CN112597705B - Multi-feature health factor fusion method based on SCVNN - Google Patents
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Abstract
A multi-feature health factor fusion method based on SCVNN relates to the technical field of fault prediction, aims at the problem that in the prior art, a model cannot reduce the influence of experience factors and remove redundant information, and adopts the idea of variational inference to carry out normal distribution modeling on an original signal, so that self-adaptive feature learning can be carried out on the signal to construct features representing the essence of the signal. Compared with the traditional feature fusion method, the model can reduce the influence of experience factors and remove redundant information. The self-normalization idea is introduced into the SCVNN model, so that the activation value can be transmitted among layers of the network in a normalized state, the over-fitting phenomenon is avoided, and the characteristics containing rich information are obtained, thereby better representing the health state of the rotary machine.
Description
Technical Field
The invention relates to the technical field of failure prediction, in particular to a multi-feature health factor fusion method based on SCVNN.
Background
As a core component of a mechanical system, a rotary machine is widely used in various fields including aerospace, military, communication, industrial control, and the like, and a fault of the rotary machine affects system functions and even causes a catastrophic accident. Therefore, in order to ensure continuous and effective operation of an industrial mechanical system, the development of the intelligent rotary machine prediction method is a trend in the field of mechanical health monitoring, and the establishment of effective health factors is a prerequisite for realizing accurate prediction of rotary machine faults.
Self-normalization Neural Networks (SNN) adopt Scaled Exponential Linear Units (SELU) as an activation function, so that an activation value can be guaranteed to be transmitted among all layers of the network in a normalized state, the value tends to a stable fixed point, once disturbance causes covariate offset, the value can be pulled back to the normalized state immediately, and the over-fitting phenomenon is avoided. Furthermore, the function has no dead zone, i.e. when the input is less than 0, the neurons can still be activated, and compared with a model using a Linear rectification Unit (ReLU) as an activation function, the SNN extracts richer features, thereby better characterizing the health status of the system.
By adding a sampling layer in the multi-layer perceptron, a Conditional Variable Neural Network (CVNN) constructs hidden variables with the same distribution as the original input. And then solving parameters of the neural network model by using the thought of variational inference, and resampling the hidden variables to obtain the output characteristics of the sampling layer. The CVNN performs normal distribution modeling on the original signal, can perform adaptive feature learning, and obtains the essential features of the original input. However, the prior art lacks a method capable of ensuring that the activation value is transmitted between layers of the network in a normalized state, and avoiding an overfitting phenomenon.
Disclosure of Invention
The purpose of the invention is: aiming at the problem that the prior art can not ensure that the activation value is transmitted between network layers in a normalized state and avoid the phenomenon of over-fitting, the multi-feature health factor fusion method based on the SCVNN is provided.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a multi-feature health factor fusion method based on SCVNN comprises the following steps:
the method comprises the following steps: collecting an original vibration signal of a rotary machine;
step two: carrying out smoothing and denoising pretreatment on an original vibration signal of the rotary machine, then extracting time domain, frequency domain and time-frequency domain characteristics of the pretreated original vibration signal of the rotary machine, constructing an original characteristic set, and then carrying out normalization treatment on the signal in the original characteristic set;
step three: constructing a sensitive feature set after screening by using the normalized original feature set;
step four: inputting the sensitive feature set into an SCVNN model for feature fusion training, and inputting data of the test set into the trained model to obtain a health factor representing the health state of the rotary machine;
the SCVNN model comprises an input layer, a first hidden layer, a sampling layer, a second hidden layer and an output layer;
The SCVNN model specifically executes the following steps:
(1) an input layer: taking a sensitive feature set obtained by screening evaluation indexes as an input sample of an input layerThe corresponding sample label isWhere M is the number of samples, xmRepresenting samples of a sample set, cmRepresenting a tag in a set of tags;
(2) first hidden layer: labeling according to sampleAnd function fθ1For the samplePerforming compression transformation to obtain output of the first hidden layer
(3) Sampling layer: outputting the first hidden layerFeature vector ofMapping into a feature compression layer to obtain a mean value mumSum variance σ2mThen random sampling is carried out on normal distribution to obtain a noise vector epsilonmAnd finally according to the mean value mumVariance σ2mSum noise vector εmSampling in a random sampling operation layer to obtain an implicit variable zm;
(4) Second hidden layer: passing functionFor hidden variable zmPerforming compression transformation to obtain feature vector
(5) An output layer: according to the feature vectorObtaining a feature vectorAnd outputting the corresponding health factor.
wherein theta is1={W1,b1Is the network parameter of the first hidden layer, W1As a weight of the first hidden layer, b1For the first hidden layer bias to be applied,is an activation function of the first hidden layer, and the activation function of the first hidden layer is a SELU.
