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Research output: Contribution to Journal/Magazine › Editorial › peer-review
Research output: Contribution to Journal/Magazine › Editorial › peer-review
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TY - JOUR
T1 - Guest Editorial for Artificial Intelligence for Machine Fault Diagnosis Special Section
AU - Yan, Ruqiang
AU - Xia, Min
AU - Wang, Peng
PY - 2022/12/31
Y1 - 2022/12/31
N2 - With the rapid technological development and production requirement, machines and equipment in modern industry, such as advanced manufacturing, transportation, aerospace, and civil infrastructure, become increasingly functional and complex. Machine fault diagnosis plays a significant role for the productivity, reliability, and safety of industrial systems. In the recent decade, data-driven solutions have become more effective and promising for fault diagnosis of complex machines due to increasing data availability and processing capacity. Artificial intelligence (AI) techniques, especially deep learning approaches, are the most powerful tools in achieving accurate fault diagnosis of complex systems. However, AI-based fault diagnosis still faces great challenges in feasibility and reliability for real applications, including the lack of fault condition data, varying working conditions, insufficient generalization capability of AI models, the black-box nature of most AI methods, etc. This special section focuses on advanced and innovative solutions to address a broad view of problems about AI-based machine fault diagnosis. Six out of 18 submissions included in this special section summarize some of the research and applications in this field.
AB - With the rapid technological development and production requirement, machines and equipment in modern industry, such as advanced manufacturing, transportation, aerospace, and civil infrastructure, become increasingly functional and complex. Machine fault diagnosis plays a significant role for the productivity, reliability, and safety of industrial systems. In the recent decade, data-driven solutions have become more effective and promising for fault diagnosis of complex machines due to increasing data availability and processing capacity. Artificial intelligence (AI) techniques, especially deep learning approaches, are the most powerful tools in achieving accurate fault diagnosis of complex systems. However, AI-based fault diagnosis still faces great challenges in feasibility and reliability for real applications, including the lack of fault condition data, varying working conditions, insufficient generalization capability of AI models, the black-box nature of most AI methods, etc. This special section focuses on advanced and innovative solutions to address a broad view of problems about AI-based machine fault diagnosis. Six out of 18 submissions included in this special section summarize some of the research and applications in this field.
U2 - 10.1109/ojim.2022.3225889
DO - 10.1109/ojim.2022.3225889
M3 - Editorial
VL - 1
SP - 1
EP - 2
JO - IEEE Open Journal of Instrumentation and Measurement
JF - IEEE Open Journal of Instrumentation and Measurement
SN - 2768-7236
ER -