CN116166997A - Intelligent main shaft service state diagnosis method, system, equipment and medium - Google Patents
Intelligent main shaft service state diagnosis method, system, equipment and medium Download PDFInfo
- Publication number
- CN116166997A CN116166997A CN202310146161.0A CN202310146161A CN116166997A CN 116166997 A CN116166997 A CN 116166997A CN 202310146161 A CN202310146161 A CN 202310146161A CN 116166997 A CN116166997 A CN 116166997A
- Authority
- CN
- China
- Prior art keywords
- channel
- intelligent
- layer
- model
- training
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention provides a method, a system, equipment and a medium for diagnosing the service state of an intelligent main shaft, which comprise the steps of constructing a data acquisition platform to realize data acquisition of the intelligent main shaft, carrying out data enhancement on an original signal by an overlapping sampling method, converting the original signal into a two-dimensional time-frequency image by CWT, and reserving the original signal after data enhancement as input of a one-dimensional channel; dividing the original signal after data enhancement and the time-frequency pattern into a training set, a testing set and a verification set according to the interval of 7:2:1 respectively; inputting the training set into an improved two-channel DenseNet model for training; inputting the verification set into the improved model to perform super-parameter optimization through Bayesian optimization; inputting the test set into the trained model to obtain an intelligent main shaft final state diagnosis result; according to the invention, through collecting intelligent spindle data, analyzing the collected signals, extracting more detail features, and providing basis for subsequent spindle performance evaluation.
Description
Technical Field
The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a method, a system, equipment and a medium for diagnosing an intelligent main shaft service state.
Background
Intelligent manufacturing is an important new market for manufacturing, wherein the core competitiveness of enterprise intelligent manufacturing comes from the digitization and networking of equipment, the mining of analytical equipment processing data, the construction of intelligent workshops, etc.; the numerical control machine tool is an important device of a workshop, and the main shaft is taken as a core component of the numerical control machine tool, so that the realization of the intellectualization of the main shaft is particularly important; in order to meet the processing requirements of modern manufacturing industry and realize the intellectualization of a main shaft system, the primary task is to solve the main shaft perception problem, and to improve the system perception precision, the mutual complementation between data can be realized by a multi-sensor information fusion technology in consideration of the contingency and the singleness of a single physical field, the diversity and the complexity of information are increased, the uncertainty caused by single-sensor information is effectively reduced, and the stability and the credibility of the data are improved; the sensor such as acceleration, displacement and temperature is added in the main shaft system, so that multiple physical field information such as vibration, displacement and temperature field is acquired, the processing state and health condition of the intelligent main shaft are judged by adopting a certain method through the information, fault diagnosis is realized under the condition of faults, and the method has important significance for improving the processing efficiency of the intelligent main shaft.
In the actual machining process, the machining precision and efficiency can be affected due to the interference of vibration, temperature rise, local cracks and other factors of the main shaft; therefore, it is important to improve the performance of the spindle system, many current machining processes are not independently performed and are in an interdependent relationship, and under the complex machining environment, once the spindle system cannot meet the actual machining requirement, the next production and machining process will be directly affected, and finally the production efficiency and the machining quality of the product are reduced; in the prior art, the autonomous sensing of the main shaft is more and more difficult to realize by adopting the technologies of expert experience, time-frequency domain analysis, wavelet analysis and the like, and the traditional machine learning methods such as BP neural network, SVM, hidden Markov model and the like are limited by the shallow structure, so that characteristic information capable of representing the running state of the main shaft cannot be extracted from huge and complex measured signal data.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent main shaft service state diagnosis method, system, equipment and medium, which can extract more complete detail characteristics of the main shaft state and provide effective basis for the identification and classification of the fault modes.
The invention is realized by the following technical scheme:
the intelligent main shaft service state diagnosis method is characterized by comprising the following steps of:
a. constructing a data acquisition platform to realize data acquisition of the intelligent main shaft, carrying out data enhancement on an original signal by an overlapping sampling method, converting the original signal into a two-dimensional time-frequency image by CWT, and reserving the original signal after data enhancement as input of a one-dimensional channel;
b. dividing the original signal after data enhancement and the time-frequency pattern into a training set, a testing set and a verification set according to the interval of 7:2:1 respectively;
c. inputting the training set into an improved two-channel DenseNet model for training;
d. inputting the verification set into the improved model to perform super-parameter optimization through Bayesian optimization;
e. and inputting the test set into the trained model to obtain the final state diagnosis result of the intelligent spindle.
