CN117515131A - Method, device, storage medium and equipment for monitoring abrasion of planetary reducer - Google Patents
Method, device, storage medium and equipment for monitoring abrasion of planetary reducer Download PDFInfo
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- 239000003638 chemical reducing agent Substances 0.000 title claims abstract description 80
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000012544 monitoring process Methods 0.000 title claims abstract description 57
- 238000005299 abrasion Methods 0.000 title claims description 29
- 238000003860 storage Methods 0.000 title claims description 20
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H57/00—General details of gearing
- F16H57/01—Monitoring wear or stress of gearing elements, e.g. for triggering maintenance
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H57/00—General details of gearing
- F16H57/01—Monitoring wear or stress of gearing elements, e.g. for triggering maintenance
- F16H2057/012—Monitoring wear or stress of gearing elements, e.g. for triggering maintenance of gearings
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
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Abstract
The method comprises the steps of collecting friction vibration signals of a planetary reducer in a robot through a collecting device, obtaining time sequence data according to the friction vibration signals in a preset time period, obtaining fractal characteristics of the time sequence data, inputting the fractal characteristics into a pre-trained monitoring model to obtain the wear state of the planetary reducer output by the monitoring model, and analyzing the friction vibration signals collected in the use state of the robot by utilizing the pre-trained monitoring model to accurately identify the wear state of the planetary reducer of the robot in real time.
Description
Technical Field
The present disclosure relates to the field of computers, and in particular, to a method, an apparatus, a storage medium, and a device for monitoring wear of a planetary reducer.
Background
In recent years, robotics have been widely used in a number of fields including manufacturing, healthcare, aerospace, and the like. In the use process, parts of the robot are subjected to unavoidable abrasion, however, for operation scenes with high precision requirements, such as medical care, aerospace and the like, precision errors caused by abrasion can bring huge losses, and therefore the abrasion states of the parts of the robot need to be detected regularly.
The planetary reducer is a mechanical transmission component for connecting a servo motor and an application load in a robot motion control system, and in the current method, the detection of the abrasion state of the planetary reducer mainly depends on periodic maintenance and visual inspection, and the methods often need shutdown inspection on one hand, and cannot detect tiny and initial defects on the other hand.
The invention provides a method, a device, a storage medium and equipment for monitoring wear of a planetary reducer.
Disclosure of Invention
The present disclosure provides a method and apparatus for monitoring wear of a planetary reducer, so as to partially solve the foregoing problems in the prior art.
The technical scheme adopted in the specification is as follows:
the specification provides a method for monitoring wear of a planetary reducer, comprising the following steps:
collecting friction vibration signals of a planetary reducer in the robot through a collecting device;
obtaining time sequence data according to the friction vibration signals in a preset time period;
obtaining fractal characteristics of the time sequence data;
and inputting the fractal characteristics into a pre-trained monitoring model to obtain the abrasion state of the planetary reducer output by the monitoring model.
Optionally, acquiring the fractal feature of the time series data specifically includes:
acquiring a multi-fractal spectrum of the time sequence data;
and extracting fractal characteristics in the multi-fractal spectrum to serve as the fractal characteristics of the time sequence data.
Optionally, acquiring the multi-fractal spectrum of the time-series data specifically includes:
acquiring a plurality of preset time window scales;
for any time window scale, according to the time window scale K n Dividing the time series data into a plurality of groups which are continuous and equal, so that each group of time series data comprises K n Successive time series data;
obtaining a generalized Hersteter index under the time window scale according to the plurality of groups of time sequence data;
and obtaining a multi-fractal spectrum according to all generalized Hersteter indexes under a plurality of preset time window scales.
Optionally, the fractal features specifically include: at least one of fractal spectrum width of multi-fractal spectrum obtained according to the time sequence data, singular index of maximum value point of fractal spectrum, height difference of left and right endpoints, singular index of left endpoint and height of left endpoint.
Optionally, the pre-training monitoring model specifically includes:
acquiring a sample friction vibration signal of a sample part, determining the current abrasion state of the sample part, and taking the abrasion state as a mark;
extracting sample fractal characteristics of the sample friction vibration signals;
inputting the sample fractal characteristics into a model to be trained to obtain a prediction result of the model to be trained;
and training the model to be trained according to the prediction result of the model to be trained and the label of the sample component.
