CN116296364A - Gear fault diagnosis method, system, motor controller and computer medium - Google Patents
Gear fault diagnosis method, system, motor controller and computer medium Download PDFInfo
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Abstract
The invention discloses a gear fault diagnosis method, a system, a motor controller and a computer medium, comprising the following steps: acquiring gear parameters of a target gear, constructing a gear system according to the gear parameters, and further performing simulation test on the gear system to obtain a simulation result; determining abnormal characteristic points in the gear system according to the simulation result, building an experimental test platform corresponding to the gear system based on the abnormal characteristic points, and performing experimental test on the experimental test platform to obtain experimental data; when the experimental data is judged to be matched with the simulation result, determining an abnormal characteristic signal contained in the experimental data, and extracting an abnormal characteristic from the abnormal characteristic signal; and generating a target feature image according to the abnormal features, and comparing the target feature image with a preset standard feature image to obtain the gear fault type corresponding to the target gear. The invention can realize the technical effects that the motor controller can carry out simulation test and experimental test on the gear system to determine the failure cause of the gear.
Description
Technical Field
The present invention relates to the field of gear fault diagnosis technologies, and in particular, to a gear fault diagnosis method, a system, a motor controller, and a computer readable storage medium.
Background
With the development of industrial automation industry, the rotary equipment plays an increasingly important role in the industrial automation field, and the gear is used as a core component in the rotary equipment due to the wide application range; however, since the gear is in a working state of continuous high-load operation under a severe environment for a long time, various faults are easy to occur, in the process of detecting the cause of the fault of the gear, a current technician often adopts a method of installing a sensor in a gear system, acquiring signal data such as vibration signals or electric signals generated in the operation process of the gear system through the sensor, analyzing and processing the signal data to perform targeted individual modeling on the gear with the fault, and then performing operations such as filtering noise reduction, feature extraction, feature recognition and the like by a fixed algorithm to complete simulation and fault diagnosis processes, so that the cause of the fault of the gear is finally determined.
However, when the gear system includes multiple gears, multiple different gear models need to be built for different gears, and when simulation analysis is performed, operations such as model improvement, network division and optimization design need to be performed on the different gear models, so that a great deal of effort is consumed by technicians, and a large error may exist between the obtained result and experimental data. In addition, when a plurality of fault phenomena occur to the gear, the algorithm which is fixed in the mode is adopted, and the diagnosis result obtained by diagnosing the faults is lower in accuracy.
Disclosure of Invention
The invention mainly aims to provide a gear fault diagnosis method, a system, a motor controller and a computer readable storage medium, aiming at enabling the motor controller to construct a sensor-free gear system according to acquired gear parameters, and further performing simulation test and experimental test on the gear system to determine the fault cause of a gear, so that the cost generated by arranging the sensor on the gear is reduced.
In order to achieve the above object, the present invention provides the gear failure diagnosis method, which includes the steps of:
acquiring gear parameters of a target gear, constructing a gear system according to the gear parameters, and further performing simulation test on the gear system to obtain a simulation result;
determining abnormal characteristic points in the gear system according to the simulation result, building an experimental test platform corresponding to the gear system based on the abnormal characteristic points, and performing experimental test on the experimental test platform to obtain experimental data;
comparing the experimental data with the simulation result to judge whether the experimental data is matched with the simulation result or not;
if the experimental data is judged to be matched with the simulation result, determining an abnormal characteristic signal contained in the experimental data, and extracting an abnormal characteristic from the abnormal characteristic signal;
And generating a target feature image according to the abnormal feature, and comparing the target feature image with a preset standard feature image to obtain the gear fault type corresponding to the target gear.
Further, the step of constructing a gear system according to the gear parameters, and further performing a simulation test on the gear system to obtain a simulation result includes:
determining a gear model corresponding to the target gear according to the gear parameters;
modifying the gear parameters to determine respective gear models corresponding to each of the other gears corresponding to the target gear;
combining the gear models to construct a gear system, setting simulation parameters corresponding to the gear system, and further performing simulation test on the gear system based on the simulation parameters to obtain a simulation result;
and the gear model and the simulation parameters realize real-time data intercommunication.
Further, the step of performing a simulation test on the gear system based on each simulation parameter to obtain a simulation result includes:
performing simulation tests on the gear system according to the simulation parameters to obtain meshing parameters generated in the meshing process of the target gear;
Determining an engagement time domain diagram corresponding to the engagement parameter, and determining a target protrusion parameter in the engagement time domain diagram and a target position of the target protrusion parameter in the gear system;
and integrating the target salient parameters and the target positions to obtain simulation results.
Further, the step of building the experimental test platform corresponding to the gear system based on the abnormal feature points includes:
determining an anomaly location within the gear system based on the anomaly characteristic points;
and constructing an experimental test platform corresponding to the gear system according to the abnormal position.
Further, the step of performing an experimental test on the experimental test platform to obtain experimental data includes:
acquiring test signals generated when the abnormal position operates under preset detection working conditions;
extracting an effective value in the test signal, and determining an initial vibration signal corresponding to the test signal according to the effective value;
and performing noise reduction processing operation on the initial vibration signal to obtain a target vibration signal, and determining the target vibration signal as experimental data.
Further, after the step of comparing the experimental data with the simulation result to determine whether the experimental data matches the simulation result, the method further includes:
If the experimental data is not matched with the simulation result, correcting the gear parameters contained in the gear system, and reestablishing the gear system based on the corrected gear parameters;
and executing simulation test on the reestablished gear system and obtaining a new simulation result.
