Gated Adaptive Hierarchical Attention Unit Neural Networks for the Life Prediction of Servo Motors

The remaining useful life (RUL) prediction of servo motors plays a greatly indispensable role in preventing abnormal and inefficient operation of servo drive system and even major safety accident. In this article, a novel variant of recurrent neural networks, called gated adaptive hierarchical attention unit (GAHAU), is constructed to efficiently predict the RULs of servo motors, which can not only pay attention to the outputs of reset and update gates but also adaptively divide the hierarchy of the sequence information in attention gate. The updating formulas of GAHAU are derived. With the raw vibration signals of servo motors, a quadratic function-based deep convolutional autoencoder is used for automatically generating the health indicator vector. Based on the constructed HI vector, the RULs of servo motors are accurately predicted by GAHAU. The experiments on servo motors demonstrate that the presented GAHAU has remarkable prediction performance especially for the long-term prediction. Compared to the existing typical RUL prediction methods, the proposed methodology has stronger predictive ability according to the comparative results.

Automated summary

This article introduces a novel variant of recurrent neural networks, called gated adaptive hierarchical attention unit (GAHAU), for efficient prediction of the remaining useful life (RUL) of servo motors. The GAHAU not only pays attention to the outputs of reset and update gates, but also adaptively divides the hierarchy of sequence information in the attention gate. Experimental results demonstrate that the presented GAHAU has remarkable prediction performance, especially for long-term prediction.