Cloud-enabled prognosis for manufacturing

TL;DR: In this paper, the authors propose an empirically grounded model and its implementation to assess the Industry 4.0 maturity of industrial enterprises in the domain of discrete manufacturing by including organizational aspects.

TL;DR: A comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”, including computational methods based on deep learning that aim to improve system performance in manufacturing.

TL;DR: In order to improve the accuracy and efficiency of PHM, digital twin (DT), an emerging technology to achieve physical–virtual convergence, is proposed for complex equipment.

TL;DR: A review of both the tangible and intangible elements of manufacturing systems and a state-of-the-art survey of published work can be found in this article, where the evolution of research in manufacturing starting from past and current trends to future developments is discussed.

TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.

TL;DR: This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.

TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.

TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.