Further, the step of extracting the abnormal feature from the abnormal feature signal includes:
performing noise reduction filtering operation on the abnormal characteristic signals to obtain target characteristic signals;
and acquiring a plurality of preset processing algorithms, and extracting abnormal features from the target feature signals based on the processing algorithms.
Further, the step of comparing the target feature image with a preset standard feature image to obtain a gear fault type corresponding to the target gear includes:
acquiring a preset standard feature image, and comparing the target feature image with the standard feature image to determine standard fault features consistent with the abnormal features contained in the target feature image in the standard feature image;
and determining the standard fault type corresponding to the standard feature in the standard feature image, and determining the standard fault type as the gear fault type corresponding to the target gear.
In addition, in order to achieve the above object, the present invention also provides a gear failure diagnosis system including:
the gear modeling simulation module is used for acquiring gear parameters of the target gear, constructing a gear system according to the gear parameters, further performing simulation test on the gear system to obtain a simulation result, and inputting the simulation result to the gear signal acquisition module; the gear modeling simulation module is used for modeling and simulating data in real time;
the gear signal acquisition module is used for acquiring the simulation result, determining abnormal characteristic points in the gear system according to the simulation result, building an experimental test platform corresponding to the gear system based on the abnormal characteristic points, performing experimental test on the experimental test platform to obtain experimental data, further determining abnormal characteristic signals contained in the experimental data if the experimental data are judged to be matched with the simulation result, and inputting the abnormal characteristic signals to the gear fault diagnosis module;
the gear fault diagnosis module is used for extracting abnormal characteristics from the abnormal characteristic signals, generating a target characteristic image according to the abnormal characteristics, and comparing the target characteristic image with a preset standard characteristic image to obtain the gear fault type corresponding to the target gear.
Further, the gear signal acquisition module includes:
the experiment test platform is used for controlling the signal acquisition unit to acquire test signals generated when the abnormal position operates under preset detection working conditions and inputting the test signals to the signal conversion unit;
and the signal conversion unit is used for extracting an effective value in the test signal, determining an initial vibration signal corresponding to the test signal according to the effective value, and further performing noise reduction processing operation on the initial vibration signal to obtain a target vibration signal, so that the target vibration signal is determined to be experimental data.
Further, the gear fault diagnosis module includes:
the filtering noise reduction unit is used for performing noise reduction filtering operation on the abnormal characteristic signals to obtain target characteristic signals, and inputting the target characteristic signals to the characteristic extraction unit;
the feature extraction unit is used for acquiring a plurality of preset processing algorithms, extracting abnormal features from the target feature signals based on the processing algorithms, and inputting the abnormal features to the feature recognition unit;
and the feature recognition unit is used for acquiring a preset standard feature image, and comparing the target feature image with the standard feature image so as to determine standard fault features consistent with the abnormal features contained in the target feature image in the standard feature image.
In addition, in order to achieve the above object, the present invention also provides a motor controller including: the gear fault diagnosis device comprises a memory, a processor and a gear fault diagnosis program which is stored in the memory and can run on the processor, wherein the gear fault diagnosis program realizes the steps of the gear fault diagnosis method when being executed by the processor.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a gear failure diagnosis program which, when executed by a processor, implements the steps of the gear failure diagnosis method as described above.
According to the gear fault diagnosis method, the motor controller and the computer readable storage medium provided by the embodiment of the invention, the gear parameters of the target gear are obtained, the gear system is constructed according to the gear parameters, and then the gear system is subjected to simulation test to obtain a simulation result; determining abnormal characteristic points in the gear system according to the simulation result, building an experimental test platform corresponding to the gear system based on the abnormal characteristic points, and performing experimental test on the experimental test platform to obtain experimental data; comparing the experimental data with the simulation result to judge whether the experimental data is matched with the simulation result or not; if the experimental data is judged to be matched with the simulation result, determining an abnormal characteristic signal contained in the experimental data, and extracting an abnormal characteristic from the abnormal characteristic signal; and generating a target feature image according to the abnormal feature, and comparing the target feature image with a preset standard feature image to obtain the gear fault type corresponding to the target gear.
In this embodiment, when the motor controller operates, firstly, gear parameters of a target gear in the motor controller are input by a technician, the motor controller further establishes a gear model corresponding to the target gear according to the obtained gear parameters, thereby constructing a gear system according to the obtained gear model, the motor controller calls a simulation module to perform simulation test on the gear system, thereby obtaining a simulation test result corresponding to the gear system, afterwards, the motor controller determines abnormal feature points in the gear system according to the simulation result, builds an experimental test platform corresponding to the gear system based on the abnormal feature points, the motor controller performs experimental test on the experimental test platform and obtains experimental data, then, the motor controller compares the obtained experimental data with the simulation result to judge whether the experimental data is matched with simulation, when the motor controller judges that the experimental data is matched with the simulation data, the motor controller determines abnormal feature signals in the output system according to the experimental data, and calls a feature extraction unit to perform feature extraction operation on the abnormal feature signals so as to obtain abnormal features corresponding to the gear system, finally, the terminal calls a feature recognition unit to generate a target feature image according to the abnormal features, meanwhile, the motor controller obtains a preset feature image, and the obtained standard feature recognition unit obtains a standard feature image corresponding to the target feature image, and then determines the type of the target feature.
