Signal Stream Clustering for Tool-Revolution-Level Tool Condition Monitoring in Milling Process



Published Mar 26, 2021
Si Jie Phua Xiang Li Wee Keong Ng Beng Siong Lim Weixiang Zhong Junhong Zhou


The researches in tool condition monitoring often collect large amount of sensor signal data from experiments to study the complex tool condition relationships with signals. In order to provide new light into this process on a real-time basis, it is critical to identify and detect abnormality at the lowest resolution possible so that the wear beha- vior on each flute within a tool revolution can be clearly shown. A signal stream clustering method is developed to separate numerous tool-revolution signals into similar groups, each representing a specific set of corresponding events. In our expe- riment, the 1000 tool-revolution signals in force signal stream are grouped into 5 clusters. These clusters in turn provide a visual mean to assess the tool condition at the most detailed level. In addi- tion, the clusters also enable complex tool condi- tion relationships to be established from the sig- natures of each set of events.

How to Cite

Phua, S. J. ., Li, X. ., Keong Ng, W. ., Siong Lim, B. ., Zhong, W. ., & Zhou, J. . (2021). Signal Stream Clustering for Tool-Revolution-Level Tool Condition Monitoring in Milling Process. Annual Conference of the PHM Society, 1(1). Retrieved from
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CBM, condition monitoring, applications: industrial, applications: manufacturing

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