WO2017210894A1 - 基于运行视频信息的一种电弧炉故障监测方法 - Google Patents
基于运行视频信息的一种电弧炉故障监测方法 Download PDFInfo
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- the invention belongs to the technical field of industrial process fault monitoring, and particularly relates to an electric arc furnace fault monitoring method based on running video information.
- Fused magnesia is a kind of refractory material widely used in metallurgical industry, glass industry, cement industry, household heater, chemical industry and other industries.
- the fused magnesium furnace is one of the most widely used production equipment in the production of fused magnesium oxide. In order to ensure the normal operation of the fused magnesium furnace, we must ensure its safety. If any failure occurs during normal operation and the fault cannot be adjusted or predicted in time, the performance of the control system may be seriously impaired, and even the entire system may be paralyzed and cause huge losses. Therefore, it is necessary to perform fault detection and diagnosis on the fused magnesium furnace.
- the fused magnesia furnace is a submerged arc furnace in which an electrode for generating a high temperature arc is inserted into a material composed of magnesium carbonate in the furnace. Through the outer surface material, the inside and outside of the furnace are thermally insulated, and a closed heat conducting space is formed inside the material to be heated.
- magnesium carbonate is decomposed into magnesium oxide and carbon dioxide gas. These gases are usually dissipated through the gap between the particles of the solid material; when some external conditions change, the gas discharge process is affected, for example, if the internal electrode is inserted too deeply or the internal temperature is too high, the generated gas cannot be immediately The release is exhausted, or the molten magnesium oxide blocks the gap between the solid materials and the like.
- the present invention provides an electric arc furnace fault monitoring method based on running video information, so as to achieve the purpose of reducing the false alarm rate and the leakage alarm rate of the furnace fault.
- An electric arc furnace fault monitoring method based on running video information comprising the following steps:
- Step 1 Collecting a video image of the surface layer of the smelting material in the fused magnesium furnace
- Step 2 dividing each captured image into a plurality of viewpoints, obtaining an RGB color mean value of each viewpoint and storing the data as a sample data according to a row vector;
- Step 3 Perform data preprocessing on the stored sample data, including standardization processing and whitening processing;
- Step 4 Based on the Hilbert-Schmidt independence criterion, the estimated signal of the nonlinear independent element signal of the sample data is obtained by the improved FastKICA algorithm, and the specific steps are as follows:
- Step 4-1 constructing an objective function based on the Hilbert-Schmidt independence criterion
- Step 4-2 According to the local parameterization process, obtain the objective function and constraint condition from the manifold space to the European space, and decompose the transformed target function to obtain the first stage sub-object optimization function and the second stage.
- the sub-objective optimization function is as follows:
- Step 4-2-1 Perform local parameterization processing in the optimal solution neighborhood in the manifold space
- Step 4-2-2 obtaining a mapping function of the transformation of the manifold space to the European space according to the local parameterization processing, thereby obtaining a constraint condition;
- Step 4-2-3 According to the mapping function, the objective function is converted from the manifold space to the European space, and the converted objective function is obtained;
- Step 4-2-4 According to the mapping function, the constraint condition is converted from the manifold space to the European space, and the transformed constraint condition is obtained;
- Step 4-2-5 decomposing the objective function to obtain a plurality of sub-objective functions
- Step 4-2-6 determining a sub-objective optimization function in the first stage of the solution process
- H i,j ( ⁇ ) represents the i-th, j sub-objective functions
- b is the unmixed vector
- m is the dimension of the semi-orthogonal de-mixing matrix
- ⁇ B ( ⁇ ) represents a mapping function
- u is a de-mixed vector in which b is mapped from a manifold space to a European space
- u j represents a j-th unmixed vector in a European space
- E k, l represents about Empirical expectation
- E k represents the empirical expectation of z k
- E l represents the empirical expectation of z l
- ⁇ ( ⁇ ) represents the Gaussian kernel function
- Step 4-2-7 determining a sub-objective optimization function of the second stage of the solution process
- Step 4-3 Under the constraint condition, the first-stage sub-objective optimization function and the second-stage sub-objective optimization function are solved by the mapping Newton method, as follows:
