arXiv:2607. 00720v1 Announce Type: cross Abstract: Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge.
By Seung Hun Han, Hyeongwon Kang, Jinwoo Park, Pilsung Kang
arXiv:2604.17998v2 Announce Type: replace
Abstract: Anomaly detection in multivariate time series is a central challenge in industrial monitoring, as failures frequently arise from complex temporal d...
By Pooyan Khosravinia, Jo\~ao Gama, Bruno Veloso
arXiv:2607. 15799v1 Announce Type: cross Abstract: Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages.
By Jaeyeong Lee, Taeseong Yoon, Wonmo Koo, Heeyoung Kim
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
By Liang Cao, Weide Liu, Yan Qin, Jun Cheng, Weisi Lin, Bhushan Gopaluni
The paper introduces a framework that uses Large Language Models (LLMs) to generate synthetic time‑series data for manufacturing processes. By fine‑tuning pre‑trained LLMs on manufacturing instructions and applying Retrieval Augmented Generation (RAG), the method enhances data diversity and realism. Evaluation against traditional models such as ARIMA and LSTMs shows that the LLM‑driven approach produces higher‑quality synthetic data, better capturing temporal dependencies and improving downstream anomaly detection performance.
By Mantek Singh, Jeshwanth Challagundla, Prateek Karnal, Gagan Ganapathy, Vineet Shah, Ridam Arora
arXiv:2608.30804v1 Announce Type: new
Abstract: Monitoring the health of heterogeneous industrial robot fleets is severely challenged by the multi-modal nature of their operational cycles and a persi...
By Martin Bonsergent-Brachet, Jesse Read, Dany Abboud