# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 1 Introduction  
In industrial production, the normal operation of mining machinery is the guarantee for the economic growth of enterprises. The gear and bearing in the gearbox are the most important transmission parts. When one of them fails, complex mechanical behavior will appear. In the unbalanced mechanical environment, other parts will appear fatigued, and further develop into the weak fault and significant fault. At the same time, their vibration signals are coupled with each other. In the case of strong noise environments and complex transmission paths, weak fault noises are submerged, and further leakage or misdiagnosis occurs [1; 2; 3; 4; 5]. Mechanical intelligence fault diagnosis usually includes three links. (1) Signal acquisition: Obtain multi-physical monitoring signals radiated by mechanical equipment to reflect the health status of equipment. (2) Feature extraction: By analyzing the acquired monitoring data, extract features to reveal fault information. (3) Fault identification and prediction: Based on the extracted features, faults are identified and predicted through artificial intelligence models and methods. Over decades, with the development of computer science, a series of different time-frequency analysis methods have emerged. In 1998, empirical mode decomposition (EMD) was proposed, and then a large number of applications in signal processing [6; 7; 8], but it has endpoint effects and mode aliasing phenomena. In 2005, ensemble empirical mode decomposition (EEMD) was proposed [9; 10; 11];it can not only overcome the endpoint effects of EMD but also weaken the mode aliasing phenomenon, so it has been applied in industrial production by a large number of scholars. However, it needs to set the times of additions and the amplitude of white noise; so far, no formula can adaptively determine the white noise amplitude value. At the same time, the number of additions is also not adaptive. When the number is too large, the calculation amount is too large; otherwise, the noise cannot be effectively averaged [12; 13; 14]. In order to improve the decomposition efficiency, complementary ensemble empirical mode decomposition (CEEMD) was proposed, which weakens the effect of the contained noise on the original signal by adding different positive and negative white noise multiple times [15], and is widely used in fault diagnosis [16; 17; 18]. CEEMD can improve the decomposition efficiency of EEMD, but the noise part of the original signal still exists. In other words, the noise component of the original signal still exists in the multiple Intrinsic mode functions(IMFs) decomposed. If it is not removed in time, it will lead to leakage or misdiagnosis. So, it is necessary to propose an effective method to eliminate noise components. In recent years, variable mode decomposition (VMD) [19; 20; 21] has been applied to fault diagnosis. It can decompose the original signal into several different time scale functions from low frequency to high frequency. The center frequency of each time scale is different, so its decomposition efficiency is higher than the EEMD and CEEMD. However, the method is limited to the choice of the number of decomposition layers and the penalty factor. So far there is still no suitable method to adaptively determine these two parameters. Therefore, in order to adaptively reduce the noise of complex vibration signals, this article uses the CEEMD decomposition method. In strong background noise, each fault feature frequency is different [22; 23; 24; 25; 26; 27; 28; 29], so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. The article employs CEEMD to adaptively reduce the noise of complex vibration signals, but CEEMD has the problem of mode aliasing and the difficulty of removing noise components.