Optionally, acquiring a sample frictional vibration signal of the sample component specifically includes:
using the sample component in a preset experimental environment;
during use of the sample member, a sample frictional vibration signal of the sample member is acquired by an acquisition device.
Optionally, the collecting device collects a friction vibration signal of the planetary reducer, and specifically includes:
and collecting friction vibration signals of a planetary reducer in the robot through a collecting device at a preset sampling frequency, wherein the sampling frequency is larger than a frequency threshold.
The present specification provides a device for monitoring wear of a planetary reducer, comprising:
the acquisition module is used for acquiring friction vibration signals of a planetary reducer in the robot through an acquisition device;
the signal acquisition module is used for acquiring time sequence data according to the friction vibration signal in a preset time period;
the feature acquisition module acquires fractal features of the time sequence data;
and the prediction module inputs the fractal characteristics into a pre-trained monitoring model to obtain the abrasion state of the planetary reducer output by the monitoring model.
The present description provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of planetary reducer wear monitoring described above.
The present specification provides an apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of planetary reducer wear monitoring described above when executing the program.
The above-mentioned at least one technical scheme that this specification adopted can reach following beneficial effect:
in the method for monitoring the abrasion of the planetary reducer, which is provided by the specification, a friction vibration signal of the planetary reducer in a robot is collected through a collecting device, time sequence data is obtained according to the friction vibration signal in a preset time period, fractal characteristics of the time sequence data are obtained, the fractal characteristics are input into a pre-trained monitoring model, and the abrasion state of the planetary reducer output by the monitoring model is obtained.
According to the method, the friction vibration signals collected in the using state of the robot are analyzed by using the pre-trained monitoring model, so that the wearing state of the planetary reducer of the robot can be accurately identified in real time.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification, illustrate and explain the exemplary embodiments of the present specification and their description, are not intended to limit the specification unduly. In the drawings:
FIG. 1 is a flow chart of a method for monitoring wear of a planetary reducer according to the present disclosure;
FIG. 2 is a schematic diagram of an apparatus for monitoring wear of a planetary reducer provided herein;
fig. 3 is a schematic view of the electronic device corresponding to fig. 1 provided in the present specification.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present specification more apparent, the technical solutions of the present specification will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present application based on the embodiments herein.
The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for monitoring wear of a planetary reducer in the present specification, where the method for monitoring wear of a planetary reducer specifically includes the following steps:
s100: and collecting friction vibration signals of a planetary reducer in the robot through a collecting device.
At present, maintenance and maintenance of a robot planetary reducer are mainly finished by manual means, and the means often need to visually inspect the planetary reducer when the robot is stopped or know the abrasion state of the planetary reducer according to the abnormality of the robot during operation. These detection means for the wear state of the robot cannot detect the minute defect of the component nor react before wear affecting the performance of use occurs. According to the method provided by the specification, the friction vibration signal of the planetary reducer can be obtained in real time in a mode of additionally arranging the acquisition device on the planetary reducer of the robot, so that the friction vibration signal is analyzed by utilizing analysis equipment in a subsequent step, and the real-time monitoring of the abrasion state of the planetary reducer is achieved. Wherein the acquisition device can be an acceleration sensor; the specific mode for acquiring the friction vibration signal of the planetary reducer can be preset sampling duration, sampling interval and sampling frequency, and the planetary reducer is subjected to signal acquisition at preset sampling duration at preset sampling intervals, so that the purpose of acquiring the wear state of the planetary reducer in real time can be achieved by analyzing the friction vibration signal of the planetary reducer of the robot acquired in each section of sampling duration in the subsequent step.
S102: and obtaining time sequence data according to the friction vibration signal in a preset time period.
Specifically, the frictional vibration signals obtained during the sampling duration are arranged in the sampling order as time series data X k (k=1, 2, …, m), m being the number of frictional vibration signals acquired by the acquisition means during an independent sampling duration, the time series data X k Conversion to random walk time series data Y k （k=1，2，…，m），So that the subsequent step extracts fractal features of the frictional vibration signal from the random walk time series data.
S104: and obtaining fractal characteristics of the time sequence data.
