A sliding-window based signal processing method for characterizing clusters in gas-solids high-density CFB reactor

Particle clusters in CFB risers were identified from the instantaneous solids holdup signals by a new sliding-window based signal processing method. By shifting the sliding time window and calculating the mean and the standard deviation within it, a non-linear threshold curve for identifying the clusters was derived instead of the conventional constant threshold. The optimal sliding window size was determined as Wb = 1024 data points based on the bisection method on the entire piece of signals. Using the proposed method, a more realistic characterization of the clusters in both the HDCFB and LDCFB was obtained by considering the bulk fluctuation of the gas-solids flow. The clusters in the HDCFB have higher solids holdup and lower velocity than that in the LDCFB. The HDCFB is also found to have a greater number of loose clusters for better gas-solids contacting and exchanges in the center of the riser.

Development of Wideband Multi-Channel Receiver for Direction Finding of Communication Signals

In wireless environments, wideband receivers are used in a communication intelligent system to detect unknown signals and obtain azimuth information. To design a wideband receiver that performs multiple signal detection and direction finding simultaneously, it is necessary to consider a reception structure composed of multiple channels. In this paper, we propose a wideband multi-channel receiver for direction finding of unknown wideband communication signals including frequency hopping signals. A signal processing method for detecting received signals and estimating azimuth information is presented, and components of the manufactured wideband receiver are described. In addition, test results of the signal detection performance by mounting the proposed wideband multi-channel receiver on the flight system are included.

The design and fabricate a pulse shape discriminator apply signal processing method using EJ-301 detector

The high quality measurements of neutron energy spectra are required in various fields of research and applications. However, in many cases the contribution of gamma background causes the inaccuracy of neutron spectrum. Therefore, the discrimination of gamma-ray events in neutron spectrum is necessary. In this article, an algorithm for digital implementation of the chargecomparison method for n/γ discrimination based on Digital Signal Processing technique is described. Furthermore, the APX-500 board was used as a hardware for the development of a Pulse Shape Disciminator, and is equipped with ADC ADM-414 14 bit-100 MSPS. The fully system has been tested with EJ-301 detector, using 252Cf neutron source.

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.