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PhD Studentship: A Hybrid Prognostic Methodology for Aircraft Systems

Cranfield University

Qualification Type: PhD
Location: Cranfield
Funding for: UK Students, EU Students, International Students
Funding amount: please see advert text.
Hours: Full Time
Placed On: 13th May 2021
Closes: 31st August 2021
Reference: SATM215

Introduction

Cranfield University is seeking an outstanding PhD candidate to support the proposed MOIRA project, which brings together early-stage researchers and experienced specialists from key players in academia and industry across Europe covering different scientific disciplines and industrial stakeholders to optimally tackle the challenges ahead.

Background

Modern technological systems increase in scale and are becoming more and more complex and sophisticated. Parallel, the revolution in electronics, digital technology and communications have drastically modified and expanded the physical diversity, scope, processing capabilities and complexity of the monitoring equipment and infrastructure used.

Millions of networked sensors are being embedded in the physical world sense, creating and communicating data. The amount of data available for capturing has been exploding and the era of Big Data is already here, as the Internet of Things (IoT) is becoming a reality. The main question which arises is how following which steps and with which tools the data can be transformed to information and knowledge.

Aims

Within the context of the MOIRA project, the successful PhD student will develop a novel hybrid prognostic methodology focusing on the assessment of the state of the health of a system. The methodology will integrate physics-based and data-driven prognostics models in order to enhance the prognostic accuracy, robustness and applicability. Dedicated experimental test rigs such as a clogged filter, a machinery fault simulator and a linear actuator failure simulator will be used to obtain reproducible datasets under different operating conditions. Finally, the performance of the developed technique will be evaluated based on the most recent prognostic evaluation metrics.

The objectives of MOIRA are:

i) the development of novel signal processing tools for the monitoring of industrial processes based on machine learning methods applied on heterogeneous time series

ii) the application of data mining technologies for the estimation of Key Performance Indicators which determine the operational profit,

iii) the conception, development, and validation of methodologies for automated monitoring of cyber-physical system fleets,

iv) the multi-sensor machine condition monitoring under variable operating conditions.

The MOIRA Fellow will be trained in the advertised innovative PhD topic as well as receiving specific theoretical and practical education in the fields of mechanical engineering and computer science, focusing on the online early accurate identification of abnormal incidents with minimum false alarms and missed detections.

Funding

This PhD position is part of the “MOIRA” (MOnItoRing of large scale complex technological systems) project, funded by the European Commission through the H2020 “Marie Skłodowska-Curie Innovative Training Networks” program (grant number 955681). The PhD candidate will be employed by Cranfield University, including a substantial salary and living allowance (supported by the EC grant). This studentship will be paid according to the Marie Skłodowska-Curie rules.

Sponsorship

This studentship is sponsored by the European Commision, Marie Skłodowska-Curie MOIRA project.

Entry requirements

Candidates must have completed their undergraduate studies in an engineering discipline, knowledge in multi-sensor diagnostics & prognostics engineering is preferred. Selection will be according to the Marie Skłodowska-Curie Actions criteria and payment according to the MSCA rules:

  • not having had main activity in the UK longer than 12 months in the past 3 years immediately before the date of recruitment.
  • not been involved in research for more than 4 years (full time equivalent) starting to count the date this person graduates his/her first MSc degree.
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