CN201017233Y - Manufacturing production process failure diagnosis device based on wavelet analyzing - Google Patents
Manufacturing production process failure diagnosis device based on wavelet analyzing Download PDFInfo
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- CN201017233Y CN201017233Y CNU2006201402898U CN200620140289U CN201017233Y CN 201017233 Y CN201017233 Y CN 201017233Y CN U2006201402898 U CNU2006201402898 U CN U2006201402898U CN 200620140289 U CN200620140289 U CN 200620140289U CN 201017233 Y CN201017233 Y CN 201017233Y
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Abstract
A wavelet-analysis-based failure diagnosis device of the industrial production process includes a field intelligent instrument, which is connected with an industrial process target, a DCS system and a host computer. The DCS system consists of a data interface, a control station and a database. The intelligent instrument, the DCS system and the host computer are orderly linked. The host computer includes a standardization disposal module, a wavelet decomposition module, a principal component analysis function module, a wavelet reconstruction function module, a vector machine and classifier supporting function module and a failure judging module. The utility model provides a wavelet-analysis-based failure diagnosis device of the industrial production process, which synchronously considers the multicollinearity, the nonlinear characteristic and the multi-scale characteristic of the industrial process data and can obtain good diagnosis effect.
Description
(I) technical field
The utility model relates to an industrial process fault diagnosis field especially relates to an industrial production process fault diagnosis device based on wavelet analysis.
(II) background of the invention
Due to the requirements of product quality, economic efficiency, safety and environmental protection, industrial processes and related control systems become very complex, and the diagnosis and detection of faults plays a very important role in industrial processes in order to ensure the normal operation of industrial systems. In recent years, the application of statistical analysis to process monitoring and fault diagnosis has been widely studied.
The method utilizes the industrial measured data and adopts a statistical method to diagnose the fault, thereby avoiding the complex mechanism analysis and being relatively convenient to solve. Since an industrial process is inherently multi-scale, such as the spatial multi-scale of micro-macro polymerization kinetics, process variables are always obtained at different sampling rates or scales, and corresponding control or operation occurs at different time and spatial scales, the multi-scale often has significant, sometimes even essential, impact on the industrial process. Therefore, the process can be accurately and reliably monitored only by comprehensively considering the complex collinearity, the nonlinear characteristic and the multi-scale characteristic of the process. However, the existing fault diagnosis method only considers complex collinearity and nonlinear characteristics of the industrial process, but does not consider multi-scale characteristics of the process, and a good diagnosis effect is often difficult to obtain for fault diagnosis of a complex industrial process with serious influence on the multi-scale characteristics.
Disclosure of the invention
In order to overcome the existing fault diagnosis system do not consider the multiscale characteristic of process, be difficult to obtain better diagnostic effect not enough, the utility model provides a consider the complex collinearity of industrial process data, nonlinear characteristic and multiscale characteristic simultaneously, can obtain good diagnostic effect's industrial production process fault diagnosis device based on wavelet analysis.