- Step 4-3-1 randomly assigning an initial iteration point u 1 and b 1 , b 1 is a unit vector;
- Step 4-3-3 obtaining the optimization direction of u j in the first stage sub-objective function:
- Step 4-3-4 performing a one-dimensional search on u j according to the obtained optimization direction, and substituting the obtained b j(t+1) into the sub-object optimization function of the first stage;
- ⁇ jt arg min ⁇ H( ⁇ B (u jt + ⁇ jt d jt ))
- ⁇ jt represents the step size in the t-th optimization direction with respect to u j ;
- Steps 4-3-5 Determine whether
- the processed semi-orthogonal de-mixing matrix B is substituted into the second-stage sub-objective optimization function;
- Step 4-3-8 judge Whether it approaches 0, and if so, obtains the optimal semi-orthogonal de-mixing matrix B; otherwise, obtains the semi-orthogonal de-mixing matrix B and the de-mixing vector b 2 ,..., b m at this time, and gives b 1 Search direction and then obtain updated b 1 and return to step 4-3-2;
- Step 4-4 obtaining an estimated signal of the non-Gaussian independent element signal of the sample data according to the whitened sample data and the obtained optimal semi-orthogonal demixing matrix B;
- Step 5 Obtain a probability density of the T 2 and SPE statistic of the sample data according to the estimated signal and perform a density curve fitting to obtain a control upper limit value corresponding to the T 2 and the SPE under the confidence limit;
- Step 6 Collect the surface video image of the molten material in the fused magnesium furnace in real time, and obtain the T 2 and SPE statistics of the real-time working condition data according to steps 4-4 and 5;
- Step 7 Determine whether the T 2 and SPE statistics of the real-time working condition data exceed the control limit under the confidence limit. If yes, the fused magnesium furnace fails and alarms; otherwise, returns to step 6.
- E k,l indicates The empirical expectation, E k represents the empirical expectation of z k , E l represents the empirical expectation of z l , and ⁇ ( ⁇ ) represents the Gaussian kernel function.
- step 4-2-2 The constraint conditions described in step 4-2-2 are as follows:
- H(U) represents the objective function in the European space
- u i represents the i-th unmixed vector in the European space
- u j represents the j-th unmixed vector in the European space.
- step 4-2-3 The converted objective function described in step 4-2-3 is as follows:
- H(U) represents the objective function in the European space
- u 1 ,..., u m is b 1 ,..., b m is mapped from the manifold space to the unmixed vector of the European space
- b 1 ,b 2 ,...b m is the first to mth unmixed vector
- m is the dimension of the semi-orthogonal demixing matrix
- m ⁇ d is the dimension of the sample data matrix
- E k,l indicates The empirical expectation
- E k represents the empirical expectation of z k
- E l represents the empirical expectation of z l
- ⁇ ( ⁇ ) represents the Gaussian kernel function.
- H(B) is the objective function
- b is the unmixing vector
- u is the de-mixing vector of b from the manifold space to the European space
- m is the dimension of the semi-orthogonal de-mixing matrix
- H ⁇ ( ⁇ ) For the sub-objective function of the first combination case, the value range of ⁇ is 1 ⁇ i, the total number of j combinations; H i,j ( ⁇ ) represents the i, j sub-objective functions, and ⁇ B ( ⁇ ) represents the mapping function.
- the plurality of viewpoints described in step 2 specifically include: 8, 10, and 12.
- the present invention divides the complex high-dimensional feature space into several smaller spheres in the optimal solution domain in the mapping Newton method. Space, where each subspace contains each pair of unmixed vectors, so the high-dimensional feature space is transformed into a number of low-dimensional spherical vector spaces containing only two different deconvolution vectors, thus reducing the overall judgment of the independence equation scale;
- FIG. 1 is a flow chart of an electric arc furnace fault monitoring method based on running video information according to an embodiment of the present invention
- FIG. 2 is a schematic view of a fused magnesium furnace with a monitoring device according to an embodiment of the present invention
- FIG. 3 is a schematic diagram of a process monitoring image based on a multi-view method according to an embodiment of the present invention
- FIG. 4 is a schematic diagram of comparison of monitoring indexes under different viewpoints according to an embodiment of the present invention, wherein (a) is a comparison chart of T 2 statistics under 4, 5, 6, 8, 10, 12, 16, and 20 viewpoints, Figure (b) is a comparison chart of T 2 statistics under 40, 250, 1000 viewpoints, and Figure (c) is a comparison chart of SPE statistics at 4, 5, 6, 8, 10, 12, 16, 20 viewpoints. (d) is a comparison chart of SPE statistics under 40, 250, 1000 viewpoints;
- FIG. 5 is a schematic diagram showing a distribution of RGB color values of a 3-2th viewpoint according to an embodiment of the present invention.
