Time-frequency Signal Processing Techniques Research Articles

The State-of-the-Art on Time-Frequency Signal Processing Techniques for High-Resolution Representation of Nonlinear Systems in Engineering

The State-of-the-Art on Time-Frequency Signal Processing Techniques for High-Resolution Representation of Nonlinear Systems in Engineering

Filter bank property of direct fast iterative filtering and its applications

The direct fast iterative filtering (dFIF) is a novel time–frequency signal processing technique for non-stationary and non-linear signals, which can be considered the improved method of fast iterative filtering (FIF). Firstly, the equivalent impulse response is analyzed based on extensive numerical experiments to reveal the performances of dFIF. And then, fractional Gaussian noise (fGn) with different Hurst exponents is constructed to study the power spectrum density (PSD) and the frequency band distribution of the equivalent filter banks of the dFIF. The corresponding comparison analysis results are also illustrated, including empirical mode decomposition (EMD), variational mode decomposition (VMD) and db4-wavelet transform. Furthermore, the zero-crossings detection and estimate of empirical variance analysis of IMFs are performed, which indicates that dFIF does not follow a dyadic filter bank. Based on these findings, two application cases are presented in extracting time-varying oscillations and trends using dFIF, EMD and VMD. The results show that with appropriate parameter selection, dFIF performs better than EMD and VMD in terms of decomposition orthogonality, accuracy and anti-mode mixing.

Application of Time-Frequency Analysis with Selection of Suitable Mother Wavelets for Transient States of HVDC Transmission Lines

Recent developments in power semiconductor devices have made High Voltage Direct Current (HVDC) transmission systems the most crucial technology for future power systems. Today several HVDC projects have been commissioned around the world. The primary concern for such an advanced HVDC grid is to follow standard grid codes to maintain the safety and stability of the electrical equipment during transient and dynamic circumstances. Although the HVDC system has been recognized as the most prominent technology to transmit electric power, some issues must be addressed, such as selective and quick detection of faults. For selective fault detection, it is essential to look into the several fault detection strategies that fortify the HVDC system with time-frequency signal processing techniques. This research uses a time frequency-based method to identify the different HVDC transmission line faults. The most suitable mother wavelet for various transient states is selected by analyzing fault voltage and current signals based on variance, standard deviation, and range parameters. An experimental setup of a thyristor-based HVDC transmission system has been developed in the MATLAB/Simulink environment, and multiple fault scenarios have been analyzed. The simulation outcomes reveal that the most effective mother wavelet for detecting DC line, symmetrical and unsymmetrical faults occurs at the 6th level of fault signal decomposition. By examining current and voltage signals, our work has determined the best mother wavelet for various faults. Using more than one mother wavelet to detect faults is more reliable than using a single mother wavelet to detect all faults. Additionally, it has been noted that each fault's voltage and current signals have two distinct mother wavelets.

Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults

The raw vibration signal carries a great deal of information representing the mechanical equipment's health conditions. However, in the working condition, the vibration response signals of faulty components are often characterized by the presence of different kinds of impulses, and the corresponding fault features are always immersed in heavy noises. Therefore, signal denoising is one of the most important tasks in the fault detection of mechanical components. As a time-frequency signal processing technique without the support of the strictly mathematical theory, empirical mode decomposition (EMD) has been widely applied to detect faults in mechanical systems. Kernel regression (KR) is a well-known nonparametric mathematical tool to construct a prediction model with good performance. Inspired by the basic idea of EMD, a new kernel regression residual decomposition (KRRD) method is proposed. Nonparametric Nadaraya–Watson KR and a standard deviation (SD) criterion are employed to generate a deep cascading framework including a series of high-frequency terms denoted by residual signals and a final low-frequency term represented by kernel regression signal. The soft thresholding technique is then applied to each residual signal to suppress noises. To illustrate the feasibility and the performance of the KRRD method, a numerical simulation and the faulty rolling element bearings of well-known open access data as well as the experimental investigations of the machinery simulator are performed. The fault detection results show that the proposed method enables the recognition of faults in mechanical systems. It is expected that the KRRD method might have a similar application prospect of EMD.

