Distributed dictionary learning for high-dimensional process monitoring

TL;DR: A novel multi-rate sampling data fusion method for fault diagnosis that can distinguish different conditions satisfactorily and has the best diagnostic accuracy among all comparison methods is proposed.

TL;DR: A jointly mode-matching and similarity-preserving dictionary learning (JMSDL) method, which updated the model by learning the data of new modes, so that the model can adaptively match the newly emerged modes.

TL;DR: In this paper , an analytical Partial Least Squares (PLS) method is proposed to overcome the shortcomings of PLS, such as uncertainty of the optimization solution, an imperfect optimization goal, and information impurity.

TL;DR: A sparsity and manifold regularized convolutional auto-encoders (SMRCAE) for fault detection of complex multivariate processes and the experimental results show the feasibility of SMRCAE in extracting representative features for process fault detection.

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.

TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.

TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.