Methods, devices, and health management platform for determining robotic arm malfunctions

Abstract

This application provides a method, apparatus, and health management platform for determining robotic arm faults. The method includes: acquiring vibration data from vibration measurement points of the robotic arm, including gears and bearings, and the vibration data including the vibration amplitude of characteristic indicators of the gears or bearings; inputting the vibration data into a corresponding fault diagnosis model to obtain the health status, wherein the fault diagnosis model corresponds one-to-one with the faults of the vibration measurement points, and the health status includes the presence of faults corresponding to the fault diagnosis model and the absence of faults corresponding to the fault diagnosis model. The fault diagnosis model is formed through logic configuration, replacing traditional expert consultation with intelligent diagnosis using the fault diagnosis model, which can quickly output a health status report of the robotic arm, reducing the cost of robotic arm fault diagnosis, solving the problem of high cost in existing robotic arm vibration health diagnosis, and simultaneously detecting a large number of vibration measurement points, greatly improving the efficiency of fault judgment.

Description

Technical Field

[0001] This application relates to the field of fault analysis technology, and more specifically, to a method, apparatus, computer-readable storage medium, processor, and robotic arm health management platform for determining robotic arm faults. Background Technology

[0002] The coal mining machine is one of the main pieces of equipment in a fully mechanized mining system. It is a large and complex system integrating mechanical, electrical, and hydraulic systems. Its operating environment is harsh, and a malfunction can lead to the interruption of the entire mining operation, directly causing economic losses to the enterprise. The rocker arm gearbox of the coal mining machine's cutting section, as a key component, directly bears the heavy responsibility of power transmission for cutting the coal face, and is also a high-risk area for coal mining machine failures.

[0003] The commonly used analytical method for the rocker arm of a coal mining machine is:

[0004] (1) Oil detection: This method cannot accurately locate the fault of the rocker arm gear of the coal mining machine, nor can it specifically analyze the fault characteristics of the rocker arm of the coal mining machine.

[0005] (2) Expert diagnosis: After the rocker arm gear has obvious faults, a handheld vibration meter is used for temporary testing. This requires manual data analysis, which is costly, slow in response time, and cannot record historical faults of the rocker arm vibration characteristics or provide early warning of rocker arm vibration.

[0006] The information disclosed above in the background section is only intended to enhance the understanding of the background art of the art described herein. Therefore, the background art may contain certain information that does not constitute prior art known to those skilled in the art in this country. Summary of the Invention

[0007] The main objective of this application is to provide a method, apparatus, computer-readable storage medium, processor, and robotic arm health management platform for determining robotic arm faults, in order to solve the problem of high cost in existing robotic arm vibration health diagnosis technologies.

[0008] According to one aspect of the present invention, a method for determining a fault in a robotic arm is provided, comprising: acquiring vibration data of vibration measurement points of the robotic arm, wherein the vibration measurement points include gears and bearings of the robotic arm, and the vibration data includes the vibration amplitude of characteristic value indicators of the gears or bearings; inputting the vibration data into a corresponding fault diagnosis model to obtain a health status, wherein the fault diagnosis model corresponds one-to-one with the faults of the vibration measurement points, and the health status includes the presence of a fault corresponding to the fault diagnosis model and the absence of a fault corresponding to the fault diagnosis model, wherein the fault diagnosis model is formed through logic configuration.

[0009] Optionally, the vibration data of the gear includes the vibration amplitude of the meshing frequency, the vibration amplitude of the rotational frequency sideband, and the passband value. Obtaining vibration data from the vibration measurement points of the robotic arm includes: acquiring the real-time meshing frequency, real-time rotational frequency, harmonics, waveform of the gear's meshing frequency, and the passband value; performing a Fourier transform on the waveform of the gear's meshing frequency to obtain the spectrum of the gear's meshing frequency; calculating the rotational frequency sideband frequency point based on the real-time meshing frequency, the real-time rotational frequency, and the harmonics, where the rotational frequency sideband frequency point is the sum of the real-time meshing frequency and the vibration frequency, and the vibration frequency is the product of the real-time rotational frequency and the harmonics; querying the corresponding vibration amplitude in the spectrum of the gear's meshing frequency based on the rotational frequency sideband frequency point to obtain the vibration amplitude of the rotational frequency sideband; and querying the corresponding vibration amplitude in the spectrum of the gear's meshing frequency based on the real-time meshing frequency to obtain the vibration amplitude of the meshing frequency.

[0010] Optionally, the vibration data is input into a corresponding fault diagnosis model to obtain a health status, including: inputting the vibration amplitude of the meshing frequency, the vibration amplitude of the rotational frequency sideband, and the passband value into a gear wear fault diagnosis model, wherein the gear wear fault diagnosis model is the fault diagnosis model corresponding to the wear fault of the gear; and determining that the health status of the gear is that a wear fault exists when the vibration amplitude of the meshing frequency is greater than a first vibration value threshold, the vibration amplitude of the rotational frequency sideband is greater than a second vibration value threshold, and the passband value is within a predetermined range.