The random walk time series data acquired in step S102 is derived from the frictional vibration signals acquired in the preceding step, which essentially contain information on the wear state of the frictional surface (the degree of collision between the rough peaks on the microscopic rough surface of the planetary reducer varies for different wear states). However, the characteristics carried by the friction vibration signals are often nonlinear, the characteristics of the collected friction vibration signals (such as the fluctuation frequency, the fluctuation degree and the like of the friction vibration signals) are directly input into a machine learning model, and the recognition effect of the model on the friction vibration signals in different wear states is not ideal. Therefore, the fractal characteristic of the friction vibration signal for identifying different wear states is selected by using the similarity and the volatility of the random walk time series data of the friction vibration signal under different observation dimensions by using the fractal thought, and higher accuracy can be provided in the aspect of the identification of the wear states.
Specifically, in one or more embodiments of the present specification, a multi-fractal spectrum of the time-series data is obtained, and a fractal feature in the multi-fractal spectrum is extracted as a fractal feature of the time-series data.
The multi-fractal spectrum takes a singular index (reflecting the fractal characteristics of data and representing the data to be single-fractal or multi-fractal) as an abscissa, and takes a fractal dimension (a measure of complexity, irregularity and non-uniformity on a split structure) as an ordinate, and reflects the fractal characteristics of time series data under different observation scales.
In one or more embodiments of the present specification, the fractal features specifically include: and according to the time sequence data, obtaining at least one of a singular index of a fractal spectrum maximum point of the multi-fractal spectrum, a height difference of a left endpoint, a singular index of a left endpoint and a height of the left endpoint and a fractal spectrum width.
In one or more embodiments of the present disclosure, the above five fractal features may be used together to identify the wear state of the robot star reducer in a state where the robot is stationary, and the identification process may achieve a false alarm rate of 1%.
S106: and inputting the fractal characteristics into a pre-trained monitoring model to obtain the abrasion state of the planetary reducer output by the monitoring model.
Specifically, the process of obtaining the wear state of the planetary reducer according to the fractal characteristics may be that the monitoring model obtains the wear fraction as an output result according to the input fractal characteristics, so that a monitoring person can automatically judge the wear state of the planetary reducer according to the corresponding relation between the wear fraction and the wear state; the monitoring model may also directly obtain the wear state according to the input fractal characteristics as an output result, which is not limited in the present specification. Wherein the wear state can be set to normal, slight wear, severe wear, so that in the implementation of the solution provided in the present specification, a robot maintainer can directly judge the maintenance solution of the planetary reducer according to the wear state of the planetary reducer (the planetary reducer does not need to be replaced in a normal state, the planetary reducer is recommended to be replaced or grease on the planetary reducer is replenished/replaced in a slight wear state, and the planetary reducer must be replaced in a severe wear state).
According to the method for monitoring the wear of the planetary reducer of the robot, which is shown in fig. 1, the friction vibration signals collected in the using state of the robot are analyzed by using a pre-trained monitoring model, so that the wear state of the planetary reducer of the robot can be accurately identified in real time.
In addition, as shown in step S104 of fig. 1, a plurality of preset time window scales are obtained, and for any time window scale, the time window scale K is used according to the time window scale n Dividing the time series data into a plurality of groups which are continuous and equal, so that each group of time series data comprises K n And obtaining generalized Hersteter indexes under the time window scale according to the cut-out groups of time sequence data, and obtaining multi-fractal spectrums according to all generalized Hersteter indexes under the preset time window scales.
After the step S102 is performed to obtain the random walk time series data, the random walk time series Y may be performed according to the predetermined n time window scales k (k=1, 2, …, m) are divided into time window dimensions K n (the time window scale is the nth in the time window scale sequence), Y is taken as an example k (k=1, 2, …, m) into consecutive, equal S groups, such that each group cut out contains K n Successive time series data for each group of the divided setsInter-sequence data, mean square error F of each group is calculated 2 （K n K) k=1, 2, …, S), then the time window scale K n The lower q-order wave function is F (q, K) n ) The generalized Hersteter index h (q) is calculated by a fluctuation function through changing the value of q, and then the generalized Hersteter index h (q) is calculated through a time window scale K n The generalized Hersteter index h (q) under the condition of calculating the time window scale K n The singular index alpha and the fractal dimension f (alpha) are calculated respectively through each time window scale, and the multi-fractal spectrum is built.
In one or more embodiments of the present disclosure, the predetermined time window scale sequence may be set to (2 4 、2 5 、……、2 9 ) To promote the recognition of the fractal features extracted in the subsequent steps.