Further, the mean value μmExpressed as:
wherein theta is2={W2,b2Is the sampling layer mean parameter, W2Is the mean weight, b2Is the mean offset of the sampled layers.
Further, the variance σ2mExpressed as:
wherein theta is3={W3,b3Is the sampling layer variance parameter, W3As a variance weight, b3Is the variance bias of the sampling layer.
Further, the hidden variable zmExpressed as:
zm=μm+σ2m×εm。
wherein theta is4={W4,b4Is the parameter to be optimized of the second hidden layer, W4As a weight of the second hidden layer, b4For second hidden layer bias, zmIn order to be a hidden variable, the method comprises the following steps of,is a second hidden layer activation function, which is SELU.
Further, the SELU is represented as:
wherein λ is 1.0507009873554804 and α is 1.6732632423543772.
Further, in the third step, the normalized original feature set is used for screening by using a correlation index, a monotonicity index and a robustness index as evaluation criteria of the feature quantity, wherein the correlation index is used for measuring the linear correlation degree between the feature parameter sequence and the time sequence; the monotonicity index is used for reflecting the monotonous ascending or descending conversion degree of the characteristic parameter sequence; the robustness index is used for describing the capacity of the characteristic parameter sequence to contain abnormal factors including noise interference.
Further, the specific steps of screening by using the normalized raw feature set in the third step are as follows:
assume that the feature quantity sequence is E ═ E (1), E (2), E (k)]The time sequence is T ═ T1,t2,...,tK],e(tk) Represents the time tkAnd (3) dividing the characteristic parameter sequence into a stationary trend term and a random residue term by adopting a moving average method according to the characteristic value, wherein K represents the total time length:
e(tk)=eT(tk)+eR(tk)
wherein e isT(tk) Represents the steady trend portion of the feature, and eR(tk) Then the random margin portion of the feature is represented,
the correlation between E and T is marked as Corr (E, T), the monotonicity index and the robustness index of E are respectively marked as Mon (E) and Rob (E), and the calculation processes of three evaluation indexes are as follows:
where K represents the total time length and δ (-) represents a simple unit step function, which is expressed as follows:
further, the loss function of the SCVNN model is:
wherein the content of the first and second substances,is the output of the SCVNN model; y istIs the true tag value.
The invention has the beneficial effects that:
(1) the SCVNN model adopts the idea of variational inference to carry out normal distribution modeling on an original signal, can carry out self-adaptive feature learning from the signal and constructs the feature representing the essence of the signal. Compared with the traditional feature fusion method, the model can reduce the influence of empirical factors and remove redundant information.
(2) The self-normalization idea is introduced into the SCVNN model, so that the activation value can be transmitted among layers of the network in a normalized state, the over-fitting phenomenon is avoided, and the characteristics containing rich information are obtained, so that the health state of the rotary machine is better represented.
Drawings
FIG. 1 is a schematic diagram of a SCVNN model structure;
fig. 2 is a schematic diagram of a SCVNN-based multi-feature health factor fusion process.
Detailed Description
It should be noted that, in the present invention, the embodiments disclosed in the present application may be combined with each other without conflict.