- FIG. 6 is a flowchart of a method for solving a semi-orthogonal demixing matrix by a mapping Newton method according to an embodiment of the present invention
- FIG. 7 is a graph showing a probability density fit curve of T 2 and SPE according to an embodiment of the present invention, wherein (a) is a T 2 statistic probability density fitting curve, and (b) is a SPE statistic probability. Density fitting curve;
- FIG. 8 is a diagram showing an effect of monitoring a control limit obtained by using a fitting density curve according to an embodiment of the present invention, wherein (a) is a T 2 monitoring diagram, and (b) is an SPE monitoring diagram;
- FIG. 9 is a comparison diagram of T 2 and SPE statistics of three different monitoring methods according to an embodiment of the present invention, wherein FIG. (a) is a comparison chart of T 2 statistics of three different monitoring methods, and FIG. Comparison of SPE statistics for different monitoring methods;
- FIG. 10 is a diagram showing the contribution of RGB variables when a fault occurs when monitored by the FastKICA method according to an embodiment of the present invention, wherein (a) is an RGB contribution diagram of the 89th sample point monitoring index, and (b) is 118th. A sample point monitors the RGB contribution map of the indicator.
- an electric arc furnace fault monitoring method based on running video information wherein the electric arc furnace is an fused magnesia furnace, and the method flow chart is as shown in FIG. 1 , and includes the following steps:
- Step 1 Collecting a video image of the surface layer of the smelting material in the fused magnesium furnace
- the embodiment of the present invention installs the camera shown in the figure at the upper end of the electrode of the fused magnesium furnace, and is required to be able to reach a high temperature of 800 ° C and is in the camera. Externally installed with a protective cover made of high temperature refractory material;
- the temperature control should be placed in the first place; the reason is that the temperature of the entire molten pool is composed of the arc heat release and the molten magnesium oxide heat radiation, and the current and voltage changes are relative to The change in temperature is frequently fluctuating; in addition, the physical manifestation of temperature can be converted into different colored lights; therefore, the present invention achieves the goal of monitoring the fused magnesium furnace by analyzing the change in the color of the molten pool; using this method, real-time acquisition using a camera The image can effectively obtain the change of the color of the molten pool to monitor the temperature change, thus avoiding the occurrence of the furnace accident.
- the frame image is relatively fuzzy, because the complicated smoke movement appears in the video; these smokes are caused by internal high temperature gas, arc and molten magnesium oxide; but still can be video Color change to judge the operation, and even the color change of the video caused by the change of smoke concentration can be analyzed to distinguish the degree of melting;
- any frame image that is, the entire surface of the furnace, can be regarded as a whole, and the reference calculus idea can also be regarded as composed of a large number of pixels; that is, each pixel and image The adjacent pixels in it have the same contribution to the entire image.
- the present invention only considers the complementarity between pixels; therefore, each image is artificially divided into a plurality of viewpoints, which can simulate multiple imaging devices in the monitoring process;
- the method can be referred to as video information failure monitoring based on a multi-view method. According to the experience of the work site, the area where the furnace accident occurs is shown in Figure 3.
- This part of the area can be separated into p ⁇ q viewpoints, and the obtained viewpoint can extract the corresponding RGB color values to be used as observation variables for process monitoring. Therefore, 3 ⁇ p ⁇ q variables can be obtained, which are composed of RGB values of respective regions;
- the embodiment of the present invention divides the area shown in FIG. 3 into 5 ⁇ 2 viewpoints, and obtains 30 process variables in total, that is, 10 sets of RGB data; for every 5 frames of training data and real-time working condition data. Collect data at sampling intervals,
- the training data of the fault is composed of 200 sets of observation samples
- the real-time working condition data is composed of 130 sets of observation samples.
