Intelligent IoT-Driven Advanced Predictive Maintenance System for Industrial Applications

被引：0

作者：

D. Dhinakaran ^{[1
]}

S. Edwin Raja ^{[1
]}

R. Velselvi ^{[2
]}

N. Purushotham ^{[3
]}

机构：

[1] Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Department of Computer Science and Engineering

[2] P.S.R Engineering College,Department of Computer Science and Engineering

[3] Mohan Babu University,Department of CSE, School of Computing

来源：

SN Computer Science | / 6卷 / 2期

关键词：

Predictive maintenance; IoT; Explainable AI; Machine learning; Supervised and reinforcement learning;

D O I：

10.1007/s42979-025-03695-x

中图分类号：

学科分类号：

摘要：

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industrial systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM approaches, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-Driven Advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (XAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" Dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM approaches—Traditional PdM, Decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior performance of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decreases the Mean Absolute Error (MAE) by 36.7% relative to DSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust performance in real-world industrial scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies.

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