飞风流机用用的 AI 动画故隔检测用为年生学士岗位
PhD Studentship: AI-Driven fault diagnosis for wind turbine generators
原帖优先：申请材料、截止时间与资格以原帖和学校官方说明为准。
AI 中文速览
- 研究内容
- 开发放动用为飞风流机用用的故隔检测系统
- 申请条件
- 2:1 期望在电子和电密工程、机械工程、物理中
- 待遇
- 原文未说明
- 申请方式
- 原文未说明
- 材料清单
- 原文未说明
由 @cf/meta/llama-3.3-70b-instruct-fp8-fast 生成，博士岗判定置信度 90%。
结构化信息
- 截止
- （Europe/London） 剩 53 天
- 学科
- 能源
- 合同类型
- 雇佣合同
- 原帖发布
- 本站收录
- 内容更新
- 导师
- Dr Abdi Jalebi Salman
- 来源
- jobs.ac.uk（英国博士项目及学术招聘） · 最近核对 2026-10-07
判定依据（原文摘录）
- is_phd
PhD Studentship
- bachelor_ok
The minimum entry requirement is 2:1
原文
Primary supervisor - Dr Abdi Jalebi Salman
Reliability is critical to the continued expansion of wind power, particularly for offshore installations, which already contribute around 10-15% of the UK’s electricity and are central to achieving net-zero targets and ensuring the resilience of national grid infrastructure. Preventive maintenance enabled by condition monitoring systems (CMSs) plays a key role in improving turbine reliability and availability while reducing operational costs and the levelised cost of energy. However, the drivetrain, comprising the gearbox, generator, and power electronics, remains a major source of failures, accounting for roughly one third of incidents and nearly half of maintenance costs in offshore turbines. Early and accurate fault detection in this subsystem is therefore essential.
Despite widespread deployment, existing CMS solutions are predominantly based on vibration measurements from accelerometers mounted on drivetrain components, and their performance remains limited. These systems often suffer from low fault detection rates, generate false alarms that reduce energy production, or fail to identify faults sufficiently early to avoid costly repairs. Moreover, they are largely ineffective at detecting electrical faults. The lack of commercially available CMS solutions based on electrical measurements is due to inherent complexity of interpreting electrical signatures of mechanical faults in real time. This creates a clear gap and opportunity for more advanced and integrated monitoring approaches.
This project aims to address the identified gap by developing hybrid fault diagnosis methods that combine analytical approaches based on drivetrain physical properties with AI-driven data analysis techniques to enhance the accuracy and effectiveness of fault detection and classification. The work will involve analytical studies, computer simulations, and finite element (FE) analysis, alongside experimental development and validation.
Entry requirements
The minimum entry requirement is 2:1 in Electrical and Electronics Engineering, Mechanical Engineering, Physics.
Mode of study
Full-time or part-time
Start date
1 February 2027
Additional Funding Information
This is a self-funded project (Students worldwide).