CN116079724A - Robot fault detection method and device, storage medium and robot - Google Patents
Robot fault detection method and device, storage medium and robot Download PDFInfo
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- CN116079724A CN116079724A CN202310015344.9A CN202310015344A CN116079724A CN 116079724 A CN116079724 A CN 116079724A CN 202310015344 A CN202310015344 A CN 202310015344A CN 116079724 A CN116079724 A CN 116079724A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/0095—Means or methods for testing manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
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Abstract
The invention provides a fault detection method and device of a robot, a storage medium and the robot, and relates to the technical field of robots. The fault detection method of the robot comprises the following steps: establishing a joint simulation model of the robot; acquiring fault data of a joint of the robot in a fault state; determining an actual noise spectrum according to the fault data; recording fault data into a joint simulation model, and obtaining a simulated noise spectrum through the joint simulation model; and determining target fault information according to the actual noise spectrum and the simulated noise spectrum.
Description
Technical Field
The present invention relates to the field of robots, and in particular, to a method and apparatus for detecting a fault of a robot, a storage medium, and a robot.
Background
In the related art, when the operation failure occurs in the joints of the robot, abnormal sound can be generated, and the failure problem of the joints can be judged theoretically according to the abnormal sound. At present, the general noise analysis method and software are difficult to realize the accurate positioning of faults, and particularly, a good diagnosis method is not available for the movement of a complex structure such as a robot joint.
Therefore, how to overcome the above technical defects is a technical problem to be solved.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art.
To this end, a first aspect of the present invention proposes a fault detection method of a robot.
A second aspect of the present invention proposes a fault detection device for a robot.
A third aspect of the present invention proposes a fault detection device for a robot.
A fourth aspect of the invention proposes a readable storage medium.
A fifth aspect of the present invention proposes a robot.
In view of the foregoing, a first aspect of the present invention provides a fault detection method for a robot, the fault detection method including: establishing a joint simulation model of the robot; acquiring fault data of a joint of the robot in a fault state; determining an actual noise spectrum according to the fault data; recording fault data into a joint simulation model, and obtaining a simulated noise spectrum through the joint simulation model; and determining target fault information according to the actual noise spectrum and the simulated noise spectrum.
The application provides a fault detection method, which is used with a robot, wherein the robot comprises a plurality of joints, and the fault detection method is used for detecting fault information of the joints.
Specifically, the fault detection method comprises the following steps: firstly, a joint simulation model of the robot is established according to the structural information of the robot, the joint simulation model can simulate the working state of the joint through a mathematical formula, and particularly, the working noise of the joint can be simulated through inputting working data. When the joints of the robot fail, failure data of the robot running in a failure state are collected, and corresponding actual noise frequency spectrums are extracted from the failure data. And then, the fault data is input into a joint simulation model to obtain a simulated noise spectrum corresponding to noise generated when the joint has common possible faults through the joint simulation model. Finally, comparing the actual noise spectrum with the simulated noise spectrum to analyze target fault information matched with the actual noise spectrum through a comparison relation and a fault simulation model, thereby completing the fault detection of the robot joint.
In the related art, there are technical schemes for deriving joint failure problems by directly analyzing noise. However, the use condition of the robot is complex, the structure is complex, the number of parts is large, so that abnormal sound causes are complex, the noise analysis result cannot be effectively related to the working state of the robot hardware by directly analyzing the noise, and the final analysis result is deviated. In this regard, by introducing the joint simulation model, the joint fault analysis process of the robot can be effectively associated with the working state of the hardware of the robot through the joint simulation model, the fault data is introduced into the joint simulation model to simulate the link of the simulated noise spectrum, namely, the link is matched with the working state of the hardware of the robot to screen fault reasons, and finally, the actual fault type can be determined in the fault types possibly occurring under the working condition through analyzing the matching degree of the actual noise spectrum and the simulated noise spectrum, and the corresponding fault information is obtained.
For example, when the joints of the robot may have a fault a and a fault B, and the actual working conditions of the joint one and the joint two are different, the fault noise is similar, at this time, it is difficult to accurately distinguish the fault a and the fault B by directly analyzing the noise, and the probability of misjudgment is high. In the technical scheme defined by the application, the joint simulation model can effectively correlate the actual working condition of the joint with the simulated noise spectrum through the collected joint data, so that the fault A or the fault B which is not matched with the fault data is eliminated in advance, and then the fault information matched with the actual working condition is accurately analyzed through comparing the actual noise spectrum with the simulated noise spectrum.
Therefore, the technical problems that the fault detection result in the related technology is separated from the actual working condition and the fault positioning precision is poor are solved by introducing the joint simulation model. And further, the technical effects of optimizing a fault detection method, improving the fault detection precision and reliability and reducing the maintenance difficulty of the robot are achieved.
In addition, the fault detection method of the robot provided by the invention can also have the following additional technical characteristics:
in the above technical solution, the joint simulation model includes a plurality of different fault modes, and fault information corresponding to each fault mode, and the step of obtaining a simulated noise spectrum through the joint simulation model includes: and controlling the joint simulation model to operate in the first fault mode, and obtaining a simulated noise spectrum corresponding to the first fault mode.
In the technical scheme, the joint simulation model comprises a plurality of different common fault modes, wherein each fault mode is preset with corresponding fault information. After the joint has an operation fault and fault data are recorded into the joint simulation model, the joint simulation model selects one fault mode from a plurality of fault modes as a first fault mode, and the joint simulation model is controlled to operate with the fault data in the first fault mode, so that a simulated noise spectrum corresponding to the fault data and the first fault mode is obtained. And if the matching degree of the simulated noise spectrum and the actual noise spectrum is higher, the current fault of the joint is indicated to correspond to the first fault mode, otherwise, the current fault is indicated to correspond to other fault modes except the first fault mode.
