# Siyuan Chen > Siyuan Chen (陈思远) is a PhD student in Production Systems at Chalmers University of Technology, Gothenburg, Sweden. Research: industrial AI, digital twins, prescriptive maintenance, reinforcement learning and robot programming. The publication pages distinguish journal articles, conference papers, preprints, a licentiate thesis and an award-winning essay. Original English abstracts are reproduced where available. Overview text is explanatory, not an additional publication. Cite the original work using its DOI and publication metadata; an arXiv DOI identifies the preprint, not published conference proceedings. ## Profile - [Personal website](https://siyuanchen2020.github.io/): Biography, education, research projects, experience and contact. - [ORCID](https://orcid.org/0000-0001-9399-3790): Researcher identity. - [Chalmers profile](https://www.chalmers.se/en/persons/siyuanc/): Institutional profile. ## Research library - [Publications](https://siyuanchen2020.github.io/publications/): English research library with links to individual publication pages. - [Chinese research library](https://siyuanchen2020.github.io/publications/zh/): Chinese explanations and original English abstracts. - [Swedish research library](https://siyuanchen2020.github.io/publications/sv/): Swedish explanations and original English abstracts. - [Bibliography](https://siyuanchen2020.github.io/publications/citations.bib): BibTeX for all listed works. ## Publication notes - [AI-enhanced digital twins in maintenance: Systematic review, industrial challenges, and bridging research–practice gaps](https://siyuanchen2020.github.io/publications/ai-digital-twins-maintenance-review/index.md): This review combines published research with industry interviews to examine AI-enhanced digital twins in maintenance. It identifies gaps between research and industrial practice and proposes a five-layer implementation framework. - [Comparison of Unsupervised Image Anomaly Detection Models for Sheet Metal Glue Lines](https://siyuanchen2020.github.io/publications/unsupervised-anomaly-detection-glue-lines/index.md): False alarms in glue-line inspection lead to unnecessary interventions and downtime. We compare 17 unsupervised models and 28 backbones on industrial images, examining detection accuracy, robustness and inference time with limited training data. - [A Transformer Approach for Remaining Useful Life Prediction and Fault Diagnosis of Mechanical Equipment](https://siyuanchen2020.github.io/publications/transformer-rul-fault-diagnosis/index.md): Equipment lifetime prediction and fault diagnosis are often modeled separately. This Transformer shares a sensor-data encoder across both tasks and is evaluated on data from 100 industrial machines. - [Retrieval-grounded robot program generation and simulation-based correction via Model Context Protocol](https://siyuanchen2020.github.io/publications/robot-program-generation-mcp/index.md): This workflow connects retrieval-grounded ABB RAPID generation to RobotStudio through MCP. Simulation feedback reveals execution errors that static code checks miss and supports iterative correction of robot programs. - [Enhancing Digital Twins with Deep Reinforcement Learning: A Use Case in Maintenance Prioritization](https://siyuanchen2020.github.io/publications/digital-twins-deep-reinforcement-learning/index.md): Maintenance priorities change with the state of a production system. This study combines a digital twin with proximal policy optimization to prioritize maintenance tasks in a lab-scale drone factory. - [LLM-enhanced multi-agent reinforcement learning for human-centric prescriptive maintenance in manufacturing](https://siyuanchen2020.github.io/publications/llm-multi-agent-prescriptive-maintenance/index.md): Maintenance logs contain diagnostic knowledge that numerical-only agents cannot use directly. This preprint combines local LLMs with hierarchical multi-agent reinforcement learning and a production-cost reward in a lab-scale drone assembly study. - [Towards Prescriptive Maintenance Using Digital Twins and Artificial Intelligence](https://siyuanchen2020.github.io/publications/prescriptive-maintenance-licentiate-thesis/index.md): This licentiate thesis examines the transition from failure prediction to actionable maintenance decisions. It brings together five studies on industrial requirements, digital twins, reinforcement learning and anomaly detection. - [Understanding Stakeholder Requirements for Digital Twins In Manufacturing Maintenance](https://siyuanchen2020.github.io/publications/stakeholder-requirements-digital-twins/index.md): Interviews and a workshop identify what industrial stakeholders need from a maintenance digital twin. The study maps implementation requirements, decision-support needs and barriers to adoption. - [Developing RAGs for robot code generation](https://siyuanchen2020.github.io/publications/rag-robot-code-generation/index.md): Robot code generation needs reliable, domain-specific engineering knowledge. This paper explores RAG architectures that ground language models in controlled sources while retaining human oversight and traceability. - [Data-Driven Smart Maintenance Decision Analysis: A Drone Factory Demonstrator Combining Digital Twins and Adapted AHP](https://siyuanchen2020.github.io/publications/digital-twin-maintenance-decision-analysis/index.md): A drone-factory digital twin supports maintenance comparisons without interrupting real production. The demonstrator evaluates what-if scenarios and combines performance indicators with structured decision analysis. - [Toward Cognitive Augmentation: Personalization and Standardization of Humanoid Social Robot Instruction](https://siyuanchen2020.github.io/publications/humanoid-robot-personalized-instruction/index.md): Adaptive robot instruction can change the demands placed on industrial trainees. This study compares standard and personalized humanoid-robot instructions, examining mental workload and task performance. - [Synthetic simulated environment for discrete manufacturing systems: A demonstrator through a computational modeling approach](https://siyuanchen2020.github.io/publications/synthetic-simulation-manufacturing/index.md): This PyTorch-based simulation generates synthetic data for discrete manufacturing systems. Timed Petri nets and a directed graph represent production flow, creating an expandable environment for manufacturing AI experiments. - [Leveraging generative AI for smart maintenance: navigating technological tipping points in manufacturing](https://siyuanchen2020.github.io/publications/generative-ai-smart-maintenance-essay/index.md): This award-winning essay examines opportunities and barriers for generative AI in industrial maintenance. It discusses synthetic data, workforce learning, implementation costs, data quality and governance. ## Research leadership and computing allocations These are three NAISS Small Compute resource allocations, with Siyuan Chen as Principal Investigator. The first two are an initial IMAP allocation and its renewal. They are computing-resource support, with no cash funding amount stated. Monthly allocations may vary over time; the current AIMOps allocation was verified on 2026-11-13. - NAISS 2025/22-1678 — Advanced AI Architectures for Integrated and Enhanced Manufacturing Operations (AIMOps). PI: Siyuan Chen. 2025-12-08 to 2026-12-01. Arrhenius · 150 GPU-h/month · 750 GiB storage. - NAISS 2024/22-1180 — Integrated Manufacturing Analytics Platform. PI: Siyuan Chen. 2024-10-01 to 2025-10-01. Alvis / C3SE · 250 GPU-h/month · 500 GiB storage. Renewal of NAISS 2023/22-245. - NAISS 2023/22-245 — Integrated Manufacturing Analytics Platform. PI: Siyuan Chen. 2023-09-18 to 2024-10-01. Alvis / C3SE · up to 500 GPU-h/month · 500 GiB storage. - [Research leadership](https://siyuanchen2020.github.io/#naiss): Project periods, roles and resources. - [Structured computing records](https://siyuanchen2020.github.io/data/computing-allocations.json): Dates, sources and allocation qualifiers. ## Optional - [Structured publication records](https://siyuanchen2020.github.io/data/publications.json): Bibliography, source URLs, abstracts and localized explanations. - [Sitemap](https://siyuanchen2020.github.io/sitemap.xml): Indexable HTML pages.