Condition monitoring on complex machinery for predictive maintenance and process control

The rotating machinery for engineering process and materials science has become faster and lightweight recently. The machinery has been required to run for longer periods of time and reliable operations. Because machine breakdowns and consequent down times severely affect the productivity of factories or the safety of products and process success depends on the reliability and the efficiency of related key components, the requirements for enhanced reliability of equipment are more critical than ever before. Firstly, this paper describes applying vibration theory to detect machinery fault via the measurement of vibration and voice monitoring machinery working condition. This paper proposes a useful way of vibration analysis and source identification in complex machinery. An actual experiment case study on a cold-roll press machine has been conducted in aluminum factory. Based on intensity measure, statistical and FFT frequency analysis methods, the experiment results indicate that fewer sensors and less measurement and analysis time can achieve condition monitoring, fault diagnosis, and damage forecasting. As a result, lower in running operation and maintenance costs and increased in productivity and efficiency can be achieved.