Through the preset fault mode, the fault detection range of the joint simulation model can cover the Yangtze river fault types, and the precision and accuracy of joint fault analysis are improved, and the possibility of misjudgment and missed judgment of joint faults is reduced.
In any of the above technical solutions, the step of determining fault information according to the actual noise spectrum and the analog noise spectrum includes: determining the similarity of an actual noise spectrum and an analog noise spectrum; based on the similarity being greater than or equal to a threshold, taking fault information corresponding to the first fault mode as target fault information; and based on the similarity being smaller than the threshold value, controlling the joint simulation model to be switched to operate in the second fault mode.
In this technical scheme, a step of determining fault information based on an actual noise spectrum and an analog noise spectrum is described. Specifically, after the joint simulation model outputs a simulated noise spectrum, analyzing the similarity of the actual noise spectrum and the simulated noise spectrum, wherein the actual noise spectrum comprises a first time domain feature and a first frequency domain feature, the simulated noise spectrum comprises a second time domain feature and a second frequency spectrum feature, determining the time domain matching degree of the first time domain feature and the second time domain feature, and the frequency domain matching degree of the first frequency domain feature and the second frequency domain feature respectively, and then synthesizing the time domain matching degree and the frequency domain matching degree to obtain the similarity.
After the similarity is determined, comparing the magnitude relation between the similarity and a preset threshold value, if the similarity is larger than or equal to the threshold value, proving that the simulated noise spectrum generated in the first fault mode is matched with the actual noise spectrum, the current fault of the joint corresponds to the first fault mode, and immediately taking the fault information corresponding to the first fault mode as target joint information to push the target joint information to a client side, thereby providing convenience for the client to troubleshoot. Otherwise, if the similarity is smaller than the threshold value, the simulated noise spectrum generated in the first fault mode is proved to be not matched with the actual noise spectrum, then the joint simulation model is controlled to be switched to work in the second fault mode, and the cycle is used, so that multiple fault modes preset in the joint simulation mode are checked one by one.
Compared with the technical scheme of batch investigation, the control flow realizes the one-by-one investigation of fault types, and the one-by-one investigation is not influenced by other fault data, and has relatively high precision and reliability. Meanwhile, the one-by-one checking process can complete the judging task when the judging result is matched, which is beneficial to reducing the data processing burden of the system and further improving the practicability of the fault detection method.
The technical scheme does not rigidly limit specific components of the target fault information, and can provide convenience conditions for a user to troubleshoot the fault.
In any of the above solutions, the step of controlling the joint simulation model to operate in the first failure mode to obtain a simulated noise spectrum corresponding to the first failure mode includes: determining a part characteristic frequency corresponding to the first fault mode; determining fault frequency according to the fault data; and modulating the characteristic frequency through the fault frequency to obtain an analog noise spectrum.
In this technical scheme, a process of generating a simulated noise spectrum by a joint simulation model is described. Specifically, after the control joint simulation model operates in a first fault mode, determining a feature frequency of a part corresponding to the first fault mode, and determining a corresponding fault frequency according to fault data. And then, the fault frequency is used as a carrier signal, and the fault frequency is used as a modulation signal to generate an analog noise spectrum through modulation.
By introducing the fault frequency modulation process, the fault analysis process can be effectively associated with the actual working condition of the joint, so that the joint fault detection process is ensured not to deviate from the actual working condition, and further the technical effects of improving the precision and reliability of the fault detection method are realized.
Specifically, after the analog noise spectrum is obtained, the analog noise spectrum may be synthesized into a sound signal, and the sound signal may be pushed to the user side. The user can compare the sound signal with the actual noise of the joint to perform preliminary investigation on the joint fault, so that the practicability of the fault detection method is improved, and the joint maintenance difficulty is reduced.
In any of the above solutions, the fault data includes a joint: static data, dynamic data, and payload data.
In this technical scheme, the fault data includes static data, and the static data includes posture data of each component, position data of the joint, and the like. The fault data also comprises dynamic data, wherein the dynamic data comprises the speed of the joint, the rotating speed of each part and the like. The fault data also includes load data of the joint.
By introducing the data types, the simulation precision of the joint simulation model can be improved, the error of the simulated noise spectrum output by the joint simulation model is reduced, and the fault detection process and the actual working condition data can be effectively associated, so that the technical effects of improving the fault detection precision and reliability are realized.
In any of the above technical solutions, the step of establishing a joint simulation model of the robot includes: establishing a frequency spectrum model according to the type information and the connection information of the parts of the joint; acquiring vibration data and noise spectrum data of the joint in a non-fault state; and calibrating the spectrum model according to the vibration data and the noise spectrum data to obtain the joint simulation model.
In this technical scheme, a procedure for establishing a joint simulation model will be described. Specifically, part type information and part connection information of each joint in the robot are acquired first, different joints correspond to different part type information and different part connection information, then a rotational speed order relation among parts is established according to the part connection relation, and a corresponding frequency spectrum model is determined according to the rotational speed order relation. And then, vibration data and noise spectrum data of the joint in a non-fault state when the joint runs at a constant speed are obtained, and spectrum orders and amplitudes in the spectrum model are calibrated through the vibration data and the noise spectrum data, so that a joint simulation model capable of accurately simulating joint working conditions is obtained.
By introducing part information, the fault detection method can be closely related to the inherent structure of the joint, so that the analysis process is ensured to be matched with the joint attribute. By introducing joint steady-state working data to calibrate the spectrum model, errors caused by external factors can be eliminated. For example, the joint simulation model cannot give consideration to environmental characteristics such as temperature, humidity and the like in a scene where the joint is located, and accuracy errors caused by external factors can be reduced by introducing steady-state data, so that a simulated noise spectrum output by the joint simulation model can be close to actual working conditions. And further, the joint simulation precision is improved, and the technical effects of practicability and reliability of the fault detection method are improved.
