CN114964779A - Bearing fault detection method, system and device - Google Patents

Abstract

The invention discloses a bearing fault detection method, a system and a device, which relate to the technical field of fault detection, and are used for acquiring impact data representing impact information of each bearing component in a bearing in a current sampling period in a time domain, processing the impact data to obtain an impact frequency spectrum, further determining the bearing component with the fault in the bearing, and further determining the fault grade of the bearing component with the fault according to the impact data. Therefore, according to the method in the application, although the temperature change is not obvious when the bearing part has a slight fault in the early stage, the grade can be timely found and determined so as to give an alarm later, developers can conveniently find and process the grade, the fault is prevented from further influencing the work of other parts in the rotating mechanical equipment comprising the bearing, and the reliability and timeliness of the scheme are further ensured; compared with a relatively lagging early warning mode in the prior art, the method and the device ensure normal work of other parts, save maintenance cost and ensure reliable and safe operation of rotary mechanical equipment.

Description

A bearing fault detection method, system and device

technical field

The invention relates to the technical field of fault detection, in particular to a bearing fault detection method, system and device.

Background technique

The main fan, also known as the axial fan, as one of the important equipment in the subway electromechanical environment control system, plays an important role in air supply, exhaust, heat exhaust and accident smoke exhaust, and maintains the operation safety of the subway. However, the main fan is unavoidable to fail during the operation, among which the fault that affects the reliable operation of the main fan is the bearing fault.

For this reason, in the prior art, in order to detect bearing faults, a temperature sensor is installed inside the main fan when it leaves the factory, and the temperature of the bearing is monitored through the temperature sensor. However, when the bearing has a slight fault in the early stage, the temperature change of the bearing is very small at this time, so the temperature sensor will not reach the temperature alarm threshold and will not alarm; It has been operating at ultra-high temperature for a period of time, and serious damage has occurred, and due to the ultra-high temperature operation of the bearing, it is very likely that other components in the main fan will also be damaged. Parts are screened to further clarify the problem.

It can be seen that the detection method in the prior art has low reliability and is essentially an untimely and post-warning method. Therefore, how to find an effective method for bearing fault detection is an urgent problem to be solved at present. .

SUMMARY OF THE INVENTION

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 find and deal with them in time, and ensure the rotating machinery including bearings. The normal operation of other components in the equipment saves maintenance costs and ensures the reliable and safe operation of rotating machinery and equipment.

In order to solve the above technical problems, the present invention provides a bearing fault detection method, including:

Acquire shock data representing the shock 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 impact information of each of the bearing components in the frequency domain;

determining the faulty bearing component in the bearing in the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault location correspondence;

A failure level of the failed bearing component during the current sampling period is determined based on the shock data.

Preferably, according to the impact spectrum and the pre-stored fault frequency-fault location correspondence, the bearing component that has failed in the bearing in the current sampling period is determined, including:

For each fault frequency in the pre-stored fault frequency-fault location correspondence, determine whether there is a peak value within a preset error frequency range corresponding to the fault frequency in the impact spectrum;

If so, determine a fault location corresponding to the fault frequency based on the fault frequency-fault location correspondence relationship, so as to determine a bearing component that has failed in the bearing within the current sampling period.

Preferably, determining the failure level of the faulty bearing component in the current sampling period based on the impact data includes:

determining, based on the impact data, an impact magnitude indicative of the impact strength experienced by the failed bearing component;

The failure level of the faulty bearing component in the current sampling period is determined according to the impact amplitude and the preset leveled alarm threshold.

Preferably, based on the impact data, determining an impact magnitude representing the impact strength of the faulty bearing component, including:

determining, based on the shock data, a shock characteristic value corresponding to the shock data that characterizes the shock condition of the failed bearing component;

An impact amplitude characterizing the impact strength experienced by the failed bearing component is determined based on the impact characteristic value.

Preferably, based on the impact data, determining an impact characteristic value corresponding to the impact data and representing the impact condition of the faulty bearing component, including:

determining an impact characteristic value corresponding to the impact data and representing the impact condition of the faulty bearing component based on the impact data and the preset data-eigenvalue relationship;

The preset data-eigenvalue relational formula is:

Wherein, SV is the shock 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 An is the corresponding number of rotations when the rotor rotates the _nth revolution. The maximum value in the impact data, where 1≤n≤N is an integer, and N is an integer not less than 1.