The first specific implementation way is as follows: referring to fig. 1 and fig. 2, the present embodiment is specifically described, and the SCVNN-based multi-feature health factor fusion method in the present embodiment includes the following steps:
the method comprises the following steps: collecting an original vibration signal of a rotary machine;
step two: carrying out smoothing and denoising pretreatment on an original vibration signal of the rotary machine, then extracting time domain, frequency domain and time-frequency domain characteristics of the pretreated original vibration signal of the rotary machine, constructing an original characteristic set, and then carrying out normalization treatment on the signal in the original characteristic set;
step three: constructing a sensitive feature set after screening by using the normalized original feature set;
Step four: inputting the sensitive feature set into an SCVNN model for feature fusion training, and inputting data of the test set into the trained model to obtain a health factor representing the health state of the rotary machine;
the SCVNN model comprises an input layer, a first hidden layer, a sampling layer, a second hidden layer and an output layer;
the SCVNN model specifically executes the following steps:
(1) an input layer: taking a sensitive feature set obtained by screening evaluation indexes as an input sample of an input layerThe corresponding sample label isWhere M is the number of samples, xmRepresenting samples of a sample set, cmRepresenting a tag in a set of tags;
(2) first hidden layer: labeling based on sampleAnd functionFor the samplePerforming compression transformation to obtain output of the first hidden layer
(3) Sampling layer: outputting the first hidden layerFeature vector ofMapping into a feature compression layer to obtain a mean value mumSum variance σ2mThen random sampling is carried out on normal distribution to obtain a noise vector epsilonmAnd finally according to the mean value mumVariance σ2mSum noise vector εmSampling in a random sampling operation layer to obtain an implicit variable zm;
(4) Second hidden layer: passing functionFor hidden variable zmPerforming compression transformation to obtain feature vector
(5) And (3) an output layer: according to the feature vectorObtaining a feature vectorAnd outputting the corresponding health factor.
The invention provides a multi-feature health factor fusion method based on a Self-normalization Conditional variable Neural network (SCVNN) by combining the characteristics of the SNN and the CVNN, which realizes the fusion of multi-feature quantities through a supervised learning method, establishes feature parameters capable of representing the degradation state of a mechanical system and further realizes the tracking of the degradation state of the rotary mechanical performance and the prediction of the residual life under multi-features.
The SCVNN model structure provided by the present invention is shown in fig. 1, and is mainly divided into an input layer, a first hidden layer, a sampling layer, a second hidden layer, and an output layer. The model is described in detail as follows:
1. input layer
Taking a sensitive feature set obtained by screening evaluation indexes as an input sample of an input layerCorresponding sample label isWhere the number of samples is M.
2. First hidden layer
Passing functionPerforming a compression transform on the samples to obtain an output of the first hidden layerComputational complexity of neuronsThe formula is as follows:
wherein theta is1={W1,b1Is the network parameter of the first hidden layer, which needs to be determined optimally, W1Is the weight of the first hidden layer, b 1Is an offset.Is the activation function of the first hidden layer. Selecting a SELU as an activation function of the first hidden layer, wherein the expression is as follows:
wherein λ 1.0507009873554804, α 1.6732632423543772. The SELU activation function has the advantages of high convergence speed and output approximate zero center, and solves the problems of gradient disappearance and neuron necrosis.
To improve the generalization capability of the model, the Dropout algorithm is used for the first layer hidden layer. The Dropout algorithm discards neurons in one layer of the neural network with a certain probability, thereby preventing adaptation between neurons in the same layer. Therefore, the Dropout algorithm makes the characteristic expression of the neuron more independent, and can improve the expression capability of the network.
3. Sampling layer
The sampling layer is composed of three sub-layers including a feature compression layer, a random sampling operation layer and a calculation layer. The feature compression layer functions similarly to the hidden layer. Feature vectorRespectively mapping to 2 parts in the feature compression layer, and correspondingly calculating the mean value mumSum variance σ2mThe calculation formula is as follows:
wherein theta is2={W2,b2And theta3={W3,b3And is the parameter to be determined of the sampling layer. Then random sampling is carried out on N (0,1) to obtain a noise vector epsilonm. The latent variable z is accomplished in a random sampling operation layer in the following way mSampling:
zm=μm+σ2m×εm (5)
in order to promote gradient calculation of the model, a re-parameterization skill is introduced in the sampling process to realize the average value mumAnd variance σ2mAnd (6) derivation. Obtaining a hidden variable z obeying a normal distributionm~N(μm,σ2m) Which is consistent with the probability distribution of the original sample. And, the generalization capability of the model is improved by adding the noise vector. Finally, calculating and determining an implicit variable z by adopting the idea of variation inference in a calculation layermThe model parameters of (1).
