Multi-Sensor Fault Diagnosis Based on Time Series in an Intelligent Mechanical System
Zhuoran Xu1, Qianmu Li1, Linfang Qian2
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|December 23, 2022
Summary
This study introduces a novel fault diagnosis method for complex intelligent mechanical systems. It uses Autoformer and transfer entropy for accurate fault identification and root cause analysis, improving system maintenance.
Area of Science:
- Intelligent mechanical systems
- Industrial automation
- Machine learning for diagnostics
Background:
- Modern mechanical systems are complex, posing challenges for integrated fault diagnosis.
- Existing methods struggle with algorithm integration and pinpointing fault origins.
- Real-time data analysis is crucial for intelligent fault diagnosis.
Purpose of the Study:
- To propose a general fault diagnosis algorithm for complex mechanical systems.
- To address the integration challenges of specialized fault diagnosis algorithms.
- To accurately locate fault origins within complex systems.
Main Methods:
- Utilized Autoformer for multi-dimensional long time series prediction.
- Employed sensor timing characteristics and transfer entropy for fault diagnosis.
- Developed a root cause analysis method based on transfer entropy for fault localization.
Main Results:
- Successfully predicted time series data for fault identification.
- Achieved accurate fault identification by analyzing prediction deviations.
- Enabled precise fault component localization through cause-effect analysis.
Conclusions:
- The proposed method offers a generalizable approach to fault diagnosis in complex systems.
- Accurate fault localization aids maintenance personnel in efficient troubleshooting.
- This approach enhances the intelligence and reliability of mechanical systems.
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