The utility model provides a technical scheme that its technical problem adopted is:
an industrial production process fault diagnosis device based on wavelet analysis comprises an on-site intelligent instrument connected with an industrial process object, a DCS and an upper computer, wherein the DCS is composed of a data interface, a control station and a database; intelligent instrument, DCS system, host computer link to each other in proper order, the host computer include:
the normalization processing module is used for performing normalization processing on the data, the mean value of each variable is 0, the variance is 1, an input matrix X is obtained, and the normalization processing module is completed by adopting the following processes:
1) Calculating an average value:
2) Calculating the variance:
3) And (3) standardization:
wherein, TX is a training sample, and N is the number of the training samples;
the wavelet decomposition function module is used for decomposing an original signal into a series of approximate information and detail information by adopting a Mallat tower decomposition algorithm, and is realized by adopting the following steps:
(1) the original signal space V 0 Can be decomposed into a series of approximation spaces V J And a detail space W j Where J is the coarsest scale, also referred to as the decomposed scale;
(2) calculating an approximation space V J . Space V J From a scale function { J，k (t), k ∈ Z } stretch, calculated using the following formula:
V J ＝{ J，k (t)| J，k (t)＝2 -J/2 (2 -J t-k)} (9)
(3) computing a detail space W j . Detail space W j From wavelet functions { psi j、k (t), J = 1.. J, k ∈ Z }, calculated using the following formula:
W j ＝{ψ j，k (t)|ψ j，k( t)＝2 -j/2 ψ(2 -j t-k)} (10)
where j is a scale factor and k is a translation factor;
(4) thus, decomposition information of the original information is obtained, and the following formula is adopted for calculation:
wherein the first term represents approximate information, the second term is detail information, and the approximation factor a J，k And detail factor d J，k Computing by adopting a Mallat algorithm;
said approximate information A J f (t) and detail information D j f (t) (J =1,2,.., J), defined as follows:
the principal component analysis functional module is used for carrying out principal component analysis and extracting principal components, and is realized by adopting a method of covariance singular value decomposition and adopting the following steps:
(1) computing the covariance matrix of X, denoted as ∑ X ；
(2) To sigma X Singular value decomposition is carried out to obtain a characteristic root lambda 1 ，λ 2 ，...，λ p Wherein λ is 1 ≥λ 2 ≥...≥λ p ，
The corresponding characteristic vector matrix is U;
(3) calculating the total variance and the variance contribution rate corresponding to each eigenvalue, and accumulating the total variance contribution rate from large to small according to the variance contribution rate of each eigenvalue until the total variance contribution rate reaches a given value;
(4) selecting the first k columns of the characteristic vector matrix U as a transformation coefficient matrix T;
(5) and (3) calculating pivot elements: f = T × X;
the wavelet reconstruction function module is used for performing wavelet reconstruction, and adding the principal elements obtained under each scale according to a wavelet theory to obtain a total principal element;
a support vector machine classifier functional module for kernel function adopting radial basis function K (x) i ，x)＝exp(-‖x-x i ‖/σ 2 ) The training process is converted into the following quadratic programming solving problem:
a classification function is obtained, i.e. the sign function of the following function:
defining that when f (x) > =0, the data sample is in a normal state; when f (x) < 0, it is in an abnormal state;
the signal acquisition module is used for setting a time interval of each sampling and acquiring a signal of the on-site intelligent instrument;
the diagnostic data determining module is used for transmitting the acquired data to a DCS real-time database, and obtaining the latest variable data from the real-time database of the DCS database at each timing period as data VX to be diagnosed;
a fault diagnosis module for obtaining during training of the data VX to be detected
And σ x 2 Carrying out standardization processing, taking the data after the standardization processing as the input of a wavelet decomposition module, carrying out wavelet decomposition on the input data by using the same parameters during training, and taking the obtained coefficient as the input of a middle principal component analysis module; transforming the input by using a transformation matrix T obtained in the training process, and inputting the transformed matrix into a wavelet reconstruction module; adding the corresponding data to obtain the principal component of the original data to be measured, and adding the obtained principal componentInputting the data into a support vector machine classifier module; substituting the input into the discriminant function obtained by training, calculating the discriminant function value, and discriminating the state of the process.
As a preferred solution: the host computer still include: and the discrimination model updating module is used for periodically adding points with normal process states into the training set VX, outputting the points to the standardization processing module, the wavelet decomposition module, the principal component analysis functional module and the wavelet reconstruction functional module, and updating the classification model of the support vector machine classifier.
As another preferable scheme: the host computer still include: and the result display module is used for transmitting the fault diagnosis result to the DCS, displaying the process state at a control station of the DCS, and transmitting the process state information to the field operation station for displaying through the DCS and the field bus.