[0011] Optionally, after inputting the vibration data into the corresponding fault diagnosis model to obtain the health status, the method further includes: matching maintenance suggestions for the vibration measuring point based on manual diagnostic data, wherein the manual diagnostic data includes the faults that occur at the vibration measuring point and the corresponding maintenance suggestions, and the faults at the vibration measuring point and the maintenance suggestions correspond one-to-one; and if the health status indicates that there is a fault corresponding to the fault diagnosis model, displaying the location of the vibration measuring point and the corresponding maintenance suggestions.

[0012] Optionally, before acquiring the vibration data of the vibration measurement points of the robotic arm, the method further includes: establishing a measurement point tree diagram based on the vibration measurement points, wherein one node of the measurement point tree diagram corresponds to one vibration measurement point; matching all the nodes with the vibration data of the corresponding vibration measurement points, so that the nodes display the vibration data in response to a predetermined operation.

[0013] According to another aspect of the present invention, a device for determining a fault in a robotic arm is also provided, comprising: an acquisition unit for acquiring vibration data of vibration measurement points of the robotic arm, wherein the vibration measurement points include gears and bearings of the robotic arm, and the vibration data includes the vibration amplitude of characteristic value indicators of the gears or the bearings; and a diagnosis unit for inputting the vibration data into a corresponding fault diagnosis model to obtain a health status, wherein the fault diagnosis model corresponds one-to-one with the faults of the vibration measurement points, and the health status includes the presence of a fault corresponding to the fault diagnosis model and the absence of a fault corresponding to the fault diagnosis model, wherein the fault diagnosis model is formed through logic configuration.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any one of the methods described.

[0015] According to another aspect of the present invention, a processor is also provided, the processor being configured to run a program, wherein the program, when running, executes any of the methods described.

[0016] According to another aspect of the present invention, a robotic arm health management platform is also provided, comprising: one or more processors, a memory, a display device, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any one of the methods described.

[0017] Optionally, the robotic arm health management platform includes: a vibration measurement point establishment system for establishing a measurement point tree diagram, wherein one node in the measurement point tree diagram corresponds to one vibration measurement point; an offline data upload interface system, communicatively connected to the vibration measurement point establishment system, for uploading vibration data of the vibration measurement points; a feature value index management system, communicatively connected to both the vibration measurement point establishment system and the offline data upload interface system, for establishing feature value indices for the gear or the bearing and matching the vibration data corresponding to the feature value indices with the nodes; a diagnostic model management system, communicatively connected to the vibration measurement point establishment system, for outputting the health status of the vibration measurement points based on the input vibration data; and a data interface display system for displaying the location and health status of the vibration measurement points.

[0018] In this embodiment of the invention, the method for determining the fault of the robotic arm firstly acquires vibration data from vibration measurement points of the robotic arm, including gears and bearings. The vibration data includes the vibration amplitude of characteristic indicators of the gears or bearings. Then, the vibration data is input into a corresponding fault diagnosis model to obtain a health status. The fault diagnosis model corresponds one-to-one with the faults of the vibration measurement points. The health status includes the presence of a fault corresponding to the fault diagnosis model and the absence of a fault corresponding to the fault diagnosis model. The fault diagnosis model is formed through logic configuration. This determination method generates a fault diagnosis model through logic configuration for fault judgment, replacing traditional expert consultation with intelligent diagnosis using a fault diagnosis model. It can quickly output a health status report of the robotic arm, reducing the cost of robotic arm fault diagnosis and solving the problem of high cost in existing robotic arm vibration health diagnosis. Furthermore, it can simultaneously detect a large number of vibration measurement points, greatly improving the efficiency of fault judgment and ensuring timely maintenance. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 A flowchart illustrating a method for determining a robotic arm fault according to an embodiment of this application is shown;

[0021] Figure 2 A schematic diagram of a measurement point tree diagram according to an embodiment of this application is shown;

[0022] Figure 3 A waveform diagram of the meshing frequency according to an embodiment of this application is shown;

[0023] Figure 4 A spectrum of the engagement frequency according to one embodiment of this application is shown;

[0024] Figure 5 A schematic diagram of a fault diagnosis model and a diagnosis report according to an embodiment of this application is shown;

[0025] Figure 6 A schematic diagram of a device for determining a robotic arm malfunction according to an embodiment of this application is shown. Detailed Implementation

[0026] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0028] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may be an intermediate element present. Furthermore, in the specification and claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element via a third element.

[0029] As mentioned in the background section, existing robotic arm vibration health diagnosis technologies are costly. To address this issue, in a typical embodiment of this application, a method, apparatus, computer-readable storage medium, processor, and robotic arm health management platform for determining robotic arm faults are provided.

[0030] According to an embodiment of this application, a method for determining robotic arm malfunctions is provided.

[0031] Figure 1 This is a flowchart of a method for determining robotic arm faults according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0032] Step S101: Obtain vibration data from vibration measurement points of the robotic arm. The vibration measurement points include gears and bearings of the robotic arm. The vibration data includes the vibration amplitude of the characteristic value index of the gears or bearings.

