葛蘭傑因果分析應用於迴轉機械異常預診與根本原因診斷之研究

Project: Government MinistryMinistry of Science and Technology

Project Details

Description

Rotary machine composed of Bearings, gears and shafts plays an important role in the machine tools. These components are often subjected to high loading during operation. Defects are then initiated, propagated, developed and finally cause machine breakdown. The quality and the estimated time of delivery will then be affected, causing a higher repair and labor cost. For these reasons, development of a prognostics system which has the ability to detect anomaly in early stage is important for both the academic and the industry field. In this study, we use the Spectral Granger Causality to detect the anomaly of a rotary machine, and the bearing data provided by the Center of Intelligent Maintenance Systems (IMS) and self-construction multi-bearing experiments is used to demonstrate this proposed algorithm. We hope the results of this study will be a significant improvement in root cause diagnosis, fault localization and early anomaly detection. When the damage occurs, we can find out the root cause and location of fault; furthermore, the early anomaly can be detected by the cause-effect analysis between components, so the repair time will be reduced.
StatusFinished
Effective start/end date2017/08/012018/07/31

Keywords

  • rotary machine
  • anomaly detection
  • fault localization
  • root cause diagnosis
  • cause-effect analysis

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.