The utility model discloses consider industrial process data's complex collinearity, nonlinear characteristic and multiscale characteristic simultaneously, combine together principal component analysis, support vector machine and wavelet analysis, carry out fault diagnosis to industrial process. The pivot analysis is used for processing the complex correlation of industrial process production data, the support vector machine classifier is used for solving the problem of nonlinear classification, and the wavelet analysis is used for acquiring information of the process under different scales.
The beneficial effects of the utility model are that: meanwhile, the characteristics of complex correlation, multi-scale characteristics, nonlinear characteristics and the like in industrial process data are considered, the characteristics of principal component analysis, multi-scale system theory and support vector machine are fully utilized, the decorrelation capability of the principal component analysis, the strong decomposition and reconstruction capability of wavelet analysis under different scales of information and the multivariable nonlinear mapping capability of the support vector machine are well combined, the advantages of the principal component analysis, the multi-scale system theory and the support vector machine are brought into play, fault diagnosis is more reliable and effective, production can be guided better, and production benefits are improved.
(IV) description of the drawings
Fig. 1 is a hardware configuration diagram of the fault diagnosis system according to the present invention.
Fig. 2 is a functional block diagram of a fault diagnosis system according to the present invention.
Fig. 3 is an exploded view of the Mallat algorithm for wavelet analysis.
Fig. 4 is a schematic block diagram of the upper computer of the present invention.
(V) detailed description of the preferred embodiments
The present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1,2, 3 and 4, the industrial production process fault diagnosis system based on wavelet analysis comprises an on-site intelligent instrument 2 connected with an industrial process object 1, a DCS system and an upper computer 6, wherein the DCS system is composed of a data interface 3, a control station 4 and a database 5; intelligent instrument 2, DCS system, upper computer 6 pass through field bus and link to each other in proper order, upper computer 6 include:
a standardization processing module 7, configured to standardize the data, where a mean value of each variable is 0 and a variance is 1, to obtain an input matrix X, and perform the following processes:
1) Calculating an average value:
2) Calculating the variance:
3) And (3) standardization:
wherein, TX is a training sample, and N is the number of the training samples;
the wavelet decomposition function module 8 is configured to decompose an original signal into a series of approximate information and detail information by using a Mallat tower decomposition algorithm, and is implemented by using the following steps:
(1) separating original signal space V 0 Can be decomposed into a series of approximation spaces V J And the detail space W j Where J is the coarsest scale, also referred to as the decomposed scale;
(2) calculating an approximation space V J . Space V J From the scale function { J，k (t), k ∈ Z } stretch, calculated using the following formula:
V J ＝{ J，k (t)| J，k (t)＝2 -J/2 (2 -J t-k)} (9)
(3) computing a detail space W j . Detail space W j From wavelet functions { psi j，k (t), J = 1.. J, k ∈ Z }, calculated using the following formula:
W j ＝{ψ j，k (t)|ψ j，k (t)＝2 -j/2 ψ(2 -j t-k)} (10)
where j is a scale factor and k is a translation factor;
(4) thus, decomposition information of the original information is obtained, and the following formula is adopted for calculation:
wherein the first term represents approximate information, the second term is detail information, and the approximation factor a J，k And detail factor d j，k Computing by adopting a Mallat algorithm;
said approximate information A J f (t) and detail information D j f (t) (J =1,2,.., J), defined as follows:
the principal component analysis functional module 9 is configured to perform principal component analysis to extract principal components, and implement the principal component analysis by using a method of covariance singular value decomposition, including the following steps:
(1) computing the covariance matrix of X, denoted as ∑ X ；
(2) To sigma X Singular value decomposition is carried out to obtain a characteristic root lambda 1 ，λ 2 ，...，λ p Wherein λ is 1 ≥λ 2 ≥...≥λ p The corresponding eigenvector matrix is U;
(3) calculating the total variance and the variance contribution rate corresponding to each eigenvalue, and accumulating the total variance contribution rate from large to small according to the variance contribution rate of each eigenvalue until the total variance contribution rate reaches a given value;
(4) selecting the first k rows of the characteristic vector matrix U as a transformation coefficient matrix T;
(5) and (3) calculating pivot elements: f = T × X;
the wavelet reconstruction function module 10 is used for performing wavelet reconstruction, and adding the principal elements obtained under each scale according to a wavelet theory to obtain a total principal element;