[0033] To facilitate the viewing of vibration data and health status at vibration measurement points, in one optional implementation, such as Figure 2 As shown, before acquiring vibration data from the vibration measurement points of the robotic arm, the above method further includes:

[0034] Step S201: Establish a tree diagram of the vibration measuring points based on the above vibration measuring points, where one node in the tree diagram corresponds to one vibration measuring point.

[0035] Step S202: Match all the above nodes with the vibration data of the corresponding vibration measurement points so that the nodes display the vibration data in response to a predetermined operation.

[0036] In the above embodiments, by establishing a tree diagram of vibration measuring points, relevant personnel can quickly find the target vibration measuring point through the tree diagram, match all the nodes with the vibration data of the corresponding vibration measuring points, so as to view the vibration data of the vibration measuring points. After determining the health status of the vibration measuring points based on the vibration data, the fault location can be quickly located through the tree diagram.

[0037] It should be noted that the aforementioned robotic arm is the left rocker arm of the coal mining machine, such as... Figure 2 As shown, the above nodes are the terminal nodes of the measurement point tree diagram. The vibration measurement points of the left rocker arm include the vertical A-axis gear, vertical B-axis gear, vertical C-axis gear, vertical D-axis gear, vertical E-axis gear, vertical F-axis gear, vertical first-stage planetary gear, and horizontal first-stage planetary gear, etc. Of course, the vibration measurement points of the left rocker arm are not limited to gears, but also include multiple bearings.

[0038] Optionally, the present invention does not limit the specific process of obtaining vibration data from the vibration measurement points of the robotic arm, and any feasible method is within the protection scope of the present invention.

[0039] For example, in one optional embodiment, the vibration data of the gear includes the vibration amplitude of the meshing frequency, the vibration amplitude of the rotational frequency sideband, and the passband value. Acquiring vibration data from the vibration measurement points of the robotic arm includes:

[0040] Step S1011: Obtain the real-time meshing frequency, real-time rotational frequency, harmonic frequency, waveform of the meshing frequency of the gear, and the passband value of the gear.

[0041] Step S1012: Perform a Fourier transform on the waveform of the meshing frequency of the gear to obtain the spectrum of the meshing frequency of the gear.

[0042] Step S1013: Calculate the frequency of the frequency shifting sideband based on the real-time meshing frequency, the real-time frequency shifting, and the frequency harmonics. The frequency of the frequency shifting sideband is the sum of the real-time meshing frequency and the vibration frequency. The vibration frequency is the product of the real-time frequency shifting and the frequency harmonics.

[0043] Step S1014: Based on the frequency point of the aforementioned frequency switching sideband, query the vibration amplitude corresponding to the meshing frequency spectrum of the aforementioned gear to obtain the vibration amplitude of the aforementioned frequency switching sideband; based on the aforementioned real-time meshing frequency, query the vibration amplitude corresponding to the meshing frequency spectrum of the aforementioned gear to obtain the vibration amplitude of the aforementioned meshing frequency.

[0044] In the above embodiments, the waveform diagram of the meshing frequency of the gears is as follows: Figure 3 As shown, the passband value is 13.725. A Fourier transform is performed on the waveform of the meshing frequency of the aforementioned gear to obtain the frequency spectrum of the meshing frequency of the aforementioned gear, as shown below. Figure 4As shown, the real-time meshing frequency M1 is 773.982 Hz. The frequency spectrum indicates that the corresponding vibration amplitude at this meshing frequency is 2.212 m / s². 2 The aforementioned real-time frequency conversion is 18.902Hz, the aforementioned real-time harmonic is 1st harmonic, and the aforementioned frequency conversion sideband frequency = 773.982Hz + 18.902Hz·1 = 792.884. From the spectrum diagram, the corresponding vibration amplitude M2 ​​of the aforementioned frequency conversion sideband is 0.607m / s. 2 .

[0045] Of course, the vibration measurement points mentioned above are gears. The characteristic indicators of gears include meshing frequency, rotational frequency sideband, and passband value. The characteristic indicators of bearings may be other characteristic indicators. In addition, not every frequency in the spectrum has corresponding data. Fuzzy calculation is used to query the vibration amplitude of the meshing frequency closest to the real-time meshing frequency.

[0046] Step S102: Input the above vibration data into the corresponding fault diagnosis model to obtain the health status. The above fault diagnosis model corresponds one-to-one with the faults of the above vibration measurement points. The above health status includes the presence of the fault corresponding to the above fault diagnosis model and the absence of the fault corresponding to the above fault diagnosis model. The above fault diagnosis model is formed through logic configuration.

[0047] Optionally, the present invention does not limit the specific process of inputting the above vibration data into the corresponding fault diagnosis model to obtain the health status, and any feasible method is within the protection scope of the present invention.

[0048] For example, in one alternative implementation, such as Figure 5 As shown, the above vibration data is input into the corresponding fault diagnosis model to obtain the health status, including:

[0049] Step S1021: Input the vibration amplitude of the meshing frequency, the vibration amplitude of the frequency sideband, and the passband value into the gear wear fault diagnosis model. The gear wear fault diagnosis model is the fault diagnosis model corresponding to the wear fault of the gear.