- the embodiment of the present invention gives partial sampling data, and the first 6 variables and 5 sets of data of the modeling data are shown in Table 1.
- the first 6 variables and 5 groups of data of the real-time working condition data are shown in Table 2:
- the selected area is divided into 11 kinds of viewpoints shown in the figure according to a certain manner, as shown in (a), (b), (c) and (d) of FIG. 4,
- viewpoints due to too many variables, the robustness of the algorithm is weakened, resulting in a large number of false positives due to high sensitivity, which is not suitable for online detection; in addition, for 20, 16, 12, 10, 8
- the comparison images at 6, 5, and 4 viewpoints can be found that their monitoring trends for T 2 and SPE are generally the same, but the corresponding T 2 statistic at 20 and 16 viewpoints is falsely reported;
- the corresponding monitoring results at 5 and 6 viewpoints are too robust for video and lack of sensitivity.
- the present invention finds that the T 2 value decreases as the viewpoint increases, and conversely, the SPE monitoring value increases as the viewpoint increases; therefore, the monitoring results under 8, 10, and 12 viewpoints can A satisfactory result is obtained. Therefore, in order to balance the robustness of the enhanced adaptive data and the sensitivity of detecting the fault and reducing the probability of false positives and false negatives during the detection, the above 10 viewpoint acquisition data are selected in the embodiment of the present invention.
- Step 2 dividing each captured image into a plurality of viewpoints, obtaining an RGB color mean value of each viewpoint and storing the data as a sample data according to a row vector;
- the data sets of the p ⁇ q group RGB are obtained and the data is stored in the manner of the row vector; any one of the viewpoints is defined as the pqth viewpoint according to the arrangement order; Each of the viewpoints has the same contribution to the color value of the entire image. Therefore, in this embodiment, the sub-images of any one of the viewpoints are randomly selected to display the RGB data distribution of the modeled samples and the faulty samples; Shown, that is, the modeled sample and test sample data distribution of the 3-2th viewpoint; by comparing the distribution of the test sample and the modeled sample, it is found that the test sample has fluctuations compared to the modeled sample, especially after 89 frames. Significant fluctuations occurred and it was observed that two abnormal peaks occurred at the 89th frame and the 118th frame, which is the point at which the fault occurred.
- Step 3 Perform data preprocessing on the stored sample data, including standardization processing and whitening processing;
- the sample data set may be represented by a random vector group x 1 , x 2 , . . . , x d .
- the set of data may be composed of non-Gaussian independent elements s 1 , s 2 , . , s m , ( m ⁇ d) nonlinear combination is expressed;
- Step 3-1 normalize the stored sample data, that is, remove the mean divided by the standard deviation
- Step 3-2 whitening the sample data
- the solution of the whitening vector is realized by singular value decomposition of the covariance matrix of the observed data, namely:
- ⁇ is the eigenvalue diagonal matrix of the covariance matrix E ⁇ x'x' T ⁇
- U is the eigenvector matrix corresponding to the covariance matrix E ⁇ x'x' T ⁇ ;
- Step 4 Based on the Hilbert-Schmidt independence criterion, the estimated signal of the nonlinear independent element signal of the sample data is obtained by the improved FastKICA algorithm, and the specific steps are as follows:
- Step 4-1 constructing an objective function based on the Hilbert-Schmidt independence criterion
- the de-mixing matrix W B T Q, so the de-mixing matrix W can be obtained by the semi-orthogonal de-mixing matrix B (B is an orthogonal matrix between columns) and the whitened data matrix Z, and the source signal can be further estimated.
- the measured data extracted in complex industrial production processes usually have outliers, nonlinearities, multi-scales, and dynamic characteristics.
