Guest Editors
Prof. Haidong Shao
Email: hdshao@hnu.edu.cn
Affiliation: College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China
Homepage:
Research Interests: deep learning, signal processing, health monitoring, fault diagnosis
Dr. Jipu Li
Email: jipu1994.li@polyu.edu.hk
Affiliation: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Homepage:
Research Interests: industrial intelligence, industrial big data, and intelligent maintenance & health management
Dr. Yun Kong
Email: kongyun@bit.edu.cn
Affiliation: School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China
Homepage:
Research Interests: fault diagnosis, vibration signals, convolutional neural network, diagnostic accuracy, deep learning models
Assist. Prof. Zhuyun Chen
Email: mezychen@gdut.edu.cn
Affiliation: Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510006, China
Homepage:
Research Interests: mechanical signal processing, data mining and analytics, fault diagnosis, intelligent prognosis, and maintenance decision
Summary
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.
Keywords
Deep learning-based industrial big data acquisition, storage, and preprocessing techniques.
Data analytics information fusion, pattern recognition, and trend analysis for industrial systems with advanced machine learning methods.
Deep transfer learning and meta learning for intelligent maintenance including abnormal detection, fault diagnosis, performance prediction.
Federated learning for data privacy and collaborative training of model.Reinforcement learning for decision optimization, including supply chain management, demand forecasting, and quality assurance.
Explainable machine learning for intelligent maintenance and decision optimization.
Digital-twin technique for decision optimization and performance management.
Published Papers
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