[0050] Step S1022: If the vibration amplitude of the meshing frequency is greater than the first vibration value threshold, the vibration amplitude of the frequency switching sideband is greater than the second vibration value threshold, and the passband value is within a predetermined range, the health condition of the gear is determined to be a wear fault.

[0051] In the above embodiments, such as Figure 5As shown, the vibration amplitude of the meshing frequency, the vibration amplitude of the rotational frequency sideband, and the passband value are input into the gear wear fault diagnosis model. First, it is determined whether the vibration amplitude of the meshing frequency and the vibration amplitude of the rotational frequency sideband are both greater than 0.4. If so, it is further determined whether the passband value is between 7.1 and 20. If so, it is determined that the gear has a wear fault.

[0052] Furthermore, to facilitate maintenance by relevant personnel, in one optional implementation, such as Figure 5 As shown, after inputting the above vibration data into the corresponding fault diagnosis model to obtain the health status, the above method further includes:

[0053] Step S301: Match maintenance suggestions for the above vibration measuring points based on the manual diagnostic data. The manual diagnostic data includes the faults that occur at the above vibration measuring points and the corresponding maintenance suggestions. The faults of the above vibration measuring points correspond one-to-one with the maintenance suggestions.

[0054] Step S302: If the above health status indicates the presence of a fault corresponding to the above fault diagnosis model, display the location of the above vibration measuring point and the corresponding above maintenance suggestions.

[0055] In the above embodiments, such as Figure 5 As shown, the left sidebar displays the diagnostic report output by the diagnostic model. The vibration measurement point mentioned above is the Z2 / Z3 gear of the left rocker arm. The Z2 / Z3 gear fault II diagnostic method was used to determine whether there is a fault in the gear. The diagnosis confirmed that the meshing frequency and harmonics of the Z2 / Z3 gear of the left rocker arm were present in the spectrum, and the vibration amplitude was within the alarm range. It was determined that the Z2 / Z3 gear of the left rocker arm had wear, damage, misalignment, looseness, etc. The report also provided maintenance suggestions, namely, to strengthen the monitoring of vibration amplitude and spectrum changes, improve the lubrication environment, not to run the equipment continuously for a long time, and to inspect the gears at appropriate times so that maintenance personnel can understand the fault and carry out efficient and effective maintenance.

[0056] In the aforementioned method for determining robotic arm faults, firstly, vibration data from vibration measurement points of the robotic arm, including gears and bearings, is acquired. The vibration data includes the vibration amplitude of characteristic indices of the gears or bearings. Then, the vibration data is input into a corresponding fault diagnosis model to obtain the health status. The fault diagnosis model corresponds one-to-one with the faults at the vibration measurement points. The health status includes the presence of faults corresponding to the fault diagnosis model and the absence of faults corresponding to the model. The fault diagnosis model is formed through logic configuration. This method generates a fault diagnosis model through logic configuration for fault judgment, replacing traditional expert consultation with intelligent diagnosis using the fault diagnosis model. It can quickly output a health status report for the robotic arm, reducing the cost of robotic arm fault diagnosis and solving the problem of high cost in existing robotic arm vibration health diagnosis. Furthermore, it can simultaneously detect a large number of vibration measurement points, greatly improving the efficiency of fault judgment and ensuring timely maintenance.

[0057] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0058] This application also provides a device for determining robotic arm faults. It should be noted that this device can be used to execute the robotic arm fault determination method provided in this application. The following describes the robotic arm fault determination device provided in this application.

[0059] Figure 6 This is a schematic diagram of a device for determining a robotic arm malfunction according to an embodiment of this application. Figure 6 As shown, the device includes:

[0060] The acquisition unit 10 is used to acquire vibration data of vibration measurement points of the robotic arm, wherein the vibration measurement points include gears and bearings of the robotic arm, and the vibration data includes the vibration amplitude of the characteristic value index of the gears or bearings.

[0061] To facilitate the viewing of vibration data and health status at vibration measurement points, in one optional implementation, such as Figure 2 As shown, before acquiring vibration data from the vibration measurement points of the robotic arm, the aforementioned device further includes:

[0062] A unit is established to create a tree diagram of the vibration measuring points based on the above vibration measuring points, wherein one node of the tree diagram corresponds to one vibration measuring point.

[0063] The matching unit is used to match all the above-mentioned nodes with the vibration data of the corresponding vibration measurement points, so that the above-mentioned nodes display the vibration data in response to a predetermined operation.

[0064] In the above embodiments, by establishing a tree diagram of vibration measuring points, relevant personnel can quickly find the target vibration measuring point through the tree diagram, match all the nodes with the vibration data of the corresponding vibration measuring points, so as to view the vibration data of the vibration measuring points. After determining the health status of the vibration measuring points based on the vibration data, the fault location can be quickly located through the tree diagram.

[0065] It should be noted that the aforementioned robotic arm is the left rocker arm of the coal mining machine, such as... Figure 2 As shown, the above nodes are the terminal nodes of the measurement point tree diagram. The vibration measurement points of the left rocker arm include the vertical A-axis gear, vertical B-axis gear, vertical C-axis gear, vertical D-axis gear, vertical E-axis gear, vertical F-axis gear, vertical first-stage planetary gear, and horizontal first-stage planetary gear, etc. Of course, the vibration measurement points of the left rocker arm are not limited to gears, but also include multiple bearings.

