Predictive maintenance in pharmaceutical manufacturing lines using deep transformers

Abstract

Inherent complexities in pharmaceutical manufacturing lines of modern industrial facilities make precise and timely detection of malfunction occurrences necessary. In fact, unpredicted malfunctions in a production line can often provoke a cascade of adverse effects that can occur everywhere in the production chain bringing the manufacturing line to a halt for undefined time periods. Such events can have unfortunate consequences that are not always confined to the damaged part itself but propagate throughout the production line. Nevertheless, modern production lines are equipped with a multitude of data sensors that enable the real-time and fine-grained monitoring of each constituent part of the production process providing a richness of information that can be exploited by intelligent data processing methods.In this work, we present ManuTrans, a deep learning-based model for monitoring real-time raw sensor data, deriving the condition of a pharmaceutical manufacturing line and predicting the next moment in time when a malfunction can occur. The model is further able to predict the severity of the next malfunction and can contribute adjunct information in corporate decision-making. The suggested approach exploits the capacity of deep transformer models for extracting both long- and short-term correlations as well as patterns in sequential data and, combined with a linear output layer, conducts both classification and regression. The proposed approach was tested on a real dataset comprising raw data from two manufacturing lines, and it achieved promising results.