Suitable Signal Processing Techniques Research Articles

A Study of 2D Roughness Periodical Profiles on a Flat Surface Generated by Milling with a Ball Nose End Mill.

This paper presents a study of 2D roughness profiles on a flat surface generated on a steel workpiece by ball nose end milling with linear equidistant tool paths (pick-intervals). The exploration of the milled surface with a surface roughness tester (on the pick and feed directions) produces 2D roughness profiles that usually have periodic evolutions. These evolutions can be considered as time-dependent signals, which can be described as a sum of sinusoidal components (the wavelength of each component is considered as a period). In order to obtain a good approximate description of these sinusoidal components, two suitable signal processing techniques are used in this work: the first technique provides a direct mathematical (analytical) description and is based on computer-aided curve (signal) fitting (more accurate); the second technique (synthetic, less accurate, providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform. This study can be seen as a way to better understand the interaction between the tool and the workpiece or to achieve a mathematical characterisation of the machined surface microgeometry in terms of roughness (e.g., its description as a collection of closely spaced 2D roughness profiles) and to characterise the workpiece material in terms of machinability by cutting.

Identifying a Suitable Signal Processing Technique for MI EEG Data

Motor imagery (MI) electroencephalography (EEG) technology is acquiring great attention from researchers due to its remarkable real-world applications. EEG signals inherit a high degree of non-stationarity, making their analysis not modest. Hence, choosing an appropriate signal processing approach becomes crucial. This comparative paper aims to identify a suitable signal processing method among famous approaches, namely short-time Fourier transform (STFT), continuous wavelet transform (CWT), and two variations of discrete wavelet transform maximal overlap DWT (MODWT) and MODWT multiresolution analysis (MODWTMRA). Different mother wavelet basis filters experimented with wavelet methods: Morse, Amor, Bump, Symlets, Daubechies, Coiflets, and Fejér-Korovkin. The different methods were tested on the classification of the right-hand and left-hand motor imagery tasks using the brain-computer interface (BCI) competition IV 2b dataset. A shallow convolutional neural network containing a single convolution layer was first trained and then used for classification. The experimental outcomes verified that MI EEG signals can be better analyzed and recognized using the maximal overlap-based signal processing methods. The classification accuracy proved that MODWT and MODWTMRA with the Symlets wavelet outperformed the other methods.

Empirical mode decomposition based techniques for imaging of shallow delamination in concrete using impact echo

An impact echo (IE) signal comprises different frequency components as a result of interaction between stress waves and targets. Empirical mode decomposition (and its improved techniques), an adaptive and fully data-driven technique have been used to separate different components of an IE signal into what is known as intrinsic mode functions (IMF). The applications of improved versions of EMD and their advantages over other new adaptive decomposition techniques have also been demonstrated. This separation of modes differentiates constituent signals such as surface wave, echo wave, and flexural wave into corresponding IMFs and thus allows reconstruction of a new signal using only desirable components. An image reconstruction strategy for the selection of appropriate IMFs based on correlation coefficient is found to produce an improved image of the delamination or flaws compared to the raw signal images. The IE signal processed with noise-assisted EMD based techniques profoundly increased the accuracy of flaw features over the raw data. This improvement is validated with a quantification approach using amplitude histogram and binary mapping of flaws in B-scan and C-scan respectively. The study demonstrated that the flexural mode of vibration produced due to shallow delamination can be used for accurate imaging of flaws provided a suitable signal processing technique is used. The technique employed in this study can also be extended for imaging of deep delamination which is exhibited by echo waves in IE signals.

Design and development of a high-speed data acquisition system for acquiring acoustic emission signals

One of the most promising non-destructive testing method suitable for in-situ detection of damages in structures and materials is Acoustic emission (AE) testing. AE waveforms can be related to various source mechanisms using suitable signal processing techniques and are characterized by low amplitude, high frequency and short duration. Data acquisition system to capture and analyze such high frequency range AE signals are expensive. One of the key components in such commercial systems are the signal conditioning units. Thus, in the present work, a signal conditioning unit (SCU) is designed and developed to process the AE signals. Further, a low-cost AE signal data acquisition system was developed by using the fabricated signal condition unit. Signals from the data acquisition system was saved using Python libraries for further analysis. The developed system was tested at lab-scale by acquiring the signals generated during pencil lead break tests on a stainless steel and composite plates. The performance of the in-house developed AE data acquisition system has been compared with the commercially available one and the comparison results are presented in this paper.