a support vector machine classifier function module 11 for kernel function using radial basis function K (x) i ，x)＝exp(-‖x-x i ‖/σ 2 ) The training process is converted into the following quadratic programming solving problem:
a classification function is obtained, i.e. the sign function of the following function:
defining that when f (x) > =0, the data sample is in a normal state; when f (x) < 0, it is in an abnormal state;
the signal acquisition module 12 is used for setting a time interval of each sampling and acquiring a signal of the on-site intelligent instrument;
the data to be diagnosed determining module 13 is configured to transmit the acquired data to the DCS real-time database, and obtain the latest variable data from the real-time database of the DCS database at each timing period as data VX to be diagnosed;
a fault diagnosis module 14 for obtaining during training the data VX to be detected
And σ x 2 Carrying out standardization processing, taking the data after the standardization processing as the input of a wavelet decomposition module, carrying out wavelet decomposition on the input data by using the same parameters during training, and taking the obtained coefficients as the input of a middle principal component analysis module; transforming the input by using a transformation matrix T obtained in the training process, and inputting the transformed matrix into a wavelet reconstruction module; adding the corresponding data to obtain a principal component of the original data to be detected, and inputting the obtained principal component into a support vector machine classifier module; substituting the input into the discriminant function obtained by training, calculating the discriminant function value, and discriminating the state of the process.
The host computer still include: and the discrimination model updating module 15 is used for periodically adding points with normal process states into the training set VX, outputting the points to the standardization processing module 7, the wavelet decomposition module 8, the principal component analysis functional module 9 and the wavelet reconstruction functional module 10, and updating the classification model of the support vector machine classifier module 11.
The host computer still include: and the result display module 16 is used for transmitting the fault diagnosis result to the DCS, displaying the process state at a control station of the DCS, and transmitting the process state information to the field operation station through the DCS and the field bus for displaying.
The hardware structure diagram of the industrial process fault diagnosis system of the present embodiment is shown in fig. 1, and the core of the fault diagnosis system is composed of five functional modules including a standardization module 7, a wavelet decomposition module 8, a principal component analysis module 9, a wavelet reconstruction module 10, a support vector machine classifier module 11, and a human-computer interface upper computer 6, and further includes: the field intelligent instrument 2, the DCS system and the field bus. The DCS system consists of a data interface 3, a control station 4 and a database 5; the industrial process object 1, the intelligent instrument 2, the DCS system and the upper computer 6 are sequentially connected through a field bus, and uploading and issuing of information flow are achieved. The fault diagnosis system runs on the upper computer 6, can conveniently exchange information with a bottom layer system, and timely deals with system faults.
The functional block diagram of the fault diagnosis system of this embodiment is shown in fig. 2, and mainly includes five functional blocks, such as a standardization processing block 7, a wavelet decomposition block 8, a principal component analysis block 9, a wavelet reconstruction block 10, and a support vector machine classifier block 11.
The fault diagnosis method of the utility model is implemented according to the following steps:
1. determining key variables used for fault diagnosis, and respectively acquiring data of the variables when the system is normal and fails from a historical database of a DCS (distributed control system) database 5 as training samples TX;
2. in a wavelet decomposition module 8, a principal component analysis module 9 and a support vector machine classifier module 11 of an upper computer 6, parameters such as the number of wavelet decomposition layers, a principal component analysis variance extraction rate, a support vector machine kernel parameter, a confidence probability and the like are respectively set, and a sampling period in DCS is set;
3. in the upper computer 6, the training sample TX is sequentially subjected to functional modules such as standardization processing 7, wavelet decomposition 8, principal component analysis 9, wavelet reconstruction 10, support vector machine 11 and the like, and the following steps are adopted to complete the training of the diagnosis system:
1) In the normalization processing function module 7 of the upper computer 6, data is normalized so that the mean value of each variable is 0 and the variance is 1, and an input matrix X is obtained. The following process is adopted to complete the process:
(1) calculating an average value:
(2) calculating the variance:
(3) and (3) standardization:
where N is the number of training samples.
