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Industrial Big Data and Artificial Intelligence
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Topic Editors

Prof. Dr. Chun Yin
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Dr. Jiusi Zhang
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
College of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry University, Xianyang 712100, China
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Industrial Big Data and Artificial Intelligence

Abstract submission deadline
20 October 2026
Manuscript submission deadline
20 December 2026
Viewed by
527

Topic Information

Dear Colleagues,

Industrial platforms now generate vast, heterogeneous, and fast-evolving data—from high-frequency sensor streams and control logs to imagery, text, and graphs. This Topic seeks contributions that convert such data into trustworthy intelligence for analysis, optimization, and decision support across industrial settings. We welcome advances in scalable spatiotemporal learning; multimodal fusion and vision–time-series co-modeling; streaming/real-time and on-device analytics; robust anomaly detection under distribution shift; AI for quality assurance, process tuning, scheduling, and control; hybrid and physics-aware modeling; causal inference, uncertainty quantification, and governance for safe, auditable AI; privacy-preserving collaboration (e.g., federated or split learning); synthetic data and simulation-validated methods; and human-in-the-loop tools and visualization. Submissions may present algorithms, system architectures, datasets and benchmarks, reproducible case studies, surveys, or best-practice guidelines. Emphasizing reliability, safety, and cost-aware scalability, this Topic aims to bridge lab-grade methods and real-world deployment across manufacturing, energy and power, process industries, robotics, logistics, and smart infrastructure.

Prof. Dr. Chun Yin
Dr. Jiusi Zhang
Dr. Quan Qian
Dr. Tenglong Huang
Topic Editors

Keywords

  • industrial big data
  • multimodal sensor fusion
  • time-series analytics
  • anomaly detection
  • digital twins
  • physics-informed machine learning
  • edge AI
  • federated learning
  • smart manufacturing
  • energy systems
  • industrial fault diagnosis
  • data-centric AI
  • foundation models
  • generative AI

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Sensors
sensors 		3.5 8.2 2001 17.8 Days CHF 2600 Submit
Energies
energies 		3.2 7.3 2008 16.8 Days CHF 2600 Submit
Applied Sciences
applsci 		2.5 5.5 2011 16 Days CHF 2400 Submit
Electronics
electronics 		2.6 6.1 2012 16.4 Days CHF 2400 Submit
Technologies
technologies 		3.6 8.5 2013 19.1 Days CHF 1800 Submit
Data
data 		2.0 5.0 2016 25 Days CHF 1600 Submit
Modelling
modelling 		1.5 2.2 2020 24.9 Days CHF 1200 Submit
Mathematics
mathematics 		2.2 4.6 2013 17.3 Days CHF 2600 Submit

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Published Papers (1 paper)

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22 pages, 1844 KB  
Article
A Hybrid Coal Flow-Centric Predictive Model for Mining–Transportation Coordination Based on an LSTM–Transformer
by Yue Wu, Guoping Li, Longlong He, Jiangbin Zhao, Ruiyuan Zhang and Xiangang Cao
Mathematics 2026, 14(4), 634; https://doi.org/10.3390/math14040634 - 11 Feb 2026
Viewed by 104
Abstract
This paper addresses the issue of coordination failures in fully mechanized mining equipment under complex operating conditions, which can lead to operational abnormalities and safety hazards. We systematically analyze the dynamic coordination relationships within the equipment system across three dimensions: temporal, spatial, and [...] Read more.
This paper addresses the issue of coordination failures in fully mechanized mining equipment under complex operating conditions, which can lead to operational abnormalities and safety hazards. We systematically analyze the dynamic coordination relationships within the equipment system across three dimensions: temporal, spatial, and geometric. Centered on the coal flow, we establish a comprehensive “mining–transportation” coordination mathematical model covering the entire production process from the coal flow cut off by the shearer to the coal flow transported out by the conveyor. Building upon this foundation, a deep learning prediction method integrating long short-term memory (LSTM) and transformer architectures is proposed to construct an intelligent prediction model for the shearer traction speed. This model effectively captures temporal features and long-term dependencies within equipment operation data, enabling the prediction of critical operational parameters for fully mechanized mining systems. It significantly enhances the early identification and warning capabilities for equipment coordination failure states. The experimental results based on the operational data of fully mechanized mining systems show that the LSTM–Transformer model performs excellently in the prediction of traction speed. The mean square error (MSE) of prediction reached 0.041, the mean absolute error (MAE) was 0.122, and the coefficient of determination (R2) was 0.996, fully demonstrating the advantages of the model in terms of prediction accuracy and stability. This article provides a theoretical basis and technical support for the judgment of the operating status of coal mine working faces and the early warning of accident risks, which is of great significance for promoting the intelligent construction of coal mines. Full article
(This article belongs to the Topic Industrial Big Data and Artificial Intelligence)
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