Journal Article10.1007/S10489-020-02128-X
Distributed dictionary learning for industrial process monitoring with big data
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TL;DR: A distributed dictionary learning algorithm based on the MapReduce framework can improve the effectiveness and robustness of process monitoring for industrial processes and solve the issue that the ability of calculation and information processing is limited at industrial sites.
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Abstract: With the development of sensor and communication technology, industrial systems have accumulated a large amount of data. This data has provided new perspectives and methods for industrial system analysis, monitoring and control, which is proven to be of great significance. However, with the collection and storage of industrial data in a 7 × 24 manner, the computing and information processing capabilities of edge controllers and computers at industrial sites face new challenges. Therefore, this paper proposes a distributed dictionary learning algorithm based on the MapReduce framework. The dictionary learning method can efficiently extract useful information from high-dimensional data for process monitoring. In addition, deploying the algorithm under the MapReduce framework can achieve the purpose of parallel distributed computing, which would solve the issue that the ability of calculation and information processing is limited at industrial sites. Based on extensive numerical experiments, the proposed method can improve the effectiveness and robustness of process monitoring for industrial processes.
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References
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
10K
A reliable data-based bandwidth selection method for kernel density estimation
Simon J. Sheather,M. C. Jones +1 more
TL;DR: The key to the success of the current procedure is the reintroduction of a non- stochastic term which was previously omitted together with use of the bandwidth to reduce bias in estimation without inflating variance.
2.8K
A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process
TL;DR: A comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM–FD) based on the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail.
1.2K
Multiscale PCA with application to multivariate statistical process monitoring
TL;DR: Multiscale Principal Component Analysis (MSPCA) as mentioned in this paper combines the ability of PCA to decorrelate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decorrelation of autocorrelated measurements.
864
Review on data-driven modeling and monitoring for plant-wide industrial processes
TL;DR: A systematic review on data-driven modeling and monitoring for plant-wide processes is presented in this paper, where the authors provide an overview of the state-of-the-art data processing and modeling procedures for the plantwide process monitoring.
595