Further, the step c is to input a training set into an improved dual-channel DenseNet model for training; the improved dual-channel DenseNet model based on feature fusion consists of a 2-input layer, a 2-convolution layer and a maximum pooling layer, a 2-three-level intensive connection block, a 2-intensive connection block with ECA, 2-three transition layers, a 2-BN layer+Conv layer+Maxpooling layer, a 2-LSTM layer, a 2-flattening layer, a localization channel combination, a full connection layer and an output layer.
Further, the step d is to input the verification set into the improved model to perform super-parameter optimization through Bayesian optimization; and optimizing the super-parameters in a given range, wherein the super-parameters to be optimized mainly comprise learning rate, batch size, training rounds, the number and size of convolution kernels and the number of neurons of a full-connection layer.
Further, the step e is to input the test set into the trained model to obtain the final state diagnosis result of the intelligent spindle;
where TP is the true positive sample; TN is a true negative sample; FP is a false positive sample; FN is a false negative sample.
Further, the improvement process of the dual-channel DenseNet model is as follows:
the extraction of local and global features of the original signal is completed by improving the combination mode of the DenseNet network and the LSTM network;
carrying out parameter adjustment and over-fitting inhibition on the network through a full connection layer and a dropout layer;
and (3) carrying out normalization processing on the output characteristics through a normalization exponential function-Softmax function, converting all output values into probabilities, wherein the sum of all probability values is 1, and the Softmax function formula is as follows:
where j=1, &.. K refers to the number of classes of a particular class.
Furthermore, an ECA attention mechanism is added into the final stage dense connecting block of the 2-three stage dense connecting blocks; based on the SE module, the ECA changes the FC learning channel attention information using the full connection layer in the SE into 1*1 convolution learning channel attention information, and the method specifically comprises the following steps:
s1: firstly, inputting a feature map, wherein the dimension of the feature map is H, W and C;
s2: performing spatial feature compression on the input feature map, and in the spatial dimension, using global average pooling GAP to obtain a 1x C feature map;
s3: and carrying out channel feature learning on the compressed feature map to realize: by 1*1 convolution, the importance among different channels is learned, and the output dimension is 1x C;
s4: channel attention combining, the channel attention feature map 1x C and the original input feature map H x W x C are multiplied channel by channel, and a feature map with channel attention is output.
Further, selecting an Adam optimizer to optimize the model, wherein the Adam optimizer is used for optimizing the model:
wherein M is the number of categories; y is ic For a sign function (0 or 1), taking 1 if the true class of sample i is equal to C, otherwise taking 0; p is p ic The predicted probability that sample i belongs to category c is observed.
An intelligent spindle service state diagnostic system, comprising:
the acquisition module is used for constructing a data acquisition platform to realize data acquisition of the intelligent main shaft, carrying out data enhancement on the original signals by an overlapping sampling method, converting the original signals into two-dimensional time-frequency images by CWT, and reserving the original signals after data enhancement as input of a one-dimensional channel;
the dividing module is used for dividing the original signal after data enhancement and the time-frequency pattern into a training set, a testing set and a verification set according to the interval of 7:2:1 respectively;
the training module is used for inputting the training set into the improved two-channel DenseNet model for training;
the optimizing module is used for inputting the verification set into the improved model and carrying out super-parameter optimizing through Bayesian optimization;
and the diagnosis module is used for inputting the test set into the trained model to obtain the final state diagnosis result of the intelligent spindle.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of a method for diagnosing a service condition of an intelligent spindle when the computer program is executed.
A computer readable storage medium storing a computer program which when executed by a processor implements the steps of a method for diagnosing a service condition of an intelligent spindle.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a method, a system, equipment and a medium for diagnosing the service state of an intelligent main shaft, which comprise the steps of constructing a data acquisition platform to realize data acquisition of the intelligent main shaft, carrying out data enhancement on an original signal by an overlapping sampling method, converting the original signal into a two-dimensional time-frequency image by CWT, and reserving the original signal after data enhancement as input of a one-dimensional channel; dividing the original signal after data enhancement and the time-frequency pattern into a training set, a testing set and a verification set according to the interval of 7:2:1 respectively; inputting the training set into an improved two-channel DenseNet model for training; inputting the verification set into the improved model to perform super-parameter optimization through Bayesian optimization; inputting the test set into the trained model to obtain an intelligent main shaft final state diagnosis result; according to the invention, through collecting intelligent main shaft data, analyzing the collected signals, through the combination of the improved dense connection network and the long-time and short-time memory network, more detail features are extracted based on a double-channel fusion mode, and a basis is provided for the subsequent main shaft performance evaluation.