Preferably, determining an impact amplitude characterizing the impact strength of the bearing component in failure based on the impact characteristic value, including:

determining, based on the impact characteristic value and a preset characteristic value-strength relationship, an impact amplitude characterizing the impact strength of the faulty bearing component;

The preset eigenvalue-intensity relationship is:

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

Preferably, the preset hierarchical alarm threshold includes an early warning threshold, a primary alarm threshold and a secondary alarm threshold, wherein the early warning threshold < the primary alarm threshold < the Level 2 alarm threshold;

Determining the failure level of the faulty bearing component in the current sampling period according to the impact amplitude and the preset classification alarm threshold, including:

When the pre-warning threshold value≤the impact amplitude value<the first-level alarm threshold value, determine the failure level of the faulty bearing component as the pre-warning level;

When the first-level alarm threshold value≤the impact amplitude value<the second-level alarm threshold value, determine that the failure level of the bearing component that has failed is first-level;

When the shock amplitude is greater than or equal to the second-level alarm threshold value, it is determined that the failure level of the faulty bearing component is the second-level.

Preferably, a sensor is provided on the bearing;

Obtain shock data representing shock information of each bearing component in the bearing in the time domain during the current sampling period, including:

The impact data representing the impact information of each bearing component in the bearing in the time domain in the current sampling period is acquired through the sensor.

Preferably, after determining the failure level of the faulty bearing component in the current sampling period based on the impact data, the method further includes:

S21: Determine whether the impact sampling times i for obtaining the impact data in the current sampling period is not less than N; if yes, go to S22; if not, go to S29; wherein, 1≤i≤N and i is an integer, and N is no an integer less than 1;

S22: Count the number of occurrences of failures corresponding to the bearing components that have failed within all the times of impact sampling under the current sampling period;

S23: Count the number of occurrences of the failure level corresponding to each of the bearing components that have failed within all the impact sampling times in the current sampling period;

S24: Use the highest fault level among all the fault levels under the current sampling period as the current alarm level judgment flag;

S25: determine whether the occurrence times corresponding to the current alarm level determination flag is greater than the comprehensive decision-making level impact alarm threshold; if so, go to S26; if not, go to S28;

S26: Determine the bearing component corresponding to the maximum number of occurrences of each failure as the alarm bearing component, and use the current alarm level determination flag as the alarm level of the alarm bearing component;

S27: output bearing component failure alarm information corresponding to the alarm bearing component and its alarm level;

S28: take the highest fault level after the current alarm level determination flag as the current alarm level determination flag, and return to S25;

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

Preferably, after outputting the bearing component fault alarm information corresponding to the alarm bearing component and its alarm level, the method further includes:

Clear all the impact data, the number of impact sampling times, the number of occurrences, the number of occurrences of failures, the bearing components that have failed, and the corresponding failure level recorded in the current sampling period.

In order to solve the above technical problems, the present invention also provides a bearing fault detection system, including:

an acquisition unit, used for acquiring impact data representing impact information of each bearing component in the bearing in the time domain in the current sampling period;

a processing unit, configured to process the impact data to obtain an impact spectrum representing impact information of each of the bearing components in the frequency domain;

a bearing fault determination unit, configured to determine the bearing component that has failed in the bearing in the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault location correspondence;

A failure level determination unit, configured to determine a failure level of the bearing component that has failed in the current sampling period based on the impact data.

In order to solve the above technical problems, the present invention also provides a bearing fault detection device, including:

memory for storing computer programs;

The processor is configured to execute the steps of the bearing fault detection method as described above.

The invention provides a bearing fault detection method, system and device, which can acquire impact data representing impact information of each bearing component in the bearing in the time domain in the current sampling period, obtain the impact spectrum by processing the impact data, and then determine the bearing. The faulty bearing components in the system are further determined according to the impact data, and the failure level of the faulty bearing components is determined. It can be seen that according to the method in this application, although the temperature change is not obvious when a minor fault occurs in the early stage of the bearing component, it is still possible to detect and determine the level in time, so as to issue an alarm later, which is convenient for developers to find and deal with, so as to prevent the fault from further affecting the components including the bearing. The work of other components in the mechanical equipment ensures the reliability and timeliness of the plan; and compared with the relatively lagging early warning methods in the prior art, it ensures the normal operation of other components, saves maintenance costs, and ensures the reliability of mechanical equipment. safe operation.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the prior art and the accompanying drawings required in the embodiments. Obviously, the drawings in the following description are only some of the present invention. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

1 is a flowchart of a bearing fault detection method provided by the present invention;

2 is a schematic structural diagram of a bearing fault detection system provided by the present invention;

FIG. 3 is a schematic structural diagram of a bearing fault detection device provided by the present invention.

