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Intelligent Maintenance with Artificial Intelligence and Industrial IoT PhD


School of Aerospace, Transport and Manufacturing (SATM)

Mr Samir Khan Applications accepted all year round Self-Funded PhD Students Only

About the Project

Cranfield University is seeking a top-class candidate to undertake research leading to PhD award. The research aims to improve maintenance practices and optimisation in resource-limited systems. The PhD candidate is expected to develop state of the art technology using existing AI knowledge but with a focus on real-time implementation.

Background

This exciting PhD focuses on cutting-edge maintenance management technology. 

Recent Artificial Intelligent (AI) advancements have led to a proliferation in the number of sensing, processing and communications tasks. These developments warrant increasingly large amounts of big data. Although this can be readily available via various Industrial Internet of Things (IIoT) sources, it application is not straightforward. This research will first study the evolution of reliability modelling technology and then develop the next generation maintenance technology. The PhD candidate will focus on AI and IIoT based intelligent maintenance to investigate (1) recent machine learning algorithms including probabilistic reliability modelling with deep learning, (2) real-time data collection, transfer, and storage through wireless smart sensors, (3) human-machine interface technologies with visual, audio and olfactory capabilities (4) integration, validation and deployment of machine learning models, (5) field testing with better decision-making. 

This project will keep the focus on real-time processing. It will focus on criteria such as learning speed, stability as well as storage and computation requirements of a peripheral device and a processing system. Due to their different operating behaviours and performance considerations, these two types of devices require different designs of learning. Emphasis will be placed on optimisation techniques to speed up the convergence, to better maintain the required maintenance performance in a dynamic system with large variations and to test the algorithm in real-world conditions to validate the simulation results. 

Aim

The aim of the PhD is to develop the next generation of maintenance technology. It focuses on the following research questions:

In particular, the research questions include:

1) How to implement the AI algorithms developed in other fields to real-time series data for industrial maintenance?

2) How to record data from wireless/remote sources that are not continuously connected to the network?

(3) How to store and process data seamlessly?

(4) How to validate models for on-field deployment?

(5) How to optimise decision-making?

 

The project encompasses lots of concepts and technologies; Internet of Things (IoT), big data analytics and machine learning. Consideration should be given to these concepts and the adaptability and scalability for future technology. The developed technology will facilitate and optimise existing systems in different areas such as a) IoT, b) communication buses, and c) real-time control. Each of these areas has quite distinct skills set and behaviours associated with them. 

Objectives

1. Determine AI algorithm implementation complexity requirements;

2. Develop a robustness evaluation strategy;

3. Addressing the technological barriers, i.e., lack of computation, knowledge representation, network performance, interfacing and communication;

4. Validation of the concept in a real-world setting.

At Cranfield, the candidate will be based at the Centre for Digital Engineering and Manufacturing, which hosts cutting-edge simulation and visualisation facilities. The student will have access to high-end computers for simulating the complex nature of maintenance. There will be relevant visits to various organisations throughout the PhD to develop and demonstrate the research. 

Entry requirements

Candidates should have a minimum of an upper second (2.1) honours degree (or equivalent) preferably in Computer Science/ Mechanical Engineering / Industrial Engineering / Mathematics / Operations Research but candidates in other degrees related to Engineering or related quantitative fields would be considered. Candidates with an MSc degree in these disciplines will be desirable.


Funding Notes

This is a self-funded PhD; open to UK, EU and International applicants.

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