Drawings
FIG. 1 is a flow chart of a method for diagnosing the service state of an intelligent spindle;
FIG. 2 is a flow chart of the spindle data acquisition and control process of the present invention;
FIG. 3 is a diagram of a dense connectivity network of the present invention;
FIG. 4 is a diagram of a modified 2D-DenseNet model of the present invention;
FIG. 5 is a diagram of a two-channel model structure of the present invention;
fig. 6 is a diagram of the attention mechanism of the ECA of the present invention.
Detailed Description
The invention will now be described in further detail with reference to specific examples, which are intended to illustrate, but not to limit, the invention.
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides a method for diagnosing the service state of an intelligent main shaft, which is shown in figure 1 and comprises the following steps:
a. constructing a data acquisition platform to realize data acquisition of the intelligent main shaft, carrying out data enhancement on an original signal by an overlapping sampling method, converting the original signal into a two-dimensional time-frequency image by CWT, and reserving the original signal after data enhancement as input of a one-dimensional channel;
b. dividing the original signal after data enhancement and the time-frequency pattern into a training set, a testing set and a verification set according to the interval of 7:2:1 respectively;
c. inputting the training set into an improved two-channel DenseNet model for training;
d. inputting the verification set into an improved model, performing super-parameter optimization through Bayesian optimization, and performing optimization on the super-parameters of the model in a global optimization mode to play a role in fine adjustment of the model;
e. and inputting the test set into the trained model to obtain the final state diagnosis result of the intelligent spindle.
In the step a, the vibration signal and the displacement signal of the main shaft are collected through an acceleration sensor and a displacement sensor; a filter and a signal amplifier are selected to perform A/D conversion on the acquired signals; storing and transmitting the processed data through a data acquisition card; carrying out data enhancement on the received original signal by a method of overlapping sampling, reserving the original signal, and carrying out time-frequency domain analysis by CWT; dividing the original signal after data enhancement and the time-frequency pattern into a training set, a verification set and a test set according to the interval of 7:2:1 respectively; and carrying out time domain, frequency domain and time-frequency domain analysis on the signals subjected to data enhancement, wherein a time-frequency diagram of the original signals is obtained through Continuous Wavelet Transform (CWT), so that the time-frequency distribution condition of vibration can be clearly and accurately represented.
It is further described that, the present application firstly utilizes the dense connection network to adaptively extract the feature information and reduce the dimension, then constructs the batch normalization layer, the convolution layer and the maximum pooling layer to further extract the deep features as the input of the LSTM layer, and finally realizes the extraction of the global features through the long-short-term memory network (LSTM); according to the invention, the local and global features are extracted by utilizing the improved DenseNet network and the LSTM network, so that the phenomenon that training speed is greatly slowed down due to the fact that training parameters are rapidly increased while a model is deepened can be avoided, modeling capacity is increased by fewer LSTM units, and the intelligent main shaft fault features are extracted more comprehensively.
Preferably, as shown in fig. 5, the step c inputs the training set into the improved dual-channel DenseNet model for training; the improved dual-channel DenseNet model based on feature fusion consists of a 2-input layer, a 2-convolution layer, a maximum pooling layer, a 2-three-level intensive connection block, a 2-intensive connection block with ECA, 2-three transition layers, a 2-BN layer+Conv layer+Maxpooling layer, a 2-LSTM layer, a 2-flattening layer, a localization channel combination, a full connection layer and an output layer;
as shown in fig. 3, the structure diagram of the dense connection network of the present invention, M1: adopts a three-level dense connection mode, and the dense connection mainly comprises two parts: a Dense block Dense block+transition layer Transition block; m2: dense layer improvement: BN, reLU and 1x1Conv, BN, reLU and 3x3Conv; m3: and (3) improving a transition layer: BN, reLU, 1x1Conv, average pool; m4: an ECA attention module is added after the last stage of the dense block.
Preferably, the step d inputs the verification set into the improved model to perform super-parameter optimization through Bayesian optimization; the super-parameters to be optimized mainly comprise learning rate, batch size, training rounds, the number and size of convolution kernels, the number of neurons of a full-connection layer and the like, and the Bayesian optimization algorithm has many advantages, less iteration times, high convergence speed and strong robustness to non-convex problems.