Detection of Sealant Delamination in Integrated Circuit Package Using Nondestructive Evaluation

Abstract This paper presents the development of a nondestructive evaluation method for delamination detection in integrated circuit (IC) packages. The developed method allows for quantitative detection of sealant delamination between integrated heat sink (IHS) and substrate, which is considered a potential failure mechanism in IC packages. The proposed method is expected to overcome the destructive nature of most existing techniques, while maintaining low cost of development. The reported development consists of the following processes: First, flash thermographic analysis is investigated and shown to be incapable of detecting a realistic sealant delamination. Then, ultrasonic guided waves (UGWs) are used as the chosen interrogation method due to their sensitivity to small-size damage and structural thickness variations. The complexity of the received ultrasonic signals caused by the geometric heterogeneity is resolved using a time–frequency signal processing technique. The extracted ultrasonic information, including time-of-arrival (TOA) and amplitude of wave modes received from different sensing paths under multiple excitation frequencies, is then used to construct a feature space. A multivariate Gaussian model is implemented as an information fusion and delamination detection tool, which investigates the distribution of feature space including correlations between features. Results from the developed model are compared with two existing evaluation methods, including pullout force measurement and a delamination metric. The developed method is seen to possess a similar level of accuracy with a nondestructive nature.

Wind Turbine Blade Fault Detection Using Wavelet Power Spectrum and Experimental Modal Analysis

The dynamic behavior of modern multi-Megawatt wind turbines has become an important design consideration. The power generated by wind turbine depends mainly on the interaction between the blades and the flowing wind. Therefore, the major aspects related to the operational reliability of a wind turbine are the aerodynamics characteristics and integrity of the blades. This paper demonstrates the results of the application of wavelet transform combined with experimental modal analysis as a tool for wind turbine blade condition monitoring. An experimental model analysis with different vibrational excitations performed for healthy and cracked cantilever stepped beam, which approximate the wind turbine blade when attached to the wind turbine hub. The beam response signals acquired for three different locations of vibration sensor. The cantilever beam with breathing crack response exhibits a non-stationary vibrational signal because of crack open-close cycles during the oscillation of the beam. The wavelet transform as a time-frequency signal processing technique is an effective tool to extract the characteristics of the non-stationary signals; therefore, used in this paper to analyze the cracked beam vibrational signals. The wavelet power spectrum (WPS) calculated for different beam health conditions and for different locations of the vibration sensors. The results reveal the effectiveness of the wavelet power spectrum over the traditional FFT spectrum to identify the existence of the crack in the wind turbine blade. Moreover, a mathematical model, which correlates the variation of the blade natural frequencies with the crack severity and location, is presented and validated with an experimental modal analysis data.

A Search for Meteoroid Lunar Impact Generated Electromagnetic Pulses

Lunar white light flashes associated with meteoroid impacts are now regularly observed using modest optical instrumentation. In this paper, we hypothesize that the developing, optically-dense hot ejecta cloud associated with these hypervelocity impacts also produce an associated complex plasma component that rapidly evolves resulting in a highly-transient electro magnetic pulse (EMP) in the VHF/UHF spectral region. Discovery of the characteristics and event frequency of impact EMPs would prove interesting to meteoroid flux and complex plasma physics studies especially if EMPs from the same event are detected from at least two locations on the Earth with relative delays appropriate to the propagation paths. We describe a prototype observational search, conducted in May 2014, for meteoroid lunar-impact EMPs that was conducted using simultaneous, overlapping-band, UHF radio observations at the Arecibo (AO; Puerto Rico) and Haystack (HO, Massachusetts, USA) Observatories. Monostatic/bistatic lunar radar imaging observations were also performed with HO transmitting and HO/AO receiving to confirm tracking, the net delay, and the pointing/timing ephemeris at both observatories. Signal analysis was performed using time–frequency signal processing techniques. Although, we did not conclusively identify EMP returns, this search detected possible EMPs and we have confirmed the search paradigm and established the sensitivity of the AO–HO system in detecting the hypothesized events. We have also characterized the difficult radio-frequency interference environment surrounding these UHF observations. We discuss the wide range of terrestrial-origin, Moon-bounce signals that were observed which additionally validate the observational technique. Further observations are contemplated.

