Industry 4.0 is collaborating directly for the technological revolution. Both machines and
managers are daily confronted with decision making involving a massive input of data and …

Recently, with the emergence of Industry 4.0 (I4. 0), smart systems, machine learning (ML)
within artificial intelligence (AI), predictive maintenance (PdM) approaches have been …

Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …

The use of a modern technological system requires a good engineering approach,
optimized operations, and proper maintenance in order to keep the system in an optimal …

The Industry 4.0 paradigm is boosting the relevance of predictive maintenance (PdM) for
manufacturing and production industries. PdM strongly relies on Internet of Things (IoT) …

Failure prediction is the task of forecasting whether a material system of interest will fail at a
specific point of time in the future. This task attains significance for strategies of industrial …

A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …

Performing deep neural network (DNN) inference in real time requires excessive network
resources, which poses a big challenge to the resource-limited industrial Internet of things …

In the context of Industry 4.0, smart factories use advanced sensing and data analytic
technologies to understand and monitor the manufacturing processes. To enhance …

The downtime of industrial machines, engines, or heavy equipment can lead to a direct loss
of revenue. Accurate prediction of such failures using sensor data can prevent or reduce the …