Abstract
Based on multi-sensor data fusion a method of analysis is presented that fuses together original statistical data obtained from means and variances of multi-channel signals and indentify the cutting tool states by means of their higher-order terms and artificial neural network and fault tree theory. Simulations show that the method is quite effective in identifying different cutting-tool wear levels. Experiments of monitoring boring breakage on FMC show that it is a new practical and feasible method to monitor cutting-tool states by the use of multi-sensor data sampled from a new type flow acoustic emission sensor and accelerator sensor.
| Original language | English |
|---|---|
| Pages (from-to) | 75-81 |
| Number of pages | 7 |
| Journal | Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology |
| Volume | 15 |
| Issue number | 1 |
| Publication status | Published - 1995 |
Keywords
- Boring tools/cutting-tool state
- Data fusion
- Fault tree analysis