# Detection of Pneumatic Conveying by Acoustic Emissions
## 1 Introduction
[PERSON] [14] used audio AE signals to monitor particle size in pneumatic conveying, and after decomposition by wavelet analysis, the energy fractions of different components could predict particle size through a neural network. The result showed that relative error was within 23% when experimental particle sizes (e.g. 75/90/110/150/200 \(\upmu\)m in the research) were in certain ranges. Additionally, the higher error range indicated that the signal processing should be improved. Among the scientific research mentioned above, only a little portion of relative researches have denoised the raw signal to extract effective signals to develop the relationship between the particle size and the AE signal. Moreover, the acoustic emission signal propagation has not been considered. The propagation process is rapid in the metal pipe. In the relative references mentioned above, the total signal introduced into the sensors is a complex mixture of signals generated by the whole system, making it difficult to detect local characteristics of the particle size. Some researchers have applied the acoustic emission to the research on particle mass flow rate monitoring in the multiphase flow. [PERSON] [15] decomposed the AE signal by utilizing the seven scales wavelet decomposition method and proposed a model of particle mass flow rate to explore the quantitative relationship between the AE energy, superficial gas velocity and particle mass flow rate. The model is under strict assumption of low speed flow conditions and uniform particle distribution. Nevertheless, particle size distribution was described by the Rosin-Rammler distribution instead of uniform particle distribution. It means that the model developed by [PERSON] was useful in limited situation. [PERSON] [16] developed a regression model between AE energy and particle mass flow rate by using the wavelet packet decomposition and partial least square method. [PERSON] [17] developed an acoustic emission monitoring system to detect the sand mass flow rate and some other parameters. The relationship between the sand mass flow rate and power spectrum amplitude is linearity. His research focused on liquid-solid phases systems instead of pneumatic conveying. [PERSON] [18] studied the relationship of the signal frequency, area of the power spectrum estimation and solid particle size and mass flow rate. The combination of AE signal and power spectrum estimation to detect parameters of pneumatic conveying is feasible and effective. However, the effects of multiple impacts of the same particle were ignored. Through the literature mentioned above, it is apparent that the researchers did not consider the mixture of the signal problem adequately. Involving in the signal processing, the wavelet packet decomposition is used widely. However, the selection of wavelet function and decomposition level is a complex process, which needs manual selection in many situations. In this paper, acoustic emission signals collected by different probes, subjected to different particle mass flow rates with different particle sizes, were studied. Compared with the wavelet packet decomposition, the ensemble empirical mode decomposition (EEMD) is a more self-adaptive algorithm. The ensemble empirical mode decomposition method was used to decompose the signal, and the effective signal is extracted to establish a specifying relationship between AE signal and mass flow rate, and the relationship of the signal and particle size is built as well.