4. Second hidden layer
Similar to the first hidden layer, the second hidden layer passes through a functionCompressing and transforming the output of the sampling layer to obtain the characteristic vectorThe formula for the neuron is as follows:
wherein theta is4={W4,b4Is the parameter to be optimized of the second hidden layer, W4Is the weight of the second hidden layer, b4Is offset. Likewise, SELU is chosen as the second hidden layer activation function sf2And the Dropout algorithm is applied to the second hidden layer.
5. Output layer
And outputting the multi-feature fusion health factor based on SCVNN by the output layer.
Fig. 2 shows a processing procedure of the SCVNN-based multi-feature health factor fusion method, which includes the following steps:
(1) collecting vibration signals of a rotating machine as data input;
(2) carrying out smooth denoising pretreatment on the original vibration data, extracting characteristics such as a time domain, a frequency domain, a time-frequency domain and the like to construct an original characteristic set, and then normalizing the characteristics;
(3) And (3) screening to obtain characteristics closely tracked with the degradation process of the rotary machine and constructing a sensitive characteristic set by using the correlation, monotonicity and robustness indexes as evaluation standards of various characteristic quantities.
(4) And inputting the sensitive feature set into an SCVNN model, performing feature fusion training, and inputting the data of the test set into the trained model to obtain a health factor representing the health state of the rotary machine.
The rotary machine performance degradation state tracking and residual life prediction method selects the rotary machine of a certain mechanical system as a research object, utilizes the vibration sensor to acquire the vibration data of the rotary machine, extracts the characteristics of the vibration data, and then inputs the vibration data into the SCVNN model to construct health factors, thereby realizing the performance degradation state tracking and the residual life prediction of the rotary machine.
For the fault prediction example of the rotating machine, the method comprises the following specific steps:
(1-1) collecting vibration data of the rotary machine by using a vibration sensor;
(2-1) performing smooth denoising pretreatment on the original vibration data by adopting a wavelet filter;
and (2-2) extracting time domain, frequency domain and time-frequency domain characteristics of the vibration data, constructing an original characteristic set and carrying out normalization processing, so as to represent comprehensive degradation state information of the rotary machine, wherein the characteristics in the original characteristic set are shown in table 1.
TABLE 1 time-Domain, frequency-Domain and time-frequency-Domain features
Where x (N), N1, 2, N represents the original vibration signal sequence acquired from the rotating machine of a mechanical system. N represents the signal sequence length. p (x (n)) represents the probability of occurrence of each data and satisfies
And (3-1) adopting correlation, monotonicity and robustness indexes as evaluation criteria of various characteristic quantities. In the evaluation criterion, the correlation index can measure the linear correlation degree between the characteristic parameter sequence and the time sequence; the monotonicity index can reflect the monotonous ascending or descending conversion degree of the characteristic parameter sequence; the robustness index may describe the ability of the characteristic parameter sequence to accommodate anomalous factors including noise interference.
Let E ═ E (1), E (2), E, (k) be assumed as the characteristic quantity sequence]The time sequence is T ═ T1,t2,...,tK],e(tk) Represents the time tkWherein K represents the total time length. Firstly, a characteristic parameter sequence is divided into a stationary trend term and a random residue term by adopting a moving average method:
e(tk)=eT(tk)+eR(tk) (7)
wherein e isT(tk) Represents the steady trend portion of the feature, and eR(tk) The random margin portion of the feature is represented.
The correlation between E and T is marked as Corr (E, T), the monotonicity index and the robustness index of E are respectively marked as Mon (E) and Rob (E), and formulas (8) to (11) describe the calculation processes of three evaluation indexes:
Where K represents the total time length and δ (-) represents a simple unit step function, which is expressed as follows:
and (3-2) screening to obtain 10 characteristics closely tracked with the degradation process of the rotary machine so as to construct a sensitive characteristic set.
(4-1) construction of training set Using remaining service-life samples of rotating machineWherein x is selectedt∈RN*1N sensitive features at time t, yt∈[0,1]Is a label associated with the percentage of degradation of the mechanical rotating component at time t. For example, assuming the rotating machine has a fault time of 2800s and the current checkpoint of 1400s, then tag yt0.5. Therefore, the SCVNN model is trained by minimizing a loss function
Wherein the content of the first and second substances,is the output of the SCVNN model; y istIs the true tag value.
(4-2) directly inputting the sensitive characteristics of the test set into the trained SCVNN model to obtain a health factor representing the health state of the rotating machine.