Specifically, the robots are provided with a plurality of different types of joints, so that each robot comprises a plurality of joint simulation models, and when the robot fails, joint model data corresponding to the part of data is included in the extracted failure data, so that joint fault models to be used are screened out from the plurality of joint simulation models through the joint model data.
In any of the above solutions, the fault detection method further includes: acquiring common fault data of joints; and importing common fault data into a joint simulation model to generate multiple fault modes.
In the technical scheme, after a joint simulation model is initially established according to the type information of the parts, the connection information of the parts and the steady-state data, common fault data of the joint is obtained, wherein the common fault data is historical data of joints of the same model, and corresponds to various faults commonly occurring in the joints of the model. The joint simulation model establishes a corresponding fault model according to the common fault data to generate a fault mode corresponding to the common fault type in the joint simulation model.
By introducing common fault data to generate a fault mode, the fault detection range of the joint simulation model can effectively cover the common fault problem of the joint, so that the possibility of erroneous judgment and missed judgment of the fault detection method is reduced, and the practicability of the fault detection method is improved.
In any of the above embodiments, the fault data includes first noise data.
In the technical scheme, when a joint fails, abnormal sound emitted by the joint is collected, and first noise data are obtained. And then obtaining a corresponding actual noise spectrum through the first noise data so as to detect faults by comparing the analog noise spectrums.
In any of the above technical solutions, the step of determining an actual noise spectrum according to the fault data includes: filtering the first noise data to obtain second noise data; extracting time domain features and frequency domain features in the second noise data; and determining the actual noise spectrum according to the time domain features and the frequency domain features.
In the technical scheme, in the process of determining the actual noise spectrum according to the fault data, band-pass filtering processing is firstly carried out on the first noise data, and noise in the first noise data is eliminated so as to obtain second noise data. And extracting time domain features and frequency domain features in the second noise data, and determining an actual noise spectrum through the time domain features and the frequency domain features, wherein the extracted time domain features correspond to the first time domain features, and the extracted frequency domain features correspond to the first frequency domain features, so that the first noise data is preprocessed, and faults are detected by matching with the analog noise spectrum.
The preprocessing process can remove interference components in the first noise data, and reduces data processing burden in the fault detection process by extracting main features, so that the fault detection efficiency is improved.
In any of the above solutions, the fault data includes model data of the joint.
In the technical scheme, the fault data further comprise joint model data, the types, the numbers and the connection relations of the parts in joints of different models are different, the joints of different types can be effectively distinguished through the joint model data, and the possibility of misuse of joint simulation models is reduced.
In any of the above solutions, the robot includes a plurality of joint simulation models, and the step of inputting fault data into the joint simulation models includes: determining a target joint simulation model from the multiple joint simulation models according to the model data; and inputting the fault data into a target joint simulation model.
In the technical scheme, the robot is provided with a plurality of different types of joints, so that each robot comprises a plurality of joint simulation models, when the robot fails, joint model data corresponding to the part of data are included in the extracted failure data, the corresponding target joint simulation model is screened out from the plurality of joint simulation models through the joint model data, and the part of failure data is input into the screened target joint simulation model. And further, the problem of joint simulation model misuse is avoided, and the precision and reliability of the fault detection method are improved.
A second aspect of the present invention provides a fault detection device for a robot, the fault detection device including: the building module is used for building a joint simulation model of the robot; the acquisition module is used for acquiring fault data of the joints of the robot in a fault state; the first determining module is used for determining an actual noise spectrum according to the fault data; the simulation module is used for inputting the fault data into a joint simulation model, and obtaining a simulated noise spectrum through the joint simulation model; and the second determining module is used for determining the target fault information according to the actual noise spectrum and the simulated noise spectrum.
The application provides a fault detection device, this fault detection device cooperation robot uses, and the robot includes a plurality of joints, and fault detection device is used for detecting the trouble information that the joint appears, and fault detection device is including establishing module, acquisition module, first determination module, simulation module and second determination module.
Specifically, the building module builds a joint simulation model of the robot according to the structural information of the robot, the joint simulation model can simulate the working state of the joint through a mathematical formula, and specifically, the working noise of the joint can be simulated through inputting working data. When the joints of the robot fail, the acquisition module acquires failure data of the robot running in a failure state, and the first determination module extracts a corresponding actual noise spectrum from the failure data. The simulation module then enters the fault data into a joint simulation model to obtain a simulated noise spectrum corresponding to noise generated when a joint is subject to common possible faults through the joint simulation model. Finally, the second determining module compares the actual noise spectrum with the simulated noise spectrum to analyze target fault information matched with the actual noise spectrum through a comparison relation and a fault simulation model, so that fault detection of the robot joint is completed.
In the related art, there are technical schemes for deriving joint failure problems by directly analyzing noise. However, the use condition of the robot is complex, the structure is complex, the number of parts is large, so that abnormal sound causes are complex, the noise analysis result cannot be effectively related to the working state of the robot hardware by directly analyzing the noise, and the final analysis result is deviated. In this regard, by introducing the joint simulation model, the joint fault analysis process of the robot can be effectively associated with the working state of the hardware of the robot through the joint simulation model, the fault data is introduced into the joint simulation model to simulate the link of the simulated noise spectrum, namely, the link is matched with the working state of the hardware of the robot to screen fault reasons, and finally, the actual fault type can be determined in the fault types possibly occurring under the working condition through analyzing the matching degree of the actual noise spectrum and the simulated noise spectrum, and the corresponding fault information is obtained.
