What question did this study set out to answer?

The aim is to develop an advanced predictive maintenance framework that integrates machine learning with edge computing.

March 6, 2026Open Access

Advanced Predictive Maintenance Framework for Industrial Equipment Based on Hybrid Machine Learning and Edge Computing Architectures

Key Points

The aim is to develop an advanced predictive maintenance framework that integrates machine learning with edge computing.
Proposed a hybrid framework combining machine learning and edge computing architectures.
Utilized supervised learning models like Random Forest and Deep Neural Networks for monitoring.
Implemented anomaly detection techniques such as Isolation Forest and Autoencoders.
Designed a modular data pipeline to process multi-modal sensor data.
Validated the framework using a simulated dataset over 24 months.
Achieved a 34% improvement in fault detection accuracy compared to traditional cloud-only systems.
Reduced response latency by 41% through edge-based inference deployment.

Abstract

Abstract Industrial enterprises operating within Industry 4.0 ecosystems increasingly require intelligent maintenance strategies to ensure operational reliability, cost efficiency, and sustainable production continuity. Traditional maintenance paradigms — reactive and preventive — demonstrate significant inefficiencies when confronted with complex multi-parameter industrial equipment operating under variable load conditions. Predictive maintenance (PdM), driven by real-time sensor data and advanced analytics, has emerged as a transformative solution capable of reducing unplanned downtime and optimizing lifecycle management. This study proposes a hybrid predictive maintenance framework integrating machine learning algorithms with edge computing architectures for industrial equipment monitoring. The framework combines supervised learning models (Random Forest, Gradient Boosting, Deep Neural Networks) with anomaly detection techniques (Isolation Forest, Autoencoders) and deploys them within distributed edge nodes for low-latency inference. The proposed system architecture enables scalable, fault-tolerant, and cyber-secure monitoring of rotating machinery, hydraulic systems, and CNC production units. A modular data pipeline is designed to process multi-modal sensor inputs, including vibration spectra, acoustic emissions, temperature gradients, power consumption, and oil quality parameters. The study introduces a hierarchical decision model that distinguishes between incipient fault detection, degradation trend analysis, and remaining useful life (RUL) estimation. The proposed framework is validated through a simulated industrial dataset representing 24 months of operational data from manufacturing equipment. Results demonstrate a 34% improvement in fault detection accuracy compared to baseline cloud-only architectures and a 41% reduction in response latency due to edge-based inference deployment. This research contributes to applied engineering by presenting an integrated system-level approach that unifies data engineering, artificial intelligence, and distributed computing in industrial predictive maintenance.

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