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The loading state of the vehicle has a significant impact on vehicle stability. On the fundamental of the load distribution, the vehicle can be controlled, which can effectively enhance the stability of the operation (Baykasoglu et al., 2013; Golias et al., 2013). Therefore, the real-time on-line monitoring of the dynamic load status and early warning can effectively reduce the probability of traffic accidents that occur due to the abnormal loading state of the vehicle as running (Faulin et al., 2011; Hirsch, 2013; Malairajan et al., 2013).
At present, few researches have been done on the method of load dynamic detection. The BP neural network method was used to identify the dynamic load of the air suspension system, but the calculation accuracy is not ideal (Yang et al., 2010). Chen et al., (2011) designed a paste-based strain sensor vehicle overload monitoring system, but the vehicle load detection sensor structure was complex and at high cost, so the practicality is poor. Based on optical fiber sensor, a kind of vehicle load calculation strategy was designed (Yuan et al., 2005). Niedzwiecki et al., (1996) studied the application of adaptive filtering in vehicle dynamic weighing. The key of the dynamic load detection is the extraction of dynamic and static loads, which has not been studied in detail.
After the proposal of the empirical mode decomposition (EMD) method, it has been widely applied in the field of signal processing due to self-adaptability of decomposition (Flandrin et al., 2004; Liu et al., 2016; Liu et al., 2006; Xun et al., 2008; Loutridis, 2004; Loh et al., 2001; Balocchi et al., 2004; Lei et al., 2013; Flandrin et al., 2004). The published literature (Huang et al., 1998) has been quoted more than 15000 times in Google Scholar. In order to solve the mode mixing problem, Wu et al., (2009) proposed the ensemble empirical mode decomposition (EEMD). Complementary ensemble empirical mode decomposition (CEEMD) method (Yeh et al., 2010) can reduce the calculation error due to added white noise; and therefore, this method was widely noted in practical engineering problems (Tang et al., 2015; Li et al., 2014; Li et al., 2015; Jallon et al., 2013). Hereafter, Complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was proposed in order to improve the algorithmic efficiency and had a quicker calculation speed (Torres et al., 2011). As the noise is added self-adaptively, the ideal decomposition results can be obtained after few times of iterations. This method is also applied in the biological and mechanical fields (Li et al., 2014; Hassan et al., 2015; Navarro et al., 2012; Lei et al., 2015; Humeau-Heurtier et al., 2015; Jimenez et al., 2014; Wu et al., 2014; Sadek et al., 2015; Cui et al., 2015; Loomis et al., 2014).