[0066] Optionally, the present invention does not limit the specific process of obtaining vibration data from the vibration measurement points of the robotic arm, and any feasible method is within the protection scope of the present invention.

[0067] For example, in one optional embodiment, the vibration data of the gear includes the vibration amplitude of the meshing frequency, the vibration amplitude of the rotational frequency sideband, and the passband value, and the acquisition unit includes:

[0068] The acquisition module is used to acquire the real-time meshing frequency, real-time rotational frequency, harmonic frequency, waveform of the meshing frequency of the gear, and the passband value of the gear.

[0069] The processing module is used to perform a Fourier transform on the waveform of the meshing frequency of the gear to obtain the spectrum of the meshing frequency of the gear.

[0070] The calculation module is used to calculate the frequency of the frequency rotation sideband based on the real-time meshing frequency, the real-time rotation frequency, and the harmonic. The frequency of the frequency rotation sideband is the sum of the real-time meshing frequency and the vibration frequency, and the vibration frequency is the product of the real-time rotation frequency and the harmonic.

[0071] The query module is used to query the vibration amplitude corresponding to the meshing frequency spectrum of the gear based on the aforementioned frequency switching sideband frequency point, and to obtain the vibration amplitude of the aforementioned frequency switching sideband. It also queries the vibration amplitude corresponding to the meshing frequency spectrum of the gear based on the aforementioned real-time meshing frequency, and to obtain the vibration amplitude of the aforementioned meshing frequency.

[0072] In the above embodiments, the waveform diagram of the meshing frequency of the gears is as follows: Figure 3 As shown, the passband value is 13.725. A Fourier transform is performed on the waveform of the meshing frequency of the aforementioned gear to obtain the frequency spectrum of the meshing frequency of the aforementioned gear, as shown below. Figure 4 As shown, the real-time meshing frequency M1 is 773.982 Hz. The frequency spectrum indicates that the corresponding vibration amplitude at this meshing frequency is 2.212 m / s². 2 The aforementioned real-time frequency conversion is 18.902Hz, the aforementioned real-time harmonic is 1st harmonic, and the aforementioned frequency conversion sideband frequency = 773.982Hz + 18.902Hz·1 = 792.884. From the spectrum diagram, the corresponding vibration amplitude M2 ​​of the aforementioned frequency conversion sideband is 0.607m / s. 2 .

[0073] Of course, the vibration measurement points mentioned above are gears. The characteristic indicators of gears include meshing frequency, rotational frequency sideband, and passband value. The characteristic indicators of bearings may be other characteristic indicators. In addition, not every frequency in the spectrum has corresponding data. Fuzzy calculation is used to query the vibration amplitude of the meshing frequency closest to the real-time meshing frequency.

[0074] The diagnostic unit 20 is used to input the above vibration data into the corresponding fault diagnosis model to obtain the health status. The above fault diagnosis model corresponds one-to-one with the faults of the above vibration measurement points. The above health status includes the presence of the fault corresponding to the above fault diagnosis model and the absence of the fault corresponding to the above fault diagnosis model. The above fault diagnosis model is formed through logic configuration.

[0075] Optionally, the present invention does not limit the specific process of inputting the above vibration data into the corresponding fault diagnosis model to obtain the health status, and any feasible method is within the protection scope of the present invention.

[0076] For example, in one alternative implementation, such as Figure 5 As shown, the diagnostic unit includes:

[0077] The input module is used to input the vibration amplitude of the meshing frequency, the vibration amplitude of the frequency sideband, and the passband value into the gear wear fault diagnosis model, wherein the gear wear fault diagnosis model is the fault diagnosis model corresponding to the wear fault of the gear.

[0078] The determination module is used to determine that the health status of the gear is a wear fault when the vibration amplitude of the meshing frequency is greater than a first vibration value threshold, the vibration amplitude of the frequency sideband is greater than a second vibration value threshold, and the passband value is within a predetermined range.

[0079] In the above embodiments, such as Figure 5As shown, the vibration amplitude of the meshing frequency, the vibration amplitude of the rotational frequency sideband, and the passband value are input into the gear wear fault diagnosis model. First, it is determined whether the vibration amplitude of the meshing frequency and the vibration amplitude of the rotational frequency sideband are both greater than 0.4. If so, it is further determined whether the passband value is between 7.1 and 20. If so, it is determined that the gear has a wear fault.

[0080] Furthermore, to facilitate maintenance by relevant personnel, in one optional implementation, such as Figure 5 As shown, the above-mentioned device also includes:

[0081] The matching module is used to input the above vibration data into the corresponding fault diagnosis model, obtain the health status, and then match maintenance suggestions for the above vibration measuring points based on the manual diagnosis data. The above manual diagnosis data includes the faults that occur at the above vibration measuring points and the corresponding maintenance suggestions. The faults of the above vibration measuring points correspond one-to-one with the above maintenance suggestions.

[0082] The display module is used to display the location of the vibration measuring points and the corresponding maintenance suggestions when the above-mentioned health status is such that a fault corresponding to the above-mentioned fault diagnosis model exists.

