A SIA-LSTM based virtual metrology for quality variables in irregular sampled time sequence of industrial processes

Abstract

In industrial processes, there are usually strongly dynamic temporal relationship between process data sequence. Hence, dynamic modeling methods are popular for soft sensing of key quality variables. For the real-life industrial processes, the data series are usually sampled with different intervals for the samples. However, most of the mainstream dynamic models, represented by long short-term memory (LSTM), is difficult to directly handle the time series with irregularly sampled data. Hence, a sampling-interval-attention LSTM (SIA-LSTM) is developed to deal with it, in which an attention network is introduced to adaptively learn the dynamics between two consecutive samples and transfer it into an attention weight of the sampling interval. Next, the previous hidden state and the attention weight are multiplied. The weighted previous hidden state is further sent to the control gates to compute the current hidden state in LSTM unit. Hence, the dynamic relationship is calculated adaptively according to the sampling interval. The effectiveness of SIA-LSTM is evaluated for quality prediction on a refining hydrocracking process.