleared, and i is cleared to restart the statistics and issue a fault alarm again when the preset impact sampling threshold N is reached again. No special limitation is made here.

[0142] It should be noted that the comprehensive decision-making graded impact alarm thresholds corresponding to different current alarm level determination marks may be the same or different, and may be set according to actual needs.

[0143] It can be understood that after the bearing component fault alarm information is output, the two parts of information corresponding to the current N impact sampling times, the faulty bearing component and the corresponding fault level, can be directly cleared, and i can be cleared to restart the statistics and issue a fault alarm when the preset impact sampling threshold N is reached again.

[0144] Specifically, as an example, assuming that the outer ring fails, the number is 1, the inner ring fails, the number is 2, the roller fails, the number is 3, and the retaining cage fails, the number is 4. If no bearing component fails, the number is 0. The preset impact sampling threshold N=10 is assumed. Assuming that the storage information of each bearing component that fails within the stored 10 impact samples is [0, 0, 1, 1, 1, 1, 2, 1, 1], the corresponding storage information of the fault level is [0, 0, 2, 2, 2, 2, 2, 1, 2, 1]. Therefore, the number of fault occurrences corresponding to each bearing component that has failed can be obtained by counting: the number of fault occurrences of the outer ring fault is 7, the number of fault occurrences of the inner ring fault is 1, the number of fault occurrences of the roller fault is 0, and the number of fault occurrences of the cage fault is 0; correspondingly, it has been explained in the above embodiment that the "2" here means that the fault level that has occurred is the first-level alarm, and the "1" here means that the fault level that has occurred is the early warning level. The number of occurrences of each fault level obtained by counting is 0 for the second-level alarm, 6 for the first-level alarm, and 2 for the early warning level. According to the above judgment logic, it can be seen that the highest level of all fault levels is the first level, which is used as the current alarm level judgment mark. Assuming that the comprehensive decision-making graded impact alarm threshold is 2, the number of occurrences 6 is greater than the comprehensive decision-making graded impact alarm threshold 2, and the maximum value of the number of occurrences of each fault is 7, the corresponding bearing component is the outer ring, then according to the above execution logic, it is determined that the alarm bearing component is the outer ring, and the alarm level is the first level.

[0145] It can be seen that through the above execution logic, the determination of the alarm bearing components and the corresponding alarm levels can be efficiently realized, which is more practical.

[0146] As a preferred embodiment, after outputting the bearing component failure alarm information corresponding to the alarm bearing component and its alarm level, the method further includes:

[0147] Clear all impact data, impact sampling times, occurrence times, fault occurrence times, faulty bearing components and corresponding fault levels recorded in the current sampling period.

[0148] In this embodiment, in order to further save storage space and avoid repeated alarms, once the alarm information is output, all data corresponding to the current N impact sampling times recorded in the current sampling period can be actively cleared so as to restart the statistics and issue a fault alarm again when the preset impact sampling threshold N is reached again. Therefore, the data here specifically includes the impact data stored in the current sampling period, the impact sampling times (that is, the impact sampling times are cleared to zero), the number of occurrences, the number of faults, the faulty bearing components and the corresponding fault levels.

[0149] Please refer to Figure 2 , Figure 2 A structural schematic diagram of a fault detection system provided by the present invention.

[0150] The fault detection system, applied to the fan, includes:

[0151] An acquisition unit 41 is used to acquire impact data representing impact information of each bearing component in the bearing in the time domain within a current sampling period;

[0152] The processing unit 42 is used to process the impact data to obtain an impact spectrum representing the impact information of each bearing component in the frequency domain;

[0153] The bearing fault determination unit 43 is used to determine the bearing component that has failed in the bearing within the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault position correspondence relationship.

[0154] The fault level determination unit 44 is used to determine the fault level of the bearing component that has failed in the current sampling period based on the impact data.

[0155] For an introduction to the bearing fault detection system provided in the present invention, please refer to the above-mentioned embodiment of the bearing fault detection method, which will not be described in detail here.