Detailed ways

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 find and deal with them in time, and ensure the rotating machinery including bearings. The normal operation of other components in the equipment saves maintenance costs and ensures the reliable and safe operation of rotating machinery and equipment.

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Please refer to FIG. 1 , which is a flowchart of a fault detection method provided by the present invention.

The bearing fault detection method includes:

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

S12: Process the impact data to obtain the impact spectrum representing the impact information of each bearing component in the frequency domain;

S13: Determine the faulty bearing component in the bearing in the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault location correspondence;

S14: Determine the failure level of the bearing component that has failed in the current sampling period based on the impact data.

In this embodiment, for the problem of bearing fault detection, a method commonly used in the prior art is to install a temperature sensor inside, and monitor the bearing temperature based on the temperature sensor and a preset temperature alarm threshold. However, when the bearing has a slight fault, the temperature change is very small, so that the temperature sensor will not detect the fault and give an alarm; when the temperature sensor gives an alarm, usually the bearing and the main fan including the bearing are in the air. Other components have also been damaged by elevated temperatures caused by bearing failures. In order to solve the above technical problems, the present application provides a bearing fault detection method, which can reliably and timely realize the detection of bearing faults.

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 particularly limited here; the bearing here is specifically a bearing in a fan For example, the bearing fault detection method can be specifically applied to a control device in the main fan, such as a diagnostic instrument in the fan, which is not particularly limited here.

Obtain the impact data representing the impact information of each bearing component in the bearing in the time domain during the current sampling period. It should be noted that this is based on the fact that the temperature change is not obvious when the bearing has a slight fault in the early stage. The setting can be less than the minimum value of the time required for each bearing component to reach the temperature alarm threshold from the time of failure to its temperature, and it is understood that if there is only a temperature alarm threshold for the entire bearing, it is only necessary to set the sampling period length. The adaptability can be set to be less than the time required for the entire bearing to reach the temperature alarm threshold from the moment when the whole bearing fails, which is not particularly limited here.

It should also be noted that each bearing component here includes, but is not limited to, the inner ring, the outer ring, the rollers and the cage.

A sensor can be installed on the bearing end cover. Therefore, for step S11, the speed tracking sampling technology can be used, and the impact data in the time domain can be collected by the sensor, and it is further considered that the signal when the bearing component fails is usually high. Therefore, the shock data collected by the above sensors can be filtered by using the resonance demodulation technology to filter out the low-frequency vibration signal, and the filtered shock data can be used as the shock data in the current sampling period, There is no particular limitation here, as long as the execution logic in the present application can be implemented.

Then, the shock data is processed to obtain shock spectra representing shock information of each bearing component in the frequency domain. Specifically, for step S12, fast Fourier transform may be performed on the shock data to obtain the shock spectrum.

Then, according to the impact spectrum and the pre-stored fault frequency-fault location correspondence, the faulty bearing component in the bearing can be determined. Specifically, the fault location in the corresponding relationship here includes each of the bearing components, and the corresponding fault frequency refers to the corresponding fault frequency value in the frequency domain when each of the bearing components fails. Furthermore, the mechanism for determining the corresponding relationship between the fault frequency and the fault location may be as follows: according to the bearing component parameters of the bearing, the corresponding relationship between the fault frequency and the fault location when each bearing component fails is pre-determined and stored, wherein , the bearing component parameters here may include the pitch diameter of the bearing, the shaft diameter of the bearing, the number of rolling elements, the diameter of the rolling elements and the contact angle of the rolling elements. And in order to distinguish the faulty bearing components, the fault label corresponding to each bearing component can be set. Taking the bearing components including the inner ring, outer ring, roller and cage as an example, when the outer ring fails, the label is 1, and the inner ring is marked as 1. When the ring fails, the label is 2, when the roller fails, the label is 3, and when the cage fails, the label is 4, so if each bearing component does not fail, it can be labeled 0.

Finally, the failure class of the bearing component that failed during the current sampling period is determined based on the shock data. It can be understood that, after determining the faulty bearing component in the bearing and the corresponding fault level, the corresponding alarm information can be further output to prompt the maintenance personnel to maintain.

In conclusion, the present application provides a bearing fault detection method. Compared with the prior art, the solution has higher reliability and timeliness, can realize early detection of bearing faults, and can further determine which bearing component has occurred. The fault is accurate detection, and the corresponding fault level, that is, graded alarm, which is convenient for developers to find and repair in time. Compared with the relatively lag early warning method in the prior art, this early warning method guarantees the rotation of bearings including bearings. The normal operation of other components in the mechanical equipment saves maintenance costs and ensures the reliable and safe operation of the rotating mechanical equipment.