Time-Frequency Enhancement Technique for Bevel Gear Fault Diagnosis

Gear failure is one of the most common causes of breakdown in rotating machineries. It is well known that vibration signals from machineries can be effectively used to detect certain gear faults. Yet it is still not an easy task to find a symptom that reflects a particular fault from vibration signals. This paper presents an advanced time-frequency signal processing technique for extracting important gear fault information from the vibration signal that is heavily corrupted by measurement noise. Experiments were performed on a bevel gearbox test rig using vibration measurements. The Time Synchronous Average (TSA) was initially utilized to eliminate all asynchronous component of vibration signal obtained from the gear. The Continuous Wavelet Transform (CWT) method was then used to capture the non-stationary behaviour of the impulse signal generated from the broken bevel gear tooth. It was shown that the diagnosis method using the Continuous Wavelet Transform combined with Time Synchronous Averaging outperformed the conventional spectral analysis, capable of identifying the angular location of broken teeth in the gear.

Damage assessment of CFRP composites using a time–frequency approach

A damage assessment methodology using a time–frequency signal processing technique is presented in this article. Delaminations are detected in composite structures with multiple stiffeners. Because Lamb waves are complex in nature, due to wave dispersion and scattering, a robust signal processing technique is required to extract features from Lamb wave signals. In this article, the matching pursuit decomposition algorithm is used for extracting wavelets from the Lamb wave signals in the time–frequency domain. A small time–frequency atom dictionary is defined to avoid the exhaustive search over the time–frequency domain and to reduce the computation costs. The propagation characteristics of Lamb waves in stiffened composite panels are investigated. The delaminations are detected by identifying the converted Lamb wave modes introduced by the structural imperfection. A two-step damage detection approach, which uses both pulse-echo and pitch-catch active sensing schemes, is developed for the identification of delaminations. The delamination is quantified using a signal energy-based damage index. The matching pursuit decomposition algorithm is further used to localize the delamination position by solving a set of nonlinear equations. The results show that the matching pursuit decomposition algorithm can be used to identify and localize the seeded delaminations in composite structures with complex geometries and material properties.

A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach

When gear fault occurs, the vibration signals always display non-stationary behavior. Therefore time-frequency analysis has become the well-accepted technique for vibration-based gear fault diagnosis. This paper presents the application of a new time-frequency signal processing technique, the Hilbert spectrum based on the maximal overlap discrete wavelet packet transform (MODWPT), to the analysis of simulation signals and gear fault vibration signals measured by the acceleration sensor fixed on the bearing house. As long as the decomposition scale and disjoint dyadic decomposition are chosen suitably, the original signal could be decomposed into a set of monocomponent signals whose instantaneous amplitude and instantaneous frequency own physical meaning. After the instantaneous amplitude and instantaneous frequency of each monocomponent signal are calculated by using MODWPT, the corresponding Hilbert spectrum could be obtained by assembling the instantaneous amplitude and instantaneous frequency. The simulation and practical application examples show that the Hilbert spectrum base on the MODWPT is superior to another competing method, namely, EMD (empirical mode decomposition)-based method, which has been widely used in the gear fault diagnosis.

Pulsed electromagnetic methods for defect detection and characterisation

The magnetic flux leakage (MFL) method has very good defect detection and location capabilities, but defect sizing capabilities, especially for sub-surface defect characterisation, are limited. The pulsed magnetic flux leakage (PMFL) technique has recently been introduced and shown to have great potential for automated defect sizing for surface-breaking defects using time-frequency signal processing techniques, but sizing of sub-surface defects has proved problematic. In this paper, pulsed magnetic reluctance (PMR), a new electromagnetic (EM) non-destructive evaluation (NDE) technique, is introduced and incorporated into a dual PMFL/PMR probe for the characterisation of surface and sub-surface defects in ferromagnetic materials. Experimental results from a comparison study of the two techniques using variety of defects analysed using time-frequency analysis show that the techniques offer complementary information, with PMFL providing defect location data and data for the characterisation of surface defects and PMR offering sub-surface defect characterisation capabilities. The work concludes that integration of these inspection techniques in the new pulsed EM probe can provide enhanced defect characterisation capabilities for flux leakage-based inspection systems using relatively simple time-frequency signal processing techniques.