PdM-FSA - predictive maintenance framework with fault severity awareness in Industry 4.0 using machine learning

Aug 4, 2026

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Predictive maintenance enables Industry 4.0 goals by reducing maintenance costs and downtime while improving production and return on investment. Despite growing adoption of machine learning for industrial systems, practical deployment remains hindered by data quality issues, limited computational resources, and equipment heterogeneity. This study presents PdM-FSA, an adaptive Industry 4.0 framework for IoT systems using an ensemble classifier to assess malfunction severity and guide whether predictive maintenance is needed. Performance evaluation on smart factory scenarios shows strong results for severity-aware forecasting.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Industry 4.0 technologies and data-driven maintenance

Challenges in ML-based predictive maintenance

Proposed Framework (PdM-FSA)

Ensemble models and soft voting strategy

Fault severity categories

PdM-FSA: predictive maintenance framework with fault severity awareness in Industry 4.0 using machine learning Donatien Koulla Moulla1, Ernest Mnkandla1, Moussa Aboubakar2, Ado Adamou Abba Ari3,4,5, Alain Abran6 1Centre for Augmented Intelligence and Data Science, School of Computing, University of South Africa, Johannesburg, South Africa 2SogetiLabs, Issy-les-Moulineaux, France 3LaRI Lab, University of Maroua, Maroua, Cameroon 4CREATIVE, Institute of Fine Arts and Innovation, University of Garoua, Garoua, Cameroon 5DAVID Lab, Université Paris-Saclay, University of Versailles Saint-Quentin-en-Yvelines, Versailles, France 6Department of Software Engineering and Information Technology, École de Technologie Supérieure, Montréal, Canada Article history: Received May 8, 2024 Revised Aug 2, 2024 Accepted Aug 6, 2024 Keywords: Artificial intelligence Fault severity classification Industry 4.0 Internet of things systems Machine learning Predictive maintenance Corresponding Author: Predictive maintenance contributes to Industry 4.0, as it enables a decrease in maintenance costs and downtime while aiming to increase production and return on investment. Despite the increasing utilization of machine learning techniques in predictive maintenance in industrial systems over the past few years, several challenges remain to be addressed in the implementation of ML, including the quality of the data collected, resource constraints, and equipment heterogeneity. This study proposes an adaptive framework for predictive maintenance in the context of Industry 4.0, specifically in internet of things (IoT) systems, using machine learning (ML) models. In particular, this study introduces PdM-FSA, a new framework based on an ensemble classifier that takes advantage of four widely adopted ML models in the predictive maintenance literature: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN) . The performance evaluation results showed that the PdM-FSA framework can perform well for predictive maintenance according to the severity of equipment malfunctions in a smart factory. The results of this study provide significant knowledge to researchers and practitioners on predictive maintenance in the context of Industry 4.0. and enables the optimization of processes and improves productivity. This is an open access article under the CC BY-SA license. Donatien Koulla Moulla Centre for Augmented Intelligence and Data Science, School of Computing, University of South Africa Johannesburg, 1709, South Africa [Email: moulldk@unisa.ac.za](Email: moulldk@unisa.ac.za) Article Info ABSTRACT 1. INTRODUCTION The term “Industry 4.0” was introduced by the German National Academy of Science and Engineering in 2011 to describe the fourth industrial revolution, with people, machines, and industrial processes intelligently networked [1] . Many technologies drive Industry 4.0, such as artificial intelligence (AI), cloud computing, the internet of things (IoT), cyber-physical systems, edge computing, digital twins, and machine learning (ML) [2], [3] . Through these technologies, Industry 4.0 enables increased productivity and efficiency. It also leads to improved production processes, higher quality products, and greater sustainability. Additionally, it decreases product development costs and shortens lead times [4] . With the widespread use of sensing devices in smart factories, the amount of data generated by production equipment have increased exponentially. These data can be leveraged to provide useful information and gain insights into manufacturing processes, production systems, and equipment. For instance, several studies using ML models in Industry 4.0 have been proposed to enable machines to learn from data and make valuable predictions to anticipate equipment malfunctions [5], [6] . Within the context of Industry 4.0, it is crucial to establish an accurate model for

FAQs

What problem does PdM-FSA address in Industry 4.0 predictive maintenance?

PdM-FSA targets shortcomings in existing ML-based predictive maintenance deployments by using an adaptive, severity-aware ensemble framework tailored to IoT/Industry 4.0 settings.

How does PdM-FSA determine malfunction severity?

PdM-FSA applies an ensemble classifier that combines predictions from random forest, SVM, XGBoost, and k-nearest neighbors, then classifies equipment malfunction severity into low, medium, and high.

Which machine learning models are combined in the PdM-FSA ensemble?

PdM-FSA leverages four widely used ML models: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN).

Aug 4, 2026

Upload by Aria