Submission Deadline: 31 October 2024 (closed) View: 1128
In recent years, the rapid development of industrial big data and artificial intelligence (AI) technologies has revolutionized the industrial landscape. Industrial systems, such as manufacturing, energy, transportation, and logistics, have become increasingly complex, generating vast amounts of data. These big data encompass a wide range of data sources, including sensor data, production logs, and maintenance records, which hold valuable insights. Moreover, machine learning-based AI techniques can be applied to extract meaningful insights from this big data. For example, deep learning allows machines to interpret and understand multisensory information, which can be utilized for quality control, defect detection, and object recognition in industrial systems. Transfer learning can improve predictive maintenance models, anomaly detection, and fault diagnosis by transferring knowledge learned from similar systems. Reinforcement learning empowers machines to learn from trial and error, making it suitable for optimization problems in industrial systems. As such, the integration of industrial big data and AI enables intelligent perception, maintenance, and decision optimization, driving the intelligent upgrade of enterprises and enhancing productivity and quality.