Fault diagnosis technology of rolling bearing based on LMD and BP neural network

Rolling bearing is a key component of rotary machinery, and its working state tends to affect the reliability and lifetime of the equipment. The traditional way to ensure productivity is to replace rolling bearings regularly which is a waste of resources and could increase maintenance costs, because of the large discreteness of rolling bearing's lifetime. So changing the fail-and-fix practices into a predict-and-prevent methodology is the most effective method. At present, the most common way used for rolling bearing fault diagnosis is vibration signal analysis. This paper explores the Local Mean Decomposition (LMD) algorithm to self-adaptively decompose the vibration signal into several PF components, and further calculates the power spectrum of each PF component, and extracts the sum of power value at the characteristic frequency band of inner ring fault/outer ring fault/ rolling element fault. The sum of power value is used to train the BP neural network. And the experimental results show that the BP neural network trained in this way has high classification ability.