- the present invention will be an independent meta-analysis method. Combined with the Hilbert-Schmidt Independence Criteria (HSIC), it provides an effective method for finding the semi-orthogonal de-mixing matrix B;
- HSIC is based on the square of the Hilbert Schmidt norm mapped to the covariance between the variables of the regenerative kernel Hilbert space (RKHSs) As a method of judging the independence of binary variables;
- H(B) is the objective function
- B is the semi-orthogonal de-mixing matrix
- B [b 1 , b 2 ,...b m ]
- b 1 , b 2 ,...b m is the first
- E k,l indicates The empirical expectation, E k represents the empirical expectation of z k , E l represents the empirical expectation of z l , and ⁇ ( ⁇ ) represents the Gaussian kernel function.
- Step 4-2 According to the local parameterization process, obtain the objective function and constraint condition from the manifold space to the European space, and decompose the transformed target function to obtain the first stage sub-object optimization function and the second stage. Sub-objective optimization function;
- Step 4-2-1 Perform local parameterization processing in the optimal solution neighborhood in the manifold space
- O(m) be a manifold space of m(m-1)/2-dimensional, and there is a smooth mapping for each point B ⁇ O(m) in the manifold:
- mapping is a differential homeomorphic mapping at 0 ⁇ R m(m-1)/2 , ie regardless of the mapping ⁇ B or its inverse mapping It is locally smooth in the neighborhood of 0 ⁇ R m(m-1)/2 ; this mapping is called local parameterization in the B neighborhood;
- Step 4-2-2 obtaining a mapping function of the transformation of the manifold space to the European space according to the local parameterization processing, thereby obtaining a constraint condition;
- Step 4-2-3 According to the mapping function, the objective function is converted from the manifold space to the European space, and the converted objective function is obtained;
- the above optimization condition is combined with a HSIC based on a regenerated kernel Hilbert space, wherein the regenerative kernel Hilbert space has a Riemannian manifold structure;
- mapping B ⁇ B (U) can be used to establish mutual interaction between the European space and the reproducing kernel Hilbert space.
- U U ⁇ R m(m-1)/2 is an element of local parameterization at point B * ;
- high-dimensional kernel space can be regarded as a high-dimensional sphere space of unit radius, so high-dimensional sphere is utilized in this embodiment
- the parametric equation represents the above mapping:
- H(U) represents the objective function in the European space
- u 1 ,..., u m is b 1 ,..., b m is mapped from the manifold space to the unmixed vector of the European space
- b 1 ,b 2 ,...b m is the first to mth unmixed vector
- m is the dimension of the semi-orthogonal demixing matrix
- m ⁇ d is the dimension of the sample data matrix
- E k,l indicates The empirical expectation
- E k represents the empirical expectation of z k
- E l represents the empirical expectation of z l
- ⁇ ( ⁇ ) represents the Gaussian kernel function.
- Step 4-2-4 According to the mapping function, the constraint condition is converted from the manifold space to the European space, and the transformed constraint condition is obtained;
- H(U) represents the objective function in the European space
- u i represents the i-th unmixed vector in the European space
- u j represents the j-th unmixed vector in the European space.
- Step 4-2-5 decomposing the objective function to obtain a plurality of sub-objective functions
- HSIC the closer the optimization result of the function H(B) is to zero, the stronger the mutual independence between the de-mixing vectors b i and b j in the semi-orthogonal de-mixing matrix B.
- H(B) is an accumulated result, and the optimal value of the objective function H(B) is zero, and the square value of the Hilbert Schmidt norm of the covariance operator It is non-negative, so the optimal solution of the optimized function H(B) can be regarded as the accumulation of the optimal solution for each component.
