CN116412162A - Digital twin model-based magnetic suspension blower fault diagnosis method and system - Google Patents
Digital twin model-based magnetic suspension blower fault diagnosis method and system Download PDFInfo
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Abstract
The invention provides a magnetic suspension blower fault diagnosis method and system based on a digital twin model, comprising the following steps: acquiring relevant parameters under the working condition of the magnetic suspension blower to be tested in real time; based on the obtained related parameters, obtaining a fault diagnosis result by using a pre-trained deep neural network model; wherein the deep neural network model adopts a form of combining a stack sparse self-encoder with a Softmax logistic regression model, the training process of the deep neural network model comprises the following steps: acquiring historical related parameters of the magnetic suspension blower under various working conditions, and taking the historical related parameters as a training set; performing initial training on the deep neural network model by utilizing the training set; performing secondary training on the model after initial training by utilizing historical related parameters corresponding to the current working condition in the training set to obtain a trained deep neural network model; the coding layer parameters of the front preset number of the stack sparse self-encoder obtained in the initial training model are used as initial parameters of corresponding layers in the stack sparse self-encoder during secondary training.
Description
Technical Field
The invention belongs to the technical field of fault diagnosis of magnetic suspension blowers, and particularly relates to a magnetic suspension blower fault diagnosis method and system based on a digital twin model.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Although the magnetic suspension blower has the defects of higher input cost in the early stage, inapplicability to the scenes of low air volume and low air pressure, incapability of maintaining the blower by non-professional staff and the like, the outstanding advantages of energy conservation, consumption reduction, no noise and the like of the magnetic suspension blower allow the magnetic suspension blower to be considered in many enterprises and municipal projects, so that the improvement of the fault diagnosis accuracy of the blower is urgent.
The inventor finds that the existing fault diagnosis method mainly adopts a data-driven scheme, and the method lacks of instantaneity, cooperativity and interactivity, so that the efficiency and accuracy of fault diagnosis are lower; and secondly, the fault data of the magnetic suspension blower are less, so that the accuracy of the offline training model is insufficient, and various unknown faults are difficult to predict.
Disclosure of Invention
The invention aims to solve the problems, and provides a magnetic suspension blower fault diagnosis method and system based on a digital twin model.
According to a first aspect of the embodiment of the invention, there is provided a magnetic suspension blower fault diagnosis method based on a digital twin model, including:
acquiring relevant parameters under the working condition of the magnetic suspension blower to be detected in real time, wherein the relevant parameters comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower;
based on the obtained related parameters, obtaining a fault diagnosis result by using a pre-trained deep neural network model;
the deep neural network model adopts a form of combining a stack sparse self-encoder with a Softmax logistic regression model, and the training process of the deep neural network model comprises the following steps: acquiring historical related parameters of the magnetic suspension blower under various working conditions, wherein samples in the training set comprise parameter values of radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower and corresponding faults; performing initial training on the deep neural network model by utilizing the training set; performing secondary training on the model after initial training by utilizing historical related parameters corresponding to the current working condition in the training set to obtain a trained deep neural network model; the coding layer parameters of the front preset number of the stack sparse self-encoder obtained in the initial training model are used as initial parameters of corresponding layers in the stack sparse self-encoder during secondary training.
Further, the related parameters of the magnetic suspension blower under the working condition to be detected are obtained in real time, and particularly, a pre-built digital twin model of the magnetic suspension blower is adopted for real-time obtaining, wherein the digital twin model comprises a geometric model, an analysis model and an evolution model; the geometric model is used for describing geometric parameters and geometric relations of the magnetic suspension blower, the analysis model adopts the neural network model for fault diagnosis, and the evolution model is used for realizing process evolution of the magnetic suspension blower.
Further, the stacked sparse self-encoder in the deep neural network model comprises a plurality of sequentially connected sparse self-encoders.
Further, in the initial training, the stack sparse self-encoder performs unsupervised training by using the training set in advance, and further performs supervised global fine tuning on the whole deep neural network model by using marked samples in the training set in combination with a Softmax logistic regression model.
Furthermore, in the training process of the deep neural network model, the cross entropy loss function is adopted to evaluate the training performance of the model.
Furthermore, in the training process of the deep neural network model, a confusion matrix method is adopted to evaluate the diagnosis performance of the model.
