Abstract:
The trait of the ball mill is chaotic in nature due its complex dynamics associated during grinding.
Grinding in ball mill generates high-intensity vibration and is too complex on account dependency of multiple
variables. In this paper, the vibration signal is acquired using a low power ZigBee based three axes wireless
MEMS accelerometer sensor mounted onto the mill shell. Firstly, the exact frequency bands of the mill are
identified under variable impact loading using non synchronized and Synchronized Frequency Estimation
method (SFE) methods. The synchronization between the mill speed and the sampling rate are put forward by
SFE to convert the random non stationary data to quasi stationary data. The actual signal length is calculated
using proposed SFE approach and further it is used as window size for wavelet decomposition. Further, to decorrelate
the auto-correlated and cross-correlated signal and signal spaces both PCA and Wavelet are used.
Finally, the combination of all this techniques, i.e., Synchronized Wavelet Based Multi-Scale Principal
Component Analysis (SWMSPCA) is used to extract the vibration feature of the ball mill in the presence of
variable density ores i.e., iron ore and limestone.