Hybrid Intelligence for Industrial IoT Monitoring and Predictive Maintenance
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The Industrial Internet of Things (IIoT) has ushered in a new era of datadriven manufacturing and industrial processes. This chapter explores the application of hybrid intelligent systems for monitoring and predictive maintenance in the IIoT environment. By combining various artificial intelligence (AI) techniques, such as machine learning, deep learning, and knowledge-based systems, hybrid models can overcome the limitations of individual approaches, leading to more robust and accurate predictions of equipment failures. This chapter presents a comprehensive overview of hybrid intelligence, its application in predictive maintenance, and a proposed methodology for a hybrid system that integrates Long Short-Term Memory (LSTM) networks for time-series data analysis with a knowledge-based system for expert-driven decision-making. The proposed system is evaluated using a real-world dataset, and the results demonstrate the superiority of the hybrid approach in terms of prediction accuracy and lead time for maintenance interventions. The chapter concludes with a discussion of the challenges and future directions in the field of hybrid intelligent systems for Industrial IoT.
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Principles of Hybrid Intelligent Systems