[0083] In the above embodiments, such as Figure 5 As shown, the left sidebar displays the diagnostic report output by the diagnostic model. The vibration measurement point mentioned above is the Z2 / Z3 gear of the left rocker arm. The Z2 / Z3 gear fault II diagnostic method was used to determine whether there is a fault in the gear. The diagnosis confirmed that the meshing frequency and harmonics of the Z2 / Z3 gear of the left rocker arm were present in the spectrum, and the vibration amplitude was within the alarm range. It was determined that the Z2 / Z3 gear of the left rocker arm had wear, damage, misalignment, looseness, etc. The report also provided maintenance suggestions, namely, to strengthen the monitoring of vibration amplitude and spectrum changes, improve the lubrication environment, not to run the equipment continuously for a long time, and to inspect the gears at appropriate times so that maintenance personnel can understand the fault and carry out efficient and effective maintenance.

[0084] In the aforementioned device for determining robotic arm faults, vibration data from vibration measurement points of the robotic arm are acquired via a single cloud. These vibration measurement points include gears and bearings of the robotic arm, and the vibration data includes the vibration amplitude of characteristic values ​​of the gears or bearings. A diagnostic unit inputs the vibration data into a corresponding fault diagnosis model to obtain the health status. The fault diagnosis model corresponds one-to-one with the faults at the vibration measurement points. The health status includes the presence of a fault corresponding to the fault diagnosis model and the absence of a fault corresponding to the fault diagnosis model. The fault diagnosis model is formed through logic configuration. This device generates a fault diagnosis model through logic configuration for fault judgment, replacing traditional expert consultation with intelligent diagnosis using a fault diagnosis model. It can quickly output a health status report for the robotic arm, reducing the cost of robotic arm fault diagnosis and solving the problem of high cost in existing robotic arm vibration health diagnosis. Furthermore, it can simultaneously detect a large number of vibration measurement points, greatly improving the efficiency of fault judgment and ensuring timely maintenance.

[0085] This application also provides a robotic arm health management platform, including: one or more processors, a memory, a display device, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any of the above-described methods.

[0086] In the aforementioned robotic arm health management platform, the fault diagnosis logic is generated into a fault diagnosis model through logic configuration. The traditional expert consultation is replaced by intelligent diagnosis using the fault diagnosis model, which can quickly output the health status report of the rocker arm, reduce the cost of rocker arm fault diagnosis, solve the problem of high cost of existing robotic arm vibration health diagnosis, and can simultaneously detect a large number of vibration measurement points, greatly improving the efficiency of fault diagnosis and ensuring timely maintenance.

[0087] In one optional implementation, the aforementioned robotic arm health management platform includes:

[0088] The vibration measurement point establishment system is used to establish a measurement point tree diagram, where one node in the tree diagram corresponds to one vibration measurement point.

[0089] The offline data upload interface system establishes a system communication connection with the aforementioned vibration measuring points. The offline data upload interface system is used to upload the vibration data of the aforementioned vibration measuring points.

[0090] The feature value index management system is connected to the vibration measurement point establishment system and the offline data upload interface system, respectively. The feature value index is used to establish the feature value index of the gear or the bearing and to match the vibration data corresponding to the feature value index with the node.

[0091] The diagnostic model management system establishes a system communication connection with the aforementioned vibration measuring points. The diagnostic model management system is used to output the health status of the aforementioned vibration measuring points based on the vibration data input from the aforementioned vibration measuring points.

[0092] The data interface display system is used to display the location of the aforementioned vibration measuring points and their health status.

[0093] In the above embodiments, taking the health management platform of the rocker arm as an example, the offline data upload interface system uses a multi-channel high-speed vibration data acquisition system to collect the vibration data of the rocker arm. It adopts a high-speed synchronous AD conversion module with a conversion resolution of 24 bits and a frequency of up to 250kHz. The multi-channel high-speed vibration data acquisition system can be powered by a battery or a power bank. The number of channels of the multi-channel high-speed vibration data acquisition system can be expanded to 32 channels. The multi-channel high-speed vibration data acquisition system can realize local storage of vibration data and can also be uploaded to the platform system via the network. The system includes a vibration measurement point establishment system for rocker arm equipment, responsible for creating a tree diagram of measurement points for each rocker arm to manage its individual vibration data; an offline data upload interface system, used to receive vibration data values ​​stored locally by the multi-channel high-speed vibration data acquisition system and transmit the local data to the database of the coal mining machine rocker arm intelligent health management platform system; a feature value index management system, used to establish and manage the feature value indices of each rocker arm gear and bearing, and after establishing the feature value index library, uses multiple algorithms such as spectrum analysis, order analysis, envelope demodulation, and gearbox annular impact diagram to extract the feature values ​​corresponding to the effective feature value indices of the rocker arm gearbox; and a diagnostic model management system. Based on expert experience, the system sets up intelligent fault diagnosis mechanism models for each gear. After the vibration data of the coal mining machine rocker arm is uploaded to the platform system, the system can output a health status assessment of the rocker arm gears based on this vibration data. The data interface display system of the coal mining machine rocker arm is used to display the current vibration value and health status of the rocker arm. The robotic arm health management platform can accurately and quickly identify faulty gears in the coal mining machine rocker arm. Vibration data analysis adopts big data model analysis, which can quickly output analysis results. It is low-cost, widely applicable, flexible in application, and easy to use. The system can be widely promoted and applied to different models of rocker arm equipment.

