Condition monitoring and lifetime prognosis of electrical machines

Electrical machines are estimated to contribute to more than 99% of the global generation and 50% of all utilisation of electrical energy.
Electric motors and generators will underpin the transition towards a more sustainable carbon neutral economy being at the heart of renewable energy generation in wind and marine power systems. They will also contribute to significant changes in our life as low emission transportation systems with "more electric" or "all electric" technologies in the automotive, marine, railway and aerospace industries are quickly growing in a market conservatively estimated to be worth over £50bn.
Reliability is of paramount importance for the acceptance of electrical drives in safety critical applications such as those in aerospace industry. Increased reliability and availability can also generate significant commercial benefits to operators and users in sectors such as industrial, transport (e.g. electric/hybrid vehicles) and renewables (e.g. offshore wind generators) where the cost of maintenance, downtime and repair can markedly affect the business case for adopting new and innovative technologies.
Electrical faults in machines, usually caused by progressive degradation of insulation materials, accounts for over 40% of the reported failures in industrial installations.
To increase availability without increasing maintenance and associated downtime, it is necessary to monitor machines during operation, autonomously, with well-founded information on the current state of machine health available in real-time to the operator. Robustness of the methods for assessing degradation is critical, since false-positives, i.e. condition alerts which do not reflect the actual condition of elements of the machine, can be equally damaging in terms of availability and operational costs.
Unfortunately, universally accepted and industrially validated methods for online condition monitoring remain elusive due to their lack of generality and robustness, the need for tuning specific algorithms for each individual application or the requirement for invasive and costly off-line testing.
The research has two main aims that will contribute to a unified solution for online condition monitoring of inverter-driven electric machines.
The first is the determination of a quantifiable model of lifetime of electrical motors under realistic operating conditions, including thermal, electrical and thermo-mechanical stresses, informing a methodology that can be used in real-time applications for continuous indication of the remaining useful life.
The second is the demonstration of an innovative concept for condition monitoring of the state-of-health of the machine insulation without the need for additional expensive testing hardware, or modification to existing drives. The method, based on the real-time measurement of the common-mode impedance of the machine and its variations over the lifetime of the drive system, can provide a quantifiable indication of the progressive degradation of the insulation material.
The research will allow a cost-effective solution to significantly improve reliability and operating costs in a large number of potential applications including transportation and renewable energy generation.

The research outlined in the proposal addresses key questions in the area of condition monitoring and prognosis of electrical machines which are attracting substantial academic and industrial interest. The work has the potential to be transformational for condition monitoring of electrical machines and therefore will have a significant direct impact on reliability constrained applications enabling a wider acceptance of innovative electrical technologies in a large range of industries, most notably the aerospace sector. More widely, potential direct beneficiaries include major electrical machines manufacturers, in particular those with significant stakes in high value, high availability application sectors such as oil and gas, renewable energy generation and transportation as well as manufacturers and operators of condition monitoring and asset management equipment and services.
The work will provide the project partners with valuable innovations such as modelling tools for lifetime prognosis to Motor Design, and validated methods for tracking insulation degradation to suppliers of aerospace-certified equipment such as Rolls-Royce and UTC Aerospace Systems. In the long term, it is anticipated that the techniques developed will be incorporated into industry standard drive systems, contributing to significant improvements in availability and operating costs.