According to a second aspect of the embodiment of the present invention, there is provided a magnetic suspension blower fault diagnosis system based on a digital twin model, including:
the data acquisition unit is used for acquiring related parameters of the magnetic suspension blower under the working condition to be detected in real time; the related parameters comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower;
a fault diagnosis unit for obtaining a fault diagnosis result using a deep neural network model trained in advance based on the obtained relevant parameters;
the deep neural network model adopts a form of combining a stack sparse self-encoder with a Softmax logistic regression model, and the training process of the deep neural network model comprises the following steps: acquiring historical related parameters of the magnetic suspension blower under various working conditions, and taking the historical related parameters as a training set; the samples in the training set comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, parameter values of fundamental frequency and double fundamental frequency of the magnetic suspension blower, and corresponding faults; performing initial training on the deep neural network model by utilizing the training set; performing secondary training on the model after initial training by utilizing historical related parameters corresponding to the current working condition in the training set to obtain a trained deep neural network model; the coding layer parameters of the front preset number of the stack sparse self-encoder obtained in the initial training model are used as initial parameters of corresponding layers in the stack sparse self-encoder during secondary training.
According to a third aspect of the embodiment of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program running on the memory, where the processor implements the method for diagnosing a fault of a magnetic suspension blower based on a digital twin model when executing the program.
According to a fourth aspect of embodiments of the present invention, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for diagnosing a magnetic levitation blower fault based on a digital twin model.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention provides a magnetic suspension blower fault diagnosis method and system based on a digital twin model, wherein the scheme provides a platform and a data source for the real-time diagnosis of the magnetic suspension blower fault by constructing a digital twin frame, and improves the fault diagnosis efficiency and accuracy;
(2) Aiming at the problems of redundancy of input features and complex and difficult traditional manual feature extraction modes in intelligent diagnosis, the technical conception of constructing a deep neural network model through SSAE is provided by considering that SSAE can effectively extract fault features of a magnetic suspension blower and eliminate redundancy of features through an unsupervised deep network mode;
(3) According to the scheme, the problem that the deep learning model cannot be trained due to the fact that a large number of magnetic suspension blower fault marking data are lacked is solved, and meanwhile, the model only needs a short training time.
(4) When the scheme of the invention is used for transfer learning, the information extracted by the lower layer of the neural network is more universal, and the information extracted by the higher layer is more proprietary, so that the parameters trained by the source domain model are selected as initial values of the target domain network, the parameters of the first two layers are fixed, only the target domain data are used for training the later layers, and the result of calculation analysis shows that the method effectively saves the model training time and improves the classification precision.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a schematic diagram of a stacked sparse self-encoder according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a transfer learning process according to an embodiment of the present invention;
fig. 3 is a flowchart of a fault diagnosis method for a magnetic suspension blower based on a digital twin model according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Embodiment one:
the embodiment aims to provide a fault diagnosis method for a magnetic suspension blower based on a digital twin model.
A magnetic suspension blower fault diagnosis method based on a digital twin model comprises the following steps:
acquiring relevant parameters under the working condition of the magnetic suspension blower to be detected in real time, wherein the relevant parameters comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower;
based on the obtained related parameters, obtaining a fault diagnosis result by using a pre-trained deep neural network model;
the deep neural network model adopts a form of combining a stack sparse self-encoder with a Softmax logistic regression model, and the training process of the deep neural network model comprises the following steps: acquiring historical related parameters of the magnetic suspension blower under various working conditions, wherein samples in the training set comprise parameter values of radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower and corresponding faults; performing initial training on the deep neural network model by utilizing the training set; performing secondary training on the model after initial training by utilizing historical related parameters corresponding to the current working condition in the training set to obtain a trained deep neural network model; the coding layer parameters of the front preset number of the stack sparse self-encoder obtained in the initial training model are used as initial parameters of corresponding layers in the stack sparse self-encoder during secondary training.
In specific implementation, the related parameters of the magnetic suspension blower under the working condition to be detected are obtained in real time, and particularly, a pre-built digital twin model of the magnetic suspension blower is adopted for real-time obtaining, wherein the digital twin model comprises a geometric model, an analysis model and an evolution model; the geometric model is used for describing geometric parameters and geometric relations of the magnetic suspension blower, the analysis model adopts the neural network model for fault diagnosis, and the evolution model is used for realizing process evolution of the magnetic suspension blower.