On the basis of the above-mentioned embodiment:

As a preferred embodiment, according to the impact spectrum and the pre-stored fault frequency-fault location correspondence, determine the bearing component that has failed in the bearing in the current sampling period, including:

For each fault frequency in the pre-stored fault frequency-fault location correspondence, determine whether there is a peak value within the preset error frequency range corresponding to the fault frequency in the impact spectrum;

If so, the fault location corresponding to the fault frequency is determined based on the fault frequency-fault location correspondence, so as to determine the bearing component that has failed in the bearing in the current sampling period.

In this embodiment, in order to determine the faulty bearing component in the bearing, based on each fault frequency stored in the pre-stored fault frequency-fault location correspondence, that is, each fault frequency is used as a reference, the impact spectrum corresponding to the fault frequency is judged. Whether there is a peak value within the preset error frequency range of The failed bearing component in the bearing corresponding to that failure 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 may be different or the same, which is not particularly limited here.

Specifically, as an example, the impact spectrum is essentially a multi-order spectral line, the abscissa is the frequency, and the ordinate is the peak value. Here, an outer ring fault is taken as an example to illustrate, assuming the pre-stored fault frequency-fault location correspondence relationship Among them, the outer ring fault frequency corresponding to the outer ring fault (fault location) is 100, and the preset error frequency range corresponding to the outer ring fault is 97-103, so according to the outer ring fault frequency as the benchmark, the preset error in the impact spectrum is judged Whether there is a peak in the frequency range of 97-103, if so, based on the above fault frequency-fault location correspondence, it can be determined that the fault location is the outer ring.

It can be seen that through the above execution logic, the determination of the faulty bearing component can be simply and reliably realized, which is convenient for implementation.

As a preferred embodiment, the failure level of the bearing component that has failed in the current sampling period is determined based on the shock data, including:

determining, based on the shock data, an impact magnitude that characterizes the impact strength experienced by the failed bearing component;

According to the impact amplitude and the preset classification alarm threshold, the failure level of the bearing component that has failed in the current sampling period is determined.

In this embodiment, a method for determining the failure level of the faulty bearing component is given, wherein the impact amplitude describes the magnitude of the impact strength that the faulty bearing component receives.

Specifically, the preset grading alarm thresholds here can be set according to actual needs, and there can be one or more. The specific preset grading alarm thresholds can be the same or different, which is not particularly limited here.

It can be seen that through the setting of the above-mentioned hierarchical alarm execution logic, quantitative and hierarchical alarms for faulty bearing components can be realized, which is convenient for follow-up maintenance personnel to deal with them in a targeted manner, and realizes early warning and grading of faulty bearing components. Call the police.

As a preferred embodiment, determining the impact magnitude representing the impact strength of the faulty bearing component based on the impact data includes:

determining, based on the shock data, a shock characteristic value corresponding to the shock data and characterizing the shock condition of the failed bearing component;

A shock magnitude that characterizes the impact strength experienced by the failed bearing component is determined based on the shock eigenvalues.

In this embodiment, the method for determining the impact amplitude of the faulty bearing component is given, which is as described above, and will not be repeated here.

As a preferred embodiment, determining an impact characteristic value corresponding to the impact data and representing the impact condition of the faulty bearing component based on the impact data includes:

Determine, based on the impact data and the preset data-eigenvalue relationship, an impact eigenvalue corresponding to the impact data and representing the impact condition of the failed bearing component;

The preset data-eigenvalue relationship is:

Among them, SV is the shock characteristic value, N is the total number of rotations of the rotor in the rotating machinery equipment including the bearing in the current sampling period, An is the maximum value in each shock data corresponding to the _nth rotation of the rotor, among which, 1≤n≤N and is an integer, and N is an integer not less than 1.

In this embodiment, a specific method for determining the impact characteristic value is given, 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 for a total of N turns in the rotating machinery equipment including the bearing, and one or more corresponding impact data can be obtained for each turn. Therefore, the impact characteristic value can be determined according to The above preset data-eigenvalue relational expression is obtained.

As a preferred embodiment, determining the impact amplitude representing the impact strength of the bearing component in failure is determined based on the impact characteristic value, including:

Determine, based on the impact eigenvalue and the preset eigenvalue-strength relationship, an impact amplitude that characterizes the impact strength of the faulty bearing component;

The preset eigenvalue-intensity relationship is:

Among them, SV is the shock characteristic value, dB is the shock amplitude, E is the rotational speed of the rotor in the rotating mechanical equipment including the bearing, and D is the shaft diameter of the bearing.

