HYDRAULIC PRESSURE SIGNAL DENOISING USING THRESHOLD SELF-LEARNING WAVELET ALGORITHM

A pre-filter combined with threshold self-learning wavelet algorithm is proposed for hydraulic pressure signals denoising. The denoising threshold is self-learnt in the steady flow state, and then modified under a given limit to make the mean square errors between reconstruction signals and desirable outputs minimum, so the corresponding optimal denoising threshold in a single operating case can be obtained. These optimal thresholds are used for the whole signal denoising and are different in various cases. Simulation results and comparative studies show that the present approach has an obvious effect of noise suppression and is superior to those of traditional wavelet algorithms and back-propagation neural networks. It also provides the precise data for the next step of pipeline leak detection using transient technique.

Neural Network Prediction Model for Ship Hydraulic Pressure Signal Under Wind Wave Background

The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then,a predictive model is set up by using the radial basis function(RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not.The practical result shows that the method can improve the signal to noise ratio(SNR) obviously.

Intelligent Identification of Rotating Stall for Centrifugal Compressor Based on Pressure Pulsation Signals and SDKAE Network

Most accidents of centrifugal compressors are caused by fluid pulsation or unsteady fluid excitation.Rotating stall,as an unstable flow phenomenon in the compressor,is a difficult point in the field of fluid machinery research.In this paper,a stack denoising kernel autoencoder neural network method is proposed to study the early warning of rotating stall in a centrifugal compressor.By collecting the pressure pulsation signals of the centrifugal compressor under different flow rates in engineering practice,a double hidden layer sparse denoising autoencoder neural network is constructed.According to the output labels of the network,it can be judged whether the rotation stall occurs.At the same time,the Gaussian kernel is used to optimize the loss function of the whole neural network to improve the signal feature learning ability of the network.From the experimental results,it can be seen that the flow state of the centrifugal compressor is accurately judged,and the rotation stall early warning of the centrifugal compressor at different speeds is realized,which lays a foundation for the research of intelligent operation and maintenance of the centrifugal compressor.

A combined signal processing approach against coherent interference with pressure and particle velocity

Comparing with traditional underwater acoustic system which only utilizes pressure information, combine sensor system processes pressure together with particle velocity information of sound field. More information certainly brings nicer processing result. By using spatial directional information collected by combine sensor, the Coherent Interference Energy Suppress (CIES) technology, which can effectively suppress coherent interference and detect linear spectrum signal and wide-band continuous-spectrum signal as well, is presented. Current research has shown favorite result, and further research is going on.