Special Session Ⅰ- Data Mining and Fault Diagnosis
Chair: Yuanjiang Li, Jiangsu University of Science and Technology, China
Co-chair: Ruijie Zhao, Jiangsu University, China
Co-organized by Jiangsu University of Science and Technology, China
Keywords:
Time series prediction
Time series anomaly detection
Fault diagnosis
Interpretable artificial intelligence
Summary:
Data mining is a creative process involving a large number of different technologies and knowledge, aimed at extracting knowledge of interest from data in large databases. This process requires predetermining the steps to be taken and the goals to be achieved to ensure the orderly implementation and success of data mining.
Fault diagnosis is a process of collecting and analyzing data aimed at determining the cause of a fault and how to prevent its recurrence. This process involves almost all industries and is an important means to ensure industrial safety and improve production efficiency.
In summary, data mining is a process of extracting useful information from large amounts of data, while fault diagnosis is a process of determining the cause of faults and taking corresponding measures by collecting and analyzing data. The two methods are interrelated in industrial applications, jointly promoting the improvement of industrial safety and efficiency.
Topics:
Univariate/Multivariate Time Series Prediction and Modeling Type Stability Analysis
Time Series Anomaly Detection - Anomalies in Point/Subsequences Frequently Analyze
Deep Learning Methods in Fault Diagnosis and Decision-making Assistance Application in the Field and its Interpretability Analysis
Knowledge Graph Method in the Field of Fault Diagnosis