The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines

Gulrukh Turabee, Muhammad Raza Khowja, Paolo Giangrande, Vincenzo Madonna, Georgina Cosma, Gaurang Vakil, Chris Gerada, Michael Galea

Research output: Journal PublicationArticlepeer-review

15 Citations (Scopus)


For a continuous mode of operation, insulating material in an electrical machine is subject to constant thermal, electrical, mechanical and environmental stresses where thermal stress is a major cause of gradual insulation deterioration, which leads to ultimate winding failure. To guarantee a satisfactory lifetime, electrical machines are designed to operate winding temperatures well below their thermal class, which results in an oversized design. Standard methods for thermal lifetime evaluation of electrical machines are based on accelerated aging tests that require several months of testing. This paper proposes an alternative approach relying on a supervised neural network that significantly shortens the time demanded by accelerated aging tests for thermal lifetime evaluation of electrical machines. The supervised neural network is based on a feedforward neural network trained with Bayesian Regularisation Backpropagation (BRP) algorithm. The network predicts the wire insulation resistance with respect to its aging time at aging temperatures of 250°C, 270°C and 290°C, which reveals a good match of prediction outcomes against the experimental findings. The mean time-to-failure at each aging temperature is extracted using the Weibull probability plot in order to compare the Arrhenius curves for both conventional and proposed method and a relative error of 0.125% is achieved in terms of their temperature indexes. In addition, the analysis shows a time saving of 1680 hours (57% time saved of experimental test procedure) when the thermal life of the insulating material is predicted using BRP neural network.

Original languageEnglish
Article number9007468
Pages (from-to)40283-40297
Number of pages15
JournalIEEE Access
Publication statusPublished - 2020


  • Neural network
  • accelerated lifetime test
  • aging time
  • thermal life of insulation

ASJC Scopus subject areas

  • Computer Science (all)
  • Materials Science (all)
  • Engineering (all)
  • Electrical and Electronic Engineering


Dive into the research topics of 'The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines'. Together they form a unique fingerprint.

Cite this