The standardized processing performed by the standardized processing functional module 7 of the upper computer 6 can eliminate the influence of various variables caused by different dimensions.
2) In the wavelet decomposition functional module 8 of the upper computer 6, a Mallat tower decomposition algorithm is adopted to decompose the original signal into a series of approximate information and detail information. The wavelet analysis of the wavelet decomposition module 8 in the upper computer 6 adopts db3 wavelets, and the number of decomposition layers is 3-7. The method is realized by adopting the following steps:
(1) separating original signal space V 0 Can be decomposed into a series of approximation spaces V J And the detail space W j Where J is the coarsest scale, also referred to as the decomposed scale;
(2) calculating an approximation space V J . Space V J From a scale function { J，k (t), k ∈ Z } stretch, calculated using the following formula:
V J ＝{ J，k (t)| J，k (t)＝2 -J/2 (2 -J t-k)} (9)
(3) computing a detail space W j . Detail space W j From wavelet functions { psi j，k (t), J = 1.. J, k ∈ Z }, calculated using the following formula:
W j ＝{ψ j，k (t)|ψ j，k (t)＝2 -j/2 ψ(2 -j t-k)} (10)
where j is the scale factor and k is the translation factor.
(4) Thus, decomposition information of the original information is obtained, and the following formula is adopted for calculation:
wherein the first item represents approximate information and the second item is detail information. Approximation factor a J，k And detail factor d j，k Calculated using the Mallat algorithm. The Mallat algorithm used therein is shown in fig. 2 as a tower exploded view.
Said approximate information A J f (t) and detail information D j f (t) (J =1,2,.., J), defined as follows:
the actual industrial process is multi-scale in nature, the information content and the embodied system characteristics are different at each scale, and if the system is directly modeled, the difference is ignored, thereby causing deviation in the result. And wavelet decomposition is used for extracting information of each scale, so that information carried by process data can be more fully mined, and the accuracy of a result is improved.
3) In the principal component analysis functional module 9 of the upper computer 6, principal component analysis is performed to extract principal components. The extraction rate of the total variance of the principal component analysis is more than 80%, and the calculation process adopts a method of covariance singular value decomposition and is realized by adopting the following steps:
(1) computing the covariance matrix of X, denoted as ∑ X ；
(2) To sigma X Singular value decomposition is carried out to obtain a characteristic root lambda 1 ，λ 2 ，...，λ p Wherein λ is 1 ≥λ 2 ≥...≥λ p The corresponding eigenvector matrix is U;
(3) calculating the total variance and the variance contribution rate corresponding to each eigenvalue, and accumulating the total variance contribution rate from large to small according to the variance contribution rate of each eigenvalue until the total variance contribution rate reaches a given value;
(4) selecting the first k rows of the characteristic vector matrix U as a transformation coefficient matrix T;
(5) and (3) calculating a pivot: f = T × X.
It is clear that the analysis system is much easier in a low-dimensional space than in a high-dimensional space. Principal component analysis strives to reduce the dimension of a high-dimensional variable space under the principle of minimum data information loss so as to obtain a few linear combinations of a variable system, and comprehensive variables formed by the linear combinations keep information on the variation of original variables as much as possible.
4) In the wavelet reconstruction function block 10 of the upper computer 6, wavelet reconstruction is performed.
And adding the principal elements obtained under each scale according to a wavelet theory to obtain a total principal element.
5) And training a classification model of a support vector machine classifier functional module 11 in the upper computer 6.