For example, when the joints of the robot may have a fault a and a fault B, and the actual working conditions of the joint one and the joint two are different, the fault noise is similar, at this time, it is difficult to accurately distinguish the fault a and the fault B by directly analyzing the noise, and the probability of misjudgment is high. In the technical scheme defined by the application, the joint simulation model can effectively correlate the actual working condition of the joint with the simulated noise spectrum through the collected joint data, so that the fault A or the fault B which is not matched with the fault data is eliminated in advance, and then the fault information matched with the actual working condition is accurately analyzed through comparing the actual noise spectrum with the simulated noise spectrum.
Therefore, the technical problems that the fault detection result in the related technology is separated from the actual working condition and the fault positioning precision is poor are solved by introducing the joint simulation model. And further, the technical effects of optimizing the fault detection device, improving the fault detection precision and reliability and reducing the maintenance difficulty of the robot are realized.
A third aspect of the present invention provides a fault detection device for a robot, the fault detection device comprising: a memory having stored thereon programs or instructions; a processor configured to implement the steps of the detection method in any of the above claims when executing a program or instructions.
In this technical scheme, a fault detection device is provided, and the fault detection device comprises a memory and a processor, and the processor can implement the detection method in any one of the above technical schemes by executing a program or instructions stored in the memory. Therefore, the fault detection device has the advantages of the fault detection method in any one of the above technical schemes, and can achieve the technical effects of the fault detection method in any one of the above technical schemes, and in order to avoid repetition, the description is omitted here.
A fourth aspect of the present invention provides a readable storage medium having stored thereon a program or instructions which, when executed by a processor, implement the steps of the detection method as in any of the above-mentioned aspects.
In this technical solution, a readable storage medium is provided, where a program or an instruction is stored, and the program or the instruction is executed by a processor to implement the steps of the detection method in any one of the above technical solutions. Therefore, the readable storage medium has the advantages of the fault detection method in any one of the above technical solutions, and can achieve the technical effects of the fault detection method in any one of the above technical solutions, and is not repeated here for avoiding repetition.
A fifth aspect of the present invention provides a robot, the robot comprising: the detecting device according to any one of the above aspects; or a readable storage medium as in any of the above.
In this technical solution, a robot including the detection device in any one of the above technical solutions or the terminal readable storage medium in any one of the above technical solutions is provided, so that the robot has the advantages of the detection device in any one of the above technical solutions, and can achieve the technical effects achieved by the detection device in any one of the above technical solutions, or the robot has the advantages achieved by the readable storage medium in any one of the above technical solutions, and can achieve the technical effects achieved by the readable storage medium in any one of the above technical solutions. To avoid repetition, no further description is provided here.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 shows one of the flow diagrams of a method of fault detection of a robot according to one embodiment of the present invention;
FIG. 2 shows a second flow diagram of a method for detecting a failure of a robot according to an embodiment of the present invention;
FIG. 3 shows a third flow diagram of a method for detecting a failure of a robot according to an embodiment of the present invention;
FIG. 4 shows a fourth flow diagram of a fault detection method for a robot according to one embodiment of the present invention;
FIG. 5 shows a fifth flow diagram of a fault detection method for a robot according to one embodiment of the present invention;
FIG. 6 shows a sixth flow diagram of a method of fault detection of a robot according to an embodiment of the present invention;
FIG. 7 shows a seventh flow diagram of a method of fault detection for a robot in accordance with one embodiment of the present invention;
FIG. 8 shows an eighth flow diagram of a method of fault detection for a robot in accordance with one embodiment of the present invention;
Fig. 9 shows one of the block diagrams of the failure detection apparatus of the robot according to one embodiment of the present invention;
FIG. 10 shows a second block diagram of a failure detection apparatus of a robot according to an embodiment of the present invention;
fig. 11 shows a block diagram of a robot according to an embodiment of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
A fault detection method of a robot, an apparatus, a storage medium, and a robot according to some embodiments of the present invention are described below with reference to fig. 1 to 11.
As shown in fig. 1, one embodiment of the present invention proposes a fault detection method for a robot, the fault detection method comprising:
102, establishing a joint simulation model of a robot;
104, acquiring fault data of a joint of the robot in a fault state;
step 108, inputting fault data into a joint simulation model, and obtaining a simulated noise spectrum through the joint simulation model;
and 110, determining target fault information according to the actual noise spectrum and the simulated noise spectrum.
The application provides a fault detection method, which is used with a robot, wherein the robot comprises a plurality of joints, and the fault detection method is used for detecting fault information of the joints.
Specifically, the fault detection method comprises the following steps: firstly, a joint simulation model of the robot is established according to the structural information of the robot, the joint simulation model can simulate the working state of the joint through a mathematical formula, and particularly, the working noise of the joint can be simulated through inputting working data. When the joints of the robot fail, failure data of the robot running in a failure state are collected, and corresponding actual noise frequency spectrums are extracted from the failure data. And then, the fault data is input into a joint simulation model to obtain a simulated noise spectrum corresponding to noise generated when the joint has common possible faults through the joint simulation model. Finally, comparing the actual noise spectrum with the simulated noise spectrum to analyze target fault information matched with the actual noise spectrum through a comparison relation and a fault simulation model, thereby completing the fault detection of the robot joint.
In the related art, there are embodiments in which the problem of joint failure is derived by directly analyzing noise. However, the use condition of the robot is complex, the structure is complex, the number of parts is large, so that abnormal sound causes are complex, the noise analysis result cannot be effectively related to the working state of the robot hardware by directly analyzing the noise, and the final analysis result is deviated. In this regard, by introducing the joint simulation model, the joint fault analysis process of the robot can be effectively associated with the working state of the hardware of the robot through the joint simulation model, the fault data is introduced into the joint simulation model to simulate the link of the simulated noise spectrum, namely, the link is matched with the working state of the hardware of the robot to screen fault reasons, and finally, the actual fault type can be determined in the fault types possibly occurring under the working condition through analyzing the matching degree of the actual noise spectrum and the simulated noise spectrum, and the corresponding fault information is obtained.
