AI-driven embedded systems for predictive maintenance in industrial IoT
1 Department of Electronics and Communication Engineering, Government Residential Polytechnic for Women’s, Shimoga, Karnataka, India.
2 Department of Electrical and Electronics Engineering, VISSJ Government Polytechnic Bhadravathi, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2020, 07(03), 338-346
Publication history:
Received on 24 August 2020; revised on 22 September 2020; accepted on 26 September 2020
Abstract:
Predictive maintenance (PdM) leverages Artificial Intelligence (AI) integrated with the Industrial Internet of Things (IIoT) to proactively monitor and predict equipment failures, significantly optimizing operational efficiency while reducing downtime and maintenance costs. PdM shifts the maintenance paradigm from reactive or preventive strategies to a data-driven, predictive approach that ensures timely intervention based on the actual condition of equipment rather than predetermined schedules. Embedded systems, serving as the backbone of PdM, are equipped with AI algorithms that enable real-time data collection, processing, and decision-making at the edge of the network. These systems are designed to interface seamlessly with IIoT devices, gathering data from various industrial sensors and analyzing it to detect anomalies, estimate the remaining useful life (RUL) of equipment, and predict potential failures. The integration of AI capabilities such as machine learning (ML) and deep learning (DL) within embedded systems allows them to handle complex data streams, identify patterns, and make intelligent predictions in real time. This paper explores the multi-faceted aspects of AI-driven embedded systems for predictive maintenance in IIoT environments. First, it delves into the architecture of these systems, highlighting the interplay between hardware components such as microcontrollers, sensors, and communication modules, and software frameworks that incorporate AI algorithms for data processing and analysis. The role of edge computing in reducing latency and enabling on-site decision-making is also emphasized. Second, the paper examines the AI algorithms commonly employed in PdM, such as neural networks, support vector machines, and ensemble methods, discussing their suitability for various industrial applications. Specific attention is given to the use of advanced techniques like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for handling time-series sensor data and identifying early warning signs of equipment degradation. Furthermore, the practical applications of these systems across industries are reviewed, showcasing use cases in sectors such as manufacturing, energy, transportation, and healthcare. For instance, AI-driven embedded systems have been used to monitor conveyor belts, wind turbines, railways, and medical equipment, providing tangible benefits like extended equipment lifespan, improved safety, and reduced operational costs. The paper also presents case studies and performance metrics to evaluate the effectiveness of AI-driven PdM systems. Metrics such as prediction accuracy, false positive rate, and computational efficiency are analyzed to demonstrate the strengths and limitations of this approach. Challenges such as the high initial cost of implementation, data privacy concerns, and the need for robust cybersecurity measures are discussed to provide a balanced perspective.
Keywords:
Predictive Maintenance (PdM); Artificial Intelligence (AI); Industrial Internet of Things (IIoT); Embedded Systems; Edge Computing; Machine Learning (ML); Deep Learning (DL); Time-Series Data Analysis; Neural Networks
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0