The kernel function of the support vector machine classifier functional module 9 in the upper computer 6 adopts a radial basis function K (x) i ，x)＝exp(-‖x-x i ‖/σ 2 ) The training process is converted into the following quadratic programming solving problem:
a classification function is thus obtained, i.e. the sign function of the following function:
defining that when f (x) > =0, the data sample is in a normal state; when f (x) < 0, the state is abnormal.
The support vector machine is based on a statistical learning theory, adopts a structure risk minimization criterion, well solves the problems of small samples, local minimum points, high dimension and the like, and can improve the classification precision when used for classification.
4. The system starts to be put into operation:
1) Setting the time interval of each sampling by using a timer;
2) The field intelligent instrument 2 detects the process data and transmits the process data to a real-time database of the DCS database 5;
3) The upper computer 6 obtains the latest variable data from the real-time database of the DCS database 5 in each timing period, and the latest variable data are used as data VX to be diagnosed;
4) Standardized processing function module 7 of upper computer 6, obtained during trainingAnd σ 2 x Standardizing the data VX to be detected, and taking the standardized data as the input of the wavelet decomposition module 8;
5) The wavelet decomposition module 8 of the upper computer 6 performs wavelet decomposition on input data by using the same parameters during training, and the obtained coefficients are used as the input of the principal component analysis module 9 in the upper computer 6;
6) The principal component analysis module 9 of the upper computer 6 transforms input by using a transformation matrix T obtained in training, and the transformed matrix is input to a wavelet reconstruction module 10 of the upper computer 6;
7) The wavelet reconstruction module 10 of the upper computer 6 adds the corresponding data to obtain the principal component of the original data to be detected, and inputs the obtained principal component into the support vector machine classifier module 11 of the upper computer 6;
8) The support vector machine classifier module 11 of the upper computer 6 substitutes the input into the discrimination function obtained by training, calculates the discrimination function value, discriminates the state of the process, and displays the state of the process on the human-computer interface of the upper computer 6
9) The upper computer 6 transmits the fault diagnosis result to the DCS, displays the process state at the control station 4 of the DCS, and transmits the process state information to the field operation station for displaying through the DCS system and the field bus, so that the field operator can deal with the fault diagnosis result in time.
5. Classifier model updating
In the system commissioning process, points with normal process states are added into the training set TX periodically, and the training process in the step 3 is repeated, so that the classification model of the support vector machine classifier 11 of the upper computer 6 is updated in time, and the classifier model has a good classification effect.
Claims (3)
1. An industrial production process fault diagnosis device based on wavelet analysis comprises a field intelligent instrument connected with an industrial process object, a DCS and an upper computer, wherein the DCS is composed of a data interface, a control station and a database; intelligent instrument, DCS system, host computer link to each other in proper order, its characterized in that: the host computer include:
the standardization processing module is used for standardizing data;
a wavelet decomposition function module for decomposing the original signal into a series of approximate information and detail information by adopting a Mallat tower decomposition algorithm;
a principal component analysis function module used for carrying out principal component analysis to extract principal components and adopting a covariance singular value decomposition method;
the wavelet reconstruction function module is used for performing wavelet reconstruction and adding the principal elements obtained under each scale according to a wavelet theory to obtain a total principal element;
using radial basis functions K (x) for kernel functions i ，x)＝exp(-‖x-x i ‖/σ 2 ) A support vector machine classifier function module which converts the training process into a secondary planning and solving problem;
the signal acquisition module is used for setting a time gap of each sampling and acquiring a signal of the on-site intelligent instrument;
the data determination module to be diagnosed is used for transmitting the acquired data to the DCS real-time database and obtaining the latest variable data from the real-time database of the DCS database at each timing period as the data VX to be diagnosed;
obtained during training for data VX to be detected
And σ x 2 Carrying out standardization processing, taking the data after the standardization processing as the input of a wavelet decomposition module, carrying out wavelet decomposition on the input data by using the same parameters during training, and taking the obtained coefficient as the input of a middle principal component analysis module; transforming the input by using a transformation matrix T obtained in the training process, and inputting the transformed matrix into a wavelet reconstruction module; adding the corresponding data to obtain the principal component of the original data to be detected, and inputting the obtained component into a support vector machine classifier module; a fault diagnosis module for substituting the input into the discriminant function obtained by training, calculating the discriminant function value, and discriminating the state of the process,
the on-site intelligent instrument is in data connection with the signal acquisition module, the signal acquisition module is connected with the data determination module to be diagnosed, the data determination module to be diagnosed is connected with the fault diagnosis module, the standardization processing module is connected with the wavelet decomposition function module, the wavelet decomposition function module is connected with the principal component analysis function module, the principal component analysis function module is connected with the wavelet reconstruction module, the wavelet reconstruction module is connected with the support vector machine classifier function module, and the support vector machine classifier function module is connected with the fault diagnosis module.
