## 1 Introduction  
To stay competitive, many companies all over the world try to go beyond the costly industry standard of preventative maintenance by implementing predictive maintenance procedures. For the industry related to oil exploitation, nuclear power and wind power, predictive maintenance procedures are the backbone in order to improve the operation and maintenance. Additionally, those industries for operation use drive trains that usually contain a gearbox as one of the key components. Vibration analysis is the most widely-used predictive maintenance procedure in gearbox predictive maintenance. In many mechanical systems, gearboxes are widely used for power transmission. Its condition is very important for the performance of the whole mechanical system. Therefore, many companies--and in the last two decades of academia also--have paid great attention to the gearbox condition monitoring. Vibration analysis can be applied very effectively to gearbox condition monitoring and predictive maintenance. In 2006, [PERSON] et al. [1] reviewed the machinery fault diagnosis and prognosis in the condition-based maintenance view. The authors considered fault diagnosis as an important part of condition-based maintenance. We know that the gearbox is composed of many parts, such as gears, shafts, bearings, etc. In addition, there are many types of gearbox. The most typical are the fixed-axis gearbox and planetary gearbox. In 2011, bearing fault diagnosis work was reviewed by [PERSON] and [PERSON] [2]. Later, [PERSON] and [PERSON] [3] reviewed the signal processing techniques used in bearing fault diagnosis. This focuses mainly on the recent developments of the fault diagnosis signal processing techniques. Then, they discussed the advantages and disadvantages of these methods used in bearing fault diagnosis. For time-frequency analysis, [PERSON] et al. [4] reviewed more than 20 major time-frequency analysis methods used in fault diagnosis since 1990 and examined their advantages and disadvantages. As a major time-frequency analysis method, multiwavelet's development in fault diagnosis has been reviewed by [PERSON] et al. [5]. Finally, they discussed the existing problems and future research directions. As we know, the fault mechanism of the planetary gearbox is very different from fixed-axis gearbox. Therefore, many authors pay great attention to the fault diagnosis of planetary gearboxes. [PERSON] et al. [6] reviewed the papers emerging in the planetary gearbox fault diagnosis domain and compared the fixed-axis and planetary gearbox fault diagnosis. Finally, they pointed to the potential research topics. Through these detailed review papers, readers will have a good knowledge of gearbox fault diagnosis. Whether for the gear or the bearing, fault will produce impulse signals with a specific period. Therefore, the main object for fault diagnosis is to detect these impulse signals. Therefore, many methods were developed to detect impulse signals. Spectral kurtosis was one such effective method used for impulse signal detection. From its proposal, it becomes an important and effective way to detect the impulse signal. Recently, [PERSON] et al. [7] reviewed the development of spectral kurtosis and pointed out its future use in fault prognosis. Nevertheless, these periodic impulse signals (PIS) produced by fault are always overwhelmed by deterministic signals produced by the regular gear mesh and structural vibration. These deterministic signals are also called discrete frequency signals or the deterministic component. In order to detect the impulse signal more easily, it is necessary to suppress the deterministic signals. There are several methods developed to subtract the deterministic component from the original signal. The most dominant were: the autoregressive (AR) model method [8; 9; 10; 11; 12; 13; 14; 15], time synchronous averaging [16; 17; 18], adaptive noise cancellation [19; 20] and the cepstrum pre-whitening method [21; 22; 23].