The stack sparse self-encoder in the deep neural network model comprises a plurality of sequentially connected sparse self-encoders; in the initial training, the stack sparse self-encoder performs unsupervised training by utilizing the training set in advance, and further performs supervised global fine tuning on the whole deep neural network model by utilizing marked samples in the training set in combination with a Softmax logistic regression model.
In the specific implementation, in the training process of the deep neural network model, a cross entropy loss function is adopted to evaluate the training performance of the model; in the training process of the deep neural network model, a confusion matrix method is adopted to evaluate the diagnosis performance of the model.
In a specific implementation, the acquiring the relevant parameters under the working condition of the magnetic suspension blower to be tested specifically adopts the parameters as shown in table 1, and includes: radial vibration, axial vibration, flow, oil temperature, rotational speed, fundamental frequency, and twice the fundamental frequency.
Table 1 magnetic levitation blower related parameters
| Sequence number | Fault parameters | Unit (B) | |
| 1 | | μm | |
| 2 | | μm | |
| 3 | Flow rate | m 3 /s | |
| 4 | Oil temperature | ℃ | |
| 5 | Rotational speed | r/min | |
| 6 | Fundamental frequency | Hz | |
| 7 | Double fundamental frequency | Hz |
In particular, for easy understanding, the following detailed description of the embodiments will be given in terms of specific implementations with reference to the accompanying drawings:
in order to solve the problems existing in the prior art, the embodiment provides a magnetic suspension blower fault diagnosis method based on a digital twin model, which has the main technical concept that: constructing a digital twin mapping model of the magnetic suspension blower by utilizing a digital twin technology; then, based on the digital twin mapping model, establishing a depth stack sparse self-encoder model to realize real-time fault prediction, further completing fault result verification, and taking the verification result as a condition for digital twin model correction and deep learning model adjustment; finally, aiming at the problem of limited data of the magnetic suspension blower in practical application, the migration learning theory is applied to other magnetic suspension blowers. Specifically, the scheme in this embodiment specifically includes the following steps:
step 1: establishing a digital twin model of the magnetic suspension blower;
the construction formula of the magnetic levitation blower twin Model (MDT) is as follows:
M DT ＝(M G ,M A ,M E ) (1)
wherein M is DT Represents a twin model of a magnetic suspension blower, M G Representing a geometric model, M A Representing an analytical model, M E Is an evolution model.
The construction formula of the geometric model is as follows:
M G ＝(p g ,r g ) (2)
wherein p is g Representing the geometric parameters of the magnetic suspension blower, r g Representing the geometric relationship of the magnetic levitation blower.
The geometric model mainly describes geometric parameters and relations of cold water entities, wherein the geometric parameters mainly comprise the shape, the position, the size, the tolerance and the like of the magnetic suspension blower and the components thereof, and the relations mainly comprise the assembly relations of the high-efficiency centrifugal impeller, the magnetic suspension bearing, the permanent magnet synchronous motor and the special frequency converter and the assembly relations of parts in each component. Modeling and assembly of the magnetic suspension blower are mainly realized through three-dimensional CAD software SolidWorks.
The analytical model was constructed as follows:
M A ＝(m d ,m m ) (3)
wherein m is d Representing a data-driven failure analysis model, m m Representing a model-driven failure analysis model. The analysis model consists of a fault analysis model, a performance analysis model, an optimization analysis model and other models, and the analysis model is used for realizing the analysis of physical entities according to various collected data. The analysis model in the embodiment mainly refers to a fault analysis model, and in order to realize real-time analysis and diagnosis of faults, the fault diagnosis model is constructed by a fault diagnosis method based on data driving and a fault result verification method based on model simulation. On one hand, the collected real-time data is analyzed by constructing a deep learning model (a deep stack sparse self-encoder model) through a fault diagnosis method based on data driving, so that the real-time diagnosis of faults is realized. On the other hand, the verification of the prediction result is realized by a fault result verification method based on model simulation.
The evolution model is constructed as follows:
M E ＝(t f ,l f ,m f ) (4)
wherein t is f Indicating the fault type, l f Indicating the fault location, m f Representing a fault model. The evolution model mainly aims at synchronizing the real running state of the magnetic suspension blower in the virtual space. Based on a geometric model, real-time data is analyzed through an analysis model to obtain the actual state of the magnetic suspension blower, and twin data and the geometric model are correspondingly updated and processed, so that the process evolution of the magnetic suspension blower is realized, and the evolution process is the process of continuously correcting the digital twin model.
