Predictive Maintenance in Industrial Internet of Things: Current Status
Keywords:
Predictive Maintenance (PdM), Industrial Internet of Things (IIoT), Artificial Intelligence, Machine Learning (ML), Industry 4.0.Abstract
Introduction/Importance of Study: Predictive Maintenance (PdM) is a key challenge within the Industrial Internet of Things (IIoT). It aims to enhance system operations by minimizing equipment failures, leading to smoother operations and increased productivity. By anticipating maintenance needs before failures occur, PdM ensures more reliable and efficient industrial processes.
Novelty Statement: This study examines maintenance techniques and datasets that leverage AI and ML for predictive maintenance in the context of industrial IoT. The primary goal is to enhance productivity, identify faults before failures occur, and minimize downtime. By utilizing advanced algorithms, the study aims to improve the efficiency and reliability of industrial systems.
Material and Method: A systematic literature review of state-of-the-art predictive maintenance in the context of industrial IoT, incorporating machine learning (ML) and artificial intelligence (AI) methods, is conducted. This review is based on research articles retrieved from the Dimensions.ai database, covering publications from 2018 to 2024.
Result and Discussion: This comprehensive analysis offers valuable insights for advancing Predictive Maintenance (PdM) strategies in the Industrial Internet of Things (IIoT), ultimately contributing to more efficient manufacturing processes. The study highlights leading publication venues and top keywords in this research area, providing a clear picture of emerging trends. It also explores the prognosis of PdM within the manufacturing industry. Additionally, the review discusses relevant models, methods, input variables, and datasets in the PdM and IIoT domain, with a particular focus on machine learning (ML) and artificial intelligence (AI) techniques. Among the most widely used techniques for PdM in IIoT are deep learning, artificial neural networks, and random forest.
Concluding Remarks: Subsequently, the study highlights various challenges, offering future research directions aimed at refining predictive maintenance techniques.
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