  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.
