Multivariate and Multi-frequency LSTM based Fine-grained Productivity Forecasting for Industrial IoT

Abstract

Thanks to Industrial Internet of Things (IIoT), traditional industry is transforming to the fine and flexible production. To comprehensively control the dynamic industrial processes that includes marketing and production, accurate productivity is a vital factor that can reduce the idle operation and excessive pressure of the equipment. Due to increasing requirements of flexible control desired by IIoT, the productivity forecast also demands finer granularity. However, due to the neglect of multiple related factors and the ignorance of the multi-frequency characteristics of productivity, existing methods fail to provide accurate fine-grained productivity forecasting service for IIoT. To fill this gap, we propose a multivariate and multi-frequency Long Short-Term Memory model (mmLSTM) to predict the productivity in the granularity of day. mmLSTM takes equipment status and order as new supporting factors and leverages a multivariate LSTM to model their relationship to productivity. mmLSTM also integrate a multi-level wavelet decomposition network to thoroughly capture the multi-frequency features of productivity. We apply the proposed method in a real-world steel factory and conduct a comprehensive evaluation of performance with the productivity data in nearly two years. The result shows that our method can effectively improve the prediction accuracy and granularity of industrial productivity.