Predict Lab Student Projects

Heterogeneous Monitoring of of Industrial Processes with Graph Neural Networks

The Industrial Internet of Things (IIoT) generates vast quantities of data from interconnected sensors and actuators operating within complex industrial systems. While conventional data-driven methods analyze these signals with temporal context, they often fail to exploit the relational structure of industrial processes, such as physical connections, control interaction and functional dependencies between process variables and equipment.

In industrial plants, sensor data can be broadly categorized into process state variables (e.g., temperature, pressure, flow), actuation or load variables (e.g., motor current, valve position), and equipment health indicators (e.g., vibration, acoustic emission). These variables are tightly coupled: process conditions influence equipment load, which in turn affects mechanical degradation. However, in current industrial practice, process monitoring and equipment condition monitoring are typically treated as separate problems, obscuring these interactions and delaying fault detection.

As a result, many equipment faults are only detected once mechanical damage has already progressed. For example, pumps, among the most failure-prone components in process manufacturing, often experience clogging due to suboptimal process operation, such as inappropriate temperature changes that induce crystallization or solidification. While vibration-based monitoring may detect the issue at a late stage, it often cannot distinguish whether the root cause lies in process conditions or intrinsic equipment failure. Capturing the relationships between process variables, load indicators, and equipment health is therefore essential for early fault detection and meaningful root cause analysis.

Graph Neural Networks (GNNs) provide a powerful framework to model such systems by explicitly representing sensors and actuators as nodes in a graph, with edges encoding their interactions. By leveraging sensor relationship graphs inferred from IIoT data and guided by physical priors and known process dependencies, GNNs can capture structured, directional dependencies that are typically lost in traditional machine learning approaches.

Thesis Goal

This thesis aims to develop a framework for heterogeneous monitoring that jointly models process behavior and equipment health. By combining data-driven graph inference with physics- and process-informed constraints, the proposed approach seeks to enable early fault detection, unsupervised fault diagnosis, and interpretable insights into how process operation affects equipment degradation.

You will work with real industrial IIoT data and addresses a practical and pressing challenge in process manufacturing, conducted as part of an early stage startup project. Ultimately, the goal is to support operators with actionable insights that enables timely and informed interventions to ensure process reliability.

Requirements

– Good knowledge of Python programming.

– Good understanding of fundamental machine learning concepts.

– Experience with GNNs is advantageous.

Professor(s)

Mengjie Zhao (Laboratoire d’automatique 3), Colin Neil Jones

Administration

Barbara Marie-Louise Frédérique Schenkel

External

KIIO

Site

https://sirop.org/app/6bd357eb-d958-4b1c-b40d-ef3ee4d31176?_k=VXrSaqjZ7UDEUApl