Vibration Signal Denoising Using Deep Learning

Structure vibration signals induced by footsteps are widely used for tasks like occupant identification, localization, human activity inference, structure health monitoring and so on. The vibration signals are collected as time series with amplitude values. However, the collected signals are always noisy in practice due to the influence of environmental noise, electromagnetic interference and other factors. The presence of noise affects the process of signal analysis, thus affecting the accuracy and error of the final tasks. In this paper, we mainly explore the denoising methods for footstep-induced vibration signals. We have considered different kinds of noise including stationary noises such as gaussian noises and non-stationary noises such as item-dropping vibration noise and music noises.

Fuzzy inference system application for oil-water flow patterns identification

With the continuous development of the petroleum industry, long-distance transportation of oil and gas has been the norm. Due to gravity differentiation in horizontal wells and highly deviated wells (non-vertical wells), the water phase at the bottom of the pipeline will cause scaling and corrosion in the pipeline. Scaling and corrosion will make the transportation process difficult, and transportation costs will be considerably increased. Therefore, the study of the oil-water two-phase flow pattern is of great importance to oil production. In this paper, a fuzzy inference system is used to predict the flow pattern of the fluid, get the prediction result, and compares it with the prediction result of the BP neural network. From the comparison of the results, we found that the prediction results of the fuzzy inference system are more accurate and reliable than the prediction results of the BP neural network. At the same time, it can realize real-time monitoring and has less error control. Experimental results demonstrate that in the entire production logging process of non-vertical wells, the use of a fuzzy inference system to predict fluid flow patterns can greatly save production costs while ensuring the safe operation of production equipment.