Preferably, the step e is to input the test set into the trained model to obtain the final state diagnosis result of the intelligent spindle;
where TP is the true positive sample; TN is a true negative sample; FP is a false positive sample; FN is a false negative sample.
As shown in fig. 4, the improvement process of the dual-channel DenseNet model is as follows:
the extraction of local and global features of the original signal is completed by improving the combination mode of the DenseNet network and the LSTM network;
parameter adjustment and overfitting inhibition are carried out on the network through a full-connection layer and a dropoff layer, the dropoff layer is generally added on the full-connection layer to prevent overfitting, the generalization capability of the model is improved, dropoff is only used when the model is trained, and dropoff is not needed when the model is evaluated; the output characteristics are normalized through a normalized exponential function, namely a Softmax function, all output values are converted into probabilities (between 0 and 1), all probability values are added up to be 1, and the Softmax function formula is as follows:
where j=1, &.. K refers to the number of classes of a particular class.
Flattening the multidimensional data into one-dimensional data through a flat () function in a flattening layer to realize flattened dimension reduction processing; and the parameter adjustment and the overfitting inhibition are carried out on the network through a full-connection layer and a dropout layer, the dropout layer is generally added on the full-connection layer to prevent the overfitting, and the generalization capability of the model is improved. Dropout is only used when training the model, and is not needed when evaluating the model. During model evaluation, the dropout layer can let all the active units pass;
as shown in fig. 6, an ECA attention mechanism is added to the last stage of dense connection blocks of the 2-three stage dense connection blocks; based on the SE module, the ECA changes the FC learning channel attention information using the full connection layer in the SE into 1*1 convolution learning channel attention information, and the method specifically comprises the following steps:
s1: firstly, inputting a feature map, wherein the dimension of the feature map is H, W and C;
s2: performing spatial feature compression on the input feature map, and in the spatial dimension, using global average pooling GAP to obtain a 1x C feature map;
s3: and carrying out channel feature learning on the compressed feature map to realize: by 1*1 convolution, the importance among different channels is learned, and the output dimension is 1x C;
s4: channel attention combining, the channel attention feature map 1x C and the original input feature map H x W x C are multiplied channel by channel, and a feature map with channel attention is output.
Preferably, an Adam optimizer is selected to optimize the model, and the Adam optimizer performs the following steps:
wherein M is the number of categories; y is ic For a sign function (0 or 1), taking 1 if the true class of sample i is equal to C, otherwise taking 0; p is p ic The predicted probability that sample i belongs to category c is observed.
The invention provides an intelligent main shaft service state diagnosis system, which comprises:
the acquisition module is used for constructing a data acquisition platform to realize data acquisition of the intelligent main shaft, carrying out data enhancement on the original signals by an overlapping sampling method, converting the original signals into two-dimensional time-frequency images by CWT, and reserving the original signals after data enhancement as input of a one-dimensional channel;
the dividing module is used for dividing the original signal after data enhancement and the time-frequency pattern into a training set, a testing set and a verification set according to the interval of 7:2:1 respectively;
the training module is used for inputting the training set into the improved two-channel DenseNet model for training;
the optimizing module is used for inputting the verification set into the improved model and carrying out super-parameter optimizing through Bayesian optimization;
and the diagnosis module is used for inputting the test set into the trained model to obtain the final state diagnosis result of the intelligent spindle.
In still another embodiment of the present invention, as shown in fig. 2, a hardware platform of a method for diagnosing a service state of an intelligent spindle includes: the device comprises a three-way acceleration sensor, a thermocouple temperature sensor, a displacement sensor, a data acquisition card, signal conditioning and storage equipment, a control module and a piezoelectric actuator; firstly, acquiring original signals transmitted by an acceleration sensor, a thermocouple temperature sensor and a displacement sensor; denoising through a filter and an amplifier, removing trend terms and the like, and preprocessing signals; then, carrying out data enhancement on the received original signal by a method of overlapping sampling, reserving the original signal, carrying out time-frequency domain analysis by CWT, extracting time-frequency characteristics of the signal, and better representing the running state of the spindle; the extraction of the signal characteristics by the model is completed in a characteristic fusion mode; finally, the softmax function is used for classifying the fault modes.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions within a computer storage medium to implement the corresponding method flow or corresponding functions; the processor provided by the embodiment of the invention can be used for the operation of an intelligent main shaft service state diagnosis method.
