Fault detection and diagnosis of batch process using dynamic ARMA-based control charts

TL;DR

This paper introduces ARMA-based control charts tailored for batch process monitoring, effectively capturing serial correlation and variability to detect faults and diagnose disturbances.

Contribution

It proposes a novel ARMA model-based control chart method that accounts for batch-to-batch and within-batch variability, improving fault detection and diagnosis in batch processes.

Findings

Effective fault detection demonstrated on simulated data

Successful application to real batch process data

Improved diagnosis of process disturbances

Abstract

A wide range of approaches for batch processes monitoring can be found in the literature. This kind of process generates a very peculiar data structure, in which successive measurements of many process variables in each batch run are available. Traditional approaches do not take into account the time series nature of the data. The main reason is that the time series inference theory is not based on replications of time series, as it is in batch process data. It is based on the variability in a time domain. This fact demands some adaptations of this theory in order to accommodate the model coefficient estimates, considering jointly the batch to batch samples variability (batch domain) and the serial correlation in each batch (time domain). In order to address this issue, this paper proposes a new approach grounded in a group of control charts based on the classical ARMA model for monitoring and diagnostic of batch processes dynamics. The model coefficients are estimated (through the ordinary least square method) for each historical time series sample batch and modified Hotelling and t-Student distributions are derived and used to accommodate those estimates. A group of control charts based on that distributions are proposed for monitoring the new batches. Additionally, those groups of charts help to fault diagnosis, identifying the source of disturbances. Through simulated and real data we show that this approach seems to work well for both purposes.