Name
Title | ||
|---|---|---|
Research on Spindle Bearings State Recognition of CNC Milling Machine Based on Noise Monitoring |
Abstract | ||
|---|---|---|
Relationship between spindle running noise and health state of spindle bearings of CNC milLing machine is studied. With an acoustic sensor system, the spindle noise signals are sampled both in normal state and fault state of bearings. With three input characteristics abstracted from the signals, such as mean of absolute value, power and variance, a three-layer Back-Propagation neural network to recognize the bearing running state is built up and trained. The optimized number of hidden layer nodes of the neural network is determined by comparison test. It is proved by the experimental results that the noise signals monitoring is effective in recognition of spindle bearings health state. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDMA.2011.252 | ICDMA |
Keywords | DocType | Citations |
fault state,cnc milling machine,health state,neural network,spindle bearings state recognition,spindle noise signal,spindle bearing,absolute value,noise monitoring,normal state,spindle bearings health state,three-layer back-propagation neural network | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |