A review on empirical mode decomposition in fault diagnosis of rotating machinery

Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.

Review of Recent Research on Data-Based Process Monitoring

The graph construction procedure essentially determines the potentials of those graph-oriented learning algorithms for image analysis. In this paper, we propose a process to build the so-called directed ?1-graph, in which the vertices involve all the samples and the ingoing edge weights to each vertex describe its ?1-norm driven reconstruction from the remaining samples and the noise. Then, a series of new algorithms for various machine learning tasks, e.g., data clustering, subspace learning, and semi-supervised learning, are derived upon the ?1-graphs. Compared with the conventional k -nearest-neighbor graph and ?-ball graph, the ?1-graph possesses the advantages: (1) greater robustness to data noise, (2) automatic sparsity, and (3) adaptive neighborhood for individual datum. Extensive experiments on three real-world datasets show the consistent superiority of ?1-graph over those classic graphs in data clustering, subspace learning, and semi-supervised learning tasks.

Learning With $\ell ^{1}$ -Graph for Image Analysis

Spectral analysis-based dimensionality reduction algorithms are important and have been popularly applied in data mining and computer vision applications. To date many algorithms have been developed, e.g., principal component analysis, locally linear embedding, Laplacian eigenmaps, and local tangent space alignment. All of these algorithms have been designed intuitively and pragmatically, i.e., on the basis of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide a systematic framework for understanding the common properties and intrinsic difference in different algorithms. In this paper, we propose such a framework, named "patch alignment,rdquo which consists of two stages: part optimization and whole alignment. The framework reveals that (1) algorithms are intrinsically different in the patch optimization stage and (2) all algorithms share an almost identical whole alignment stage. As an application of this framework, we develop a new dimensionality reduction algorithm, termed discriminative locality alignment (DLA), by imposing discriminative information in the part optimization stage. DLA can (1) attack the distribution nonlinearity of measurements; (2) preserve the discriminative ability; and (3) avoid the small-sample-size problem. Thorough empirical studies demonstrate the effectiveness of DLA compared with representative dimensionality reduction algorithms.

/pdf/patch-alignment-for-dimensionality-reduction-4vcll07w7y.pdf

Patch Alignment for Dimensionality Reduction

Recovery of carboxylic acids produced by fermentation

https://nip.sjtu.edu.cn/pdf/Spike%20sorting%20based%20on%20automatic%20template%20reconstruction%20with%20a%20partial%20solution.pdf

Jingqi Yuan

Papers

Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem

Multivariate statistical process control based on multiway locality preserving projections

Macrokinetic model for methylotrophic Pichia pastoris based on stoichiometric balance.

Statistical monitoring of fed-batch process using dynamic multiway neighborhood preserving embedding

Multi-model predictive control method for nuclear steam generator water level