Multi-Scale Aggregation with Self-Attention Network for Modeling Electrical Motor Dynamics

Modeling induction motor dynamics is a crucial problem in the industry. The previous works mainly model the dynamics based on the physical model assumption and state equation. However, due to the complex internal structure of motors, the traditional methods cannot estimate dynamics precisely. To address this issue, we adopt a deep learning-based approach that takes the time-series motor data measured from the sensor to estimate the dynamics without making any assumptions about the motor’s interior. In this paper, we propose a multi-scale feature aggregation with self-attention network (MASNet) to deal with modeling motor dynamics. First, our model extracts multi-scale features from motor signals by various convolutional kernel sizes. Then, the proposed adaptive feature aggregation module fuses different receptive signals effectively. To further refine these high-level motor features, two-stream components, containing Bidirectional LSTM and self-attention module, are applied to improve motor contextual information. Moreover, we present a novel temporal relative loss to enhance consecutive signal consistency, which improves the performance of modeling dynamics. To deploy the service in the real-world scenario, our network is very lightweight and reduces the number of parameters from 0.62M to 0.09M (around 85% reduction) but outperforms state-of-the-art algorithms by extensive experiments on simulation and real-world motor datasets.