Efficient Energy-based Orthogonal Matching Pursuit Algorithm for Multiple Sound Source Localization with Unknown Source Count

In this paper, we propose a compressed sensing (CS) sound source localization algorithm based on signal energy to solve the problem of stopping iteration condition of orthogonal matching pursuit reconstruction algorithm in compressed sensing. The orthogonal matching tracking algorithm needs to stop iteration according to the number of sound sources or the change of residual. Generally, the number of sound sources cannot be known in advance, and the residual often leads to unnecessary calculation. Because the sound source is sparsely distributed in space, and its energy is concentrated and higher than that of the environmental noise, the comparison of the signal energy at different positions in each iteration reconstruction signal is used to determine whether the new sound source is added in this iteration. At the same time, the block sparsity is introduced by using multiple frequency points to avoid the problem of different iteration times of different frequency points in the same frame caused by the uneven energy distribution in the signal frequency domain. Simulation and experimental results show that the proposed algorithm retains the advantages of the orthogonal matching tracking sound source localization algorithm, and can complete the iteration well. Under the premise of not knowing the number of sound sources, the maximum error between the number of iterations and the set number of sound sources is 0.31.

Gear tooth breakage analysis based on multi-order modulated sideband RMS trending method

The fault characteristic signal energy for early gear tooth breakage is relatively weak and easily drowned by other signals, which is not conducive to the study of the fault development stage. A multi-order modulated sideband RMS (Root Mean Square) trend analysis method is proposed to analyse the development trend of the broken gear fault characteristics. By using this method to analyse gear breakage faults, the multi-order modulated sideband RMS trend analysis method can effectively determine the fault deterioration and fault stabilisation stages.

Fatigue Damage Detection and Risk Assessment via Wavelet Transform and Neural Network Analysis of Ultrasonic Signals

This paper develops a data-driven autonomous method for detection of fatigue damage and classification of the associated damage risk in mechanical structures, based on ultrasonic signal energy. The underlying concept is built upon attenuation of the signal and stability of the attenuation process. The attenuation provides pertinent information for damage quantification, whereas the stability represents resistance towards the fatigue damage growth. The proposed neural network (NN) model has been trained using the scaled conjugate-gradient back-propagation method. The NN model is capable of damage detection and damage classification into five classes of increasing risk. The Daubechies wavelet transform has been used to reduce the noisy pattern of the ultrasonic signal energy by using the associated approximation coefficients. The results show that the proposed method of approximation signal energy can detect and classify the damage with an accuracy of up to ∼ 9 8 . 5 % .

Adaptive Unsaturated Bistable Stochastic Resonance Multi-Frequency Signals Detection Based on Preprocessing

Stochastic resonance (SR) has been widely used for extracting single-frequency weak periodic signals. For multi-frequency weak signals, empirical mode decomposition (EMD) can adaptively decompose the complex signal, but this method also suffers from mode mixing, which affects the accuracy of detection. SR can convert part of the noise energy into signal energy, which compensates for the defects of EMD. According to the advantages of SR and EMD, we constructed a multi-frequency signals detection method using adaptive unsaturated bistable SR based on EMD (EMD-AUBSR). In this study, we avoid the inherent saturation of SR by reconstructing the potential function and improve the multi-frequency signals detection ability by adding the preprocessing element. For strong background noise, the experimental results show that this proposed can effectively detect multi-frequency weak signals and decrease signal aliasing, whereas EMD alone cannot.

Assessment of the Mechanism of Adhesive Destruction Coatings

Information on deformation of coatings at different loading levels during peeling is given. It is shown that the deformations of the organosilicon coating appear already at the initial stages of loading, and the obtained data correlate with the data on acoustic emission. For a polyvinyl acetate coating at low loading levels, equal to 0.3-0.4 R, the release of acoustic signal energy is not observed. The absence of signals with a large amplitude at loading levels up to 0.7-0.8 R indicates the development of plastic deformations in the contact zone of the coating with the substrate. It was found that early localization of bond breaking in the contact zone, leading to the formation of a fracture focus, occurs in organosilicon coatings KO-168. For PVAC coatings, an abrupt growth of cracks is characteristic, which is preceded by its slow growth. The pseudoplastic mechanism of destruction of PVAC coatings has been established. The influence of the nature of the substrate on the change in the nature of the peeling of the coatings is considered.

Water Content Monitoring in Water-in-Crude-Oil Emulsions Using an Ultrasonic Multiple-Backscattering Sensor

This work shows the application of an ultrasonic multiple-scattering sensor for monitoring water-in-petroleum emulsions. The sensor consists of a commercial ultrasonic transducer with an array of cylindrical scatterers placed in the near field. The scatterers are thin metal bars arranged in rows in front of the transducer. The backscattering signals were analyzed by calculating the wave energy and by a cross-correlation between signal segments; they were also used to determine the propagation velocity in the emulsions. The tests performed used emulsions with water volume concentrations from 0 to 50%. The results showed that both the signal energy and propagation velocity strongly depended on the concentration of water in the emulsion. Therefore, the ultrasonic multiple-scattering sensor can be used for on-line and real-time monitoring of the water content in water-in-crude-oil emulsions.