For example, when the joints of the robot may have a fault a and a fault B, and the actual working conditions of the joint one and the joint two are different, the fault noise is similar, at this time, it is difficult to accurately distinguish the fault a and the fault B by directly analyzing the noise, and the probability of misjudgment is high. In the embodiment defined by the application, the joint simulation model can effectively correlate the actual working condition and the simulated noise spectrum of the joint through the collected joint data, so that the fault A or the fault B which is not matched with the fault data is eliminated in advance, and then the fault information matched with the actual working condition is accurately analyzed through comparing the actual noise spectrum and the simulated noise spectrum.
Therefore, the technical problems that the fault detection result in the related technology is separated from the actual working condition and the fault positioning precision is poor are solved by introducing the joint simulation model. And further, the technical effects of optimizing a fault detection method, improving the fault detection precision and reliability and reducing the maintenance difficulty of the robot are achieved.
In the above embodiment, the joint simulation model includes a plurality of different fault modes, and fault information corresponding to each fault mode, and the step of obtaining the simulated noise spectrum through the joint simulation model includes: and controlling the joint simulation model to operate in the first fault mode, and obtaining a simulated noise spectrum corresponding to the first fault mode.
In this embodiment, the joint simulation model includes a plurality of different common fault modes, where each fault mode is preset with its corresponding fault information. After the joint has an operation fault and fault data are recorded into the joint simulation model, the joint simulation model selects one fault mode from a plurality of fault modes as a first fault mode, and the joint simulation model is controlled to operate with the fault data in the first fault mode, so that a simulated noise spectrum corresponding to the fault data and the first fault mode is obtained. And if the matching degree of the simulated noise spectrum and the actual noise spectrum is higher, the current fault of the joint is indicated to correspond to the first fault mode, otherwise, the current fault is indicated to correspond to other fault modes except the first fault mode.
Through the preset fault mode, the fault detection range of the joint simulation model can cover the Yangtze river fault types, and the precision and accuracy of joint fault analysis are improved, and the possibility of misjudgment and missed judgment of joint faults is reduced.
As shown in fig. 2, in any of the above embodiments, the step of determining the fault information according to the actual noise spectrum and the simulated noise spectrum includes:
and 206, controlling the joint simulation model to be switched to operate in the second fault mode based on the similarity being smaller than the threshold value.
In this embodiment, the step of determining the failure information from the actual noise spectrum and the analog noise spectrum is explained. Specifically, after the joint simulation model outputs a simulated noise spectrum, analyzing the similarity of the actual noise spectrum and the simulated noise spectrum, wherein the actual noise spectrum comprises a first time domain feature and a first frequency domain feature, the simulated noise spectrum comprises a second time domain feature and a second frequency spectrum feature, determining the time domain matching degree of the first time domain feature and the second time domain feature, and the frequency domain matching degree of the first frequency domain feature and the second frequency domain feature respectively, and then synthesizing the time domain matching degree and the frequency domain matching degree to obtain the similarity.
After the similarity is determined, comparing the magnitude relation between the similarity and a preset threshold value, if the similarity is larger than or equal to the threshold value, proving that the simulated noise spectrum generated in the first fault mode is matched with the actual noise spectrum, the current fault of the joint corresponds to the first fault mode, and immediately taking the fault information corresponding to the first fault mode as target joint information to push the target joint information to a client side, thereby providing convenience for the client to troubleshoot. Otherwise, if the similarity is smaller than the threshold value, the simulated noise spectrum generated in the first fault mode is proved to be not matched with the actual noise spectrum, then the joint simulation model is controlled to be switched to work in the second fault mode, and the cycle is used, so that multiple fault modes preset in the joint simulation mode are checked one by one.
Compared with the embodiment of batch investigation, the control flow realizes the one-by-one investigation of fault types, and the one-by-one investigation is not influenced by other fault data, and has relatively higher precision and reliability. Meanwhile, the one-by-one checking process can complete the judging task when the judging result is matched, which is beneficial to reducing the data processing burden of the system and further improving the practicability of the fault detection method.
The target fault information comprises information such as a fault position, a fault reason, a fault type, a fault grade and the like, and the specific components of the target fault information are not rigidly limited in the embodiment, so that convenience conditions can be provided for a user to troubleshoot the fault.
As shown in fig. 3, in any of the above embodiments, the step of controlling the joint simulation model to operate in the first failure mode to obtain a simulated noise spectrum corresponding to the first failure mode includes:
In this embodiment, a process in which a joint simulation model generates a simulated noise spectrum is described. Specifically, after the control joint simulation model operates in a first fault mode, determining a feature frequency of a part corresponding to the first fault mode, and determining a corresponding fault frequency according to fault data. And then, the fault frequency is used as a carrier signal, and the fault frequency is used as a modulation signal to generate an analog noise spectrum through modulation.
By introducing the fault frequency modulation process, the fault analysis process can be effectively associated with the actual working condition of the joint, so that the joint fault detection process is ensured not to deviate from the actual working condition, and further the technical effects of improving the precision and reliability of the fault detection method are realized.
Specifically, after the analog noise spectrum is obtained, the analog noise spectrum may be synthesized into a sound signal, and the sound signal may be pushed to the user side. The user can compare the sound signal with the actual noise of the joint to perform preliminary investigation on the joint fault, so that the practicability of the fault detection method is improved, and the joint maintenance difficulty is reduced.
In any of the above embodiments, the fault data comprises articular: static data, dynamic data, and payload data.
In this embodiment, the failure data includes static data including posture data of each component, position data of the joint, and the like. The fault data also comprises dynamic data, wherein the dynamic data comprises the speed of the joint, the rotating speed of each part and the like. The fault data also includes load data of the joint.
