CN116643170B - Motor shafting vibration testing method and device and computer equipment - Google Patents
Motor shafting vibration testing method and device and computer equipment Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
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Abstract
The application relates to a motor shafting vibration test method, a motor shafting vibration test device and computer equipment. The method comprises the following steps: respectively adopting excitation sources of different types to excite motors of different fault types to obtain fault data of a motor shaft system; types of excitation sources include: time-varying interference and time-invariant interference; constructing a machine learning model which is preset in training according to the fault data so that the machine learning model can identify the fault type of the motor; vibration data of a motor shafting to be detected are collected in real time, the vibration data are input into a machine learning model, and the fault type of the motor is output. By adopting the method, the faults in the motor shaft system can be accurately identified.
Description
Technical Field
The application relates to the technical field of motor shafting testing, in particular to a motor shafting vibration testing method, a motor shafting vibration testing device and computer equipment.
Background
Motor vibration testing plays an important role in fault prediction and maintenance. However, the current motor vibration test method has an inaccurate problem in terms of failure prediction. Traditional fault prediction methods rely primarily on rules and experience and can only provide prediction results based on known fault patterns. Due to the complexity and diversity of motor systems, as well as the effects of environmental and load variations, conventional methods cannot accurately predict motor faults. In addition, conventional approaches typically consider only a single or a limited number of vibration characteristics, ignoring the rich information and complex relationships in the vibration signal. This leads to inaccuracy and limitation of the prediction result. In addition, the conventional method has a certain difficulty in processing a large amount of data and monitoring in real time.
Disclosure of Invention
Based on the above, it is necessary to provide a method, a device and a computer device for testing vibration of a motor shaft system.
A method of testing vibration of a motor shaft system, the method comprising:
respectively adopting excitation sources of different types to excite motors of different fault types to obtain fault data of a motor shaft system; the types of the excitation source include: time-varying interference and time-invariant interference;
constructing a machine learning model which is preset in training according to the fault data so that the machine learning model can identify the fault type of the motor;
and collecting vibration data of a motor shafting to be tested in real time, inputting the vibration data into the machine learning model, and outputting the fault type of the motor.
In one embodiment, the method further comprises: converting the fault data into digital signals, and extracting time-varying interference signals, time-non-varying interference signals and basic signals in the digital signals; constructing multidimensional features according to the time-varying interference signals, the time-varying interference signals and the basic signals; training the machine learning model according to the multi-dimensional features.
In one embodiment, the time-varying interference comprises: environmental change stimulus, temperature change stimulus, and load change stimulus.
In one embodiment, the time-invariant interference comprises: power supply interference, electromagnetic interference, and mechanical resonance interference.
In one embodiment, the machine learning model is a vector machine, a decision tree, or a random forest.
A motor shafting vibration testing apparatus, the apparatus comprising:
the off-line excitation module is also used for exciting motors of different fault types by adopting excitation sources of different types respectively to obtain fault data of a motor shaft system; the types of the excitation source include: time-varying interference and time-invariant interference;
the training module is also used for constructing a machine learning model which is preset in training according to the fault data so that the machine learning model can identify the fault type of the motor;
the fault analysis module is also used for collecting vibration data of the motor shafting to be tested in real time, inputting the vibration data into the machine learning model and outputting the fault type of the motor.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
respectively adopting excitation sources of different types to excite motors of different fault types to obtain fault data of a motor shaft system; the types of the excitation source include: time-varying interference and time-invariant interference;
constructing a machine learning model which is preset in training according to the fault data so that the machine learning model can identify the fault type of the motor;
and collecting vibration data of a motor shafting to be tested in real time, inputting the vibration data into the machine learning model, and outputting the fault type of the motor.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
respectively adopting excitation sources of different types to excite motors of different fault types to obtain fault data of a motor shaft system; the types of the excitation source include: time-varying interference and time-invariant interference;
constructing a machine learning model which is preset in training according to the fault data so that the machine learning model can identify the fault type of the motor;
and collecting vibration data of a motor shafting to be tested in real time, inputting the vibration data into the machine learning model, and outputting the fault type of the motor.
The motor shafting vibration testing method, the motor shafting vibration testing device, the computer equipment and the storage medium have the following beneficial effects:
the fault prediction accuracy is improved: the application utilizes the machine learning and data driving method to comprehensively capture key characteristics and modes in motor vibration signals. Compared with the traditional method, the method can more accurately predict the faults of the motor and provide more reliable prediction results.
Fully utilizes vibration signal information: the application adopts big data analysis and machine learning methods, and can fully mine information in the vibration signals. By comprehensively considering a plurality of characteristics and modes, the application can more comprehensively evaluate the health state of the motor system and improve the precision and the comprehensiveness of fault prediction.
Real-time monitoring and timely maintenance: the method is suitable for monitoring the motor vibration signal in real time and can timely identify potential fault conditions. By finding and predicting motor faults in advance, timely maintenance measures can be taken, so that downtime is reduced and maintenance cost is reduced.
Data-driven and automated: the application utilizes the machine learning and data driving method, can automatically learn and extract the characteristics from the vibration data, and reduces the influence of manual intervention and subjective judgment. This may improve the efficiency, consistency and repeatability of the test.
