Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
AI-Driven Analytics and Intelligent Sensing for Industrial Systems
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

AI-Driven Analytics and Intelligent Sensing for Industrial Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1407

Special Issue Editors

Prof. Dr. Janis Terpenny

E-Mail Website
Guest Editor
1. Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA
2. Department of Mechanical Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: smart integrated systems and processes for manufactured products and systems; design education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thomas Kurfess

E-Mail Website
Guest Editor
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: next-generation secure digital manufacturing systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vittal Prabhu

E-Mail Website
Guest Editor
Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802, USA
Interests: sensing and modeling of processes for control and decision-making, especially in manufacturing and service industries
Special Issues, Collections and Topics in MDPI journals
Dr. Pongchalat Chaisiriroj

E-Mail Website
Guest Editor Assistant
1. Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA
2. Department of Mechanical Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: high-fidelity CFD and thermal-fluid simulation; model-based systems engineering; data-driven venture development

Special Issue Information

Dear Colleagues,                    

This Special Issue highlights the transformative role of AI-driven analytics and intelligent sensing technologies across the design, operation, and lifecycle management of modern industrial systems. We invite contributions that integrate heterogeneous sensor data, physics-informed models, and data-driven intelligence to enable more adaptive, efficient, and resilient engineering systems.

Research addressing new sensing concepts, architectures, and optimization methods that enhance modeling, control, and decision-making in complex industrial environments is especially welcome. Submissions may explore system design, real-time operation, process optimization, or autonomous decision support in domains such as manufacturing, energy, transportation, logistics, or supply chains.

We also encourage studies demonstrating how AI, machine learning, and digital twins can couple with sensor technologies to support performance prediction, fault diagnosis, scheduling, and system redesign—advancing both scientific understanding and industrial practice.

We look forward to your contributions that expand the frontier of intelligent, sensor-enabled industrial systems.

Prof. Dr. Janis Terpenny
Prof. Dr. Thomas Kurfess
Prof. Dr. Vittal Prabhu
Guest Editors

Dr. Pongchalat Chaisiriroj
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI-driven analytics
  • intelligent sensing
  • physics-informed learning
  • industrial systems
  • manufacturing
  • transportation
  • logistics
  • supply chains
  • digital twins
  • operational optimization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

28 pages, 5944 KB  
Article
3D Vision-Guided Adaptive 3D Ultrasonic Scanning for Robotic Arms: Nondestructive Testing of Aerospace Components
by Xiaolong Wei, Zijian Kang, Yizhen Yin, Jingtao Zhang, Caizhi Li, Yu Cai and Weifeng He
Sensors 2026, 26(7), 2129; https://doi.org/10.3390/s26072129 - 30 Mar 2026
Viewed by 614
Abstract
In view of the bottleneck problems existing in the 3D ultrasonic testing of aircraft composite laminated structures—including heavy reliance on manual operation, resulting in low detection efficiency, and the inability of traditional robotic arms to adapt to the testing of complex curved surfaces [...] Read more.
In view of the bottleneck problems existing in the 3D ultrasonic testing of aircraft composite laminated structures—including heavy reliance on manual operation, resulting in low detection efficiency, and the inability of traditional robotic arms to adapt to the testing of complex curved surfaces due to their dependence on predefined fixed trajectories—this paper proposes an automated 3D ultrasonic testing method based on 3D vision guidance for robotic arms. Firstly, the proposed Yolo-Mask model is adopted to realize the visual recognition and segmentation of composite component regions, after which the segmentation results are mapped to the depth map and further converted into the surface point cloud of the material. Secondly, on the basis of point cloud preprocessing and trajectory point extraction, the automatic planning of the robotic arm’s scanning trajectory is achieved, which drives the robotic arm to perform precise motion and to synchronously collect spatial pose and ultrasonic testing data. Finally, 3D reconstruction is completed via a fusion algorithm, and 3D images of the material’s internal structures are generated. Experimental verification shows that the proposed method achieves a Segm-mAP of 97.4%, a detection speed of 11.7 fps, and a 3D imaging error of less than 0.1 mm, thereby realizing fully automated detection throughout the entire process. This research provides an effective solution for the non-destructive testing of aircraft composite structures. Full article
(This article belongs to the Special Issue AI-Driven Analytics and Intelligent Sensing for Industrial Systems)
►▼ Show Figures

Figure 1

24 pages, 1252 KB  
Article
A Reinforcement Learning-Based Framework for Tariff-Aware Load Shifting in Energy-Intensive Manufacturing
by Jersson X. Leon-Medina, Mario Eduardo González Niño, Claudia Patricia Siachoque Celys, Bernardo Umbarila Suarez and Francesc Pozo
Sensors 2026, 26(6), 1858; https://doi.org/10.3390/s26061858 - 15 Mar 2026
Viewed by 444
Abstract
Optimizing energy-intensive manufacturing under time-varying electricity tariffs requires scheduling strategies that reduce cost without compromising operational feasibility. This study is grounded in readily available industrial sensing: we exclusively use time-series measurements of aggregated active power and energy at the main distribution board of [...] Read more.
Optimizing energy-intensive manufacturing under time-varying electricity tariffs requires scheduling strategies that reduce cost without compromising operational feasibility. This study is grounded in readily available industrial sensing: we exclusively use time-series measurements of aggregated active power and energy at the main distribution board of a quicklime production plant. We propose a tariff-aware load-shifting framework in which a Proximal Policy Optimization (PPO) reinforcement learning agent is trained in a custom Gymnasium environment to apply discrete consumption scaling actions constrained to 80–125% of a baseline profile during the operating shift (08:00–16:00), explicitly accounting for demand-charge exposure in the TOU peak window (13:00–15:00). The reward design combines instantaneous electricity cost with cumulative energy-tracking penalties and terms associated with operational constraints. Multi-day validation over N=30 working days shows consistent economic benefits, with a median total cost reduction on the order of 10% (narrow IQR) driven by reduced peak-window energy and demand peaks. However, the script-based binary compliance indicators (viol_energy, viol_prod_min) reveal deviations from the energy-balance criterion and occasional minimum-production shortfalls under the tolerances used, highlighting the cost–production trade-off and the need for stricter constraint handling for industrial deployment. In addition, we benchmark against dynamic programming (DP), an alternative RL policy (DQN), and a greedy heuristic (GREEDY), comparing cost; operational performance; and, when applicable, computational efficiency, which positions PPO as a competitive alternative among the considered methods. Overall, this work demonstrates how learning-based decision making can be coupled with real-world industrial sensing infrastructures, providing a data-driven tariff-aware scheduling layer for industrial energy management under practical constraints. Full article
(This article belongs to the Special Issue AI-Driven Analytics and Intelligent Sensing for Industrial Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop