On November 10, 2023, from 9:30 to 11:00 in the morning, Room 501, Block B of the Science and Education Building at the Feicui Lake Campus of Hefei University of Technology, we welcomed a brilliant academic report by Professor Yan Ruqiang from Xi'an Jiaotong University. The theme of the report was "Fault Diagnosis in the Era of Artificial Intelligence: From Wavelet Analysis to Trustworthy Deep Learning". As a renowned expert in the field of artificial intelligence, Professor Yan Ruqiang, with his rich research experience and outstanding academic achievements, brought a profound analysis of fault diagnosis in the era of artificial intelligence to the teachers and students present.

In the report, Professor Yan Ruqiang introduced the performance-driven to risk-sensitive mode change faced with the widespread application of deep learning in various fields, especially in key areas such as industrial equipment health management. He emphasized that in areas related to human life safety, a wrong diagnosis could lead to irreversible consequences, so it is particularly important to achieve interpretable and trustworthy deep learning. In response to the opacity of deep learning algorithms, insufficient signal analysis prior knowledge, and uninterpretable results, Professor Yan shared his recent research work, focusing on signal processing, especially wavelet analysis techniques, on how to empower deep learning and apply it to intelligent diagnosis. He detailed the advantages of signal processing as a kind of inductive prior, including well-developed theoretical development, rich signal prior, and strong interpretability, and demonstrated how to improve the credibility of deep learning through this method.

After the report, the teachers and students present actively participated in the question and answer interaction, and had an in-depth discussion with Professor Yan Ruqiang on hot issues in the field of artificial intelligence, achieving good academic exchange effects. This report not only provided our teachers and students with an opportunity to deeply understand the frontier issues of fault diagnosis in the era of artificial intelligence, but also promoted academic exchanges and provided beneficial guidance for research and practice in related fields.