Drawings
FIG. 1 is a flow chart of a method for testing vibration of a motor shaft system according to an embodiment;
FIG. 2 is a block diagram of a motor shafting vibration testing apparatus in one embodiment;
FIG. 3 is an internal block diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. 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 application.
In one embodiment, as shown in fig. 1, there is provided a motor shafting vibration testing method, including the steps of:
step 102, respectively adopting different types of excitation sources to excite motors with different fault types to obtain fault data of motor shafting.
Types of excitation sources include: time-varying interference and time-invariant interference.
In order to achieve the above-described excitation, it is necessary to prepare excitation devices having excitation capabilities, including excitation sources capable of generating different types of excitation. Specifically, the excitation source may include devices that generate time-varying interference and time-varying interference, such as a frequency converter, a load simulator, and the like, so as to ensure that the excitation source can simulate the influence of different fault types on the vibration of the motor shaft system.
And different types of excitation sources are used for exciting motors with different fault types, vibration data of a motor shaft system are collected, the installation positions and parameter settings of the excitation sources and the vibration sensors are reasonable, and the collected vibration data have enough coverage range and sample size.
It is worth to say that, for motor shafting data, because of lack of sample data, and the signal to noise ratio of the motor shafting data of direct collection is big, and the degree of difficulty of directly extracting real vibration data from the vibration data of motor shafting is higher, therefore when utilizing neural network model to carry out on-line monitoring, can't truly reflect whether vibration data is unusual. In the embodiment, the method of generating the time-varying interference and the time-varying interference to excite is applied to the fault motor, so that fault data are collected, and it can be known that the sensitivity degree of different fault types to the time-varying interference and the time-varying interference is different, so that the point can be accurately learned through a machine learning model, and a basis is provided for online detection of machine learning.
And 104, constructing a machine learning model which is preset in training according to the fault data, so that the machine learning model identifies the fault type of the motor.
In the step, a machine learning model which is preset in a training mode is built by utilizing the collected fault data. The machine learning model may employ a supervised learning algorithm, such as deep learning, support Vector Machines (SVMs), and the like. The input data may include vibration data and characteristic parameters related to the type of fault. The model can identify characteristic modes of different fault types by inputting fault data into a machine learning model for training and optimizing. And the accuracy and the robustness of the model are ensured by using a cross-validation and model evaluation technology.
It should be noted that, in a normal state, the motor shaft vibration is also subject to time-varying interference and time-varying interference, but the interference is weak, so that it is difficult to extract the signals respectively. However, when the motor fails, the vibration data is obviously changed through the time-varying interference and the time-varying interference of active excitation, so that the vibration data can be easily separated.
And 106, collecting vibration data of the motor shafting to be tested in real time, inputting the vibration data into a machine learning model, and outputting the fault type of the motor.
When the motor to be tested runs, vibration data of a shaft system of the motor to be tested is collected in real time. And inputting the collected vibration data of the shafting into a trained machine learning model, analyzing and processing the vibration data by the model, and outputting a fault type diagnosis result of the motor. A real-time monitoring system may be employed to update and feed back the fault diagnosis results in real time.
In the motor shafting vibration test method, the machine learning model is not beneficial to direct recognition due to the fact that the signal-to-noise ratio of vibration data collected in real time is high, and the machine learning model can recognize and learn key features through signal excitation in an off-line training mode. In addition, in feature selection, time-varying interference sensitive to fault types and non-time-varying interference sensitive to faults are selected as machine learning features, influences of different fault types on motor shafting vibration are simulated, and comprehensiveness and accuracy of fault diagnosis are improved.
In one embodiment, the fault data are converted into digital signals, and time-varying interference signals, time-invariant interference signals and basic signals in the digital signals are extracted; constructing multidimensional features according to the time-varying interference signals, the time-varying interference signals and the basic signals; the machine learning model is trained based on the multi-dimensional features.
Specifically, vibration data of a motor shaft system is collected by using a vibration sensor, and is converted into a digital signal through an analog-to-digital converter (ADC) or a data collection system, so that the sampling rate is ensured to be high enough to capture details of the vibration signal. And carrying out time-frequency analysis or other corresponding signal processing methods on the digital signals to extract time-varying interference signals and time-non-varying interference signals. The time-varying interference signal includes a frequency component that varies with time, while the time-varying interference signal exhibits a fixed frequency or amplitude component. Basic signal extraction: the basic signal, i.e. the vibration signal remaining after removal of the interference signal, is extracted from the digital signal. Filtering, noise reduction, or other signal processing techniques may be employed. Based on the extracted time-varying interference signal, time-non-varying interference signal and basic signal, a multidimensional feature is constructed. The three main dimensions of the multi-dimensional feature are a time-varying interference signal, a time-varying interference signal and a basic signal, and each main dimension simultaneously comprises a frequency domain feature (such as a frequency spectrum peak value, frequency band energy and the like), a time domain feature (such as a mean value, a standard deviation and the like) and a statistical feature (such as a correlation coefficient, kurtosis and the like). Ensuring that the selected features are able to adequately characterize the variation and characteristics of the vibration signal. And constructing a training data set by utilizing the extracted multidimensional features, and training a model by using a machine learning algorithm. Suitable supervised learning algorithms, such as Support Vector Machines (SVMs), random forests, deep neural networks, etc., may be selected. Through repeated training and optimization, the model can accurately identify time-varying interference and non-time-varying interference, and prediction and diagnosis of motor shafting faults are realized.
