IoT based Maintenance and Asset Management PhD

Introduction

Applications are invited for a Ph.D. degree in the area of IoT based Maintenance and Asset Management. The research would focus on developing and leveraging AI optimised IoT sensory systems for feature engineering, prognostics, and data analytics in existing state-of-the-art as well as the next-generation systems, such as cyber physical (CPS), all-electric aircraft, smart grid, and autonomous vehicles.

Background

With the developments in IoT (Internet of Things), sensor data is streamed wirelessly from systems, sub-systems, or assets, to remote servers in the cloud. In this manner, all data relevant to health estimation (e.g., environmental conditions, maintenance, and operating data) are available for health monitoring and prognostic assessment. The sharing of information across assets and platforms enables the development of a complete operating picture and the flexibility to assess and manage new and even previously unknown maintenance and management risks. This, however, becomes intensively complex to handle and arrive at optimal asset maintenance and management decisions in a shorter period of time with higher accuracy, especially in mission-critical environments. Cognitive/AI optimised approach for IoT based maintenance and asset management holds the key to increasing efficiency of such complex IoT structures.

This research project aims at designing and developing Cognitive/AI optimised IoT sensory systems for feature engineering, prognostics, and data analytics that augment the efficiency of existing IoT based maintenance and asset management activities. Novel machine learning algorithms/deep neural networks may be, therefore, developed and validated to support mission-critical environments for their optimum maintenance and management.

Cranfield is a unique learning environment with world-class programmes, unrivaled facilities, and close links with business, industry, and governments, all combining to attract the best students and teaching staff from around the world. In 2014, 81% of research at Cranfield was rated as world-leading or internationally excellent in the Research Excellence Framework (REF).

Upon successful completion of the project, the potential candidate will be able to carry out research activities independently and more vigorously. This research will be formative for the potential candidate in building his/her analytical logic and algorithm craftsmanship. The understanding of the essence and application of IoT for maintenance and asset management would broaden the employability scope appreciably.