In this way, the invention adopts the mode of inputting the gear parameter of the target gear in the simulation test platform, then modeling the target gear according to the gear parameter by the simulation test platform and establishing a sensorless gear system, thereby carrying out simulation test on the gear system to determine the abnormal characteristic points in the gear system, then constructing the experiment test platform according to the abnormal characteristic points, then carrying out experiment test on the experiment test platform to determine the fault characteristics in the gear system, and screening the preset standard characteristic image according to the obtained fault characteristics to determine the gear fault type corresponding to the target gear, namely, the invention solves the problems that the current technician needs to face when the gear system contains multiple gears by modeling the target gear according to the obtained gear parameter to generate the gear system, the invention solves the technical problems that a plurality of different gear models are required to be respectively built for different gears, and the operations such as model improvement, network division, optimal design and the like are required to be respectively carried out on the different gear models when simulation analysis is carried out, so that a great deal of energy is consumed, simultaneously, the invention solves the problem that a technician needs to face how to determine the distribution position and the use number of the sensors in the gear system by constructing the gear system without sensors, simultaneously, the invention solves the problem that the accuracy of a diagnosis result obtained by diagnosing the faults is lower when a plurality of fault phenomena occur to the gears by adopting a fixed algorithm in the prior art by screening standard characteristic images according to fault characteristics, thereby realizing that the motor controller can construct the gear system without the sensors according to the acquired gear parameters, and then carry out simulation test and experimental test to the gear system in order to confirm the technological effect of the fault reason of gear, reduced the cost that the enterprise needs to arrange the sensor on the gear and produced.
Drawings
FIG. 1 is a schematic diagram of a motor controller of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a gear fault diagnosis method according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a modeling simulation flow involved in an embodiment of a gear fault diagnosis method according to the present invention;
FIG. 4 is a flow chart of a simulation guidance experiment setup involved in an embodiment of a gear fault diagnosis method of the present invention;
FIG. 5 is a schematic diagram of a signal acquisition process according to an embodiment of the gear fault diagnosis method of the present invention;
FIG. 6 is a schematic diagram of a fault diagnosis flow according to an embodiment of the gear fault diagnosis method of the present invention;
FIG. 7 is a graph of a gear wear failure spectrum according to an embodiment of the gear failure diagnostic method of the present invention;
fig. 8 is a schematic diagram of functional modules related to an embodiment of a gear fault diagnosis method according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of a motor controller of a hardware running environment according to an embodiment of the present invention.
It should be noted that fig. 1 is a schematic structural diagram of a hardware operating environment of the motor controller. The motor controller of the embodiment of the invention can be equipment for executing the gear fault diagnosis method aiming at the fault gear, and can be a mobile terminal, a data storage control terminal, a PC or a portable computer and other terminals.
As shown in fig. 1, the motor controller may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the configuration shown in fig. 1 is not limiting of the motor controller and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, an operating system, a data storage module, a network communication module, a user interface module, and a gear failure diagnosis program may be included in the memory 1005 as one type of storage medium.
In the motor controller shown in fig. 1, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the motor controller of the present invention may be disposed in the motor controller, and the motor controller calls the gear fault diagnosis program stored in the memory 1005 through the processor 1001 and executes the gear fault diagnosis method provided by the embodiment of the present invention.
Based on the motor controller, various embodiments of the gear fault diagnosis method of the present invention are provided;
referring to fig. 2, fig. 2 is a flowchart illustrating a gear fault diagnosis method according to a first embodiment of the present invention.
It should be understood that while a logical sequence is illustrated in the flow chart, in some cases the gear fault diagnosis method of the present invention may of course perform the steps illustrated or described in a different order than that which is illustrated herein.
In this embodiment, the gear failure diagnosis method of the present invention may include the steps of:
step S10: acquiring gear parameters of a target gear, constructing a gear system according to the gear parameters, and further performing simulation test on the gear system to obtain a simulation result;
in this embodiment, when the motor controller is running, firstly, gear parameters of a target gear in the motor controller are input by a technician, the motor controller inputs the obtained gear parameters to a modeling unit, the modeling unit models the target gear according to the gear parameters and obtains a gear model corresponding to the target gear, the modeling unit further assembles the gear model to establish a gear system without a sensor, and the motor controller calls a simulation unit to perform simulation test on the gear system, so that a simulation test result corresponding to the gear system is obtained.
For example, when the motor controller operates, for example, firstly, the gear coordinates (x, y), the tooth number z, the modulus m, the pressure angle alpha, the tooth thickness d, the tooth width b, the inner hole diameter k, the key groove depth p, the height q and other first gear parameters and gear types of a plurality of target gears input by a technician are obtained, when the modeling unit determines that the gear types corresponding to the target gears are straight gears, the modeling unit determines involute standard straight gear engagement relationships corresponding to the straight gears in preset gear engagement relationships, the modeling unit respectively calculates the first gear parameters such as the gear coordinates (x, y), the tooth number z, the modulus m, the pressure angle alpha, the tooth thickness d, the tooth width b, the inner hole diameter k, the key groove depth p, the height q of the target gears according to the involute standard straight gear engagement relationships, and then, respectively, sets up the second gear parameters corresponding to the involute standard straight gears corresponding to the straight gears, respectively, controls the motor, sets up the first gear parameters corresponding to the target gears and the target gears, respectively, sets up a model according to the first gear parameters corresponding to the target gears, respectively, sets up a model according to the parameters, respectively, sets up a model, respectively, sets up the model according to the model, and then, respectively sets up the model, the simulation unit performs simulation test on the gear system to obtain a simulation result.
It should be understood that in this embodiment, the gear type may include other gear types such as helical gears and herringbone gears in addition to spur gears, and similarly, the modeling unit should preset the gear engagement relationships corresponding to the other gear types such as helical gears and herringbone gears, and of course, there are many ways to specifically set the gear type and the gear engagement relationship, which is not limited in this aspect of the present invention.
