Data-driven fault detection and diagnosis in industrial process systems: A systematic review and perspective
Advances in fault diagnosis are explored via collective investigation and analysis...
Shuaiyu Zhao
I am a Ph.D. student in Chemical Engineering at Texas A&M University (TAMU), working with Prof. Qingsheng Wang and Prof. Faisal Khan.
My research interests focus on artificial intelligence application in chemical process safety, including large language models, deep learning, and generative models.
Feel free to reach out if you would like to talk about life, research, or career. I am open to conversations, discussions, and potential collaborations.
News
- January 2026 We conducted a PRISMA-based study to evaluate future perspectives and challenges for industrial fault diagnosis, with the findings published in Reliability Engineering & System Safety!
- January 2025 New semester 25 Spring beginning!
- December 2024 The dynamic control research collaborated with Mingfeng Yu accepted by Process Safety & Environmental Protection.
- September 2024 We developed a cutting-edge industrial deep learning framework and published our results in Reliability Engineering & System Safety.
Publications
Fine-tuned large language models for natech analytics: Evidence from two decades of Texas chemical emission incidents
Natural-hazard-triggered technological accidents (Natechs) pose compound risks to the...
A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis
Intelligent fault diagnostic techniques are crucial for ensuring the long-term...
PPO-based resilient control framework for safer operation of industrial exothermic CSTR
The safety of chemical reactor is one of the major problems in process safety...
Prevention of bund overtopping after a catastrophic tank failure accident: Effects of bund design, liquids and scale-up
Hazardous materials are usually stored in large tanks surrounded by bunds...