By introducing the data types, the simulation precision of the joint simulation model can be improved, the error of the simulated noise spectrum output by the joint simulation model is reduced, and the fault detection process and the actual working condition data can be effectively associated, so that the technical effects of improving the fault detection precision and reliability are realized.
As shown in fig. 4, in any of the above embodiments, the step of establishing a joint simulation model of the robot includes:
and step 406, calibrating the spectrum model according to the vibration data and the noise spectrum data to obtain a joint simulation model.
In this embodiment, a step of creating a joint simulation model will be described. Specifically, part type information and part connection information of each joint in the robot are acquired first, different joints correspond to different part type information and different part connection information, then a rotational speed order relation among parts is established according to the part connection relation, and a corresponding frequency spectrum model is determined according to the rotational speed order relation. And then, vibration data and noise spectrum data of the joint in a non-fault state when the joint runs at a constant speed are obtained, and spectrum orders and amplitudes in the spectrum model are calibrated through the vibration data and the noise spectrum data, so that a joint simulation model capable of accurately simulating joint working conditions is obtained.
By introducing part information, the fault detection method can be closely related to the inherent structure of the joint, so that the analysis process is ensured to be matched with the joint attribute. By introducing joint steady-state working data to calibrate the spectrum model, errors caused by external factors can be eliminated. For example, the joint simulation model cannot give consideration to environmental characteristics such as temperature, humidity and the like in a scene where the joint is located, and accuracy errors caused by external factors can be reduced by introducing steady-state data, so that a simulated noise spectrum output by the joint simulation model can be close to actual working conditions. And further, the joint simulation precision is improved, and the technical effects of practicability and reliability of the fault detection method are improved.
Specifically, the robots are provided with a plurality of different types of joints, so that each robot comprises a plurality of joint simulation models, and when the robot fails, joint model data corresponding to the part of data is included in the extracted failure data, so that joint fault models to be used are screened out from the plurality of joint simulation models through the joint model data.
As shown in fig. 5, in any of the foregoing embodiments, the fault detection method further includes:
In this embodiment, after a joint simulation model is initially established according to the component type information, the component connection information and the steady-state data, common fault data of the joint is obtained, where the common fault data is historical data of the joint with the same model, and corresponds to various faults commonly occurring in the joint with the model. The joint simulation model establishes a corresponding fault model according to the common fault data to generate a fault mode corresponding to the common fault type in the joint simulation model.
By introducing common fault data to generate a fault mode, the fault detection range of the joint simulation model can effectively cover the common fault problem of the joint, so that the possibility of erroneous judgment and missed judgment of the fault detection method is reduced, and the practicability of the fault detection method is improved.
In any of the above embodiments, the fault data comprises first noise data.
In this embodiment, when the joint fails, abnormal sound emitted from the joint is collected, and first noise data is obtained. And then obtaining a corresponding actual noise spectrum through the first noise data so as to detect faults by comparing the analog noise spectrums.
As shown in fig. 6, in any of the above embodiments, the step of determining the actual noise spectrum according to the fault data includes:
In this embodiment, in the process of determining the actual noise spectrum according to the fault data, band-pass filtering processing is performed on the first noise data first, so as to eliminate noise in the first noise data, and obtain second noise data. And extracting time domain features and frequency domain features in the second noise data, and determining an actual noise spectrum through the time domain features and the frequency domain features, wherein the extracted time domain features correspond to the first time domain features, and the extracted frequency domain features correspond to the first frequency domain features, so that the first noise data is preprocessed, and faults are detected by matching with the analog noise spectrum.
The preprocessing process can remove interference components in the first noise data, and reduces data processing burden in the fault detection process by extracting main features, so that the fault detection efficiency is improved.
In any of the above embodiments, the fault data comprises model data of the joint.
In this embodiment, the fault data further includes joint model data, and differences exist in types, numbers and connection relationships of parts in joints of different models, so that different types of joints can be effectively distinguished through the joint model data, and the possibility of misuse of the joint simulation model is reduced.
As shown in fig. 7, in any of the above embodiments, the robot includes a plurality of joint simulation models, and the step of entering the fault data into the joint simulation models includes:
In this embodiment, the robots are provided with a plurality of different types of joints, so each robot includes a plurality of joint simulation models, when the robot fails, the extracted failure data includes joint model data corresponding to the portion of data, so as to screen out a corresponding target joint simulation model from the plurality of joint simulation models through the joint model data, and the portion of failure data is input into the screened target joint simulation model. And further, the problem of joint simulation model misuse is avoided, and the precision and reliability of the fault detection method are improved.
As shown in fig. 8, in a specific embodiment of the present invention, the fault detection flow of the robot is as follows:
step 808, acquiring fault data of the joints of the robot in a fault state;
step 810, determining a part characteristic frequency corresponding to the first failure mode;
step 814, modulating the characteristic frequency by the fault frequency to obtain an analog noise spectrum;
step 822, determining a target joint simulation model from the plurality of joint simulation models according to the model data;
step 824, controlling the joint simulation model to operate in the first failure mode, so as to obtain a simulated noise spectrum corresponding to the first failure mode;
in step 830, the joint simulation model is controlled to switch to operate in the second failure mode.
As shown in fig. 9, a second aspect of the present invention provides a fault detection device 900 of a robot, the fault detection device 900 of the robot including: a building module 902, configured to build a joint simulation model of the robot; an acquiring module 904, configured to acquire fault data of a joint of the robot in a fault state; a first determining module 906, configured to determine an actual noise spectrum according to the fault data; the simulation module 908 is used for inputting the fault data into a joint simulation model, and obtaining a simulated noise spectrum through the joint simulation model; a second determining module 910 is configured to determine target fault information according to the actual noise spectrum and the simulated noise spectrum.