It should be noted that the detailed description is only for explaining and explaining the technical solution of the present invention, and the scope of protection of the claims is not limited thereby. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.
Claims (5)
1. A multi-feature health factor fusion method based on SCVNN is characterized by comprising the following steps:
The method comprises the following steps: collecting an original vibration signal of a rotary machine;
step two: carrying out smoothing and denoising pretreatment on an original vibration signal of the rotary machine, then extracting time domain, frequency domain and time-frequency domain characteristics of the pretreated original vibration signal of the rotary machine, constructing an original characteristic set, and then carrying out normalization treatment on the signal in the original characteristic set;
step three: constructing a sensitive feature set after screening by using the normalized original feature set;
step four: inputting the sensitive feature set into an SCVNN model for feature fusion training, and inputting data of the test set into the trained model to obtain a health factor representing the health state of the rotary machine;
the SCVNN model comprises an input layer, a first hidden layer, a sampling layer, a second hidden layer and an output layer;
the SCVNN model specifically executes the following steps:
(1) an input layer: taking a sensitive feature set obtained by screening evaluation indexes as an input sample of an input layerCorresponding sample label isWhere M is a sampleNumber, xmRepresenting samples of a sample set, cmRepresenting a tag in a set of tags;
(2) first hidden layer: labeling based on sampleAnd functionFor the samplePerforming compression transformation to obtain output of the first hidden layer
(3) Sampling layer: output of the first hidden layerFeature vector ofMapping into a feature compression layer to obtain a mean value mumSum variance σ2mThen random sampling is carried out on normal distribution to obtain a noise vector epsilonmAnd finally according to the mean value mumVariance σ2mSum noise vector εmSampling in a random sampling operation layer to obtain an implicit variable zm;
(4) Second hidden layer: passing functionFor hidden variable zmPerforming compression transformation to obtain feature vector
(5) An output layer: according to the feature vectorObtaining a feature vectorThe corresponding health factor is output;
wherein theta is1={W1,b1Is the network parameter of the first hidden layer, W1As a weight of the first hidden layer, b1For the first hidden layer bias to be applied,the activation function of the first hidden layer is SELU;
the mean value mumExpressed as:
wherein theta is2={W2,b2Is the sampling layer mean parameter, W2Is the mean weight, b2Mean bias for the sampled layer;
the variance σ2mExpressed as:
wherein theta is3={W3,b3Is the sampling layer variance parameter, W3As a variance weight,b3Is the variance bias of the sampling layer;
the latent variable zmExpressed as:
zm=μm+σ2m×εm;
wherein theta is4={W4,b4Is the parameter to be optimized of the second hidden layer, W 4Is the weight of the second hidden layer, b4For the second hidden layer bias, zmIn order to be a hidden variable, the method comprises the following steps of,is a second hidden layer activation function, which is SELU.
3. The SCVNN-based multi-feature health factor fusion method according to claim 1, wherein in the third step, the normalized original feature set is used for screening by using a correlation index, a monotonicity index and a robustness index as evaluation criteria of feature quantities, wherein the correlation index is used for measuring the linear correlation degree between a feature parameter sequence and a time sequence; the monotonicity index is used for reflecting the monotonous ascending or descending conversion degree of the characteristic parameter sequence; the robustness index is used for describing the capacity of the characteristic parameter sequence to contain abnormal factors including noise interference.
4. The SCVNN-based multi-feature health factor fusion method according to claim 3, wherein the screening with the normalized raw feature set in the third step comprises:
Let E ═ E (1), E (2), E, (k) be assumed as the characteristic quantity sequence]The time sequence is T ═ T1,t2,...,tK],e(tk) Represents the time tkAnd (3) dividing the characteristic parameter sequence into a stationary trend term and a random residue term by adopting a moving average method according to the characteristic value, wherein K represents the total time length:
e(tk)=eT(tk)+eR(tk)
wherein e isT(tk) Represents the steady trend portion of the feature, and eR(tk) Then the random margin portion of the feature is represented,
the correlation between E and T is marked as Corr (E, T), the monotonicity index and the robustness index of E are respectively marked as Mon (E) and Rob (E), and the calculation processes of three evaluation indexes are as follows:
where K represents the total time length and δ (-) represents a simple unit step function, which is expressed as follows:
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