- the high-dimensional space where B is located is converted into m(m-1)/2 conventional three-dimensional spherical spaces at the non-degenerate critical point B * ; and the objective function H(B) can be rewritten into the following form. :
- H(B) is the objective function
- b is the unmixing vector
- u is the de-mixing vector of b from the manifold space to the European space
- m is the dimension of the semi-orthogonal de-mixing matrix
- H ⁇ ( ⁇ ) The sub-objective function of the first combination case, the range of ⁇ is 1 ⁇ i, the total number of j combinations;
- H i,j ( ⁇ ) represents the i,j sub-objective functions, and the order of i,j is in accordance with i
- the order to j is arranged in ascending order, and ⁇ B ( ⁇ ) represents a mapping function;
- Arbitrary sub-objective functions are non-negative, so when each component of the optimized function finds a minimum, then the global optimal solution is also obtained, that is, the entire matrix can be based on the search sub-objective function H i,j (b) obtaining the optimal solution;
- Step 4-2-6 determining a sub-objective optimization function in the first stage of the solution process
- H i,j ( ⁇ ) represents the i-th, j sub-objective functions
- b is the unmixed vector
- m is the dimension of the semi-orthogonal de-mixing matrix
- ⁇ B ( ⁇ ) represents a mapping function
- u is a de-mixed vector in which b is mapped from a manifold space to a European space
- u j represents a j-th unmixed vector in a European space
- E k,l indicates Empirical expectation
- E k represents the empirical expectation of z k
- E l represents the empirical expectation of z l
- ⁇ ( ⁇ ) represents the Gaussian kernel function
- Step 4-2-7 determining a sub-objective optimization function in the first stage of the solution process
- Step 4-3 Under the constraint condition, the first-stage sub-objective optimization function and the second-stage sub-objective optimization function are solved by the mapping Newton method.
- the specific flow chart is shown in Figure 6, as follows:
- Step 4-3-1 randomly assigning an initial iteration point u 1 and b 1 , b 1 is a unit vector;
- t max 10000
- Step 4-3-3 obtaining the optimization direction of u j in the first stage sub-objective function:
- Step 4-3-4 performing a one-dimensional search on u j according to the obtained optimization direction, and substituting the obtained b j(t+1) into the sub-object optimization function of the first stage;
- ⁇ jt arg min ⁇ H( ⁇ B (u jt + ⁇ jt d jt ))
- ⁇ jt represents the step size in the t-th optimization direction with respect to u j ;
- Steps 4-3-5 Determine whether
- 2 is close to 0 or whether the number of iterations reaches the maximum value. If yes, perform step 4-3-6. Otherwise, the number of iterations t t+ 1 and return to step 4-3-3;
- the processed semi-orthogonal de-mixing matrix B is substituted into the second-stage sub-objective optimization function;
- Step 4-3-8 judge Whether it approaches 0, and if so, obtains the optimal semi-orthogonal de-mixing matrix B; otherwise, obtains the semi-orthogonal de-mixing matrix B and the de-mixing vector b 2 ,..., b m at this time, and gives b 1 Search direction Further obtaining the updated b 1 and returning to step 4-3-2;
- the objective function constructed in the embodiment of the present invention is required. It should contain all the sub-objective functions related to the unmixing vector b 1 so that there will be a great probability of finding the optimal b 1 . Therefore, if you can give a good initial iteration value, it will make the calculation easier and the result more accurate. Therefore, according to this conclusion, in the embodiment of the present invention, the near-optimal solution b 1 obtained each time can be continuously optimized, thereby obtaining a set of more perfect global optimal solutions.
- step 4-3 if you want to obtain a more accurate semi-orthogonal de-mixing matrix, you need to traverse all the components; divide the sub-optimal targets into (m-1) groups respectively; then the first-stage sub-goals at this time
- the function group is:
- the r-th sub-objective function of the second stage is:
- Step 4-4 obtaining an estimated signal of the non-Gaussian independent element signal of the sample data according to the whitened sample data and the obtained optimal semi-orthogonal demixing matrix B;
- the sample z and the semi-orthogonal de-mixing matrix B are whitened by the fused magnesium furnace industrial process to obtain a potential independent meta-signal;
- D is the variance matrix of the high dimensional samples, An estimate of the whitened sample
- Step 5 Obtain a probability density of the T 2 and SPE statistics of the sample data according to the estimated signal and perform a density curve fitting to obtain a control upper limit value corresponding to the T 2 and the SPE under the confidence limit;
- the T 2 and SPE control limits of the new sample are determined by the respective T 2 and SPE values corresponding to the fitted probability density obtained by the standard modeling samples, and the set of control limits corresponds to 95% confidence of the density function. Limit, as shown in Figure 7 (a) and Figure (b);
- Step 6 Collect the surface video image of the molten material in the fused magnesium furnace in real time, and obtain the T 2 and SPE statistics of the real-time working condition data according to steps 4-4 and 5;
- Step 7 Determine whether the T 2 and SPE statistics of the real-time working condition data exceed the control limit under the confidence limit. If yes, the fused magnesium furnace fails and alarms; otherwise, returns to step 6.