In addition, in this embodiment, the first gear parameter may be set by a technician to obtain other gear parameters besides the number of teeth, modulus, pressure angle, tooth thickness, tooth width, inner hole diameter, key groove depth and height of the gear, and likewise, the second gear parameter may be set by a technician to obtain other gear parameters besides the base circle diameter, tip circle diameter, base circle diameter and root circle diameter of the gear, which is not limited in this invention.
Further, in a possible embodiment, the step of constructing a gear system according to the gear parameter and performing a simulation test on the gear system to obtain a simulation result in the step S10 may specifically include:
Step S101: determining a gear model corresponding to the target gear according to the gear parameters;
step S102: modifying the gear parameters to determine respective gear models corresponding to each of the other gears corresponding to the target gear;
step S103: combining the gear model wheels to construct a gear system, setting simulation parameters corresponding to the gear system, and further performing simulation test on the gear system based on the simulation parameters to obtain a simulation result;
step S104: the gear model and the simulation parameters realize real-time data intercommunication;
for example, referring to fig. 3, fig. 3 is a schematic diagram of a modeling simulation flow related to an embodiment of a gear fault diagnosis method according to the present invention, as shown in fig. 3, a modeling unit first models a target gear according to gear parameters of the target gear to obtain a gear model, then the modeling unit determines gear parameters corresponding to each other gear corresponding to the target gear in a field gear system, modifies acquired gear parameters based on the gear parameters of each other gear to generate gear models corresponding to each other gear, then the modeling unit assembles each generated gear model to generate a gear system, inputs the gear system to the simulation unit, sets simulation parameters such as material properties, grid division, load parameters, contact conditions, boundary conditions, etc. in the gear system by the simulation unit, and then the simulation unit performs a simulation test on the gear system according to each simulation parameter to generate a simulation result, where the gear model generated by the modeling unit on the modeling simulation platform and each simulation parameter in the simulation test unit in the platform can be intercommunicated in real time.
Further, in a possible embodiment, the step of performing the simulation test on the gear system to obtain the simulation result in step S103 based on each simulation parameter may specifically include:
step S1031: performing simulation tests on the gear system according to the simulation parameters to obtain meshing parameters generated in the meshing process of the target gear;
step S1032: determining an engagement time domain diagram corresponding to the engagement parameter, and determining a target protrusion parameter in the engagement time domain diagram and a target position of the target protrusion parameter in the gear system;
step S1033: integrating the target salient parameters and the target positions to obtain simulation results;
for example, when the simulation unit obtains the gear system, the simulation unit firstly sets simulation parameters such as material properties, grid division, load parameters, contact conditions and boundary conditions in the gear system, then the simulation unit calls the internal configured visualization subunit, the visualization subunit calculates the simulation parameters according to a preset dynamics analysis solving method to generate meshing parameters such as meshing force, rigidity and strain value generated by each gear in the gear system in the meshing process, then the visualization subunit generates time domain diagrams of each meshing parameter which change along with time, further determines the change trend of each meshing parameter in the running process of the gear system according to each time domain diagram, generates analysis reports corresponding to each change trend, finally the simulation unit determines the target protrusion parameters in the gear system according to the analysis reports, determines the target positions of the target protrusion signals in the gear system, integrates the target protrusion parameters and the target position determination to obtain simulation results, and uploads the simulation results to the motor controller.
Step S20: determining abnormal characteristic points in the gear system according to the simulation result, building an experimental test platform corresponding to the gear system based on the abnormal characteristic points, and performing experimental test on the experimental test platform to obtain experimental data;
in this embodiment, when the motor controller obtains the simulation result, the abnormal feature points are determined in the gear system according to the target positions included in the simulation result, meanwhile, the motor controller obtains the physical model corresponding to the target gear, builds the physical model based on the abnormal feature points to obtain an experimental test platform corresponding to the target gear, the motor controller further calls the working condition control unit to control the experimental test platform to enter preset working conditions respectively, the signal acquisition unit acquires signal parameters generated by the experimental test platform under the working conditions, and the motor controller further obtains experimental data according to the signal parameters.
For example, when the motor controller obtains the simulation result, the abnormal feature points are determined in the gear system according to the target positions included in the simulation result, then the motor controller obtains the physical models corresponding to the gears in the gear system, splices the physical models based on the abnormal feature points to obtain the experimental test platforms corresponding to the gear system, then the motor controller controls the experimental test platforms to operate under different working conditions such as forward speed, reverse speed, constant speed and acceleration and deceleration respectively through the working condition control unit, meanwhile, the motor controller collects test signals sent by the abnormal feature points in the experimental test platforms in the operation process through the signal collection unit in the operation process of the experimental test platforms, and finally the motor controller calls the signal conversion unit configured inside to convert the obtained test signals to obtain experimental data.
Further, in a possible embodiment, the step of "building an experimental test platform corresponding to the gear system based on the abnormal feature points" in the step S20 may specifically include:
step S201: determining an anomaly location within the gear system based on the anomaly characteristic points;
step S202: constructing an experimental test platform corresponding to the gear system according to the abnormal position;
for example, the motor controller determines an abnormal characteristic point in the gear system according to a target position included in a simulation result, determines an abnormal position of the abnormal characteristic point in the gear system, obtains physical models corresponding to gears in the gear system, and splices the physical models based on the abnormal position to obtain an experimental test platform corresponding to the gear system.
