39 results about "Kernel partial least squares" patented technology

The invention relates to an industrial process fault diagnosis method based on a direction kernel partial least square. The method is characterized in that historical normal data of an input variable and an output variable of an industrial process is acquired, wherein a fault is easily generated in the industrial process; an operation based on the direction kernel partial least square is performed on the historical normal data; a control limit of Hotelling statistics of the historical normal data and a control limit of a squared prediction error of the historical normal data are calculated; sampling data of the input variable of the industrial process is collected and the operation based on the direction kernel partial least square is performed on the sampling data so as to acquire process monitoring statistics of the sampling data and a squared prediction error of the sampling data are obtained; when the process monitoring statistics control limit of the sampling data or the squared prediction error of the sampling data exceeds the control limit, the sampling data possesses one kind of fault; historical fault data of a known fault type is acquired; reconstruction based on the Hotelling statistics and reconstruction based on the squared prediction error are performed on the historical fault data of the known fault type and a fault type of the sampling data is determined.

The use of machine learning for pattern recognition in magnetocardiography (MCG) that measures magnetic fields emitted by the electrophysiological activity of the heart is disclosed herein. Direct kernel methods are used to separate abnormal MCG heart patterns from normal ones. For unsupervised learning, Direct Kernel based Self-Organizing Maps are introduced. For supervised learning Direct Kernel Partial Least Squares and (Direct) Kernel Ridge Regression are used. These results are then compared with classical Support Vector Machines and Kernel Partial Least Squares. The hyper-parameters for these methods are tuned on a validation subset of the training data before testing. Also investigated is the most effective pre-processing, using local, vertical, horizontal and two-dimensional (global) Mahanalobis scaling, wavelet transforms, and variable selection by filtering. The results, similar for all three methods, were encouraging, exceeding the quality of classification achieved by the trained experts. Thus, a device and associated method for classifying cardiography data is disclosed, comprising applying a kernel transform to sensed data acquired from sensors sensing electromagnetic heart activity, resulting in transformed data, prior to classifying the transformed data using machine learning.

The invention relates to an industrial process fault diagnosis method based on a direction kernel partial least square. The method is characterized in that historical normal data of an input variable and an output variable of an industrial process is acquired, wherein a fault is easily generated in the industrial process; an operation based on the direction kernel partial least square is performed on the historical normal data; a control limit of Hotelling statistics of the historical normal data and a control limit of a squared prediction error of the historical normal data are calculated; sampling data of the input variable of the industrial process is collected and the operation based on the direction kernel partial least square is performed on the sampling data so as to acquire process monitoring statistics of the sampling data and a squared prediction error of the sampling data are obtained; when the process monitoring statistics control limit of the sampling data or the squared prediction error of the sampling data exceeds the control limit, the sampling data possesses one kind of fault; historical fault data of a known fault type is acquired; reconstruction based on the Hotelling statistics and reconstruction based on the squared prediction error are performed on the historical fault data of the known fault type and a fault type of the sampling data is determined.

The invention discloses a simplifying soft measurement method for a primary variable in a production process integrating KPLS (Kernel Partial Least Squares) and FNN (False Nearest Neighbors). The method is characterized by comprising the following steps: determining n original auxiliary variables possibly related to the primary variable, collecting value data of the n original auxiliary variables and the primary variable, and forming a sample set; respectively calculating weighted values of the n original auxiliary variables by using a method of integrating the KPLS and the FNN; forming an original auxiliary variable sequence; modeling and determining the best auxiliary variable according to a minimum mean square error (MSE); and acquiring a simplifying soft measurement model. According to the method, an auxiliary variable set containing the auxiliary variables with the least number can be found for modeling the primary variable on the basis of the best modeling effect, so that the simplifying soft measurement on the primary variable can be realized.

