In this study, we investigate how computationally simplified activation functions affect predictive performance, inference latency, and energy usage in long short-term memory-based temperature prediction for wind turbine generator bearings. We tested three different types of long short-term memory (LSTM) cells, along with bidirectional LSTM (biLSTM) networks, to determine their effectiveness in modeling dynamic changes in gearbox bearing temperatures. We compared several activation-function variants, focusing on variants that are either computationally simple or known to give good performance in deep recurrent networks. The results show that the best-performing architectures achieved root mean squared errors (RMSEs) between 0.0798 and 0.0822, corresponding to coefficients of determination in the range of R2=0.84–0.85. When applied across five turbines, the best-performing architectures (peephole and bidirectional) achieved root mean squared errors of 0.0898, 0.0882, and 0.042, respectively. The best activation function-enhanced variant (the peephole) improved accuracy by approximately 3% while maintaining low model complexity. These findings provide a practical and efficient solution for embedded predictive maintenance systems, providing high accuracy without incurring the computational cost of deeper or bidirectional architectures.
Jankauskas et al. (Wed,) studied this question.