成人直播

Predictive Maintenance (PdM) is one of the maintenance strategies that has attracted the attention of businesses in this Industry 4.0 era. Even though the PdM strategy has been introduced more than two decades ago, its adoption and implementation in the industry have been rapidly accelerated in the last few years along with the booming of digital technologies.

One of the key elements for a successful implementation of the PdM strategy is the usage of technologies that can effectively convert relevant measurement data into actionable information, such as the health condition, and/or the remaining useful life of critical assets. Currently, Artificial Intelligence (AI) based big data analytics tools have been massively developed in the research community to address this challenge. These AI-based analytics tools are data-driven and black-box, so the interpretation of how predictions are made by such techniques is still an open problem. Although these techniques have been successfully demonstrated in laboratory environments, however, the barrier to deploying such pure-data-driven black-box techniques in the industry is still high. One of the main reasons is that the performance of such techniques highly depends on a large amount of good-quality data. Unfortunately, the availability of good-quality data is typically limited for high-value critical assets.

This PhD project will focus on developing, evaluating, and demonstrating physics-informed machine learning or domain knowledge-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique will be first explored, tailored, and extended into the PdM context of high-value critical assets.

It is expected that combining the domain knowledge and the existing data analytics tools will help deploy these technologies in the industry context without the need for big datasets.

You will gain from the experience in numerous ways, whether it be transferable skills in the technical area of optimisation, industrial exposure, or soft skills including presentation skills, project management, and communication skills. There are also numerous employability opportunities that the PhD will offer whether it be in Industry or Academia.