In yet another embodiment of the present invention, a storage medium, specifically a computer readable storage medium (Memory), is a Memory device in a computer device, for storing a program and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the above-described embodiments with respect to a method for diagnosing a service condition of an intelligent spindle.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the technical solutions according to the embodiments of the present invention.
Claims (10)
1. The intelligent main shaft service state diagnosis method is characterized by comprising the following steps of:
a. constructing a data acquisition platform to realize data acquisition of the intelligent main shaft, carrying out data enhancement on an original signal by an overlapping sampling method, converting the original signal into a two-dimensional time-frequency image by CWT, and reserving the original signal after data enhancement as input of a one-dimensional channel;
b. dividing the original signal after data enhancement and the time-frequency pattern into a training set, a testing set and a verification set according to the interval of 7:2:1 respectively;
c. inputting the training set into an improved two-channel DenseNet model for training;
d. inputting the verification set into the improved model to perform super-parameter optimization through Bayesian optimization;
e. and inputting the test set into the trained model to obtain the final state diagnosis result of the intelligent spindle.
2. The intelligent spindle service state diagnosis method according to claim 1, wherein the step c is characterized in that a training set is input into an improved two-channel DenseNet model for training; the improved dual-channel DenseNet model based on feature fusion consists of a 2-input layer, a 2-convolution layer and a maximum pooling layer, a 2-three-level intensive connection block, a 2-intensive connection block with ECA, 2-three transition layers, a 2-BN layer+Conv layer+Maxpooling layer, a 2-LSTM layer, a 2-flattening layer, a localization channel combination, a full connection layer and an output layer.
3. The intelligent spindle service state diagnosis method according to claim 1, wherein the step d is characterized in that a verification set is input into an improved model to perform super-parameter optimization through Bayesian optimization; and optimizing the super-parameters in a given range, wherein the super-parameters to be optimized mainly comprise learning rate, batch size, training rounds, the number and size of convolution kernels and the number of neurons of a full-connection layer.
4. The method for diagnosing the service state of the intelligent spindle according to claim 1, wherein the step e is to input a test set into a trained model to obtain a final state diagnosis result of the intelligent spindle;
where TP is the true positive sample; TN is a true negative sample; FP is a false positive sample; FN is a false negative sample.
5. The intelligent spindle service state diagnosis method according to claim 1, wherein the improvement process of the dual-channel DenseNet model is as follows:
the extraction of local and global features of the original signal is completed by improving the combination mode of the DenseNet network and the LSTM network;
carrying out parameter adjustment and over-fitting inhibition on the network through a full connection layer and a dropout layer;
and (3) carrying out normalization processing on the output characteristics through a normalization exponential function-Softmax function, converting all output values into probabilities, wherein the sum of all probability values is 1, and the Softmax function formula is as follows:
where j=1, &.. K refers to the number of classes of a particular class.
6. The intelligent spindle service state diagnosis method according to claim 1, wherein an ECA attention mechanism is added to a final-stage dense connecting block of the 2-three-stage dense connecting block; based on the SE module, the ECA changes the FC learning channel attention information using the full connection layer in the SE into 1*1 convolution learning channel attention information, and the method specifically comprises the following steps:
s1: firstly, inputting a feature map, wherein the dimension of the feature map is H, W and C;
s2: performing spatial feature compression on the input feature map, and in the spatial dimension, using global average pooling GAP to obtain a 1x C feature map;
s3: and carrying out channel feature learning on the compressed feature map to realize: by 1*1 convolution, the importance among different channels is learned, and the output dimension is 1x C;
s4: channel attention combining, the channel attention feature map 1x C and the original input feature map H x W x C are multiplied channel by channel, and a feature map with channel attention is output.
7. The intelligent spindle service state diagnosis method according to claim 1, wherein an Adam optimizer is selected to optimize the model, and the Adam optimizer performs the following steps:
wherein M is the number of categories; y is ic For a sign function (0 or 1), taking 1 if the true class of sample i is equal to C, otherwise taking 0; p is p ic The predicted probability that sample i belongs to category c is observed.