[0156] Please refer to Figure 3 , Figure 3 A structural schematic diagram of a bearing fault detection device provided by the present invention.

[0157] The bearing fault detection device comprises:

[0158] A memory 51, used for storing computer programs;

[0159] The processor 52 is used to execute the steps of the bearing fault detection method as described above.

[0160] For an introduction to the bearing fault detection device provided in the present invention, please refer to the above-mentioned embodiment of the bearing fault detection method, which will not be described in detail here.

[0161] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0162] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0163] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0164] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A bearing fault detection method, characterized in that: include: Acquire impact data representing impact information of each bearing component in the bearing in the time domain during the current sampling period; Processing the impact data to obtain an impact spectrum representing the impact information of each bearing component in the frequency domain; Determine the bearing component that has failed in the bearing within the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault position correspondence relationship; Determining a fault level of the bearing component having a fault in the current sampling period based on the impact data; After determining the fault level of the bearing component having a fault in the current sampling period based on the impact data, the method further includes: S21: Determine whether the number of impact sampling times i for acquiring the impact data in the current sampling period is not less than N; if so, proceed to S22; if not, proceed to S29; wherein 1≤i≤N and i is an integer, and N is an integer not less than 1; S22: Counting the number of faults corresponding to each bearing component that has a fault within all the impact sampling times in the current sampling period; S23: Counting the number of occurrences of the fault level corresponding to each of the bearing components that have failed within all the impact sampling times in the current sampling period; S24: taking the highest fault level among all fault levels in the current sampling period as the current alarm level determination mark; S25: Determine whether the number of occurrences corresponding to the current alarm level determination mark is greater than the comprehensive decision-making graded impact alarm threshold; if so, proceed to S26; if not, proceed to S28; S26: determining the bearing component corresponding to the maximum value of the number of occurrences of each fault as an alarm bearing component, and using the current alarm level determination mark as the alarm level of the alarm bearing component; S27: outputting bearing component failure alarm information corresponding to the alarm bearing component and its alarm level; S28: taking the highest fault level after the current alarm level determination mark as the current alarm level determination mark, and returning to S25; S29: Let i=i+1, and return to the step of obtaining impact data representing the impact information of each bearing component in the bearing in the time domain within the current sampling period.

2. The bearing fault detection method according to claim 1, characterized in that: Determining a bearing component that has a fault in the bearing within the current sampling period according to the impact spectrum and a pre-stored fault frequency-fault position correspondence relationship includes: For each fault frequency in the pre-stored fault frequency-fault position correspondence relationship, determining whether there is a peak in the impulse spectrum within a preset error frequency range corresponding to the fault frequency; If so, the fault position corresponding to the fault frequency is determined based on the fault frequency-fault position correspondence relationship to determine the bearing component in which the fault occurs in the bearing during the current sampling period.

3. The bearing fault detection method according to claim 1, characterized in that: Determining the fault level of the bearing component having a fault in the current sampling period based on the impact data includes: Determining an impact amplitude representing the impact intensity of the failed bearing component based on the impact data; The fault level of the bearing component that fails in the current sampling period is determined according to the impact amplitude and the preset graded alarm threshold.

4. The bearing fault detection method according to claim 3, characterized in that: Determining an impact amplitude representing the impact strength of the failed bearing component based on the impact data includes: Determine, based on the impact data, an impact characteristic value corresponding to the impact data and characterizing an impact condition of the failed bearing component; An impact amplitude representing the impact intensity of the failed bearing component is determined based on the impact characteristic value.

5. The bearing fault detection method according to claim 4, characterized in that: Determining an impact characteristic value corresponding to the impact data and characterizing an impact condition of the failed bearing component based on the impact data includes: Determine, based on the impact data and a preset data-characteristic value relationship, an impact characteristic value corresponding to the impact data and characterizing an impact condition of the failed bearing component; The preset data-characteristic value relationship is: ； Wherein, SV is the impact characteristic value, N is the total number of revolutions of the rotor in the rotating mechanical equipment including the bearing in the current sampling period, and A n is the maximum value among the impact data corresponding to the nth rotation of the rotor, wherein 1≤n≤N is an integer, and N is an integer not less than 1.

6. The bearing fault detection method according to claim 4, characterized in that: Determining an impact amplitude representing the impact strength of the failed bearing component based on the impact characteristic value includes: Determining an impact amplitude representing the impact strength of the failed bearing component based on the impact characteristic value and a preset characteristic value-strength relationship; The preset characteristic value-intensity relationship is: ； Wherein, SV is the impact characteristic value, dB is the impact amplitude, E is the rotation speed of the rotor in the rotating mechanical equipment including the bearing, and D is the shaft diameter of the bearing.

7. The bearing fault detection method according to claim 3, characterized in that: The preset graded alarm thresholds include a warning threshold, a first-level alarm threshold and a second-level alarm threshold, wherein the warning threshold < the first-level alarm threshold < the second-level alarm threshold; Determining the fault level of the bearing component that has failed in the current sampling period according to the impact amplitude and the preset graded alarm threshold includes: When the warning threshold value ≤ the impact amplitude value < the first-level alarm threshold value, determining that the fault level of the failed bearing component is a warning level; When the first-level alarm threshold value ≤ the impact amplitude value < the second-level alarm threshold value, determining that the fault level of the faulty bearing component is level one; When the impact amplitude is greater than or equal to the level 2 alarm threshold value, it is determined that the fault level of the faulty bearing component is level 2.

8. The bearing fault detection method according to claim 1, characterized in that: A sensor is provided on the bearing; Obtain impact data representing the impact information of each bearing component in the bearing in the time domain during the current sampling period, including: The sensor is used to obtain impact data representing the impact information of each bearing component in the bearing in the time domain during the current sampling period.

9. The bearing fault detection method according to claim 1, characterized in that: After outputting the bearing component failure alarm information corresponding to the alarm bearing component and its alarm level, the method further includes: Clear all impact data, impact sampling times, occurrence times, fault occurrence times, faulty bearing components and corresponding fault levels recorded in the current sampling period.

10. A bearing fault detection system, characterized in that: include: An acquisition unit, used to acquire impact data representing impact information of each bearing component in the bearing in the time domain within a current sampling period; A processing unit, configured to process the impact data to obtain an impact spectrum representing the impact information of each bearing component in the frequency domain; A bearing fault determination unit, configured to determine a bearing component that has failed in the bearing within the current sampling period according to the impact spectrum and a pre-stored fault frequency-fault position correspondence relationship; A fault level determination unit is used to determine the fault level of the bearing component having a fault in the current sampling period based on the impact data: The bearing fault detection system is also used for: S21: Determine whether the number of impact sampling times i for acquiring the impact data in the current sampling period is not less than N; if so, proceed to S22; if not, proceed to S29; wherein 1≤i≤N and i is an integer, and N is an integer not less than 1; S22: Counting the number of faults corresponding to each bearing component that has a fault within all the impact sampling times in the current sampling period; S23: Counting the number of occurrences of the fault level corresponding to each of the bearing components that have failed within all the impact sampling times in the current sampling period; S24: taking the highest fault level among all fault levels in the current sampling period as the current alarm level determination mark; S25: Determine whether the number of occurrences corresponding to the current alarm level determination mark is greater than the comprehensive decision-making graded impact alarm threshold; if so, proceed to S26; if not, proceed to S28; S26: determining the bearing component corresponding to the maximum value of the number of occurrences of each fault as an alarm bearing component, and using the current alarm level determination mark as the alarm level of the alarm bearing component; S27: outputting bearing component failure alarm information corresponding to the alarm bearing component and its alarm level; S28: taking the highest fault level after the current alarm level determination mark as the current alarm level determination mark, and returning to S25; S29: Let i=i+1, and return to the step of obtaining impact data representing the impact information of each bearing component in the bearing in the time domain within the current sampling period.

11. A bearing fault detection device, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the steps of the bearing fault detection method according to any one of claims 1 to 9.

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