Further, in a possible embodiment, the step of performing the experimental test on the experimental test platform to obtain the experimental data in the step S20 may specifically include:
step S203: acquiring test signals generated when the abnormal position operates under preset detection working conditions;
step S204: extracting an effective value in the test signal, and determining an initial vibration signal corresponding to the test signal according to the effective value;
Step S205: performing noise reduction processing on the initial vibration signal to obtain a target vibration signal, and determining the target vibration signal as experimental data;
for example, referring to fig. 5, fig. 5 is a schematic diagram of a signal acquisition flow related to an embodiment of the gear fault diagnosis method of the present invention, as shown in the drawing, a motor controller controls an experiment test platform to enter different preset working conditions such as forward speed, reverse speed, constant speed, acceleration and deceleration respectively through a working condition control unit, and simultaneously, the motor controller controls the signal acquisition unit to acquire electric signals such as current, voltage and potential generated when an abnormal position in the experiment test platform operates under each working condition through a CPU, then, the motor controller controls the signal acquisition unit to input the acquired electric signals to a signal conversion unit through a CAN communication serial port, the signal conversion unit recognizes the electric signals through an internal vibration recognition chip and converts the electric signals into initial vibration signals, the signal conversion unit inputs the acquired initial vibration signals to a gear fault diagnosis module through the CAN communication serial port, a noise reduction filter unit configured in the gear fault diagnosis module performs noise reduction processing and filtering processing on the initial vibration signals, thereby improving a signal to reduce noise interference in the initial vibration signals, and further obtaining target vibration signals, and the gear fault diagnosis module determines the target vibration signals as experimental data.
Step S30: comparing the experimental data with the simulation result to judge whether the experimental data is matched with the simulation result or not;
for example, the motor controller compares the obtained experimental data with the simulation result, determines a deviation value between the experimental data and the simulation result, and further judges whether the experimental data is matched with the simulation result according to the magnitude of the deviation value.
Step S40: if the experimental data is judged to be matched with the simulation result, determining an abnormal characteristic signal contained in the experimental data, and extracting an abnormal characteristic from the abnormal characteristic signal;
in this embodiment, when the motor controller determines that the deviation value between the experimental data and the simulation result is smaller than the preset deviation threshold, it is determined that the experimental data is matched with the simulation result, the motor controller further determines an abnormal signal generated in the experimental data as an abnormal feature signal corresponding to the gear system, the abnormal feature signal is input to the feature extraction unit, and the feature extraction unit performs feature extraction on the abnormal feature signal to obtain an abnormal feature.
For example, referring to fig. 4, fig. 4 is a schematic diagram of a simulation guidance experiment setup flow chart according to an embodiment of the gear fault diagnosis method of the present invention, as shown in fig. 4, when the motor controller determines that the deviation value between the experimental data and the simulation result is smaller than the preset deviation percentage, the motor controller determines that the experimental data is matched with the simulation result, the motor controller further determines an abnormal signal appearing in the target vibration signal as an abnormal feature signal corresponding to the gear system, and inputs the abnormal feature signal to the feature extraction unit, and the feature extraction unit performs feature extraction on the abnormal feature signal according to a plurality of preset processing algorithms to obtain an abnormal feature corresponding to the gear system.
Further, in a possible embodiment, the step of extracting the abnormal feature from the abnormal feature signal in the step S40 may specifically include:
step S401: performing noise reduction filtering operation on the abnormal characteristic signals to obtain target characteristic signals;
step S402: acquiring a plurality of preset processing algorithms, and extracting abnormal features from the target feature signals based on the processing algorithms;
for example, referring to fig. 6, fig. 6 is a schematic diagram of a fault diagnosis flow related to an embodiment of the gear fault diagnosis method according to the present invention, the motor controller inputs the obtained abnormal characteristic signal to the gear fault diagnosis module, the gear fault diagnosis module invokes the internally configured filtering noise reduction unit to process the vibration spectrum corresponding to the abnormal characteristic signal, thereby dividing the frequency signal contained in the vibration spectrum into a high frequency part and a low frequency part according to different frequencies, then the filtering noise reduction unit windows the low frequency part according to a preset first time interval, thereby obtaining first information in the low frequency part, and simultaneously, the filtering noise reduction unit windows the high frequency part according to a second time interval smaller than the first time interval to obtain second information in the high frequency part, then the filtering noise reduction unit integrates the obtained first information and second information to complete the filtering operation of the vibration signal, integrates the obtained first information and second information to obtain a target characteristic signal and inputs the target characteristic signal to the feature extraction unit, then the feature extraction unit obtains the target characteristic signal according to a preset third time interval, thereby obtaining the target characteristic signal, and obtaining the discrete feature signal, and finally obtaining the characteristic component of the vibration signal by a discrete frequency profile, the discrete signal extraction method is obtained by the discrete frequency profile, the discrete signal extraction unit is obtained by the discrete frequency profile, the discrete signal extraction method is performed, and finally, the characteristic feature extraction method is shown in the discrete signal extraction method is performed, and the characteristic profile is shown by the discrete signal extraction method, and the characteristic profile is obtained by the discrete signal, and the characteristic signal extraction method is obtained by the discrete signal, and the characteristic feature-feature extraction method is obtained by the characteristic, one or more of a plurality of processing algorithms such as ensemble average empirical mode decomposition, convolutional neural network, reinforcement learning, deep learning and the like perform image enhancement processing, texture analysis, image segmentation, image feature recognition and image matching operations on the target feature image, so that abnormal features are determined in the target feature image.
