Machinery health prognostics: A systematic review from data acquisition to RUL prediction

TL;DR: In this article, the authors provide an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015.

TL;DR: In this article , a federated framework called FedCAE is proposed for fault diagnosis, where each client is equipped with a convolutional autoencoder (CAE) trained by its corresponding local data and all CAEs are aggregated to a global CAE according to an adaptive weighted selection mechanism.

TL;DR: The results show that the proposed method can well explain the bearing degradation process from the graph perspective and will achieve superior performance to the existing methods.

TL;DR: In this article , the sum of weighted normalized square envelope was proposed as a generalized framework of some well-known sparsity measures including kurtosis, negative entropy, smoothness index, and Gini index.

TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.

TL;DR: The gaussian processes for machine learning is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can get it instantly.

TL;DR: A critical analysis questions the validation of crack-propagation laws using limited data, suggesting that agreement with a wide range of data from multiple specimens and varying crack-extension rates is necessary for validation, proposing a new empirical law for this purpose.

TL;DR: How the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms are demonstrated and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems are demonstrated.