The invention discloses a soft measurement method for load parameters of a mill. The method adopts various different signal decomposition technologies to decompose the original cylinder vibration signal and vibration-sound signals into a series of sub-signals based on different perspectives for multi-scale and unsteady characteristics of the cylinder vibration and vibration-sound signals. The selected sub-signal spectrum and the original signal spectrum are taken as multisource multi-scale information to construct a soft measurement model for the load parameters of the mill. A global optimization selective ensemble kernel partial least squares (GOSENKPLS) based on an adaptive genetic algorithm (AGA) and a branch-and-bound (BB) algorithm is adopted to carry out optimization selection on structural parameters and learning parameters of candidate sub-models and a selective ensemble model (SEN model), so that the effectively selective fusion of the multisource multi-scale signals is realized. According to the method, the accuracy for soft measurement of the load parameters of the mill can be improved.

The invention provides a penicillin fermentation process fault diagnosis method based on kernel partial least squares reconstruction, comprising: collecting offline historical normal data of penicillin fermentation process; Standardize and standardize variable offline historical normal data sets; use the improved nuclear partial least squares method to establish a fault monitoring model for the fermentation process of penicillin; monitor the faults of the penicillin fermentation process online; Correlation direction model for fermentation process faults; fault diagnosis for penicillin fermentation process. The invention divides the input space into: the pivot space directly related to the output, the pivot space irrelevant to the output and the residual space irrelevant to the output. Compared with the traditional method, it not only monitors the input variables related to the output, but also accurately monitors the variables related to the input.

The invention discloses a method for detecting operation faults of an electro-fused magnesia furnace based on public subspace separation. According to the method, correlation processing is performed on a process variable and a quality variable which are obtained during operation of the electro-fused magnesia furnace by means of kernel partial least squares regression, extraction of public subspace is performed by means of processed quality correlative process data through a local tangent space alignment algorithm, original data space is further divided into quality correlative public subspace and quality correlative special subspace, and a corresponding kernel principal component analysis monitoring model is established. By means of combined monitoring of the quality correlative public subspace shared by a plurality of operation modes of the electro-fused magnesia furnace and the quality correlative special subspace of each operation mode, the false alarm rate of faults is reduced when the operation modes are switched, and sensitivity of fault detection is increased.

The invention discloses a mill load parameter soft measuring method based on a virtual sample. The mill load parameter soft measuring method comprises the steps of acquiring a multi-dimension time domain sub-signal of mill cylinder vibration and vibration sound sample signal by means of ensemble empirical mode decomposition (EEMD) technology, and performing further processing for obtaining high-dimension spectral data with different time dimensions; then constructing a feasibility-based planning (FBP) model based on the high-dimension spectral data according to an improved selective integrated kernel partial least squares (IGASEN-KPLS) method, and generating a new virtual sample based on priori knowledge and an FBP model; then obtaining a mixed sampling model after mixing the new virtual sample with a true training sample, performing adaptive selection of a multiple-dimension spectral characteristic by means of a mutual information (MI) based characteristic selecting method, constructing a soft measuring model by means of the selected spectral characteristics and performing soft measurement.

The invention belongs to the technical field of convexity prediction, and in particular relates to a board convexity prediction method based on kernel partial least squares combined with a support vector machine, comprising the following steps: S1, using a high-precision monitoring device to collect on-site data; S2, collecting the collected Data preprocessing; S3, establishment of KPLS regression prediction model; S4, establishment of KPLS-SVM plate convexity prediction model. In the present invention, the data-driven algorithm is used as a mathematical tool, and abnormal values ​​can be removed from a large amount of collected on-site rolling process data. The present invention establishes a continuous strip rolling strip convexity prediction model based on the kernel-partial least squares method combined with the support vector machine, The prediction of the crown of the strip continuous rolling is realized, and the invention adopts the particle swarm optimization algorithm to optimize the built model, and further improves the prediction accuracy of the strip continuous rolling crown. The invention is used for the prediction of the crown of the continuous rolling strip.