2. The industrial process fault diagnosis device based on wavelet analysis according to claim 1, wherein: the host computer still include:
and the discrimination model updating module is used for periodically adding points with normal process states into the training set VX, outputting the points to the standardization processing module, the wavelet decomposition module, the principal component analysis functional module and the wavelet reconstruction functional module, and updating the classification model of the SVM classifier, and the discrimination model updating module is connected with the SVM classifier functional module.
3. The industrial process fault diagnosis device based on wavelet analysis according to claim 1 or 2, wherein: the host computer still include:
and the result display module is used for transmitting the fault diagnosis result to the DCS, displaying the process state at a control station of the DCS, transmitting the process state information to the field operation station through the DCS and the field bus for displaying, and is connected with the fault diagnosis module.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101738998B (en) * | 2009-12-10 | 2012-05-30 | 浙江大学 | System and method for monitoring industrial process based on local discriminatory analysis |
| CN103162710A (en) * | 2011-12-15 | 2013-06-19 | 洛阳理工学院 | MEMS gyro fault detection system based on wavelet entropy and detection method thereof |
| CN103699945A (en) * | 2013-12-31 | 2014-04-02 | 北京理工大学 | Method and device for extracting different-scale production performance signal of RMS (Reconfigurable Manufacturing System) |
| CN102713777B (en) * | 2010-01-22 | 2015-07-01 | 株式会社日立制作所 | Diagnostic apparatus and diagnostic method |
| CN108536128A (en) * | 2018-05-14 | 2018-09-14 | 浙江大学 | A kind of machine learning fault diagnosis system of parameter optimization |
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2006
- 2006-11-23 CN CNU2006201402898U patent/CN201017233Y/en not_active Expired - Fee Related
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101738998B (en) * | 2009-12-10 | 2012-05-30 | 浙江大学 | System and method for monitoring industrial process based on local discriminatory analysis |
| CN102713777B (en) * | 2010-01-22 | 2015-07-01 | 株式会社日立制作所 | Diagnostic apparatus and diagnostic method |
| CN103162710A (en) * | 2011-12-15 | 2013-06-19 | 洛阳理工学院 | MEMS gyro fault detection system based on wavelet entropy and detection method thereof |
| CN103162710B (en) * | 2011-12-15 | 2015-11-11 | 洛阳理工学院 | Based on MEMS gyro fault detection system and the detection method thereof of Wavelet Entropy |
| CN103699945A (en) * | 2013-12-31 | 2014-04-02 | 北京理工大学 | Method and device for extracting different-scale production performance signal of RMS (Reconfigurable Manufacturing System) |
| CN103699945B (en) * | 2013-12-31 | 2017-09-29 | 北京理工大学 | Extract the method and device of Reconfigurable Manufacturing System different scale production performance signal |
| CN108536128A (en) * | 2018-05-14 | 2018-09-14 | 浙江大学 | A kind of machine learning fault diagnosis system of parameter optimization |
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