Advances in fault detection techniques for automated manufacturing systems in industry 4.0

Fault detection and diagnosis are essential for maintaining the continuous operation of manufacturing systems. To achieve this, an innovative tool is required to immediately identify any faults in the production process and recommend the appropriate mechanisms to be adopted proactively to prevent future mishaps or accidents. This capability is critical for many industries to improve the efficiency and effectiveness of their production processes. Several methods can be used to detect trends or patterns in any given process and determine if the process variable is within normal limits. However, these techniques may only detect evident process characteristics or defects while leaving behind latent ones. This paper aims to review recent achievements and classics in fault diagnosis and detection, and suggest steps that can be taken to plan and implement this process. It will also explore emerging research streams, critical issues in the field, and strategies that can be applied to overcome these barriers. The paper outlines how the performance of fault detection and diagnostics can be improved in production processes and how a safer and fully efficient production environment can be promoted.

Authors

Institutions

Publication Details

Journal
Frontiers in Mechanical Engineering
Published
2025-04-17
DOI
https://doi.org/10.3389/fmech.2025.1564846
Citations
10
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
12.15

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advances in fault detection techniques for automated manufacturing systems in industry 4.0

Abba Abdulhamid Abubakar, Abul Fazal M. Arif, Yassmin Seid Ahmed, Fadi Al-Badour
10 citations
Frontiers in Mechanical Engineering
Fault Detection and Control Systems
12.15
article

Advances in fault detection techniques for automated manufacturing systems in industry 4.0

Abba Abdulhamid Abubakar, Abul Fazal M. Arif, Yassmin Seid Ahmed, Fadi Al-Badour
article en
10 citations

Abstract

Fault detection and diagnosis are essential for maintaining the continuous operation of manufacturing systems. To achieve this, an innovative tool is required to immediately identify any faults in the production process and recommend the appropriate mechanisms to be adopted proactively to prevent future mishaps or accidents. This capability is critical for many industries to improve the efficiency and effectiveness of their production processes. Several methods can be used to detect trends or patterns in any given process and determine if the process variable is within normal limits. However, these techniques may only detect evident process characteristics or defects while leaving behind latent ones. This paper aims to review recent achievements and classics in fault diagnosis and detection, and suggest steps that can be taken to plan and implement this process. It will also explore emerging research streams, critical issues in the field, and strategies that can be applied to overcome these barriers. The paper outlines how the performance of fault detection and diagnostics can be improved in production processes and how a safer and fully efficient production environment can be promoted.

Frontiers in Mechanical EngineeringVol. 11
Saudi Aramco (Saudi Arabia) (SA)
King Fahd University of Petroleum and Minerals
Openalex Percentile: Top 1%
Fault Detection and Control Systems
12.15
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Advances in fault detection techniques for automated manufacturing systems in industry 4.0 — Abba Abdulhamid Abubakar, Abul Fazal M. Arif, et al. · Frontiers in Mechanical Engineering (2025) | TGRS Research Map | TGRS