Fault severity classification of ball bearing using SinGAN and deep convolutional neural network

Condition monitoring and diagnosis of a bearing are very important for any rotating machine as it governs the safety while the machine is in operating condition. To construct a feature vector selection of suitable signal processing techniques is a challenge for vibration-based condition monitoring techniques. In the methodology proposed, Short Time Fourier Transform (STFT), Walsh Hadamard Transform (WHT) and Variable Mode Decomposition (VMD) are used to generate 2-D time-frequency spectrograms from the various fault conditions of bearing. When Deep learning techniques apply for fault diagnosis, a large amount of dataset is required for training of machine learning model. To overcome this issue single image Generative Adversarial Network (SinGAN) as a data augmentation technique, utilized for generating additional 2-D time-frequency spectrograms from various fault conditions of ball bearing. To detect fault severity, four deep learning algorithms, ResNet 34, ResNet50, VGG16, and MobileNetV2 are used as a classifier. Experiments are conducted on a rolling bearing dataset provided by the bearing data center of Case Western Reserve University (CWRU) for validating the utility of methodology proposed. Results show that the proposed methodology enables to detect fault severity level with high classification accuracy.

Enhanced GNSS Authentication Based on the Joint CHIMERA/OSNMA Scheme

The authentication of the navigation signals can be considered as the contribution of the system to the robustness against spoofing attacks and it is becoming an important requirement for a growing number of user communities. GPS and Galileo systems are proposing evolutions of their civil signals to embed features of authentication. For Galileo, the Open Service Navigation Message Authentication (OSNMA) is integrated in the Galileo E1 OS signal. For the GPS, the Chips-Message Robust Authentication (Chimera) solution, designed for the GPS L1C signal, is foreseen to be tested soon. On the other hand, suitable signal processing techniques can be implemented inside the receiver to monitor the quality of the received signals and protect against spoofing attacks. Such techniques shall work as a complement to the authentication strategies, to further increase the signals’ robustness. Within this context, the paper presents the Joint Chimera/OSNMA scheme , designed to be adopted by a multi-constellation receiver that already exploits both OSNMA and Chimera enhancements. The idea is to further strengthen the robustness with respect to the individual use of the two solutions, to tackle sophisticated spoofing attacks, which are able to avoid detection from navigation message authentication (NMA) techniques. The manuscript proves the high performance of the joint scheme, presenting the results of a wide bench of tests, under different scenarios of spoofing, and user conditions.

Characterization of 400 Volt High Impedance Fault With Current and Magnetic Field Measurements

Electrical faults, which can occur at all voltage levels in an electricity supply system, are a health and safety risk. Multi-branch distribution networks represent a significant ongoing challenge for fault detection, with the greatest challenge being high impedance fault (HIF) detection. To date, research has focused on higher voltage levels, and fault monitoring sensors have traditionally only been installed in limited locations within the higher voltage networks. The main contributions of this paper are to characterize a high impedance fault (HIF) involving a tree branch and to experimentally verify the feasibility of giant magneto-resistive (GMR) sensors, located distant from the overhead lines, for fault detection. In a purpose-built 400 V physical simulation test facility, we have collected current and magnetic field data during HIF involving a tree branch. We have identified new characteristics in the early stages of this fault type, which persist for a reasonable length of time but are only observable when suitable signal processing techniques are applied. New detection schemes will, therefore, need to be developed to detect such faults. GMR sensors were found to be suitable for observing the characteristics of HIF, validating their potential use for fault detection.

Phase sensitive low-coherence interferometry using microwave photonics.

