Grand Challenge 1.2: Manufacturing of features for in-service monitoring
This work-package aims to develop and evaluate manufacturing methods for embedding high reliability sensor system into electrical machines leading to the realisation of a step-change in the functionality and robustness of in-service monitoring systems.
Routine conditioning monitoring of industrial electric machines is well-established in sectors such as in off-shore oil and gas industry where the additional costs are warranted. With the adoption of electrical machines in evermore safety critical applications – the aerospace sector in particular – there is a growing interest and research activity in innovative and robust methods for reliably monitoring the real-time health status of electrical machines with very dynamic operating cycles and rapidly changing operating environments. Existing methods for in-service monitoring consider the electrical machine as a given equipment with sensors retrofitted to provide independent indications of various attributes of the machine condition.
This programme of research aims to establish design for manufacturing techniques and manufacturing technologies to reliably embed an array of sensors within a high integrity electrical machine, to provide a finer degree of spatial granularity and reduced uncertainty over methods which are solely reliant on external sensing and estimations. This will also support the calibration/referencing of real-time models used for prognostic and diagnostic purposed.
The sensors to be explored include Fibre Bragg Grating (FBG) sensing incorporated in winding design and multi-functional thin metal film shielding of winding coils for in-service monitoring. Another factor which will be researched is the interplay between manufacturing tolerances (principally geometrical and material properties) and the ability to reliably monitor machine condition. High tolerances will tend to provide a more distinctive signature when health is deteriorating, particularly in the case of methods which are underpinned by detecting asymmetry or a deviation in a parameter from a prescribed norm.
The research in the first phase of the project will focus on use of FBG sensors as a distributed and robust means of sensing strain (and thereby mechanical load and vibration) or changes in temperature for through-life monitoring of electrical machine behaviour. The work program will address the practical aspects of integrating fibres into a machine during manufacture, develop analysis methods and algorithms for the detection of mechanical/thermal behaviour during coil winding and other stages of machine manufacture, extract useful equipment health management (EHM) data during service, and demonstrate this overall capability on a series of component and full-machine demonstrators.
For more information on this project please contact Professor Jiabin Wang from The University of Sheffield.