In one embodiment, the time-varying interference comprises: environmental change stimulus, temperature change stimulus, and load change stimulus.
In one embodiment, the non-time-varying interference comprises: power supply interference, electromagnetic interference, and mechanical resonance interference.
In one embodiment, the machine learning model is a vector machine, a decision tree, or a random forest.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
In one embodiment, as shown in fig. 2, there is provided a motor shaft vibration testing apparatus, including: an offline excitation module 202, a training module 204, and a fault analysis module 206, wherein:
the offline excitation module 202 is further configured to excite motors with different fault types by using different excitation sources respectively, so as to obtain fault data of a motor shaft system; the types of the excitation source include: time-varying interference and time-invariant interference;
the training module 204 is further configured to construct a machine learning model that is set in advance according to the fault data, so that the machine learning model identifies a fault type of the motor;
the fault analysis module 206 is further configured to collect vibration data of the motor shaft system to be tested in real time, input the vibration data into the machine learning model, and output a fault type of the motor.
In one embodiment, the offline excitation module is further configured to convert the fault data into a digital signal, and extract a time-varying interference signal, a time-varying interference signal and a basic signal from the digital signal; constructing multidimensional features according to the time-varying interference signals, the time-varying interference signals and the basic signals; training the machine learning model according to the multi-dimensional features.
In one embodiment, the time-varying interference comprises: environmental change stimulus, temperature change stimulus, and load change stimulus.
In one embodiment, the time-invariant interference comprises: power supply interference, electromagnetic interference, and mechanical resonance interference.
In one embodiment, the machine learning model is a vector machine, a decision tree, or a random forest.
For specific limitations of the motor shaft vibration testing device, reference may be made to the above limitations of the motor shaft vibration testing method, and no further description is given here. All or part of each module in the motor shafting vibration testing device can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store fault data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a robot positioning recovery method.
It will be appreciated by those skilled in the art that the structure shown in FIG. 3 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment a computer device is provided comprising a memory storing a computer program and a processor implementing the steps of the method of the above embodiments when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method of the above embodiments.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. A method for testing vibration of a motor shaft system, the method comprising:
respectively adopting excitation sources of different types to excite motors of different fault types to obtain fault data of a motor shaft system; the types of the excitation source include: time-varying interference and time-invariant interference; the fault data includes: vibration data and characteristic parameters related to the type of fault;
constructing a supervised learning algorithm which is preset in training according to the fault data so that the supervised learning algorithm can identify the fault type of the motor;
collecting vibration data of a motor shafting to be tested in real time, inputting the vibration data into the supervised learning algorithm, and outputting the fault type of the motor;
the method further comprises the steps of:
converting the vibration data into a digital signal, and extracting a time-varying interference signal, a time-non-varying interference signal and a basic signal from the digital signal; the time-varying interference signal includes a time-varying frequency component, the time-varying interference signal including: fixed frequency or amplitude components;
constructing multidimensional features according to the time-varying interference signals, the time-varying interference signals and the basic signals;
and training the supervised learning algorithm according to the multi-dimensional features.
2. The motor shafting vibration testing method of claim 1, wherein the time-varying disturbance comprises: environmental change stimulus, temperature change stimulus, and load change stimulus.
3. The motor shafting vibration testing method of claim 1, wherein the non-time-varying disturbance comprises: power supply interference, electromagnetic interference, and mechanical resonance interference.
4. The method of claim 1, wherein the supervised learning algorithm is a vector machine, a decision tree, or a random forest.
5. A motor shafting vibration testing apparatus, the apparatus comprising:
the off-line excitation module is also used for exciting motors of different fault types by adopting excitation sources of different types respectively to obtain fault data of a motor shaft system; the types of the excitation source include: time-varying interference and time-invariant interference; the fault data includes: vibration data and characteristic parameters related to the type of fault;
the training module is also used for constructing a supervised learning algorithm which is preset in training according to the fault data so that the supervised learning algorithm can identify the fault type of the motor;
the fault analysis module is also used for collecting vibration data of the motor shafting to be tested in real time, inputting the vibration data into the supervision and learning algorithm and outputting the fault type of the motor;
the off-line excitation module is also used for converting the vibration data into digital signals, extracting time-varying interference signals, time-varying interference signals and basic signals in the digital signals; the time-varying interference signal includes a time-varying frequency component, the time-varying interference signal including: fixed frequency or amplitude components;
constructing multidimensional features according to the time-varying interference signals, the time-varying interference signals and the basic signals;
and training the supervised learning algorithm according to the multi-dimensional features.
6. The motor shafting vibration testing apparatus of claim 5, wherein the time-varying disturbance comprises: environmental change stimulus, temperature change stimulus, and load change stimulus.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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