Adaptive Fourier Decomposition Based Time-Frequency Analysis

The attempt to represent a signal simultaneously in time and frequency domains is full of challenges. The recently proposed adaptive Fourier decomposition （AFD） offers a practical approach to solve this problem. This paper presents the principles of the AFD based time-frequency analysis in three aspects： instantaneous frequency analysis, frequency spectrum analysis, and the spectrogram analysis. An experiment is conducted and compared with the Fourier transform in convergence rate and short-time Fourier transform in time-frequency distribution. The proposed approach performs better than both the Fourier transform and short-time Fourier transform.

A quantitative analysis method for GPR signals based on optimal biorthogonal wavelet

Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.

Fish as a source of acoustic signal measurement in an aquaculture tank:Acoustic sensor based time frequency analysis

Acoustic signals travels rapidly in water without attenuating fish telemetry.The digital sonar and passive acoustic has been used for fish monitoring and fish feeding.However,it is an urgent need to introduce new techniques in order to monitor the growth rate of fish during harvesting and without causing adverse effects to the harvested fish.Therefore,a novel technique was introduced to probe the acoustic signal frequency ratio in absence and presence of the fish in tanks,which basically uses an acoustic sensor(hydrophone),acoustic signal processing system(scope meter),and a signal monitoring system(fluke view).Acoustic signals were selected from 48-52 Hz frequency,measure of dispersion of frequency signal represented as a function of time via Xlstat software.Measure of dispersion displayed a significant effect of acoustic signal in the presence and absence of the fish in tanks.These optimised protocols of this study will help to control and prevent excessive wastage of feed and enhance proper utilization of feed that chiefly enhance fish growth in aquaculture.

Feature Extraction of Acoustic/Seismic Signal of Target

A microphone and a seismic sensor always become a basic unit of UGS(unattended ground sensors) system. The mechanism of acoustic and seismic property of target and its propagation are described. The acoustic and seismic signals of targets are analyzed with time frequency distribution according to its non stationary property. Narrow band energy function (NEF) and local power spectral density (LPSD) are proposed to extract features for target identification. Experiment results show that local power spectral density indicates corresponding target clearly.

Quantitative detection of locomotive wheel polygonization under non-stationary conditions by adaptive chirp mode decomposition

Wheel polygonal wear is a common and severe defect,which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive.Due to non-stationary running conditions(e.g.,traction and braking)of the locomotive,the passing frequencies of a polygonal wheel will exhibit time-varying behaviors,which makes it too difficult to effectively detect the wheel defect.Moreover,most existing methods only achieve qualitative fault diagnosis and they cannot accurately identify defect levels.To address these issues,this paper reports a novel quantitative method for fault detection of wheel polygonization under non-stationary conditions based on a recently proposed adaptive chirp mode decomposition(ACMD)approach.Firstly,a coarse-to-fine method based on the time–frequency ridge detection and ACMD is developed to accurately estimate a time-varying gear meshing frequency and thus obtain a wheel rotating frequency from a vibration acceleration signal of a motor.After the rotating frequency is obtained,signal resampling and order analysis techniques are applied to an acceleration signal of an axle box to identify harmonic orders related to polygonal wear.Finally,the ACMD is combined with an inertial algorithm to estimate polygonal wear amplitudes.Not only a dynamics simulation but a field test was carried out to show that the proposed method can effectively detect both harmonic orders and their amplitudes of the wheel polygonization under non-stationary conditions.

Improved Symbiotic Organism Search with Deep Learning for Industrial Fault Diagnosis

Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems.Both structural and non-structural data of industrial systems are collected,which covers data formats of time-series,text,images,sound,etc.Several researchers discussed above were mostly qualitative,and ceratin techniques need expert guidance to conclude on the condition of gearboxes.But,in this study,an improved symbiotic organism search with deep learning enabled fault diagnosis(ISOSDL-FD)model for gearbox fault detection in industrial systems.The proposed ISOSDL-FD technique majorly concentrates on the identification and classification of faults in the gearbox data.In addition,a Fast kurtogram based time-frequency analysis can be used for revealing the energy present in the machinery signals in the time-frequency representation.Moreover,the deep bidirectional recurrent neural network(DBiRNN)is applied for fault detection and classification.At last,the ISOS approach was derived for optimal hyperparameter tuning of the DL method so that the classification performance will be improvised.To illustrate the improvised performance of the ISOSDL-FD algorithm,a comprehensive experimental analysis can be performed.The experimental results stated the betterment of the ISOSDLFD algorithm over current techniques.

Dynamic Positioning Control of Surge−Pitch Coupled Motion for Small-Waterplane-Area Marine Structures

For general dynamic positioning systems,controllers are mainly based on the feedback of motions only in the horizontal plane.However,for marine structures with a small water plane area and low metacentric height,undesirable surge and pitch oscillations may be induced by the thruster actions.In this paper,three control laws are investigated to suppress the induced pitch motion by adding pitch rate,pitch angle or pitch acceleration into the feedback control loop.Extensive numerical simulations are conducted with a semi-submersible platform for each control law.The influences of additional terms on surge−pitch coupled motions are analyzed in both frequency and time domain.The mechanical constraints of the thrust allocation and the frequency characters of external forces are simultaneously considered.It is concluded that adding pitch angle or pitch acceleration into the feedback loop changes the natural frequency in pitch,and its performance is highly dependent on the frequency distribution of external forces,while adding pitch rate into the feedback loop is always effective in mitigating surge−pitch coupled motions.