The application provides a fault detection device 900 of robot, the fault detection device 900 of this robot cooperates the robot to use, and the robot includes a plurality of joints, and fault detection device 900 of robot is used for detecting the fault information that the joint appears, and fault detection device 900 of robot includes establishment module 902, acquisition module 904, first determination module 906, emulation module 908 and second determination module 910.
Specifically, the building module 902 builds a joint simulation model of the robot according to the structural information of the robot, the joint simulation model can simulate the working state of the joint through a mathematical formula, and specifically can simulate the working noise of the joint through inputting working data. When the joints of the robot fail, the acquisition module acquires failure data of the robot running in a failure state, and the first determination module 906 extracts a corresponding actual noise spectrum from the failure data. Thereafter, the simulation module 908 logs the fault data into a joint simulation model to obtain a simulated noise spectrum corresponding to noise generated when a joint is subject to common possible faults through the joint simulation model. Finally, the second determining module 910 compares the actual noise spectrum with the simulated noise spectrum, so as to analyze the target fault information matched with the actual noise spectrum by matching the comparison relation with the fault simulation model, thereby completing the fault detection of the robot joint.
In the related art, there are embodiments in which the problem of joint failure is derived by directly analyzing noise. However, the use condition of the robot is complex, the structure is complex, the number of parts is large, so that abnormal sound causes are complex, the noise analysis result cannot be effectively related to the working state of the robot hardware by directly analyzing the noise, and the final analysis result is deviated. In this regard, by introducing the joint simulation model, the joint fault analysis process of the robot can be effectively associated with the working state of the hardware of the robot through the joint simulation model, the fault data is introduced into the joint simulation model to simulate the link of the simulated noise spectrum, namely, the link is matched with the working state of the hardware of the robot to screen fault reasons, and finally, the actual fault type can be determined in the fault types possibly occurring under the working condition through analyzing the matching degree of the actual noise spectrum and the simulated noise spectrum, and the corresponding fault information is obtained.
For example, when the joints of the robot may have a fault a and a fault B, and the actual working conditions of the joint one and the joint two are different, the fault noise is similar, at this time, it is difficult to accurately distinguish the fault a and the fault B by directly analyzing the noise, and the probability of misjudgment is high. In the embodiment defined by the application, the joint simulation model can effectively correlate the actual working condition and the simulated noise spectrum of the joint through the collected joint data, so that the fault A or the fault B which is not matched with the fault data is eliminated in advance, and then the fault information matched with the actual working condition is accurately analyzed through comparing the actual noise spectrum and the simulated noise spectrum.
Therefore, the technical problems that the fault detection result in the related technology is separated from the actual working condition and the fault positioning precision is poor are solved by introducing the joint simulation model. And further, the fault detection device 900 of the robot is optimized, the fault detection precision and reliability are improved, and the technical effect of the maintenance difficulty of the robot is reduced.
In any of the above embodiments, the joint simulation model includes a plurality of different failure modes, and failure information corresponding to each failure mode, and the simulation module 908 further includes: and controlling the joint simulation model to operate in the first fault mode, and obtaining a simulated noise spectrum corresponding to the first fault mode.
In any of the foregoing embodiments, the second determining module 910 specifically includes: determining the similarity of an actual noise spectrum and an analog noise spectrum; based on the similarity being greater than or equal to a threshold, taking fault information corresponding to the first fault mode as target fault information; and based on the similarity being smaller than the threshold value, controlling the joint simulation model to be switched to operate in the second fault mode.
In any of the above embodiments, the second determining module 910 further includes: determining a part characteristic frequency corresponding to the first fault mode; determining fault frequency according to the fault data; and modulating the characteristic frequency through the fault frequency to obtain an analog noise spectrum.
In any of the foregoing embodiments, the establishing module 902 specifically includes: establishing a frequency spectrum model according to the type information and the connection information of the parts of the joint; acquiring vibration data and noise spectrum data of the joint in a non-fault state; and calibrating the spectrum model according to the vibration data and the noise spectrum data to obtain the joint simulation model.
In any of the above embodiments, the establishing module 902 further includes: acquiring common fault data of joints; and importing common fault data into a joint simulation model to generate multiple fault modes.
In any of the foregoing embodiments, the fault data includes first noise data, and the first determining module 906 specifically includes: filtering the first noise data to obtain second noise data; time domain features and frequency domain features in the second noise data; and determining the actual noise spectrum according to the time domain features and the frequency domain features.
In any of the above embodiments, the fault data comprises model data of the joint, and the simulation module 908 further comprises: determining a target joint simulation model from the multiple joint simulation models according to the model data; and inputting the fault data into a target joint simulation model.
As shown in fig. 10, a third aspect of the present invention provides a fault detection device 1000 of a robot, the fault detection device including: a memory 1002 having stored thereon programs or instructions; processor 1004, when configured to execute a program or instructions, implements the steps of the detection method as in any of the embodiments described above.
In this embodiment, a fault detection apparatus is provided, which includes a memory 1002 and a processor 1004, where the processor 1004 executes a program or instructions stored in the memory 1002 to implement the detection method in any of the above embodiments. Therefore, the fault detection device has the advantages of the fault detection method in any of the above embodiments, and can achieve the technical effects achieved by the fault detection method in any of the above embodiments, and in order to avoid repetition, the description is omitted here.
A fourth aspect of the present invention provides a readable storage medium having stored thereon a program or instructions which when executed by a processor performs the steps of the detection method as in any of the embodiments described above.
In this embodiment, a readable storage medium is provided, in which a program or an instruction is stored, and the program or the instruction is executed by a processor to implement the steps of the detection method in any of the above embodiments. Therefore, the readable storage medium has the advantages of the fault detection method in any of the above embodiments, and can achieve the technical effects of the fault detection method in any of the above embodiments, and is not repeated here.
