AI-based anomaly detection of asset failures in industrial process plants

A comparison of neural network and statistical approaches

Abstract

The recent advances in industrial process control systems have led to vast amounts of production data, especially time-series
data from various equipment in a process plant. This data provides an opportunity for AI-based use cases that can help plant
operators to understand and predict complicated plant scenarios, allowing them to run their plants more efficiently and securely. Here, asset fault detection can provide a valuable AI use case, due to the time and cost-intensive process of discovering and rectifying from such faults. In this paper asset fault detection in industrial process plants is addressed with the help of neural network architectures and statistical machine learning models.