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Accurate measurement of the mill level is a key factor to improve the ball mill's productive efficiency, safety and economy. Aiming at solving the critical problem of the mill level soft sensor, feature extraction of the processing parameters, a novel method based on Deep Belief Network (DBN) is proposed. DBN is one of the deep learning methods, which focuses on learning deep hierarchical models of...
Online fault diagnosis has been a crucial task for industrial processes. Reconstruction-based fault diagnosis has been drawing special attentions as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific fault subspace that can well eliminate alarming signals from a bunch of alternatives that have been prepared based on historical fault data...
In order to solve the problem that the excessive dimensions of feature vector will lead to probabilistic neural network (PNN) 's structure becoming complicated and recognition rate slowing down when we take the wavelet energy spectrum of the rolling bearing vibration signal as the feature vector, a novel approach based on wavelet energy spectrum, principal component analysis (PCA) and probabilistic...
For the fault diagnosis problems of the underwater vehicle sensor systems, the solution is combined by the Principal Component Analysis (PCA) and Self-Organizing Fuzzy Cerebellar Model Articulation Controller (SOFCMAC). The signal prediction model approach based on PCA and SOFCMAC is proposed in this paper. According to the history data, it can predict the signal data in the future time using the...
At present, the ratio of unemployment was rising slightly in China, and the employment pressure was bigger and bigger than before. To make a scientific explanation for unemployment is the key to solve the focus problem of employment. The study listed seven factors that affect unemployment of labor force by the method of factor analysis and principal component analysis. Seven factors were defined as...
Automatic gender classification of an individual can be very useful in video-based surveillance systems and human-computer interaction systems. In this paper, we propose an approach to integrate information from multi-view gait at the feature level. First, gait energy images (GEI) are constructed from the video streams for different viewpoints. Then, the feature fusion is performed by putting GEI...
Modeling of distributed parameter systems (DPSs) is difficult because of their infinite dimensional time-space nature. For a class of nonlinear distributed parameter systems described by parabolic partial differential equations (PDEs), Kernel Principal Component Analysis (KPCA) method is utilized to extract the nonlinear basis functions in dominant space, and the time-space decomposition is carried...
Average neighborhood margin maximization (ANMM) is a feature extraction method to make homogeneous points collect as near as possible and heterogeneous points disperse as far away as possible. To enhance the anti-noise ability of ANMM, correntropy based average neighborhood margin maximization (CANMM) is proposed in this paper. This method utilizes correntropy to substitute the Euclidean distance...
In this paper multilinear mean component analysis (MMCA) is introduced as a new algorithm for gait recognition. Compared with traditional PCA and MPCA, the new method MMCA is based on dimensionality reduction by preserving the squared length, and implicitly also the direction of the mean vector of the each mode's original input. The solution is not necessarily corresponding to the top eigenvalues...
The handling of twist-locks has been a heavy burden for the container industry. There have been many efforts in developing automated twist-lock handling solutions. To address this challenge, we are developing a customized mobile manipulator for twist-lock grasping. The technical challenge is 3D irregular object recognition in unstructured port environment. In this paper, we use PCA and KPCA to do...
Since the changes of raw material properties, external environment and other conditions, during practical industrial processes, multiple stable operation modes may arise, and between any two stable modes may undergo slowly changing transition modes. The existing multimode process monitoring methods haven't monitored dynamic characteristics of the transition modes efficiently. This paper adopts differential...
The traditional principal component analysis (PCA) method divides the variable space into two parts: Principal subspace and Residual subspace by orthogonal decomposition. It has been widely used in fault detection process, but it is difficult to interpret the modes of the fault because of model compound effect, and the ability to distinguish the pattern which is no significant is affected. In industrial...
Fault detection for multi-mode process are becoming a hotspot. By effective mode division and accurate online identification, a multi-mode hybrid data set can be transformed into multiple single-mode data sets, then the traditional PCA-based approach can still be adopt to process monitoring. However, in the treatment of “fast response” multi-mode procedure, the above idea does not seem to apply, which...
In order to evaluate sub-health state, a new method based on Principal Component Analysis (PCA) was discussed in this paper. Simultaneous multi-information acquisition of ECG signals and pulse images was achieved by using self-designed simultaneous acquisition system. According to the change of grid area in each frame, pulse beat waves were obtained from pulse image. Then the extraction and PCA of...
Aiming to the problem of weak primary user signal detection rate in low signal-to-noise ratio environments, we propose a novel spectrum sensing method based on the principal component analysis (PCA) and random forest (RF). From the received radio signal, a set of cyclic spectrum features are first calculated, and the PCA is applied to extract the most discriminate feature vector for classification...
A fault detection method based on empirical likelihood is presented to deal with the incipient fault in process and equipment. The problem of incipient fault detection is studied in the view of distribution test by a moving window approach. The original fault detection problem is transformed into distribution test, and a set of empirical likelihood values is computed. Based on the likelihood values,...
As one of the most widely used parts and components of rotating machineries, fault detection of rolling bearing is of great significance. In this paper, a new method named EMD-DPCA is proposed based on Empirical Mode Decomposition (EMD) and Dynamic Principal Component Analysis (DPCA). Firstly, the vibration signals are decomposed by EMD and Intrinsic Mode Functions (IMFs) are achieved. Then DPCA model...
In this paper, we propose an online object tracking algorithm, which combines incremental subspace learning with sparse representation. In the particle filter framework, we take Gaussian random sampling and use sub-sampling to filter the samples. We update the state of the training set through incremental PCA algorithm, then construct sparse subspace model using the eigenvectors of the training set...
In this paper, a PLS-based fault-relevant reconstruction method is proposed for fault detection and identification. The PLS-based proposed method finds a set of latent variables to project the training data onto a process space to analyze the problem of reconstruction and is used to find the directions which can best characterize the fault effects relevant to normal status. According to the fault...
Batch processes are often characterized by uneven-length durations and multistage characteristics. To reflect the inherent stage nature to improve the performances of process monitoring, simultaneously considering dynamic characteristics within the process variables for some complicated cases, stage-based variable sampling period multi-model dynamic principal component analysis (VSP-MDPCA) modeling...
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