Step 2: the fault diagnosis method of the magnetic suspension blower;
in the feature extraction stage, the traditional manual feature extraction mode is complex and difficult. The stack self-encoder can effectively extract fault characteristics of the magnetic suspension blower through an unsupervised deep network mode, eliminates characteristic redundancy and solves the problem that a common characteristic extraction method is difficult. But the stack self-encoder generalization capability is poor and is not suitable for data traffic with large fluctuations. Meanwhile, the single-layer sparse self-encoder is based on the self-encoder, the problem of training over-fitting can be effectively reduced by adding the sparse constraint, and the multi-layer network can generate more general and beneficial characteristics in the characteristic learning. Therefore, as shown in fig. 1, the scheme of the embodiment combines the multi-layer stacking mode of the stacked self-encoder and the sparsity of the sparse self-encoder to construct the stacked sparse self-encoder, so that the stacked sparse self-encoder can deeply mine the characteristic information contained in the historical data, and meanwhile, the model universality is considered, and the requirement of constructing a fault diagnosis model is met.
Specifically, the sparse self-Encoder (Sparse Autoencoder, SAE) is a feed-forward neural network that attempts to learn, through unsupervised learning, an approximation of the same function used to reconstruct a given input into a compressed representation in output, consisting of an input layer, a hidden layer, an output layer (reconstructed data) 3-layer network structure, with reference to fig. 1, in which the forward conduction of the input layer to the hidden layer is called encoding (Encoder), the forward conduction of the hidden layer to the output layer is called decoding (Decoder), the hidden layer mainly playing the role of feature extraction.
The algorithm of the sparse self-encoder comprises the following steps: given data x= { X 1 ,x 2 ,......,x i ,......,x N }，x i ∈R M Wherein N represents the number of samples, M refers to the number of neurons in the input layer, and the number of neurons in the input layer is mapped to the hidden layer through a nonlinear activation function f (·), so as to obtain a coding result, which is:
by remapping the coding result Z to the reconstruction layer by the same operation, h can be obtained w,b (x)：
Wherein X is the characteristic expression of the original data; w (W) 1 、W 2 、b 1 、b 2 The weights and offset coefficients between the input layer and the hidden layer, and between the hidden layer and the reconstructed layer, respectively.
Adjusting W 1 、W 2 、b 1 、b 2 Let X and h w,b (x) Approximation between, and adding constraints of sparse representation
The loss function is obtained as:
in the formula, lambda is a regular term coefficient; s is(s) l The number of neurons in the layer I;
representing the connection weights between the l+1 layer neurons j and the l layer neurons i; beta is a sparse penalty term weight sparse coefficient; ρ is a sparsity parameter; />
The training sample is the average activation of the jth neuron on the hidden layer; />
Is a sparse penalty factor.
Softmax regression is a generalization of logistic regression that generalizes the solution to the problem of two classifications only to the problem of multiple classifications. As a nonlinear classifier, softmax regression is widely used in supervised learning classifiers for deep networks. The method is combined with unsupervised learning in a deep network, and classification performance can be improved well. The Softmax model is as follows for dataset { (x) (1) ,y (1) )}，{(x (2) ,y (2) )}，......，{(x (m) ,y (m) ) "have y (i) E {1,2, once again, k. Given a test set sample, the probability that the sample is judged as j classes is p (y=j|x), so for a k classification problem, a k-dimensional vector (vector element sum is 1) is output as:
in which theta is 1 ,θ 2 ,……,θ k ∈R n Is a parameter of a model
The probability distribution is further normalized such that the sum of the probabilities is 1.1 {.cndot }, is expressed as a sexual function, and the cost function of the regression model is:
when the algorithm is actually used, a weight attenuation term is added to the cost function to modify the cost function, and then the cost function formula becomes:
wherein lambda is the weight attenuation coefficient,
is a weight decay term.
In the embodiment, the software max algorithm is used for carrying out supervised global fine adjustment on the neural network which is pre-trained by the SSAE, the classification of the extracted features of the neural network can be improved through global fine adjustment by using class mark data, and compared with the multi-class feature extraction algorithm, the SSAE-software max combined processing algorithm provided by the invention has better classification effect.