- the present invention constructs a fitted curve to approximate the distribution of probability densities of all modeling data T 2 and SPE; as shown, the distribution of observed samples T 2 and SPE is represented by blue splines Observing the distribution of samples T 2 and SPE represented by splines, it is generally considered that the statistical indicators of normal data obey the non-Gaussian distribution, so that it can be inferred that the original modeling data or observation data used are consistent with non-Gaussian distribution. This is another reason why KPCA is not suitable for monitoring the failure of the furnace; in addition, in the figure (a) and (b) of Fig. 7, in the embodiment of the present invention, T 2 is inferred by calculation.
- the probability value of SPE When the probability value of SPE is 95%, it corresponds to T 2 of 20 and SPE of 2.8 respectively.
- the T 2 and SPE statistics of 89th sample point of real-time working condition data are 379.78 and 38.27, which are greater than the control limit. Therefore, fused
- the magnesium furnace fails and alarms; the present invention refers to the probability value of 95% as the fault control limit with a 95% confidence limit, and uses this control limit for fault monitoring as shown in Fig. 8 (a) and (b).
- the value of the test data T 2 and SPE can be monitored by the above-described confidence limits to diagnose whether a fault has occurred.
- the two-norm value of each sample is selected as the abscissa;
- the KPCA and KICA algorithms are applied to compare with the algorithm proposed by the present invention, and the monitoring results are as follows:
- the above variables for monitoring the process are extracted into 10-dimensional row vectors in the same manner as R, G, and B as variables, respectively. Then, the present invention uses abnormal data to test the fault monitoring performance of different algorithms.
- the statistical indicators T 2 and SPE after fault detection and diagnosis are shown in Fig. 9 (a) and (b), and Fig. 10 (a) and (b).
- the relative degree of dispersion is defined here (Relative Discrete Degree). , RDD) as a relative trend between the description data, which is defined as the ratio of each sample data to the sample mean, using RDD( ⁇ ) Indicates:
- n is the number of samples collected
- the present invention compares the monitoring results of FastKICA, KICA and KPCA in the same coordinate system; from Fig. 9 (a) and (b) Among them, the performance of the algorithm FastKICA and KICA is significantly better than KPCA;
- KPCA clearly fails to indicate the occurrence of the fault at the 89th sample point compared to FastKICA and KICA. This is due to the fact that KPCA ignores high-order statistics information, and KPCA is in a false positive state for KPCA's SPE monitoring for red values; therefore, for KPCA, using KPCA algorithm to monitor furnace failure is Very difficult; but notably, the other two algorithms are able to detect faults well throughout the process; however, compared to FastKICA, for the T 2 and SPE statistics for blue values, the KICA algorithm is at frame 95 to A false alarm occurred during the 115-frame monitoring process, as shown in Figure (a) and Figure (b) of Figure 9;
- KICA also cannot clearly distinguish the two fault conditions of the red value T 2 ; the reason why KICA provides false positive information is that the independence constraint function is based on negative entropy, which is approximate and is included in the traditional KICA algorithm.
- KPCA its dimensionality reduction of data makes some data lost, and it also makes the application of observation information incomplete;
- the present invention concludes that FastKICA has strong data adaptability and more accurate diagnostic capabilities. Based on the above analysis, it is possible to determine that FastKICA has many advantages for application to the field of fault diagnosis. Therefore, as shown in (a) and (b) of FIG. 10, the present invention only considers the contribution of the red value, the green value, and the blue value change to the occurrence of the fault under the FastKICA method; in FIG. 10, the figure (a) And Figure (b) is a comparison of the contribution of RGB values to the T 2 and SPE statistics at frames 89 and 118; respectively, according to the two figures, the green variable values are decisive for these two statistics; Therefore, in order to reduce the size of the observed data, it is possible to analyze only all the green variables.
- the improved FastKICA method can effectively improve the fused furnace furnace monitoring process.
