AI-Enabled Acoustic Emission System for Industrial Condition Monitoring (Ref: AAE-EZ-2512)

About the Project

This research project focuses on the development of an advanced AI-driven system specialised in interpreting Acoustic Emission (AE) data, which aims to facilitate real-time monitoring of critical industrial components, such as electric vehicle batteries and hydrogen fuel cells. AE sensors, operational within a frequency range of several MHz, have the capacity to capture stress waves emanating from materials undergoing deformation. By deploying an array of AE sensors, the system excels in accurately identifying and quantifying micro-mechanical activities occurring within a structure.

The primary aim of this PhD project is to establish an intelligent monitoring system that leverages AE data analysis to enhance the operational reliability and performance of vital industrial systems. Utilising advanced AI algorithms, the system is engineered to deliver real-time insights into the operational health and integrity of components such as electric vehicle batteries and hydrogen fuel cells.

Expected outcomes of this research include real-time insights into the health and performance of monitored components, proactive identification and mitigation of potential issues, leading to enhanced operational reliability and extended longevity of critical industrial systems.

The successful PhD researcher will be an integral part of the Risk and Reliability and the Electric Vehicles and Advanced Propulsion research groups. They will have access to cutting-edge resources, including advanced battery and fuel cell test facilities, a dedicated AE system, and a specialised AI workstation. This project offers an excellent opportunity for a forward-thinking individual to actively engage in the innovative field of intelligent system monitoring through acoustic emissions.

Name of primary supervisor/CDT lead:
Dr Eve Zhang y.zhang@lboro.ac.uk
+441509227208
https://www.lboro.ac.uk/departments/aae/people/eve-zhang/

Entry requirements:

Students should have, or expect to achieve, at least a minimum of a 2:1 honours degree (or equivalent international qualification) in engineering, mathematics, computer science or a related discipline.

English language requirements:

Applicants must meet the minimum English language requirements. Further details are available on the international website (http://www.lboro.ac.uk/international/applicants/english/).

Bench fees required: No

Closing date of advert: 30th September 2026

Start date: January 2026, April 2026, July 2026

Full-time/part-time availability: Full-time 3 years, Part-time 6 years

Fee band: 2025/26 Band RB (UK £5,006, international £28,600)

How to apply:

• Stage 1: You are strongly advised to contact Dr Eve Zhang, in the first instance on Y.Zhang@lboro.ac.uk with a CV, academic transcripts, a reference letter, and confirmation of funding source. Informal discussions are also welcome.
• Stage 2: Following discussion with Dr Eve Zhang, applicants will be invited to make a formal application online. Under programme name, select ‘Aeronautical and Automotive Engineering’ and quote the advert reference number 'AAE-EZ-2512', in your application.

Project search terms: acoustics, artificial intelligence, automotive engineering, machine learning, structural engineering, acoustic emission, condition monitoring, industrial fault diagnosis

Email Address AACME: aacme.pgr@lboro.ac.uk