Three sparse self-encoders and Softmax classification layers were designed in this example to build a deep neural network (Deep Neural Network, DNN). And constructing a deep SSAE network by using SAE1, SAE2 and SAE3 stacks, wherein the deep SSAE network takes the characteristics output by the SAE1 hidden layer as input data of SAE2, SAE3 is similar, the SSAE network is trained layer by using a gradient descent method, and the weight matrix and the bias matrix of each layer of network are updated through multiple iterations, so that the characteristics of the input original data are finally extracted. A Softmax layer is added on top of the SSAE network to diagnose the maglev blower failure.
Step 3: fault diagnosis model based on transfer learning
In the migration learning theory, a field with enough label samples is called a source field, and a target field is usually limited in data, so that a model cannot be effectively trained. The traditional machine learning method is to learn each task from the beginning and respectively construct a separate model for the different tasks, and the migration learning is mainly to use a deep learning model trained by source domain data as a source domain model, selectively migrate the model structure of the source domain and trained parameters into a target domain model, fully mine the historical data in the source domain and provide valuable information for the target domain. When the fault diagnosis model is built, the problems of insufficient data, data loss and the like of the novel water chilling unit are met, the time cost consumed for building the digital twin model for each magnetic suspension blower from the head is too high, and if the model trained by using sufficient historical data is used as a source domain model through transfer learning, information can be provided for different water chilling unit systems, and the efficiency and the precision of building the digital twin model can be improved.
D s ＝{X s ,T s } (13)
D t ＝{X t ,T t } (14)
Wherein D is s And D t Respectively representing source domain data and target domain data, D s And D t Is data with different distributions or with different feature spaces, but they are located in related or similar domains; x is X s And X t Respectively representing a source domain sample and a target domain sample; t (T) s And T t Is its corresponding tag. The source domain task and the target domain task are respectively expressed as follows:
Y s ＝f s (X s ,δ s ) (15)
Y t ＝f t (X t ,δ t ) (16)
wherein f s Represents the slave X s To source domain predictor Y s Is a function mapping of f t Represents the slave X t To target domain predictor Y t S and t are model parameters of the source domain and the target domain, respectively; migration learning is to find source domain data { X ] s ,T s Correlation properties in }, and obtain mapping f s The method comprises the steps of carrying out a first treatment on the surface of the Then map f again s After migration to the target domain, from the target domain number { X } t ,T t Mapping f of learning target tasks in the basis t . The method can be concretely represented as follows:
wherein:
δ s ＝δ 0 +δ 1 (19)
δ t ＝δ 0 +δ 2 (20)
in equations (17) and (18), L is the loss function, and the model training process is to minimize the loss function, n s And n t The number of samples of the source domain data and the target domain data are respectively, and the source domain model is fully trained to obtain the optimal model parameter delta s The method comprises the steps of carrying out a first treatment on the surface of the In (19) and (20), δ s And delta t The model parameters of the source domain and the target domain, respectively, which have some identical part delta 0 And some different parts, delta 1 Representing the portion of the source domain parameters that cannot migrate to the target domain, delta 2 A parameter portion representing the object domain; the purpose of the transfer learning is to utilize the common part delta of these parameters when training the model in the target task 0 Optimal model parameters delta in source domain s Selecting the most suitable delta 0 Part(s).
Each layer of the stack self-encoder has own weight and bias parameters, the weight and bias parameters are obtained by training a source domain model, and then the parameters of the source domain which can be directly migrated to a target domain are determined by migration learning. In the study, the trained parameters of the source domain model are used as the initial values of the parameters of the target domain model, the coding layer in the stack self-encoder is used as a feature extractor for extracting the features of input fault data, and the more the neural network is, the more proprietary the information extracted from the higher layer is, the more general information of the first layers is, so that the parameters of the first two layers are fixed, the parameters of the later layers are only trained by using the target domain data, and the accuracy and the time cost of the obtained target domain prediction result are both optimal as shown in the transfer learning model structure in fig. 2.
