Vibration Feature Extraction and Analysis of Industrial Ball Mill Using MEMS Accelerometer Sensor and Synchronized Data Analysis Technique

Abstract

The use of advanced technologies such as Micro-electromechanical system (MEMS) sensors and low power wireless communication hold a great promise for optimal performance of industrial wet ball mill. The direct translation of the natural phenomena of the batch mill in a lab setup to a continuous process mill in the industry is quite perplexed in the nature of their intent and operating conditions. In this paper, the vibration signatures are analyzed for industrial wet ball mill using a MEMS accelerometer sensor. The signals are acquired using two wireless accelerometer sensors; mounted at feed and discharge end of the ball mill to validate the grinding status of the copper ore. The vibration spectrum before and after feed are compared to estimate the actual grinding status of the ore inside the mill. A limiting threshold level for the intensity is identified from the spectral analysis to monitor the desired grinding status of the ore. The high frequency (ZigBee) transmission loss due to diffraction is also compensated by the novel arrangement of the sensor transceiver. Finally, Pearson correlation technique is used to analyze the effect of sample length and its dependency with the rpm of the mill in determining the actual vibration signature.