Fibre Bragg Grating Sensor Based Electric Machine Condition Monitoring and Fault Detection Techniques

Electric machines are becoming a key technology in the electrification of the automotive and aerospace sectors. The application of electric machines in these sectors not only involves complex operating cycles and harsh operating conditions, but also ever-more demands on reliability and robustness. Hence, the development of advanced condition monitoring and fault detection technology is essential for the energy transition and transportation electrification. This thesis reports on the development and evaluation of methods for embedding high reliability sensor systems into electrical machines with the potential to achieve a step-change in the functionality and robustness of in-service monitoring.
This research is focused on the use of fibre Bragg grating (FBG) sensors as a distributed and robust method of strain and temperature sensing to realise through-life in-service monitoring. By comprehensively considering the application scenarios and technical requirements of electric machines and the advantages of FBG sensors, such as small size, EMI immunity, multiplexing of individual gratings and robustness in harsh environments, two main topics are investigated in this thesis.
The first topic is the feasibility of using FBG sensors to measure the stress/strain of insulation coatings, as a method for predicting the lifetime consumption of winding insulation due to temperature cycling. Finite element analysis (FEA) is used to simulate FBG behaviour in a representative machine winding and hence evaluate likely measurement accuracy. Furthermore, an FEA model of a reference machine is used to study the stress and strain distribution in the winding. By combining an FEA model with the data from a previous thermal cycling experiment, the possibility of using FBG measured strain to estimate insulation lifetime is investigated.
The second topic is the deployment of FBG strain sensors to measure and monitor vibration as pre-cursor to fault detection. FEA is used to predict strain distribution in a high-speed permanent magnet synchronous machine stator and end windings. The sensitivities of FBG measured strains at different locations to operating conditions and different faults are studied and compared. The preferred FBG sensor mounting locations and methods for strain/vibration monitoring and fault detection are determined. Experimental work is conducted to validate the effectiveness of the proposed FBG mounting and machine fault detection strategies.