Further, in a possible embodiment, after the step S30, the gear fault diagnosis method of the present invention may further include:
step A10: if the experimental data is not matched with the simulation result, correcting the gear parameters contained in the gear system, and reestablishing the gear system based on the corrected gear parameters;
step A20: executing simulation test on the reestablished gear system and obtaining a new simulation result;
for example, when the motor controller determines that the deviation value between the experimental data and the simulation result is greater than the preset deviation percentage, the experimental data and the simulation result are not matched, so that each gear parameter in the gear system is modified and muted again to obtain each new gear model, then the motor controller builds each new gear model through the modeling unit to generate a new gear system, inputs the new gear system into the simulation unit, and the simulation unit performs a simulation test on the new gear system to obtain the simulation result.
Step S50: generating a target feature image according to the abnormal features, and comparing the target feature image with a preset standard feature image to obtain a gear fault type corresponding to the target gear;
For example, referring to fig. 7, fig. 7 is a gear wear failure spectrogram according to an embodiment of the gear failure diagnosis method of the present invention, the motor controller inputs the obtained abnormal feature to a feature recognition unit configured in the gear failure diagnosis module, the feature recognition unit generates a target feature image as shown in fig. 7 according to the obtained abnormal feature, and at the same time, the motor controller reads the storage device to obtain a standard feature image, inputs the standard feature image to the feature recognition unit, and the feature recognition unit compares the obtained target feature image with the standard feature image to determine the gear failure type corresponding to the gear system.
Further, in a possible embodiment, the step of comparing the target feature image with a preset standard feature image to obtain the gear fault type corresponding to the target gear in the step S50 may specifically include:
step S501: acquiring a preset standard feature image, and comparing the target feature image with the standard feature image to determine standard fault features consistent with the abnormal features contained in the target feature image in the standard feature image;
Step S502: determining the standard fault type corresponding to the standard feature in the standard feature image, and determining the standard fault type as the gear fault type corresponding to the target gear;
for example, the motor controller first reads the storage device to acquire and store standard feature images of standard fault types corresponding to each standard fault feature and each standard fault feature, and inputs the standard feature images to the feature recognition unit in the gear fault diagnosis module, and the feature recognition unit screens the standard feature images based on the acquired target feature images to determine standard fault features consistent with abnormal features contained in the target feature images in the standard feature images, and determines the standard fault types corresponding to the standard fault features, and further determines the standard fault types as gear fault types corresponding to the gear system.
In this embodiment, when the motor controller operates, firstly, gear parameters of a target gear in the motor controller are input by a technician, the motor controller inputs the acquired gear parameters to a modeling unit, the modeling unit models the target gear according to the gear parameters and obtains a gear model corresponding to the target gear, the modeling unit further assembles the gear model to establish a gear system without a sensor, the motor controller calls a simulation unit to perform simulation test on the gear system so as to obtain a simulation test result corresponding to the gear system, then, when the motor controller acquires the simulation result, an abnormal feature point is determined in the gear system according to a target position contained in the simulation result, and meanwhile, the motor controller acquires a physical model corresponding to the target gear and builds the physical model based on the abnormal feature point to obtain an experimental test platform corresponding to the target gear, the motor controller further calls a working condition control unit to control the experimental test platform to enter preset working conditions respectively, and acquires signal parameters generated by the experimental test platform under the working conditions through a signal acquisition unit, the motor controller further obtains experimental data according to the signal parameters, and then, the motor controller compares the acquired data with the simulation test result so as to determine whether the acquired experimental data and the simulation result matches with the experimental data of the simulation result, and if the experimental result matches with the experimental result, and the experimental result is determined to be the experimental data corresponding to the experimental result, and the experimental result is small, and the experimental result is determined to be the experimental data is determined, and the experimental result is matched with the experimental result, and the experimental result is determined to be the experimental result, and has a small value and experimental result, and has a small experimental result, and inputting the abnormal characteristic signals to a characteristic extraction unit, carrying out characteristic extraction on the abnormal characteristic signals by the characteristic extraction unit to obtain abnormal characteristics, finally, inputting the obtained abnormal characteristics to a characteristic recognition unit configured in a gear fault diagnosis module by a motor controller, generating a target characteristic image according to the obtained abnormal characteristics by the characteristic recognition unit, simultaneously, reading a storage device by the motor controller to obtain a standard characteristic image, inputting the standard characteristic image to the characteristic recognition unit, and comparing the obtained target characteristic image with the standard characteristic image by the characteristic recognition unit to determine the gear fault type corresponding to the gear system.
In this way, the invention adopts the mode of inputting the gear parameter of the target gear in the simulation test platform, then modeling the target gear according to the gear parameter by the simulation test platform and establishing a sensorless gear system, thereby carrying out simulation test on the gear system to determine the abnormal characteristic points in the gear system, then constructing the experiment test platform according to the abnormal characteristic points, then carrying out experiment test on the experiment test platform to determine the fault characteristics in the gear system, and screening the preset standard characteristic image according to the obtained fault characteristics to determine the gear fault type corresponding to the target gear, namely, the invention solves the problems that the current technician needs to face when the gear system contains multiple gears by modeling the target gear according to the obtained gear parameter to generate the gear system, the invention solves the technical problems that a plurality of different gear models are required to be respectively built for different gears, and the operations such as model improvement, network division, optimal design and the like are required to be respectively carried out on the different gear models when simulation analysis is carried out, so that a great deal of energy is consumed, simultaneously, the invention solves the problem that a technician needs to face how to determine the distribution position and the use number of the sensors in the gear system by constructing the gear system without sensors, simultaneously, the invention solves the problem that the accuracy of a diagnosis result obtained by diagnosing the faults is lower when a plurality of fault phenomena occur to the gears by adopting a fixed algorithm in the prior art by screening standard characteristic images according to fault characteristics, thereby realizing that the motor controller can construct the gear system without the sensors according to the acquired gear parameters, and then carry out simulation test and experimental test to the gear system in order to confirm the technological effect of the fault reason of gear, reduced the cost that the enterprise needs to arrange the sensor on the gear and produced.
