The International Conference on AI for Industrial Monitoring, Control and Supervision (AIMCS 2026) will take place from 28 to 30 October 2026 in Paris, France.

AIMCS 2026 aims to bring together researchers, engineers, and industry experts from around the world to share their latest research findings and innovative ideas on AI-enabled supervision, monitoring, and control of industrial systems. The conference will feature keynote speeches, oral and poster presentations, and industrial sessions.

As industrial systems become increasingly complex, interconnected, and data-rich, artificial intelligence plays a pivotal role in enabling smart supervision, real-time condition monitoring, advanced diagnostics, and data-driven decision support for operations and maintenance. AIMCS 2026 will showcase state-of-the-art research, practical applications, and cross-disciplinary perspectives illustrating how AI technologies enhance reliability, safety, efficiency, and sustainability across modern industrial environments.

Topics of interest for submission include, but are not limited to, the following tracks:

Track 1: AI for Condition Monitoring & Diagnostics

Host Institution: SSHMC Laboratory, Dongguan University of Technology, China

Data-driven condition monitoring
Fault detection, isolation, and diagnosis
Anomaly detection in industrial systems
Sensor fusion and feature extraction
Health indicators and degradation modeling
Machine learning for predictive maintenance
Deep learning for signal and image-based monitoring
Real-time monitoring in IoT/IIoT environments

Track 2: AI for Supervision, Control & Optimization

Host Institution: Roberval Laboratory, University of Technology of Compiègne, France

Supervisory control and data acquisition (SCADA) with AI
Reinforcement learning for control
Model predictive control and AI-enhanced controllers
Operational optimization and decision support
Digital twins for supervisory control
Human–machine interaction and decision support systems
Adaptive and robust control using AI
Safety and reliability in AI-enabled control systems

Track 3: AI for Language, Speech & Human-Centric Systems

Host Institution: Faculty of Information Technology, Dai Nam University, Vietnam

Natural language processing for industrial and educational applications
Speech recognition, speaker identification, and audio analytics
Conversational AI and intelligent virtual assistants
Human–AI interaction and user-centered intelligent systems
Multimodal learning from text, speech, image, and sensor data
AI for language learning, assessment, and educational support
Sentiment analysis, discourse understanding, and social signal processing
Trustworthy, explainable, and ethical AI for human-centric applications

Track 4: Applications & Case Studies

Host Institution: University of Paris-Saclay, France

Manufacturing systems and production lines
Energy systems, power plants, and smart grids
Process industries (oil & gas, chemical, pharma)
Transportation, logistics, and autonomous systems
Robotics and collaborative robots (cobots)
Infrastructures and critical assets
Cross-industry benchmarks and comparative studies
Industrial success stories and lessons learned

Track 5: AI for Mechatronics and Automation

Host Institution: Faculty of Engineering and Technology, Thai Nguyen University of Information and Communication Technology (ICTU), Vietnam

Robotics and Intelligent Mechatronics
Autonomous System, Automated Guided Vehicles, UAVs
Industrial Automation and Intelligent Control Systems
Industrial IoT and AIoT
Cyber Security for IoT and AIoT
Additive Manufacturing


All submitted papers will undergo a rigorous peer-review process conducted by two to three independent reviewers from the conference technical committees. Accepted papers, upon successful registration and oral/poster presentation, will be included in the conference proceedings and submitted for indexing in major international databases.

IEEE Xplore
EI Compendex
Scopus (Elsevier)
Web of Science (Clarivate Analytics)
IET (The Institution of Engineering and Technology)
Crossref