The application of the migration learning proposed by the scheme in the embodiment in the construction of the fault diagnosis model is shown in the following fig. 3, and the specific process is as follows:
1) Preprocessing source domain data, constructing a source domain model structure, training and debugging the source domain model structure, finding an optimal source domain diagnosis model, and storing the source domain model structure and parameters for a target domain;
2) And loading the saved source domain model structure, taking the weight parameter migration of the source domain model as the parameter initial value of the target domain, fixing the first two layers of parameters, preprocessing the target domain data, and training the rest layers of the network by using the target domain data to obtain the target domain diagnosis model.
Step 4: model evaluation
(1) Model training evaluation index
For the model training performance of the proposed method, we used Cross entropy (Cross entropy) loss function to evaluate. The specific principle of the evaluation method can be expressed as follows:
regarding the two probability distributions p and q of the sample set, p is set as the true distribution, q is the fitted distribution. The expected coding length required to identify a sample is measured in terms of true distribution, i.e. the information entropy:
if the fit distribution q is used to represent the expectation of the coding length from the true distribution p, i.e. the cross entropy:
for the difference between the true distribution p and the fitted distribution q, we can measure by KL divergence, also called relative entropy:
in the classification problem of machine learning, we want to reduce the gap between model prediction and labels, and the label set is unchanged, so that only the cross entropy needs to be focused in the optimization process. In the multi-classification task of the present study, the cross entropy loss function used to describe the training performance of the fault classification model can be calculated as:
wherein p= [ p ] 0 ,…,p C-1 ]Is a probability distribution of each element p i Representing the probability that the sample belongs to the class II; y= [ y ] 0 ,…,y C-1 ]Is a one-hot representation of the sample tag.
(2) Model diagnostic performance index
To verify the diagnostic performance of the proposed method, the study uses a confusion matrix (fusion matrix) to evaluate the performance of the different methods. The confusion matrix is also called an error matrix, and is a standard format for representing precision evaluation, and is represented by a matrix form of n rows and n columns. Accuracy (Accuracy) and Precision (Precision) are both performance metrics derived from the confusion matrix. According to the form of the confusion matrix, accury is defined as the ratio of the number of correctly diagnosed samples to the total number of samples, expressed in percentage, and the calculation formula is as follows:
in the multi-classification problem, accuracy may represent the Accuracy of the overall judgment of the model. The same rate of the fault label output by the model and the actual fault label is higher, and the more accurate the classification is, the higher the diagnosis accuracy is.
Precision is also called Precision, which indicates the proportion of all predictions that are correctly predicted to be positive. The calculation formula is as follows:
in multi-classification problems, precision may calculate the classification accuracy of a model for a certain class of problems.
Embodiment two:
the embodiment aims to provide a magnetic suspension blower fault diagnosis system based on a digital twin model.
A digital twinning model-based magnetic levitation blower fault diagnosis system, comprising:
the data acquisition unit is used for acquiring related parameters of the magnetic suspension blower under the working condition to be detected in real time; the related parameters comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower;
a fault diagnosis unit for obtaining a fault diagnosis result using a deep neural network model trained in advance based on the obtained relevant parameters;
the deep neural network model adopts a form of combining a stack sparse self-encoder with a Softmax logistic regression model, and the training process of the deep neural network model comprises the following steps: acquiring historical related parameters of the magnetic suspension blower under various working conditions, and taking the historical related parameters as a training set; the samples in the training set comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, parameter values of fundamental frequency and double fundamental frequency of the magnetic suspension blower, and corresponding faults; performing initial training on the deep neural network model by utilizing the training set; performing secondary training on the model after initial training by utilizing historical related parameters corresponding to the current working condition in the training set to obtain a trained deep neural network model; the coding layer parameters of the front preset number of the stack sparse self-encoder obtained in the initial training model are used as initial parameters of corresponding layers in the stack sparse self-encoder during secondary training.
Further, the system in this embodiment corresponds to the method in the first embodiment, and the technical details thereof are described in the first embodiment, so that the details are not repeated here.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of embodiment one. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented as a hardware processor executing or implemented by a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The magnetic suspension blower fault diagnosis method and system based on the digital twin model provided by the embodiment can be realized, and has wide application prospect.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A magnetic suspension blower fault diagnosis method based on a digital twin model is characterized by comprising the following steps:
acquiring relevant parameters under the working condition of the magnetic suspension blower to be detected in real time, wherein the relevant parameters comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower;
based on the obtained related parameters, obtaining a fault diagnosis result by using a pre-trained deep neural network model;
the deep neural network model adopts a form of combining a stack sparse self-encoder with a Softmax logistic regression model, and the training process of the deep neural network model comprises the following steps: acquiring historical related parameters of the magnetic suspension blower under various working conditions, wherein samples in the training set comprise parameter values of radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower and corresponding faults; performing initial training on the deep neural network model by utilizing the training set; performing secondary training on the model after initial training by utilizing historical related parameters corresponding to the current working condition in the training set to obtain a trained deep neural network model; the coding layer parameters of the front preset number of the stack sparse self-encoder obtained in the initial training model are used as initial parameters of corresponding layers in the stack sparse self-encoder during secondary training.
2. The method for diagnosing the fault of the magnetic suspension blower based on the digital twin model as claimed in claim 1, wherein the related parameters of the magnetic suspension blower under the working condition to be tested are obtained in real time, specifically, a pre-constructed digital twin model of the magnetic suspension blower is adopted for real-time obtaining, wherein the digital twin model comprises a geometric model, an analysis model and an evolution model; the geometric model is used for describing geometric parameters and geometric relations of the magnetic suspension blower, the analysis model adopts the neural network model for fault diagnosis, and the evolution model is used for realizing process evolution of the magnetic suspension blower.
3. A method for diagnosing a fault in a magnetic levitation blower based on a digital twin model as defined in claim 1, wherein the stacked sparse self-encoders in the deep neural network model comprise a plurality of sequentially connected sparse self-encoders.
4. A method for diagnosing a fault in a magnetic levitation blower based on a digital twin model as defined in claim 1, wherein in the initial training, the stack sparse self-encoder performs unsupervised training in advance by using the training set, and further performs supervised global fine tuning on the entire deep neural network model by using marked samples in the training set in combination with a Softmax logistic regression model.
5. A method for diagnosing a fault in a magnetic levitation blower based on a digital twin model as claimed in claim 1, wherein the training performance of the model is evaluated by using a cross entropy loss function during the training of the deep neural network model.
6. A method for diagnosing a fault in a magnetic levitation blower based on a digital twin model as claimed in claim 1, wherein the diagnosis performance of the model is evaluated by using a confusion matrix method during the training of the deep neural network model.
7. A digital twinning model-based magnetic levitation blower fault diagnosis system, comprising:
the data acquisition unit is used for acquiring related parameters of the magnetic suspension blower under the working condition to be detected in real time; the related parameters comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, fundamental frequency and double fundamental frequency of the magnetic suspension blower;
a fault diagnosis unit for obtaining a fault diagnosis result using a deep neural network model trained in advance based on the obtained relevant parameters;
the deep neural network model adopts a form of combining a stack sparse self-encoder with a Softmax logistic regression model, and the training process of the deep neural network model comprises the following steps: acquiring historical related parameters of the magnetic suspension blower under various working conditions, and taking the historical related parameters as a training set; the samples in the training set comprise radial vibration, axial vibration, flow, oil temperature, rotating speed, parameter values of fundamental frequency and double fundamental frequency of the magnetic suspension blower, and corresponding faults; performing initial training on the deep neural network model by utilizing the training set; performing secondary training on the model after initial training by utilizing historical related parameters corresponding to the current working condition in the training set to obtain a trained deep neural network model; the coding layer parameters of the front preset number of the stack sparse self-encoder obtained in the initial training model are used as initial parameters of corresponding layers in the stack sparse self-encoder during secondary training.
8. A digital twin model based magnetic levitation blower fault diagnosis system according to claim 7, wherein in the initial training, the stack sparse self-encoder performs unsupervised training in advance using the training set, and further performs supervised global fine tuning of the entire deep neural network model using marked samples in the training set in combination with a Softmax logistic regression model.
9. An electronic device comprising a memory, a processor and a computer program stored for execution on the memory, wherein the processor, when executing the program, implements a method for diagnosing a fault in a magnetic levitation blower based on a digital twin model as defined in any one of claims 1-6.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a digital twin model based magnetic levitation blower fault diagnosis method according to any of claims 1-6.
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| CN117150446B (en) * | 2023-10-30 | 2024-02-09 | 武汉华信数据系统有限公司 | Blower operation state identification method and device, electronic equipment and storage medium |
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