8. An intelligent spindle service state diagnosis system, characterized in that, based on any one of the intelligent spindle service state diagnosis methods of claims 1-7, it comprises:
the acquisition module is used for constructing a data acquisition platform to realize data acquisition of the intelligent main shaft, carrying out data enhancement on the original signals by an overlapping sampling method, converting the original signals into two-dimensional time-frequency images by CWT, and reserving the original signals after data enhancement as input of a one-dimensional channel;
the dividing module is used for dividing the original signal after data enhancement and the time-frequency pattern into a training set, a testing set and a verification set according to the interval of 7:2:1 respectively;
the training module is used for inputting the training set into the improved two-channel DenseNet model for training;
the optimizing module is used for inputting the verification set into the improved model and carrying out super-parameter optimizing through Bayesian optimization;
and the diagnosis module is used for inputting the test set into the trained model to obtain the final state diagnosis result of the intelligent spindle.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of a method for diagnosing a service condition of an intelligent spindle according to any one of claims 1-7 when the computer program is executed by the processor.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of a method for diagnosing a service condition of an intelligent spindle according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310146161.0A CN116166997A (en) | 2023-02-21 | 2023-02-21 | Intelligent main shaft service state diagnosis method, system, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310146161.0A CN116166997A (en) | 2023-02-21 | 2023-02-21 | Intelligent main shaft service state diagnosis method, system, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116166997A true CN116166997A (en) | 2023-05-26 |
Family
ID=86410975
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310146161.0A Pending CN116166997A (en) | 2023-02-21 | 2023-02-21 | Intelligent main shaft service state diagnosis method, system, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116166997A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118247269A (en) * | 2024-05-27 | 2024-06-25 | 西安理工大学 | Workpiece surface quality prediction method and device and computing equipment |
-
2023
- 2023-02-21 CN CN202310146161.0A patent/CN116166997A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118247269A (en) * | 2024-05-27 | 2024-06-25 | 西安理工大学 | Workpiece surface quality prediction method and device and computing equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xue et al. | A novel intelligent fault diagnosis method of rolling bearing based on two-stream feature fusion convolutional neural network | |
| Lee et al. | Fault detection based on one-class deep learning for manufacturing applications limited to an imbalanced database | |
| US20200402221A1 (en) | Inspection system, image discrimination system, discrimination system, discriminator generation system, and learning data generation device | |
| CN108256556A (en) | Wind-driven generator group wheel box method for diagnosing faults based on depth belief network | |
| CN111738363A (en) | Alzheimer disease classification method based on improved 3D CNN network | |
| CN113837000A (en) | Small sample fault diagnosis method based on task sequencing meta-learning | |
| CN114429152A (en) | Rolling bearing fault diagnosis method based on dynamic index antagonism self-adaption | |
| Shajihan et al. | CNN based data anomaly detection using multi-channel imagery for structural health monitoring | |
| Zhang et al. | Intelligent machine fault diagnosis using convolutional neural networks and transfer learning | |
| CN112418175A (en) | Rolling bearing fault diagnosis method and system based on domain migration and storage medium | |
| CN113435321A (en) | Method, system and equipment for evaluating state of main shaft bearing and readable storage medium | |
| CN117784710A (en) | Remote state monitoring system and method for numerical control machine tool | |
| CN115290326A (en) | Rolling bearing fault intelligent diagnosis method | |
| CN116166997A (en) | Intelligent main shaft service state diagnosis method, system, equipment and medium | |
| CN116630728A (en) | Machining precision prediction method based on attention residual error twin network | |
| CN112596016A (en) | Transformer fault diagnosis method based on integration of multiple one-dimensional convolutional neural networks | |
| CN116842379A (en) | Mechanical bearing residual service life prediction method based on DRSN-CS and BiGRU+MLP models | |
| Wang et al. | A novel rolling bearing fault diagnosis method based on adaptive denoising convolutional neural network under noise background | |
| Sun et al. | Data-driven fault diagnosis method based on second-order time-reassigned multisynchrosqueezing transform and evenly mini-batch training | |
| CN116977708A (en) | Bearing intelligent diagnosis method and system based on self-adaptive aggregation visual view | |
| CN117369418A (en) | Fault diagnosis method, system, storage medium and equipment for feeding system of numerical control machine tool | |
| CN110320802B (en) | Complex system signal time sequence identification method based on data visualization | |
| CN115310499B (en) | Industrial equipment fault diagnosis system and method based on data fusion | |
| CN114202110B (en) | Service fault prediction method and device based on RF-XGBOOST | |
| CN116296396A (en) | Rolling bearing fault diagnosis method based on mixed attention mechanism residual error network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |