Reducing response delay in multivariate process monitoring by a stacked long-short term memory network and real-time contrasts

Abstract

Response delay is a critical performance measure for detecting the process shift. Reducing response delay in multivariate process monitoring, especially under big streaming data, is one of many data analysis challenges on smart manufacturing. In the literature, researchers introduced the real-time contrast control chart (RTC) to fast detect the process shift. However, most of the RTCs are underperformed in the big data environment. In this research, a new RTC combined with a stacked long short-term memory network named LSTM-RTC was proposed to monitor big streaming data with short response delay. The LSTM-RTC consists of two LSTM layers, a dropout layer between the LSTM layers, and a dense layer to extend the capability to catch the streaming data's hierarchical representation. A variety of synthesized multivariate normal and bivariate gamma datasets were used to evaluate the proposed LSTM-RTC with other benchmark methods. The experiment results show that the proposed LSTM-RTC outperforms the other benchmark RTC methods with the lowest response delay. The evaluation on two real-world cases also highlights the advantage of the proposed LSTM-RTC on real-world process monitoring applications.