In this embodiment, a specific method for determining the impact amplitude is given, as described above, and the implementation method is simple and reliable. It should be noted that the impact amplitude represents the magnitude of the impact strength to which the faulty bearing component is subjected. Specifically, in practical application, the unit of the rotational speed E of the rotor in the preset eigenvalue-strength relational formula may be revolutions per minute; the unit of the shaft diameter D of the bearing here may be millimeters.

As a preferred embodiment, the preset hierarchical alarm thresholds include an early warning threshold, a primary alarm threshold and a secondary alarm threshold, wherein the early warning threshold < primary alarm threshold < secondary alarm threshold value;

Determine the failure level of the bearing component that has failed in the current sampling period according to the impact amplitude and the preset classification alarm threshold, including:

When the pre-warning threshold value≤impact amplitude＜the first-level alarm threshold value, determine the fault level of the faulty bearing component as the pre-warning level;

When the first-level alarm threshold value ≤ the impact amplitude < the second-level alarm threshold value, the failure level of the faulty bearing component is determined to be the first-level;

When the impact amplitude is greater than or equal to the second-level alarm threshold value, it is determined that the failure level of the faulty bearing component is the second-level.

In this embodiment, the preset hierarchical alarm thresholds here may include an early warning threshold, a primary alarm threshold, and a secondary alarm threshold. The failure level of the secondary alarm is higher than that of the primary alarm, and the primary alarm The fault level of the 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.

When the impact amplitude is less than the early warning threshold, it means that although the faulty bearing component has some minor faults, the alarm can be temporarily stopped and follow-up continuous monitoring can be carried out; it should be noted that it is convenient to distinguish different fault levels , the binary notation method can be used here, for example, the failure level of the bearing failure component corresponding to the embodiment here is marked as 0000, or the decimal notation method can be directly used, for example, the failure level of the bearing failure component corresponding to the embodiment here can be marked as 0000. level is marked as 0;

When the early warning threshold value≤impact amplitude<first-level alarm threshold value, it means that the fault level of the faulty bearing component has reached the early warning level, so the fault level of the faulty bearing component is determined to be the early warning level. It should be noted that, to facilitate distinguishing different failure levels, a binary notation method can be used here, for example, the failure level of the bearing faulty component corresponding to the embodiment here is marked as 0001, or a decimal notation method can be directly used, such as the corresponding The failure level of the bearing failure component in the embodiment herein is marked as 1;

When the first-level alarm threshold value ≤ the impact amplitude < the second-level alarm threshold value, it means that the failure level of the bearing component that has failed at this time has reached the failure level of the first-level alarm, so the failure level of the faulty bearing component is determined. for the first level. It should be noted that, in order to distinguish different failure levels, binary notation can be used here, for example, the failure level of the bearing faulty component corresponding to the embodiment here is marked as 0010, or the decimal notation method can be directly used, such as the corresponding The failure level of the bearing failure component in the embodiment herein is marked as 2;

When the impact amplitude is greater than or equal to the second-level alarm threshold, it means that the fault level of the faulty bearing component at this time is very high and has reached the second-level alarm failure level, so it is determined that the fault level of the faulty bearing component is the second-level alarm. . It should be noted that, in order to distinguish different failure levels, binary notation can be used here, for example, the failure level of the bearing faulty component corresponding to the embodiment here is marked as 0011, or the decimal notation method can be directly used, such as the corresponding The failure level of the bearing failure component in the embodiment here is marked as 3, which is not particularly limited here.

It should also be noted that the preset grading alarm thresholds are 3 as an example. In practical applications, of course, more or less preset grading alarm thresholds can be set, which can be set according to actual needs. There is no particular limitation here.

It can be seen that the above-mentioned preset grading alarm threshold and the setting of the corresponding execution logic can reliably realize the grading alarm for the faulty bearing component, which is convenient for subsequent maintenance personnel to deal with it in a targeted and timely manner.

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

Obtain shock data representing shock information of each bearing component in the bearing in the time domain during the current sampling period, including:

The impact data representing the impact information of each bearing component in the bearing in the time domain in the current sampling period is acquired through the sensor.

In this embodiment, in order to acquire the impact data in the current sampling period, a sensor may be provided on the bearing, for example, on the end cover of the bearing, through which the sensor can reliably acquire each bearing component in the bearing within the first preset time period Shock data for shock information in the time domain.