We report on a low-coherence interferometer based on Microwave Photonics (MWP) which allows, for the first time to the best of our knowledge, stable determination of the interferogram's phase. The interferometer is built on suppressed carrier, double-sideband modulation, dispersive propagation in a chirped fiber Bragg grating, demodulation by electro-optical frequency down-conversion, and suitable signal processing techniques to account for modulation impairments. Taking as a reference a direct normalization of the link's microwave response, the system retrieves high-resolution interferograms, both in amplitude and phase and free from distortion induced by higher-order dispersion, in an optical path difference of 16.3 mm, surpassing previously reported values based on MWP implementations. We present representative applications targeted to the characterization of C-band sources and components, such as direct analysis of interferograms with 5.5 fs temporal resolution, Fourier-transform spectroscopy with 14 GHz spectral resolution, and optical low-coherence reflectrometry of the impulse response's amplitude of fiber Bragg gratings with 0.55 μm spatial resolution.

The Design of a Low Cost Phasor Measurement Unit

The widespread diffusion of Phasor Measurement Units (PMUs) is a becoming a need for the development of the “smartness” of power systems. However, PMU with accuracy compliant to the standard Institute of Electrical and Electronics Engineers (IEEE) C37.118.1-2011 and its amendment IEEE Std C37.118.1a-2014 have typically costs that constitute a brake for their diffusion. Therefore, in this paper, the design of a low-cost implementation of a PMU is presented. The low cost approach is followed in the design of all the building blocks of the PMU. A key feature of the presented approach is that the data acquisition, data processing and data communication are integrated in a single low cost microcontroller. The synchronization is obtained using a simple external Global Positioning System receiver, which does not provide a disciplined clock. The synchronization of sampling frequency, and thus of the measurement, to the Universal Time Coordinated, is obtained by means of a suitable signal processing technique. For this implementation, the Interpolated Discrete Fourier Transform has been used as the synchrophasor estimation algorithm. A thorough metrological characterization of the realized prototype in different test conditions proposed by the standards, using a high performance PMU calibrator, is also shown.

On the selection of advanced signal processing techniques for guided wave damage identification using a statistical approach

Advanced signal processing techniques are widely used to detect damage in structures. The current study proposed a statistical approach to the identification of structural damages using guided waves. Various signal processing techniques were applied in order to determine and improve the ways in which damage in a structure can be identified for remediation. The proposed statistical approach not only provides a quantitative identification of the damages, but can also quantify the uncertainties associated with the damage identification results. This allows the performance of various signal processing techniques to be evaluated and compared in terms of accuracy and the degree of uncertainty associated with the damage identification results. Damage identification was initially conducted using time domain guided wave signals so that the results could be used as a benchmark. Four signal processing techniques were then considered. Hilbert transform was used to extract the signal envelopes. Fast Fourier transform was applied to transform the guided wave signals from the time domain to the frequency domain. Gabor wavelet transform was employed to extract the wavelet coefficients from time–frequency domain. Discrete wavelet transform was used to decompose the guided wave signals. The frequency domain signal, signal envelopes, wavelet coefficients and discrete wavelet decomposed signals were then employed separately to identify the damages in conjunction with the proposed statistical damage identification approach. Laboratory experiments were conducted and the data were used to verify the proposed statistical approach. The levels of accuracy and degree of uncertainty associated with the damage identification results by each of the signal processing techniques were compared in detail. The results reported in this paper show that a suitable signal processing technique combined with the proposed statistical approach produces more robust identification of damages in a structure.

