A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective
In the Engineering discipline, prognostics play an essential role in improving system safety,
reliability and enabling predictive maintenance decision-making. Due to the adoption of …
reliability and enabling predictive maintenance decision-making. Due to the adoption of …
Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice
E Zio - Reliability Engineering & System Safety, 2022 - Elsevier
We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …
building a new World in which the digital, physical and human dimensions are interrelated in …
[HTML][HTML] Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods
C Ferreira, G Gonçalves - Journal of Manufacturing Systems, 2022 - Elsevier
Abstract Approaches such as Cyber-Physical Systems (CPS), Internet of Things (IoT),
Internet of Services (IoS), and Data Analytics have built a new paradigm called Industry 4.0 …
Internet of Services (IoS), and Data Analytics have built a new paradigm called Industry 4.0 …
Fusing physics-based and deep learning models for prognostics
Physics-based and data-driven models for remaining useful lifetime (RUL) prediction
typically suffer from two major challenges that limit their applicability to complex real-world …
typically suffer from two major challenges that limit their applicability to complex real-world …
Artificial intelligence in prognostics and health management of engineering systems
S Ochella, M Shafiee, F Dinmohammadi - Engineering Applications of …, 2022 - Elsevier
Prognostics and health management (PHM) has become a crucial aspect of the
management of engineering systems and structures, where sensor hardware and decision …
management of engineering systems and structures, where sensor hardware and decision …
A novel entropy-based sparsity measure for prognosis of bearing defects and development of a sparsogram to select sensitive filtering band of an axial piston pump
Y Zhou, A Kumar, C Parkash, G Vashishtha, H Tang… - Measurement, 2022 - Elsevier
This study aims to establish a novel entropy-based sparsity measure for two main purposes:
first is for the prognosis of bearing defects, secondly it is employed to construct sparsogram …
first is for the prognosis of bearing defects, secondly it is employed to construct sparsogram …
Bayesian deep-learning for RUL prediction: An active learning perspective
Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …
[HTML][HTML] Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics
I de Pater, A Reijns, M Mitici - Reliability Engineering & System Safety, 2022 - Elsevier
The increasing availability of condition monitoring data for aircraft components has
incentivized the development of Remaining Useful Life (RUL) prognostics in the past years …
incentivized the development of Remaining Useful Life (RUL) prognostics in the past years …
Bearing remaining useful life prediction based on regression shapalet and graph neural network
Remaining useful life (RUL) prediction of bearing is essential to guarantee its safe
operation. In recent years, deep learning (DL)-based methods attract a lot of research …
operation. In recent years, deep learning (DL)-based methods attract a lot of research …