Machinery Vibration Signals Analysis and Monitoring for Fault Diagnosis and Process Control

Abstract

The vibration signals contain a wealth of complex information that characterizes the dynamic behavior of the machinery. Monitoring rotating machinery is often accomplished with the aid of vibration sensors. Transforming this information into useful knowledge about the health of the machine can be challenging due to the presence of extraneous noise sources and variations in the vibration signal itself. This paper describes applying vibration theory to detect machinery fault, via the measurement of vibration and voice monitoring machinery working condition, also proposes a useful way of vibration analysis and source identification in complex machinery. An actual experiment case study has been conducted on a mill machine. The experiment results indicate that fewer sensors and less measurement and analysis time can achieve condition monitoring, fault diagnosis, and damage forecasting. Further applications allow feedback to the process control on production line.