[0094] The aforementioned device for determining robotic arm malfunctions includes a processor and a memory. The aforementioned acquisition and determination units are all stored as program units in the memory, and the processor executes the aforementioned program units stored in the memory to achieve the corresponding functions.

[0095] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address the high cost of existing robotic arm vibration health diagnostics.

[0096] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0097] This invention provides a computer-readable storage medium storing a program that, when executed by a processor, implements the aforementioned method for determining robotic arm malfunctions.

[0098] This invention provides a processor for running a program, wherein the program executes the method for determining the fault of the robotic arm.

[0099] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0100] Step S101: Obtain vibration data from vibration measurement points of the robotic arm. The vibration measurement points include gears and bearings of the robotic arm. The vibration data includes the vibration amplitude of the characteristic value index of the gears or bearings.

[0101] Step S202: Match all the above nodes with the vibration data of the corresponding vibration measurement points so that the nodes display the vibration data in response to a predetermined operation.

[0102] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0103] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0104] Step S101: Obtain vibration data from vibration measurement points of the robotic arm. The vibration measurement points include gears and bearings of the robotic arm. The vibration data includes the vibration amplitude of the characteristic value index of the gears or bearings.

[0105] Step S202: Match all the above nodes with the vibration data of the corresponding vibration measurement points so that the nodes display the vibration data in response to a predetermined operation.

[0106] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0108] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0110] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0111] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0112] 1) In the method for determining robotic arm faults in this application, firstly, vibration data from vibration measurement points of the robotic arm are acquired. These vibration measurement points include gears and bearings of the robotic arm, and the vibration data includes the vibration amplitude of characteristic values ​​of the gears or bearings. Then, the vibration data is input into a corresponding fault diagnosis model to obtain a health status. The fault diagnosis model corresponds one-to-one with the faults at the vibration measurement points. The health status includes the presence of a fault corresponding to the fault diagnosis model and the absence of a fault corresponding to the fault diagnosis model. The fault diagnosis model is formed through logic configuration. This method generates a fault diagnosis model through logic configuration for fault judgment, replacing traditional expert consultation with intelligent diagnosis using a fault diagnosis model. It can quickly output a health status report for the robotic arm, reducing the cost of robotic arm fault diagnosis and solving the problem of high cost in existing robotic arm vibration health diagnosis. Furthermore, it can simultaneously detect a large number of vibration measurement points, greatly improving the efficiency of fault judgment and ensuring timely maintenance.

[0113] 2) In the robotic arm fault determination device of this application, vibration data of vibration measurement points of the robotic arm are acquired by a single cloud. The vibration measurement points include gears and bearings of the robotic arm, and the vibration data includes the vibration amplitude of the characteristic value index of the gears or bearings. The diagnostic unit inputs the vibration data into the corresponding fault diagnosis model to obtain the health status. The fault diagnosis model corresponds one-to-one with the faults of the vibration measurement points. The health status includes the presence of the fault corresponding to the fault diagnosis model and the absence of the fault corresponding to the fault diagnosis model. The fault diagnosis model is formed through logic configuration. This determination device generates a fault diagnosis model through logic configuration of the fault judgment logic, replacing the traditional expert consultation with intelligent diagnosis using the fault diagnosis model. It can quickly output a health status report of the robotic arm, reducing the cost of robotic arm fault diagnosis, solving the problem of high cost of existing robotic arm vibration health diagnosis, and can simultaneously detect a large number of vibration measurement points, greatly improving the efficiency of fault judgment and ensuring timely maintenance.

[0114] 3) In the robotic arm health management platform of this application, the fault judgment logic is generated into a fault diagnosis model through logic configuration. The traditional expert consultation is replaced by intelligent diagnosis using the fault diagnosis model. It can quickly output the health status report of the rocker arm, reduce the cost of rocker arm fault diagnosis, solve the problem of high cost of existing robotic arm vibration health diagnosis, and can detect a large number of vibration measurement points at the same time, which greatly improves the efficiency of fault judgment and ensures the timeliness of maintenance.

[0115] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining a robotic arm malfunction, characterized in that, include: The vibration data of the vibration measurement points of the robotic arm are obtained. The vibration measurement points include the gears and bearings of the robotic arm. The vibration data includes the vibration amplitude of the characteristic value index of the gear or the bearing. The vibration data is input into the corresponding fault diagnosis model to obtain the health status. The fault diagnosis model corresponds one-to-one with the fault of the vibration measuring point. The health status includes the presence of the fault corresponding to the fault diagnosis model and the absence of the fault corresponding to the fault diagnosis model. The fault diagnosis model is formed through logic configuration. The vibration data of the gear includes the vibration amplitude of the meshing frequency, the vibration amplitude of the rotational frequency sideband, and the passband value. The vibration data of the vibration measurement point of the robotic arm is obtained, including: obtaining the real-time meshing frequency, real-time rotational frequency, harmonic frequency, waveform of the meshing frequency of the gear, and the passband value. The waveform of the gear's meshing frequency is subjected to Fourier transform to obtain the spectrum of the gear's meshing frequency. The rotational frequency sideband is calculated based on the real-time meshing frequency, the real-time rotational frequency, and the harmonics. The rotational frequency sideband is the sum of the real-time meshing frequency and the vibration frequency, and the vibration frequency is the product of the real-time rotational frequency and the harmonics. The vibration amplitude of the rotational frequency sideband is obtained by querying the corresponding vibration amplitude in the spectrum of the gear's meshing frequency based on the rotational frequency sideband. The vibration amplitude of the meshing frequency is then obtained by querying the corresponding vibration amplitude in the spectrum of the gear's meshing frequency based on the real-time meshing frequency.

2. The method according to claim 1, characterized in that, The vibration data is input into the corresponding fault diagnosis model to obtain the health status, including: The vibration amplitude of the meshing frequency, the vibration amplitude of the frequency sideband, and the passband value are input into the gear wear fault diagnosis model, which is the fault diagnosis model corresponding to the wear fault of the gear. If the vibration amplitude of the meshing frequency is greater than a first vibration threshold, the vibration amplitude of the frequency sideband is greater than a second vibration threshold, and the passband value is within a predetermined range, then the health condition of the gear is determined to be a wear fault.

3. The method according to claim 1 or 2, characterized in that, After inputting the vibration data into the corresponding fault diagnosis model to obtain the health status, the method further includes: Based on manual diagnostic data, fault matching and maintenance suggestions are made for the vibration measuring points. The manual diagnostic data includes the faults that occur at the vibration measuring points and the corresponding maintenance suggestions. The faults at the vibration measuring points correspond one-to-one with the maintenance suggestions. If the health status indicates the presence of a fault corresponding to the fault diagnosis model, the location of the vibration measuring point and the corresponding maintenance suggestion are displayed.

4. The method according to claim 1 or 2, characterized in that, Before acquiring vibration data from vibration measurement points of the robotic arm, the method further includes: A tree diagram of the vibration measuring points is established based on the vibration measuring points, wherein one node of the tree diagram corresponds to one vibration measuring point; Match all the nodes with the vibration data of the corresponding vibration measurement points so that the nodes display the vibration data in response to a predetermined operation.

5. A device for determining a robotic arm malfunction, characterized in that, include: The acquisition unit is used to acquire vibration data from vibration measurement points of the robotic arm, wherein the vibration measurement points include gears and bearings of the robotic arm, and the vibration data includes the vibration amplitude of the characteristic value index of the gear or the bearing; The diagnostic unit is used to input the vibration data into the corresponding fault diagnosis model to obtain the health status. The fault diagnosis model corresponds one-to-one with the fault of the vibration measuring point. The health status includes the presence of the fault corresponding to the fault diagnosis model and the absence of the fault corresponding to the fault diagnosis model. The fault diagnosis model is formed through logic configuration. The vibration data of the gear includes the vibration amplitude of the meshing frequency, the vibration amplitude of the rotational frequency sideband, and the passband value. The acquisition unit includes: an acquisition module for acquiring the real-time meshing frequency, real-time rotational frequency, harmonics, waveform of the meshing frequency, and passband value of the gear; a processing module for performing a Fourier transform on the waveform of the meshing frequency to obtain the spectrum of the meshing frequency; a calculation module for calculating the rotational frequency sideband frequency point based on the real-time meshing frequency, the real-time rotational frequency, and the harmonics, wherein the rotational frequency sideband frequency point is the sum of the real-time meshing frequency and the vibration frequency, and the vibration frequency is the product of the real-time rotational frequency and the harmonics; and a query module for querying the corresponding vibration amplitude in the spectrum of the meshing frequency based on the rotational frequency sideband frequency point to obtain the vibration amplitude of the rotational frequency sideband, and querying the corresponding vibration amplitude in the spectrum of the meshing frequency based on the real-time meshing frequency to obtain the vibration amplitude of the meshing frequency.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program performs the method according to any one of claims 1 to 4.

7. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 4 when it runs.

8. A robotic arm health management platform, characterized in that, include: One or more processors, a memory, a display device, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 4.

9. The management platform according to claim 8, characterized in that, The robotic arm health management platform includes: A vibration measuring point establishment system is used to establish a measuring point tree diagram, wherein one node of the measuring point tree diagram corresponds to one vibration measuring point; An offline data upload interface system establishes a system communication connection with the vibration measuring point, and the offline data upload interface system is used to upload the vibration data of the vibration measuring point. The feature value index management system is communicatively connected to the vibration measurement point establishment system and the offline data upload interface system, respectively. It is used to establish feature value indices for the gear or the bearing and match the vibration data corresponding to the feature value indices with the nodes. A diagnostic model management system establishes a system communication connection with the vibration measuring point. The diagnostic model management system is used to output the health status of the vibration measuring point based on the vibration data input from the vibration measuring point. The data interface display system is used to display the location of the vibration measuring point and its health status.

Images