Before executing the flow shown in fig. 1, the method provided in the present specification further includes pre-training a monitoring model, where training the monitoring model specifically includes: the method comprises the steps of obtaining a sample friction vibration signal of a sample part, determining the current abrasion state of the sample part, taking the abrasion state as a label, extracting sample fractal characteristics of the sample friction vibration signal, inputting the sample fractal characteristics into a model to be trained, obtaining a prediction result of the model to be trained, and training the model to be trained according to the prediction result of the model to be trained and the label of the sample part.
The method provided by the specification uses the friction vibration signals and the abrasion states of the sample parts to train the model to be trained so as to obtain a monitoring model for processing fractal characteristics in the abrasion monitoring process of the planetary reducer. The sample component can be a component which is made of the same material or has the same function as the planetary reducer; the method for acquiring the friction vibration signal of the sample component can be that the friction vibration signal is acquired through historical data or through a preset experiment; the method for identifying the abrasion state of the sample part can be visual inspection or use test of the sample part, or can be to detect the friction coefficient between the fixed friction pair material and the sample part through setting experiments, and the abrasion state of the sample part can be obtained through comparing the friction coefficients of various sample parts with different abrasion degrees and model parts.
Further, the acquiring a sample frictional vibration signal of the sample member specifically includes: and using the sample component in a preset experimental environment, and acquiring a sample friction vibration signal of the sample component through an acquisition device in the process of using the sample component.
In the process of training a monitoring model for monitoring the wear of the planetary reducer, a simulated friction vibration signal of a sample component can be obtained by presetting an experimental environment which simulates the working environment of the sample component, and then using the sample component in the experimental environment, so that the obtained sample friction vibration signal has higher similarity with a friction vibration signal generated by the sample component in a normal use state, and therefore, the accuracy of identifying the wear state of the planetary reducer in the normal use state by using the monitoring model is improved in a subsequent step.
In one or more embodiments of the present description, the sample member is the same model as the planetary reducer. That is, the solution provided in the present specification can train the monitoring model for a specific planetary reducer model, so that each trained detection model has higher identifiability for the specific model of planetary reducer; a general monitoring model can be trained by using collected friction vibration signals generated by various planetary reducers so as to simplify the preparation process of the scheme provided by the specification.
In step S100 shown in fig. 1, a frictional vibration signal of a planetary reducer in a robot is acquired by an acquisition device at a preset sampling frequency, where the sampling frequency is greater than a frequency threshold.
The sampling frequency of the acquisition device influences the signal to noise ratio of the acquired friction vibration signals, and when the sampling frequency is too low, most of the friction vibration signals acquired by the acquisition device are meaningless noise, so that fractal features with identification effect are difficult to extract from the friction vibration signals. In particular, when the planetary reducer is a joint reducer, the frequency threshold may be 25,600 Hz.
The method for monitoring the wear of the planetary reducer provided by one or more embodiments of the present specification is based on the same thought, and the present specification also provides a corresponding device for monitoring the wear of the planetary reducer, as shown in fig. 2.
Fig. 2 is a schematic diagram of a device for monitoring wear of a planetary reducer provided in the present specification, specifically including:
the acquisition module 200 is used for acquiring friction vibration signals of a planetary reducer in the robot through an acquisition device;
the signal acquisition module 202 obtains time sequence data according to the friction vibration signal in a preset time period;
a feature acquisition module 204, configured to acquire fractal features of the time-series data;
and the prediction module 206 inputs the fractal characteristics into a pre-trained monitoring model to obtain the abrasion state of the planetary reducer output by the monitoring model.
Optionally, the feature acquisition module 204 is specifically configured to: obtaining a multi-fractal spectrum of the time sequence data, and extracting fractal characteristics in the multi-fractal spectrum as the fractal characteristics of the time sequence data.
Optionally, the feature acquisition module 204 is specifically configured to: acquiring a plurality of preset time window scales, aiming at any time window scale, and according to the time window scale K n Dividing the time series data into a plurality of groups which are continuous and equal, so that each group of time series data comprises K n And obtaining generalized Hersteter indexes under the time window scale according to the cut-out groups of time sequence data, and obtaining multi-fractal spectrums according to all generalized Hersteter indexes under the preset time window scales.
Optionally, the fractal features specifically include: at least one of fractal spectrum width of multi-fractal spectrum obtained according to the time sequence data, singular index of maximum value point of fractal spectrum, height difference of left and right endpoints, singular index of left endpoint and height of left endpoint.
Optionally, the model training module 208 is further included, and obtains a sample friction vibration signal of the sample component, determines a current abrasion state of the sample component, uses the abrasion state as a label, extracts sample fractal characteristics of the sample friction vibration signal, inputs the sample fractal characteristics into a model to be trained, obtains a prediction result of the model to be trained, and trains the model to be trained according to the prediction result of the model to be trained and the label of the sample component.
Optionally, the model training module 208 is specifically configured to: and using the sample component in a preset experimental environment, and acquiring a sample friction vibration signal of the sample component through an acquisition device in the process of using the sample component.
Optionally, the acquisition module 200 is specifically configured to: and collecting friction vibration signals of a planetary reducer in the robot through a collecting device at a preset sampling frequency, wherein the sampling frequency is larger than a frequency threshold.
The present specification also provides a computer readable storage medium having stored thereon a computer program operable to perform the method of planetary reducer wear monitoring provided in fig. 1 above.
The present specification also provides a schematic structural diagram of the electronic device shown in fig. 3. As shown in fig. 3, at the hardware level, the device for monitoring wear of the planetary reducer includes a processor, an internal bus, a network interface, a memory, and a nonvolatile memory, and may of course include hardware required by other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement the method of planetary reducer wear monitoring described above with respect to fig. 1. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present description, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present disclosure and is not intended to limit the disclosure. Various modifications and alterations to this specification will become apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of the present description, are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method of planetary reducer wear monitoring, the method comprising:
collecting friction vibration signals of a planetary reducer in the robot through a collecting device;
obtaining time sequence data according to the friction vibration signals in a preset time period;
obtaining fractal characteristics of the time sequence data;
and inputting the fractal characteristics into a pre-trained monitoring model to obtain the abrasion state of the planetary reducer output by the monitoring model.
2. The method of claim 1, wherein obtaining fractal features of the time series data comprises:
acquiring a multi-fractal spectrum of the time sequence data;
and extracting fractal characteristics in the multi-fractal spectrum to serve as the fractal characteristics of the time sequence data.
3. The method of claim 2, wherein obtaining the multi-fractal spectrum of the time series data comprises:
acquiring a plurality of preset time window scales;
for any time window scale, according to the time window scale K n Dividing the time series data into a plurality of groups which are continuous and equal, so that each group of time series data comprises K n Successive time series data;
obtaining a generalized Hersteter index under the time window scale according to the plurality of groups of time sequence data;
and obtaining a multi-fractal spectrum according to all generalized Hersteter indexes under a plurality of preset time window scales.
4. The method of claim 1, wherein the fractal features specifically include: at least one of fractal spectrum width of multi-fractal spectrum obtained according to the time sequence data, singular index of maximum value point of fractal spectrum, height difference of left and right endpoints, singular index of left endpoint and height of left endpoint.
5. The method of claim 1, wherein pre-training the monitoring model, in particular, comprises:
acquiring a sample friction vibration signal of a sample part, determining the current abrasion state of the sample part, and taking the abrasion state as a mark;
extracting sample fractal characteristics of the sample friction vibration signals;
inputting the sample fractal characteristics into a model to be trained to obtain a prediction result of the model to be trained;
and training the model to be trained according to the prediction result of the model to be trained and the label of the sample component.
6. The method of claim 5, wherein obtaining a sample frictional vibration signal of the sample component, in particular comprises:
using the sample component in a preset experimental environment;
during use of the sample member, a sample frictional vibration signal of the sample member is acquired by an acquisition device.
7. The method according to claim 1, wherein the collecting means collects the frictional vibration signal of the planetary reducer, and specifically comprises:
and collecting friction vibration signals of a planetary reducer in the robot through a collecting device at a preset sampling frequency, wherein the sampling frequency is larger than a frequency threshold.
8. A device for monitoring wear of a planetary reducer, comprising:
the acquisition module is used for acquiring friction vibration signals of a planetary reducer in the robot through an acquisition device;
the signal acquisition module is used for acquiring time sequence data according to the friction vibration signal in a preset time period;
the feature acquisition module acquires fractal features of the time sequence data;
and the prediction module inputs the fractal characteristics into a pre-trained monitoring model to obtain the abrasion state of the planetary reducer output by the monitoring model.
9. A computer readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-7.
10. An apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of the preceding claims 1-7 when the program is executed by the processor.
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