Recent advancements in industrial AI are increasingly focused on enhancing operational efficiency and safety through intelligent systems that leverage diverse data sources. The integration of retrieval-augmented generation techniques with time-series models is addressing challenges in predictive maintenance, particularly in scenarios with limited data and complex dynamics, as demonstrated in applications like pressure regulating valves. Meanwhile, the development of Model Context Protocol adapters is facilitating seamless communication between AI assistants and industrial protocols, enabling more effective workflow automation. Cyber-resilient digital twins are emerging as critical tools for detecting and mitigating cyber threats in industrial systems, offering real-time anomaly detection without costly shutdowns. Additionally, frameworks for defect detection in manufacturing, such as self-evolving systems for photovoltaic modules and comprehensive datasets for steel defect classification, are improving quality control. Collectively, these innovations are poised to solve pressing commercial challenges by enhancing predictive capabilities, operational resilience, and decision-making processes in industrial environments.
While RAG has greatly enhanced LLMs, extending this paradigm to Time-Series Foundation Models (TSFMs) remains a challenge. This is exemplified in the Predictive Maintenance of the Pressure Regulating ...
AI assistants can decompose multi-step workflows, but they do not natively speak industrial protocols such as Modbus, MQTT/Sparkplug B, or OPC UA, so this paper presents INDUSTRICONNECT, a prototype s...
Industrial Cyber-Physical Systems (ICPS) face growing threats from cyber-attacks that exploit sensor and control vulnerabilities. Digital Twin (DT) technology can detect anomalies via predictive model...
Steel surface defect detection is essential for ensuring product quality and reliability in modern manufacturing. Current methods often rely on basic image classification models trained on label-only ...
Reliable photovoltaic (PV) power generation requires timely detection of module defects that may reduce energy yield, accelerate degradation, and increase lifecycle operation and maintenance costs dur...
Intelligent fault diagnosis (IFD) has emerged as a powerful paradigm for ensuring the safety and reliability of industrial machinery. However, traditional IFD methods rely heavily on abundant labeled ...
Soft sensor modeling plays a crucial role in process monitoring. Causal feature selection can enhance the performance of soft sensor models in industrial applications. However, existing methods ignore...
Accurate fault detection in high-dimensional industrial environments remains a major challenge due to the inherent complexity, noise, and redundancy in sensor data. This paper introduces CLAIRE, i.e.,...
Industrial maintenance platforms contain rich but fragmented evidence, including free-text work orders, heterogeneous operational sensors or indicators, and structured failure knowledge. These sources...
Over the past decade, Artificial Intelligence has significantly advanced, mostly driven by large-scale neural approaches. However, in the chemical process industry, where safety is critical, these met...