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

Abstract

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. INDEX TERMS Neural network, aging time, thermal life of insulation, accelerated lifetime test.

Predicting Insulation Resistance of Enamelled Wire using Neural Network and Curve Fit Methods Under Thermal Aging

“…Thus, the proposed approaches show clear benefits in terms of time-saving during the experimental test procedure (i.e. conventional method), where at least three aging temperatures are required to complete the process of thermal qualification [6,19]. Additional results, regarding different aging temperatures will be addressed in separate publications.…”

“…In order to forecast the time-to-failure of each thermally aged specimen, an end of life breakdown criterion is chosen. The latter is fixed as an 87.77% variation in the monitored diagnostic marker [6]. This implies that the specimen's life comes to an end when its insulation resistance, under thermal aging, is reduced by 87.77% from its unaged value.…”

“…Alongside the early detection of abnormal deterioration, another issue is the time required for the experimental test procedure, aiming to determine the lifetime of electrical machines, which can take thousands of testing hours lasting even months [6,7].…”

Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

“…The FITNET is used for curve-fitting and regression. In Figure 2, is the input neuron, and are the weights, represents the neuron numbers, and is the neuron output [18][19][20]. In Figure 2, the general architecture of an artificial neural network (ANN) is shown.…”

“…The FITNET is used for curve-fitting and regression. In Figure 2 , is the input neuron, and are the weights, represents the neuron numbers, and is the neuron output [ 18 , 19 , 20 ].…”