Detection of Simulated Steam Leak into Sodium in Steam Generator of PFBR by Argon Injection Using Signal Analysis Techniques

The Prototype Fast Breeder Reactor (PFBR) is pool-type, sodium-cooled reactor. PFBR has eight steam generators (SGs) in its secondary circuit. The availability of SG units in the secondary circuit is of critical importance, as this determines the entire plant availability. Constant exposure to high temperature and high pressure may result in water or steam leaks in the tubes of SGs by which sodium and water reaction will take place. The growth in a leak may lead to tube rupture and large pressure increase in the secondary circuit. So, an important requirement from the viewpoints of safety and economics is to detect leaks at the incipient stage. Hydrogen detection methods, which are currently used to detect the initiation of steam leaks to sodium, involve transport delays, as the hydrogen evolved during the leak has to reach the sensor location. It is possible to detect a leak of 1 g/s within 1 s by acoustic leak detection. Another advantage is that it is possible to locate the position of a leak by installing several acoustic sensors on a SG.Experiments to develop a suitable signal-processing technique were carried out in the Steam Generator Test Facility (SGTF); argon was injected into sodium at argon pressure of 2 to 10 MPa through a 0.5-mm orifice. Signal-processing techniques, an autoregressive noise variance technique, and a wavelet detailed coefficient variance technique were studied and compared for their sensitivity to detect a steam/water leak. The acoustic technique employing wavelet decomposition is found to be promising for detecting a leak at the incipient stage. This paper discusses the details of the experiments carried out and the instrumentation and signal analysis techniques used for leak detection in SGs.

Escape time characterization of pendular Fabry-Perot

In a pendular Fabry-Perot interferometer the system placed inside one of the minimum of the optomechanical potential undergoes an escape if it crosses the point of sudden change of reflectivity near the top of the potential well. We demonstrate that the loss of information that occurs retaining only the sequence of escapes, rather than the full trajectory, is mild if suitable signal processing techniques are applied to reveal the noise intensity or the presence of a coherent signal.

Soil moisture assessment by means of compressional and shear wave velocities: Theoretical analysis and experimental setup

In previous works, the authors presented the outline of a method for measuring the moisture content in agricultural soils via elastic waves, along with experimental results obtained on a specimen of sandy soil. This work illustrates other aspects and results of the research, regarding both the underlying theory and the design and realization of an improved measurement system. Firstly, the derivation of the simplified equations which are at the basis of the moisture measurement is thoroughly illustrated and discussed, starting from the more complex (and generally unmanageable) equations of elastic waves in unconsolidated porous media. The analysis suggests that by measuring the velocities of low-frequency compressional and shear waves in soils, it is possible not only to measure the water content, but also the uniformity in the water distribution. Secondly, the design and the practical realization of an experimental setup, which is able to measure the velocity of compressional and shear waves in soils, is illustrated in detail. The use of custom-built compressional and shear waves electromechanical actuators, together with geophones, low noise preamplifiers, and suitable signal processing techniques, brought to the realization of an effective and reasonably accurate measurement system.

Signal Processing Techniques for Robust Speech Recognition

In this paper, signal processing techniques which can be applied to automatic speech recognition to improve its robustness are reviewed. The choice of signal processing techniques is strongly dependent on the scenario of the applications. The analysis of scenario and the choice of suitable signal processing techniques are shown through two examples.

Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform

Monitoring of tool condition on the basis of sensor signals requires a selection of suitable signal processing technique and monitoring index, to assess the tool condition. In this paper, wavelet packet transform is used as a tool, to characterise the acoustic emission signals released from glass/phenolic polymeric composite during drilling. The results show that the selected monitoring indices from the wavelet packet coefficients are capable of detecting the drill condition effectively.

Maritime surveillance radar. Part 1: Radar scattering from the ocean surface

The successful optimisation of the detection performance of maritime surveillance radars requires a detailed knowledge and understanding of both forward and backscattering from the ocean surface. Such an understanding enables the development of suitable signal processing techniques. In part I the authors discuss radar scattering from an ocean environment. It is shown, through the analysis of full-scale measurements, how the amplitude and correlation properties of high-resolution radar backscatter (sea clutter) can be accurately represented by the compound K-distribution model which has the unique characteristic of providing realistic performance predictions for a wide range of signal processing techniques. In addition to modelling noncoherent clutter, it is shown how the spectral and polarisation characteristics of coherently detected sea clutter relate to those observed in the noncoherent case. Results are also presented of forward-scattering and multipath propagation. This includes a consideration of the importance of the spatial and temporal coherence of the forward-scattered wavefront.