As shown in fig. 11, a fifth aspect of the present invention provides a robot 1100, the robot 1100 comprising: the failure detection device 900 of the robot in any of the embodiments described above; or a readable storage medium 1120 as in any of the embodiments described above.
In this embodiment, a robot 1100 including the failure detection device 900 of the robot in any of the embodiments or the readable storage medium 1120 in any of the embodiments is proposed, and therefore, the robot 1100 has the advantages of the failure detection device 900 of the robot in any of the embodiments, the technical effects that can be achieved by the failure detection device 900 of the robot in any of the embodiments can be achieved, or the advantages that can be achieved by the readable storage medium 1120 in any of the embodiments can be achieved by the robot 1100. To avoid repetition, no further description is provided here.
It is to be understood that in the claims, specification and drawings of the present invention, the term "plurality" means two or more, and unless otherwise explicitly defined, the orientation or positional relationship indicated by the terms "upper", "lower", etc. are based on the orientation or positional relationship shown in the drawings, only for the convenience of describing the present invention and making the description process easier, and not for the purpose of indicating or implying that the apparatus or element in question must have the particular orientation described, be constructed and operated in the particular orientation, so that these descriptions should not be construed as limiting the present invention; the terms "connected," "mounted," "secured," and the like are to be construed broadly, and may be, for example, a fixed connection between a plurality of objects, a removable connection between a plurality of objects, or an integral connection; the objects may be directly connected to each other or indirectly connected to each other through an intermediate medium. The specific meaning of the terms in the present invention can be understood in detail from the above data by those of ordinary skill in the art.
In the claims, specification, and drawings of the present invention, the descriptions of terms "one embodiment," "some embodiments," "particular embodiments," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In the claims, specification and drawings of the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (15)
1. A method for detecting a failure of a robot, comprising:
establishing a joint simulation model of the robot;
acquiring fault data of a joint of the robot in a fault state;
determining an actual noise spectrum according to the fault data;
recording the fault data into the joint simulation model, and obtaining a simulated noise spectrum through the joint simulation model;
and determining target fault information according to the actual noise spectrum and the simulated noise spectrum.
2. The method for detecting a fault according to claim 1, wherein the joint simulation model includes a plurality of different fault modes, and fault information corresponding to each fault mode, and the step of obtaining the simulated noise spectrum by the joint simulation model includes:
and controlling the joint simulation model to run in a first fault mode, and obtaining the simulated noise spectrum corresponding to the first fault mode.
3. The fault detection method according to claim 2, wherein the step of determining fault information from the actual noise spectrum and the simulated noise spectrum comprises:
determining a similarity of the actual noise spectrum and the simulated noise spectrum;
based on the similarity being greater than or equal to a threshold, taking fault information corresponding to the first fault mode as the target fault information;
and controlling the joint simulation model to switch to operate in a second fault mode based on the similarity being smaller than the threshold.
4. A fault detection method according to claim 3, wherein the step of controlling the joint simulation model to operate in a first fault mode to obtain the simulated noise spectrum corresponding to the first fault mode comprises:
determining a part characteristic frequency corresponding to the first fault mode;
determining fault frequency according to the fault data;
and modulating the characteristic frequency through the fault frequency to obtain the analog noise spectrum.
5. The method of claim 4, wherein the fault data comprises the joint: static data, dynamic data, and payload data.
6. The fault detection method according to claim 2, wherein the step of establishing a joint simulation model of the robot includes:
establishing a frequency spectrum model according to the part type information and the part connection information of the joint;
obtaining vibration data and noise spectrum data of the joint in a non-fault state;
and calibrating the spectrum model according to the vibration data and the noise spectrum data to obtain the joint simulation model.
7. The fault detection method of claim 6, further comprising:
acquiring common fault data of the joint;
and importing the common fault data into the joint simulation model to generate the plurality of fault modes.
8. The fault detection method according to any one of claims 1 to 7, wherein the fault data includes first noise data.
9. The fault detection method of claim 8, wherein the step of determining an actual noise spectrum from the fault data comprises:
filtering the first noise data to obtain second noise data;
extracting time domain features and frequency domain features in the second noise data;
And determining the actual noise spectrum according to the time domain features and the frequency domain features.
10. The failure detection method according to any one of claims 1 to 7, wherein the failure data includes model data of the joint.
11. The method of claim 10, wherein the robot includes a plurality of the joint simulation models, and wherein the step of entering the fault data into the joint simulation models includes:
determining a target joint simulation model from a plurality of joint simulation models according to the model data;
and recording the fault data into the target joint simulation model.
12. A fault detection device for a robot, comprising:
the building module is used for building a joint simulation model of the robot;
the acquisition module is used for acquiring fault data of the joints of the robot in a fault state;
the first determining module is used for determining an actual noise spectrum according to the fault data;
the simulation module is used for inputting the fault data into the joint simulation model, and obtaining a simulated noise spectrum through the joint simulation model;
and the second determining module is used for determining target fault information according to the actual noise spectrum and the simulated noise spectrum.
13. A fault detection device for a robot, comprising:
a memory having stored thereon programs or instructions;
a processor configured to implement the steps of the detection method according to any one of claims 1 to 11 when executing the program or instructions.
14. A readable storage medium having stored thereon a program or instructions, which when executed by a processor, implement the steps of the detection method according to any of claims 1 to 11.
15. A robot, comprising:
the detection device of claim 12 or 13; or (b)
The readable storage medium of claim 14.
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| CN117169639A (en) * | 2023-11-02 | 2023-12-05 | 启东市航新实用技术研究所 | Product detection method and system for power adapter production |
| CN117169639B (en) * | 2023-11-02 | 2023-12-29 | 启东市航新实用技术研究所 | Product detection method and system for power adapter production |
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