As a preferred embodiment, after determining the failure level of the bearing component that has failed in the current sampling period based on the shock data, the method further includes:

S21: Determine whether the shock sampling times i for obtaining shock data in the current sampling period is not less than N; if so, go to S22; if not, go to S29; where 1≤i≤N and i is an integer, and N is an integer not less than 1 ;

S22: Count the number of fault occurrences corresponding to each bearing component that has failed within all impact sampling times in the current sampling period;

S23: Count the number of occurrences of the failure level corresponding to each bearing component that has failed within all the impact sampling times in the current sampling period;

S24: take the highest fault level among all the fault levels in the current sampling period as the current alarm level judgment flag;

S25: determine whether the number of occurrences corresponding to the current alarm level determination flag is greater than the comprehensive decision level impact alarm threshold; if so, go to S26; if not, go to S28;

S26: Determine the bearing component corresponding to the maximum number of occurrences of each fault as the alarm bearing component, and use the current alarm level judgment flag as the alarm level of the alarm bearing component;

S27: output the bearing component fault alarm information corresponding to the alarm bearing component and its alarm level;

S28: take the fault level with the highest level after the current alarm level determination flag as the current alarm level determination flag, and return to S25;

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

In this embodiment, the inventor considers that in order to further ensure the practicability of the method, when the number of shock sampling times reaches the preset shock sampling threshold N, the corresponding bearing component fault alarm information can be output.

First, under each impact sampling times, the faulty bearing components and their corresponding failure levels are stored, so after determining the failure level of the bearing components that have failed in the current sampling period based on the impact data, it is judged to obtain the current sampling period. Whether the number of shock sampling times i for shock data under the cycle is not less than N;

If no, it means that the number of shock sampling i has not reached the preset shock sampling threshold N at this time, and it can continue to be stored without giving an alarm temporarily, so the number of shock sampling i is accumulated and returned to obtain the bearing representing the motor in the current sampling period. The steps in the shock data of the shock information of each bearing component in the time domain to continue sampling.

If so, it means that the number of shock sampling i has reached the preset shock sampling threshold N at this time, and it is necessary to count the stored information and output the corresponding bearing component fault alarm information for repairing as soon as possible. Therefore, first of all, count the number of fault occurrences corresponding to each bearing component that has failed within all impact sampling times under the current sampling period (of course, you can also count the number of fault occurrences corresponding to each faulty bearing component accounting for the total number of impact sampling times N Percentage), count all the shock sampling times in the current sampling period, that is, the occurrence times of the occurrences of each fault level within N (of course, the percentage of the occurrence times of the fault level to the total shock sampling times N can also be counted).

Then, considering that a fault with a higher level is more dangerous, starting from the fault level with the highest level in the current sampling period, the fault level with the highest level among all the fault levels is used as the current alarm level judgment flag. Judging whether the number of occurrences corresponding to the current alarm level judgment flag is greater than the comprehensive decision-making grading impact alarm threshold;

If so, the current alarm level judgment flag is used as the alarm level of the alarm bearing component, and the corresponding failure level is usually the same based on the bearing component with the most failures under normal circumstances, so the maximum value of the number of failures is determined. The corresponding bearing component is the alarm bearing component; finally, the bearing component fault alarm information corresponding to the alarm bearing component and its alarm level is output;

If no, it means that the alarm condition is not reached under the current alarm level judgment flag, and there is a fault level order from high to low based on the fault level. Therefore, the highest fault level after the current alarm level judgment flag is used as the current alarm level judgment mark, and return to the decision logic of S25. It can be understood that if there is no fault level that can be used as a new current alarm level judgment mark after the current alarm level judgment mark, the cycle can be stopped, and the stored number of impact sampling times corresponding to the current N times can be further directly stored. , the information of the two parts of the faulty bearing component and the corresponding fault level is cleared, and i is cleared to zero, so that the statistics can be restarted and the fault alarm will be performed when the preset impact sampling threshold N is reached again. Here There is no particular limitation.

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

It can be understood that after the bearing component fault alarm information is output, the stored information corresponding to the current N times of impact sampling times, the faulty bearing component and the corresponding fault level can be directly cleared. 0, and reset i to 0, so that the statistics can be restarted and the fault alarm will be performed when the preset shock sampling threshold N is reached again.

Specifically, as an example, it is assumed that the outer ring is labeled 1 when it fails, the inner ring is labeled 2 when it fails, 3 when the rollers fail, and 4 when the cage fails. The fault label is 0, the preset shock sampling threshold N=10, and it is assumed that the stored information of each bearing component that has failed in the stored 10 shock samples is [0, 0, 1, 1, 1, 1, 1, 2, 1 , 1], and the storage information of the corresponding fault level is [0, 0, 2, 2, 2, 2, 2, 1, 2, 1]. Therefore, the number of fault occurrences corresponding to the faulty bearing components obtained by statistics can be obtained: the number of fault occurrences of the outer ring fault is 7, the number of fault occurrences of the inner ring fault is 1, and the number of fault occurrences of the roller fault is 0. The number of fault occurrences of rack faults is 0; correspondingly, it has been clarified in the above-mentioned embodiments that "2" here means that the fault level that occurs is a first-level alarm, and "1" here means that the fault level that occurs is Early warning level, the number of occurrences of each fault level obtained by statistics, the number of occurrences of secondary alarms is 0, the number of occurrences of primary alarms is 6, and the number of occurrences of early warning levels is 2. According to the above judgment logic, it can be seen that the highest fault level among all the fault levels is the first level, and it is used as the current alarm level judgment mark. It is assumed that the comprehensive decision-making level shock alarm threshold is 2, and the number of occurrences of 6 is greater than all If the comprehensive decision-making graded impact alarm threshold is 2, and the maximum number of occurrences of each fault is 7, and 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 for the first level.

It can be seen that, through the above execution logic, the determination of the alarm bearing component and the corresponding alarm level can be efficiently realized, and the practicability is higher.

As a preferred embodiment, after outputting the bearing component fault 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.

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 times of impact sampling times recorded in the current sampling period can be actively cleared, so as to restart Statistics and the fault alarm will be issued when the preset impact sampling threshold N is reached again, so the data here specifically includes the impact data stored in the current sampling period, the number of impact sampling (that is, the number of impact sampling is reset to zero), the number of occurrences, and the number of fault occurrences. , The faulty bearing components and the corresponding fault level.

Please refer to FIG. 2 , which is a schematic structural diagram of a fault detection system provided by the present invention.

The fault detection system, applied to fans, includes:

an obtaining unit 41, configured to obtain shock data representing shock information of each bearing component in the bearing in the time domain in the current sampling period;

The processing unit 42 is configured to process the impact data to obtain the impact spectrum representing the impact information of each bearing component in the frequency domain;

The bearing fault determination unit 43 is configured to determine, according to the impact spectrum and the pre-stored fault frequency-fault location correspondence relationship, the bearing component that has failed in the bearing in the current sampling period.

The failure level determination unit 44 is configured to determine the failure level of the bearing component that has failed in the current sampling period based on the impact data.

For the introduction of the bearing fault detection system provided in the present invention, please refer to the above embodiments of the bearing fault detection method, which will not be repeated here.

Please refer to FIG. 3 , which is a schematic structural diagram of a bearing fault detection device provided by the present invention.

The bearing fault detection device includes:

memory 51 for storing computer programs;

The processor 52 is configured to execute the steps of the bearing fault detection method as described above.

For the introduction of the bearing fault detection device provided in the present invention, please refer to the above embodiments of the bearing fault detection method, which will not be repeated here.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. As 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 part can be referred to the description of the method.

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 these entities or operations. There is no such actual relationship or sequence between operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

Professionals may further realize 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 possibilities of hardware and software. Interchangeability, the above description has generally described the components and steps of each example in terms of function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention.

The above description of the disclosed embodiments enables any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (12)

1. a bearing fault detection method, is characterized in that, comprises: Acquire shock data representing the shock 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 impact information of each of the bearing components in the frequency domain; determining the faulty bearing component in the bearing in the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault location correspondence; A failure level of the failed bearing component during the current sampling period is determined based on the shock data. 2 . The bearing fault detection method according to claim 1 , wherein the faulty bearing component in the bearing in the current sampling period is determined according to the impact frequency spectrum and a pre-existing fault frequency-fault location correspondence relationship. 3 . ,include: For each fault frequency in the pre-stored fault frequency-fault location correspondence, determine whether there is a peak value within a preset error frequency range corresponding to the fault frequency in the impact spectrum; If so, determine a fault location corresponding to the fault frequency based on the fault frequency-fault location correspondence relationship, so as to determine a bearing component that has failed in the bearing within the current sampling period. 3 . The bearing fault detection method according to claim 1 , wherein determining the failure level of the faulty bearing component in the current sampling period based on the impact data, comprising: 3 . determining, based on the impact data, an impact magnitude indicative of the impact strength experienced by the failed bearing component; The failure level of the faulty bearing component in the current sampling period is determined according to the impact amplitude and the preset leveled alarm threshold. 4 . The bearing fault detection method according to claim 3 , wherein determining, based on the impact data, an impact magnitude representing the impact strength of the faulty bearing component, comprising: 4 . determining, based on the shock data, a shock characteristic value corresponding to the shock data that characterizes the shock condition of the failed bearing component; An impact amplitude characterizing the impact strength experienced by the failed bearing component is determined based on the impact characteristic value. 5 . The bearing fault detection method according to claim 4 , wherein determining, based on the impact data, an impact characteristic value corresponding to the impact data and representing the impact condition of the bearing component that has failed, comprising: 6 . determining an impact characteristic value corresponding to the impact data and representing the impact condition of the faulty bearing component based on the impact data and the preset data-eigenvalue relationship; The preset data-eigenvalue relational formula is:

Wherein, SV is the shock 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 An is the corresponding number of rotations when the rotor rotates the _nth revolution. The maximum value in the impact data, where 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 , wherein determining, based on the impact characteristic value, an impact amplitude representing the impact strength of the faulty bearing component, comprising: 6 . determining, based on the impact characteristic value and a preset characteristic value-strength relationship, an impact amplitude characterizing the impact strength of the faulty bearing component; The preset eigenvalue-intensity relationship is:

Wherein, SV is the characteristic value of the impact, dB is the amplitude of the impact, E is the rotational 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 , wherein the preset grading alarm threshold includes an early warning threshold, a primary alarm threshold and a secondary alarm threshold, wherein the early warning Threshold value＜the first-level alarm threshold value＜the second-level alarm threshold value; Determining the failure level of the faulty bearing component in the current sampling period according to the impact amplitude and the preset classification alarm threshold, including: When the pre-warning threshold value≤the impact amplitude value<the first-level alarm threshold value, determine the failure level of the faulty bearing component as the pre-warning level; When the first-level alarm threshold value≤the impact amplitude value<the second-level alarm threshold value, determine that the failure level of the bearing component that has failed is first-level; When the shock amplitude is greater than or equal to the second-level alarm threshold value, it is determined that the failure level of the faulty bearing component is the second-level. 8. The bearing fault detection method according to claim 1, wherein a sensor is provided on the bearing; Obtain shock data representing shock information of each bearing component in the bearing in the time domain during the current sampling period, including: The impact data representing the impact information of each bearing component in the bearing in the time domain in the current sampling period is acquired through the sensor. 9. The bearing fault detection method according to any one of claims 1 to 8, wherein after determining the failure level of the faulty bearing component in the current sampling period based on the shock data, the method further comprises: : S21: Determine whether the impact sampling times i for obtaining the impact data in the current sampling period is not less than N; if yes, go to S22; if not, go to S29; wherein, 1≤i≤N and i is an integer, and N is no an integer less than 1; S22: Count the number of occurrences of failures corresponding to the bearing components that have failed within all the times of impact sampling under the current sampling period; S23: Count the number of occurrences of the failure level corresponding to each of the bearing components that have failed within all the impact sampling times in the current sampling period; S24: Use the highest fault level among all the fault levels under the current sampling period as the current alarm level judgment flag; S25: determine whether the occurrence times corresponding to the current alarm level determination flag is greater than the comprehensive decision-making level impact alarm threshold; if so, go to S26; if not, go to S28; S26: Determine the bearing component corresponding to the maximum number of occurrences of each failure as the alarm bearing component, and use the current alarm level determination flag as the alarm level of the alarm bearing component; S27: output bearing component failure alarm information corresponding to the alarm bearing component and its alarm level; S28: take the highest fault level after the current alarm level determination flag as the current alarm level determination flag, and return to S25; S29: Let i=i+1, and return to the step of acquiring the impact data representing the impact information of each bearing component in the bearing in the time domain in the current sampling period. 10 . The bearing fault detection method according to claim 9 , wherein after outputting the bearing component fault alarm information corresponding to the alarm bearing component and its alarm level, the method further comprises: 10 . Clear all the impact data, the number of impact sampling times, the number of occurrences, the number of occurrences of failures, the bearing components that have failed, and the corresponding failure level recorded in the current sampling period. 11. A bearing fault detection system, comprising: an acquisition unit, used for acquiring impact data representing impact information of each bearing component in the bearing in the time domain in the current sampling period; a processing unit, configured to process the impact data to obtain an impact spectrum representing impact information of each of the bearing components in the frequency domain; a bearing fault determination unit, configured to determine the bearing component that has failed in the bearing in the current sampling period according to the impact spectrum and the pre-stored fault frequency-fault location correspondence; A failure level determination unit, configured to determine a failure level of the bearing component that has failed in the current sampling period based on the impact data. 12. A bearing fault detection device, comprising: memory for storing computer programs; A processor for executing the steps of the bearing fault detection method as claimed in any one of claims 1 to 10.

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