Rotating machine fault diagnosis using empirical mode decomposition

In this paper, an empirical mode decomposition (EMD) based approach for rotating machine fault diagnosis is investigated. EMD is a new time–frequency analyzing method for nonlinear and non-stationary signals. By using EMD a complicated signal can be decomposed into a number of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. The IMFs, working as the basis functions, represent the intrinsic oscillation modes embedded in the signal. However, our research shows that IMFs sometimes fail to reveal the signal characteristics due to the effect of noises. Hence, combined mode function (CMF) is presented. With CMF, the neighboring IMFs are combined to obtain an oscillation mode depicting signal features more precisely. The adaptive filtering features of EMD and CMF are discussed, and the simulation signals are applied to test their performance. Finally, a practical fault signal of a power generator from a thermal-electric plant is analyzed to diagnose the fault by using EMD and CMF. The results show that EMD and CHF can extract the rotating machine fault characteristics and identify the fault patterns effectively.