Maintenance performance in the age of Industry 4.0: A bibliometric performance analysis and a systematic literature review

S Werbińska-Wojciechowska, K Winiarska - Sensors, 2023 - mdpi.com
Featured Application This article is focused on a literature review to provide a valuable
resource for understanding the latest developments in the Maintenance 4.0 approach. The …

Can a byte improve our bite? An analysis of digital twins in the food industry

E Henrichs, T Noack, AM Pinzon Piedrahita, MA Salem… - Sensors, 2021 - mdpi.com
The food industry faces many challenges, including the need to feed a growing population,
food loss and waste, and inefficient production systems. To cope with those challenges …

Edge intelligence for data handling and predictive maintenance in IIOT

T Hafeez, L Xu, G Mcardle - IEEE Access, 2021 - ieeexplore.ieee.org
The use of IoT has become pervasive and IoT devices are common in many domains.
Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments …

Knowledge graph based hard drive failure prediction

TR Chhetri, A Kurteva, JG Adigun, A Fensel - Sensors, 2022 - mdpi.com
The hard drive is one of the important components of a computing system, and its failure can
lead to both system failure and data loss. Therefore, the reliability of a hard drive is very …

A digital twin framework for prognostics and health management

M Toothman, B Braun, SJ Bury, J Moyne, DM Tilbury… - Computers in …, 2023 - Elsevier
Despite rapid advances in modeling and analysis technology, the manufacturing industry
has been slow to implement prognostic and health management strategies. A cause of this …

A machine learning-based workflow for automatic detection of anomalies in machine tools

M Züfle, F Moog, V Lesch, C Krupitzer, S Kounev - ISA transactions, 2022 - Elsevier
Despite the increased sensor-based data collection in Industry 4.0, the practical use of this
data is still in its infancy. In contrast, academic literature provides several approaches to …

Predictive maintenance in industry 4.0

GM Sang, L Xu, P De Vrieze, Y Bai, F Pan - Proceedings of the 10th …, 2020 - dl.acm.org
In the context of Industry 4.0, the manufacturing related processes have shifted from
conventional processes within one organization to collaborative processes cross different …

Digital food twins combining data science and food science: system model, applications, and challenges

C Krupitzer, T Noack, C Borsum - Processes, 2022 - mdpi.com
The production of food is highly complex due to the various chemo-physical and biological
processes that must be controlled for transforming ingredients into final products. Further …

Review on the evolution and impact of iot-driven predictive maintenance: assessing advancements, their role in enhancing system longevity, and sustainable …

JO Gidiagba, NK Nwaobia, PW Biu… - Computer Science & IT …, 2024 - fepbl.com
This study provides a comprehensive review of the evolution and impact of Internet of Things
(IoT)-driven predictive maintenance, focusing on advancements in technology, their role in …

Applied machine learning for IIOT and smart production—Methods to improve production quality, safety and sustainability

A Frankó, G Hollósi, D Ficzere, P Varga - Sensors, 2022 - mdpi.com
Industrial IoT (IIoT) has revolutionized production by making data available to stakeholders
at many levels much faster, with much greater granularity than ever before. When it comes to …