The paper presents a two‑stage multivariate monitoring framework for high‑dimensional, temporally dependent data from end‑of‑line testing in automotive manufacturing. Stage one cleans and aligns the data, while stage two applies nonlinear dimensionality reduction and a control‑chart‑based anomaly detection procedure that can operate in unsupervised or supervised modes. Applied to Ford Motor Company production data, the framework outperforms the existing model, raising accuracy from 0.50 to 0.625, recall from 0.30 to 1.00, and F1 score from 0.429 to 0.769.
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.
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...
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.
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
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.
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...
arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.
arXiv:2606. 05274v1 Announce Type: new Abstract: Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation.
arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based f...
arXiv:2606. 01691v1 Announce Type: cross Abstract: Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents.
arXiv:2606. 03112v1 Announce Type: cross Abstract: With the increasing scale and number of wind farms, wind turbines' daily operation and maintenance costs are increasing.
arXiv:2608. 13937v1 Announce Type: cross Abstract: Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management.