All Time-Scale Decomposition Method and Its Application in Gear Fault Diagnosis

Adaptive signal decomposition methods, especially without parameters, have become a popular way of diagnosing mechanical faults due to their capability to process mechanical vibration signals adaptively. Empirical mode decomposition (EMD), local mean decomposition (LMD), and local characteristic-scale decomposition (LCD) are typical parameterless adaptive signal decomposition methods currently applied to mechanical fault diagnosis. All of these methods use extreme points to construct baselines, and the mono-component signals are decomposed from an original signal by multiple sift. However, since these methods define time-scale parameters only through extreme points, they are prone to lose the local feature information of an original signal and lead to mode mixing. Aiming at the above problems, the time-scale parameters is defined by using extreme points and zero crossing points simultaneously in this paper. Therefore, we propose a new adaptive signal decomposition method called all time-scale decomposition (ATD). A complex signal can be adaptively decomposed into multiple independent all time-scale components by the ATD method. The baselines of ATD are constructed jointly by extreme points and zero crossing points, so ATD can extract more local feature information of a signal to suppress the mode mixing. First, the principle of ATD is proposed and the method of determining zero crossing points is introduced in this paper. Then, an empirical formula for compensation factor used to determine zero crossing points is deduced. Finally, ATD is verified by the simulation signals and gear signals, respectively. The results indicate that ATD has stronger mode mixing suppression capability and decomposition performance than EMD, LMD, and LCD, and it can be effectively used for gear fault diagnosis.