In addition, the present invention also provides a gear fault diagnosis system, please refer to fig. 8, fig. 8 is a schematic diagram of functional modules related to an embodiment of a gear fault diagnosis method according to the present invention, and as shown in fig. 8, the gear fault diagnosis system of the present invention includes:
the gear modeling simulation module 10 is used for acquiring gear parameters of a target gear, constructing a gear system according to the gear parameters, further performing simulation test on the gear system to obtain a simulation result, and inputting the simulation result to the gear signal acquisition module; the gear modeling simulation module is used for modeling and simulating data in real time;
the gear signal acquisition module 20 is configured to obtain the simulation result, determine an abnormal feature point in the gear system according to the simulation result, build an experimental test platform corresponding to the gear system based on the abnormal feature point, perform an experimental test on the experimental test platform to obtain experimental data, determine an abnormal feature signal contained in the experimental data if the experimental data is determined to be matched with the simulation result, and input the abnormal feature signal to the gear fault diagnosis module;
The gear fault diagnosis module 30 is configured to extract an abnormal feature from the abnormal feature signal, generate a target feature image according to the abnormal feature, and compare the target feature image with a preset standard feature image to obtain a gear fault type corresponding to the target gear.
Further, the gear signal acquisition module 20 includes:
the experiment test platform is used for controlling the signal acquisition unit to acquire test signals generated when the abnormal position operates under preset detection working conditions and inputting the test signals to the signal conversion unit;
and the signal conversion unit is used for extracting an effective value in the test signal, determining an initial vibration signal corresponding to the test signal according to the effective value, and further performing noise reduction processing operation on the initial vibration signal to obtain a target vibration signal, so that the target vibration signal is determined to be experimental data.
Further, the gear failure diagnosis module 30 includes:
the filtering noise reduction unit is used for performing noise reduction filtering operation on the abnormal characteristic signals to obtain target characteristic signals, and inputting the target characteristic signals to the characteristic extraction unit;
the feature extraction unit is used for acquiring a plurality of preset processing algorithms, extracting abnormal features from the target feature signals based on the processing algorithms, and inputting the abnormal features to the feature recognition unit;
And the feature recognition unit is used for acquiring a preset standard feature image, and comparing the target feature image with the standard feature image so as to determine standard fault features consistent with the abnormal features contained in the target feature image in the standard feature image.
In addition, the invention also provides a motor controller, which is provided with a gear fault diagnosis program capable of running on a processor, and the motor controller realizes the steps of the gear fault diagnosis method according to any one of the embodiments when executing the gear fault diagnosis program.
The specific embodiment of the motor controller of the present invention is substantially the same as the embodiments of the gear fault diagnosis method described above, and will not be described herein.
Furthermore, the present invention provides a computer-readable storage medium having stored thereon a gear failure diagnosis program which, when executed by a processor, implements the steps of the gear failure diagnosis method according to any one of the above embodiments.
The specific embodiments of the computer readable storage medium are basically the same as the embodiments of the gear fault diagnosis method described above, and are not described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. 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 system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a motor controller (which may be a device for performing the gear failure diagnosis method of the present invention for a failed gear, which may be a mobile terminal, a data storage control terminal, a PC or a portable computer, etc. terminal) to perform the method according to the embodiments of the present invention.
The foregoing description of the preferred embodiments of the present invention should not be taken as limiting the scope of the invention, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the following description and drawings, or by direct or indirect application to other relevant art(s).
Claims (13)
1. A gear failure diagnosis method, characterized by comprising the steps of:
acquiring gear parameters of a target gear, constructing a gear system according to the gear parameters, and further performing simulation test on the gear system to obtain a simulation result;
determining abnormal characteristic points in the gear system according to the simulation result, building an experimental test platform corresponding to the gear system based on the abnormal characteristic points, and performing experimental test on the experimental test platform to obtain experimental data;
comparing the experimental data with the simulation result to judge whether the experimental data is matched with the simulation result or not;
if the experimental data is judged to be matched with the simulation result, determining an abnormal characteristic signal contained in the experimental data, and extracting an abnormal characteristic from the abnormal characteristic signal;
And generating a target feature image according to the abnormal feature, and comparing the target feature image with a preset standard feature image to obtain the gear fault type corresponding to the target gear.
2. The gear fault diagnosis method according to claim 1, wherein the step of constructing a gear system according to the gear parameters, and further performing a simulation test on the gear system to obtain a simulation result comprises:
determining a gear model corresponding to the target gear according to the gear parameters;
modifying the gear parameters to determine respective gear models corresponding to each of the other gears corresponding to the target gear;
combining the gear models to construct a gear system, setting simulation parameters corresponding to the gear system, and further performing simulation test on the gear system based on the simulation parameters to obtain a simulation result;
and the gear model and the simulation parameters realize real-time data intercommunication.
3. The gear fault diagnosis method according to claim 2, wherein the step of performing a simulation test on the gear system based on each of the simulation parameters to obtain a simulation result comprises:
Performing simulation tests on the gear system according to the simulation parameters to obtain meshing parameters generated in the meshing process of the target gear;
determining an engagement time domain diagram corresponding to the engagement parameter, and determining a target protrusion parameter in the engagement time domain diagram and a target position of the target protrusion parameter in the gear system;
and integrating the target salient parameters and the target positions to obtain simulation results.
4. The gear fault diagnosis method according to claim 3, wherein the step of building an experimental test platform corresponding to the gear system based on the abnormal feature points comprises:
determining an anomaly location within the gear system based on the anomaly characteristic points;
and constructing an experimental test platform corresponding to the gear system according to the abnormal position.
5. The gear fault diagnosis method according to claim 4, wherein the step of performing an experimental test on the experimental test platform to obtain experimental data comprises:
acquiring test signals generated when the abnormal position operates under preset detection working conditions;
extracting an effective value in the test signal, and determining an initial vibration signal corresponding to the test signal according to the effective value;
And performing noise reduction processing operation on the initial vibration signal to obtain a target vibration signal, and determining the target vibration signal as experimental data.
6. The gear fault diagnosis method according to claim 1, wherein after the step of comparing the experimental data with the simulation result to determine whether the experimental data matches the simulation result, the method further comprises:
if the experimental data is not matched with the simulation result, correcting the gear parameters contained in the gear system, and reestablishing the gear system based on the corrected gear parameters;
and executing simulation test on the reestablished gear system and obtaining a new simulation result.
7. The gear failure diagnosis method according to claim 1, wherein the step of extracting an abnormal feature from the abnormal feature signal includes:
performing noise reduction filtering operation on the abnormal characteristic signals to obtain target characteristic signals;
and acquiring a plurality of preset processing algorithms, and extracting abnormal features from the target feature signals based on the processing algorithms.
8. The gear fault diagnosis method according to claim 1, wherein the step of comparing the target feature image with a preset standard feature image to obtain a gear fault type corresponding to the target gear comprises:
Acquiring a preset standard feature image, and comparing the target feature image with the standard feature image to determine standard fault features consistent with the abnormal features contained in the target feature image in the standard feature image;
and determining the standard fault type corresponding to the standard feature in the standard feature image, and determining the standard fault type as the gear fault type corresponding to the target gear.
9. A gear failure diagnosis system, characterized by comprising:
the gear modeling simulation module is used for acquiring gear parameters of the target gear, constructing a gear system according to the gear parameters, further performing simulation test on the gear system to obtain a simulation result, and inputting the simulation result to the gear signal acquisition module; the gear modeling simulation module is used for modeling and simulating data in real time;
the gear signal acquisition module is used for acquiring the simulation result, determining abnormal characteristic points in the gear system according to the simulation result, building an experimental test platform corresponding to the gear system based on the abnormal characteristic points, performing experimental test on the experimental test platform to obtain experimental data, further determining abnormal characteristic signals contained in the experimental data if the experimental data are judged to be matched with the simulation result, and inputting the abnormal characteristic signals to the gear fault diagnosis module;
The gear fault diagnosis module is used for extracting abnormal characteristics from the abnormal characteristic signals, generating a target characteristic image according to the abnormal characteristics, and comparing the target characteristic image with a preset standard characteristic image to obtain the gear fault type corresponding to the target gear.
10. The gear fault diagnosis system of claim 9, wherein the gear signal acquisition module comprises:
the experiment test platform is used for controlling the signal acquisition unit to acquire test signals generated when the abnormal position operates under preset detection working conditions and inputting the test signals to the signal conversion unit;
and the signal conversion unit is used for extracting an effective value in the test signal, determining an initial vibration signal corresponding to the test signal according to the effective value, and further performing noise reduction processing operation on the initial vibration signal to obtain a target vibration signal, so that the target vibration signal is determined to be experimental data.
11. The gear fault diagnosis system of claim 10, wherein the gear fault diagnosis module comprises:
the filtering noise reduction unit is used for performing noise reduction filtering operation on the abnormal characteristic signals to obtain target characteristic signals, and inputting the target characteristic signals to the characteristic extraction unit;
The feature extraction unit is used for acquiring a plurality of preset processing algorithms, extracting abnormal features from the target feature signals based on the processing algorithms, and inputting the abnormal features to the feature recognition unit;
and the feature recognition unit is used for acquiring a preset standard feature image, and comparing the target feature image with the standard feature image so as to determine standard fault features consistent with the abnormal features contained in the target feature image in the standard feature image.
12. A motor controller, the motor controller comprising: a memory, a processor, and a gear failure diagnosis program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the gear failure diagnosis method according to any one of claims 1 to 8.
13. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a gear failure diagnosis program which, when executed by a processor, implements the steps of the gear failure diagnosis method according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117309299A (en) * | 2023-11-28 | 2023-12-29 | 天津信天电子科技有限公司 | Servo driver vibration test method, device, equipment and medium |
| CN117874471A (en) * | 2024-03-11 | 2024-04-12 | 四川能投云电科技有限公司 | Water and electricity safety early warning and fault diagnosis method and system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117309299A (en) * | 2023-11-28 | 2023-12-29 | 天津信天电子科技有限公司 | Servo driver vibration test method, device, equipment and medium |
| CN117309299B (en) * | 2023-11-28 | 2024-02-06 | 天津信天电子科技有限公司 | Servo driver vibration test method, device, equipment and medium |
| CN117874471A (en) * | 2024-03-11 | 2024-04-12 | 四川能投云电科技有限公司 | Water and electricity safety early warning and fault diagnosis method and system |
| CN117874471B (en) * | 2024-03-11 | 2024-05-14 | 四川能投云电科技有限公司 | Water and electricity safety early warning and fault diagnosis method and system |
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