- the FastKICA method can provide accurate and reliable alarm information, which can effectively reduce the unnecessary parking maintenance loss; therefore, the improved FastKICA method is based on Multi-view video information
- the production operation monitoring of fused magnesium furnace furnace failures will provide full support and assistance to the factory, whether it is accuracy, adaptability or economic budget for the factory.
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- 一种基于运行视频信息的一种电弧炉故障监测方法,其特征在于,包括以下步骤:步骤1、采集电熔镁炉炉内熔炼物料表层视频图像;步骤2、将采集的每帧图像划分为多个视点,获得每个视点的RGB颜色均值并按照行向量的方式进行存储,作为样本数据;步骤3、对存储的样本数据进行数据预处理,包括标准化处理和白化处理;步骤4、基于希尔伯特-施密特独立性准则,通过所改进的FastKICA算法,获得样本数据非线性独立元信号的估计信号,具体步骤如下:步骤4-1、基于希尔伯特-施密特独立性准则构建目标函数;步骤4-2、根据局部参数化处理,获得由流形空间向欧式空间转换的目标函数和约束条件,并对转换后的目标函数进行分解,获得第一阶段的子目标优化函数和第二阶段的子目标优化函数,具体如下:步骤4-2-1、在流形空间中最优解邻域内进行局部参数化处理;步骤4-2-2、根据局部参数化处理,获得流形空间向欧式空间转换的映射函数,进而获得约束条件;步骤4-2-3、根据映射函数,将目标函数由流形空间向欧式空间转换,获得转换后的目标函数;步骤4-2-4、根据映射函数,将约束条件由流形空间向欧式空间转换,获得转换后的约束条件;步骤4-2-5、将目标函数分解获得多个子目标函数;步骤4-2-6、确定求解过程第一阶段的子目标优化函数;具体公式如下:将i被j替换后,获得如下公式:其中,μB(·)表示映射函数,u为b由流形空间映射到欧式空间的解混向量,uj表示欧式空间下的第j个解混向量;为白化处理后的第k个样本与第l个样本之间的差值,Ek,l表示关于的经验期望值,Ek表示关于zk的经验期望值,El表示关于zl的经验期望值,φ(·)表示高斯核函数;步骤4-2-7、确定求解过程第二阶段的子目标优化函数;具体公式如下:步骤4-3、在约束条件下,采用映射牛顿法对获得的第一阶段的子目标优化函数和第二阶段的子目标优化函数进行求解,具体如下:步骤4-3-1、随机赋给一个初始迭代点u1和b1,b1为单位向量;步骤4-3-2、初始化i=1,t=0,且设置迭代次数t的最大值;步骤4-3-3、获得第一阶段子目标函数中uj的寻优方向:步骤4-3-4、按照所获的寻优方向对uj进行一维搜索,并将搜索获得的bj(t+1)代入第一阶段的子目标优化函数中;具体公式如下:其中,ωjt表示关于uj的第t次寻优方向上的步长;步骤4-3-5、判断||Hj(b)||2是否趋近于0或迭代次数是否到达最大值,若是,则执行步骤4-3-6,否则,迭代次数加1并返回执行步骤4-3-3;步骤4-3-6、判断i是否等于m,若是,则执行步骤4-3-7;否则,将i加1且t=0,并返回执行步骤4-3-3;步骤4-3-7、对半正交解混矩阵B=(b1,b2,...,bm)做施密特正交化处理,再对bi做归一化处理,将处理后的半正交解混矩阵B代入第二阶段的子目标优化函数中;步骤4-4、根据白化处理后的样本数据和求得的最优半正交解混矩阵B,获得样本数据非高斯独立元信号的估计信号;步骤5、根据估计信号,获得样本数据的T2和SPE统计量的概率密度并进行密度曲线拟合,获得置信限下对应T2和SPE的控制上限值;步骤6、实时采集电熔镁炉炉内熔炼物料表层视频图像,根据步骤4-4和步骤5获得实时工况数据的T2和SPE统计量;步骤7、判断实时工况数据的T2和SPE统计量是否超过置信限下的控制限,若是,则电熔镁炉发生故障,进行报警;否则,返回执行步骤6。
- 根据权利要求1所述的基于运行视频信息的一种电弧炉故障监测方法,其特征在于,步骤2所述的多个视点,具体包括:8个、10个和12个。
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