Sampling-Interval-Aware LSTM for Industrial Process Soft Sensing of Dynamic Time Sequences With Irregular Sampling Measurements

Abstract

In modern industrial processes, dynamics and nonlinearities are two main difficulties for soft sensing of key quality variables. Thus, nonlinear dynamic models like long short-term memory (LSTM) network have been applied for data sequence modeling due to its powerful representation ability. Nevertheless, most dynamic methods cannot deal with data series with irregular sampling intervals, which is a common phenomenon in many industrial plants. To handle this problem, a novel sampling-interval-aware LSTM (SIA-LSTM) is proposed in this paper, which takes the sampling intervals between sequential samples into consideration to adjust the influence of the previous sample on the current one. To this end, two non-increasing functions of the sampling interval are designed to weight different sampling intervals in the dynamic data sequence. Then, each sampling-interval weight is multiplied to the corresponding previous hidden state to adjust its impact. Finally, the adjusted hidden state is used as an adaptive input for the three control gates in each LSTM unit to obtain the current hidden state. The proposed SIA-LSTM is applied to an actual hydrocracking process for soft sensor of the C5 content in the light naphtha and the final boiling point of the heavy naphtha.