Hilbert-Huang Transform Technique Research Articles

An experimental investigation on the dual-resonance revealed in the VIV of flexible cantilevers with orthotropic bending stiffness

This article presents an experimental investigation on the dynamic behavior of orthotropically stiffened cantilevered flexible cylinders, mounted in vertical configuration, undergoing vortex-induced vibrations (VIV). The investigation was motivated by intriguing results published in 2001, regarding a flexible cylinder with orthotropic bending stiffness, presenting the first natural frequencies ratio parameter f1*=f1x/f1y=4.08 (x and y are the in-line and crosswise directions, respectively), in which a new high amplitude-frequency response branch was revealed in the range U*>12 (amplitudes measured at the free tip), then named “High Speed Mode Branch” (HSMB). The present investigation goes further, by studying the influence of the first natural frequency ratio parameter f1* on VIV. Four cantilevered flexible cylinders were tested, each one presenting a distinct nominal frequencies ratio, namely f1*=1:1, 2:1, 3:1 and 4:1. The experiments were carried out in a recirculating water-channel with the stream flow varied continuously, by slowly increasing the velocity, instead of doing it by steps. An innovative data analysis methodology was developed to deal with the resulting slowly nonstationary signals by combining the Galerkin's projection scheme with the Hilbert-Huang Transform technique. The influence of the first natural frequency ratio parameter is shown as determining the position and width of the reduced velocity range in which dual resonance occurs. Moreover, f1*>1.0 allows higher reduced velocities to be reached, therefore, enabling the appearance of higher modes of crosswise vibration. The HSMB appearance is now recognized as a result of the combination of the first crosswise mode response and the onset of amplitudes related to the second crosswise mode of vibration. The results here presented may serve as a new benchmark data for modeling and simulations involving VIV of flexible cylinders equipped with orthotropic bending stiffness.

Detection and classification of multi-complex power quality events in a smart grid using Hilbert–Huang transform and support vector machine

This article examines the potential ability of a chosen Hilbert–Huang transform (HHT) in detecting and identifying a multi-complex electric power quality events (PQE) signal in smart grid power systems under various noise situations. HHT is an active signal processing technique, comprising the units of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA) to detect the non-stationary electric signals. The function of EMD is to detect and decompose the non-stationary electric signal in different scales of frequency modules ranging from maximum to minimum values, and thereby the attained signals are characterized as intrinsic mode functions (IMFs). HSA details the IMF signals individually to produce a unique Hilbert spectrum which carries the information of original signal. By observing the instant time-varying deviations of frequency and amplitude of the resultant signals, it is possible to categorize the disturbing signals from the original signal. The discussed slots of work is simulated under MATLAB environment, and the results report that the HHT successfully detects the single PQE, complex PQE and multi-complex PQE signals under 25 dB, 50 dB and without noise situations. The outcomes of HHT technique are compared with other transforms such as S-transform and wavelet transform to highlight superior qualities of HHT. The identified PQE signals from HHT are classified using support vector machine to improve its classification accuracy. It is wiser to disclose that the proposed system with inbuilt monitoring and identification of PQE signals will suit present smart grid system.

Investigation of Galling Wear Using Acoustic Emission Frequency Characteristics

In the sheet metal stamping process, during sliding contact between the tool and sheet, it is expected that severe events such as tool wear or fracture on the sheet generate acoustic emission (AE) burst waveforms. Attempts have been made in the literature to correlate the AE burst waveform with the wear mechanisms. However, there is a need for additional studies to understand the frequency characteristics of the AE burst waveform due to the severity and progression of the galling wear. This paper will determine the AE frequency characteristics that can be used to monitor galling wear, independent of the experimental process examined. The AE burst waveforms generated during the stamping and scratch tests are analysed in this paper to understand the change in the AE frequency characteristics with the galling severity. These AE burst waveforms were investigated using the Hilbert Huang Transform (HHT) time-frequency technique, band power, and mean-frequency. Subsequently, these AE frequency features are correlated with the wear behaviour observed via high-resolution profilometer images of the stamped parts and scratch surfaces. Initially, the HHT technique is applied to the AE burst waveform to understand the influence of wear severity in the power distribution over the wide AE frequency range. Later, the AE bandpower feature is used to quantitatively analyse the power in each frequency interval during the unworn and worn tool conditions. Finally, the mean-frequency of AE signal is identified to be able to determine the onset of galling wear. The new knowledge defined in this paper is the AE frequency features and wear measurement feature that can be used to indicate the onset of galling wear, irrespective of the processes examined.

RETRACTED ARTICLE: Detection and classification of power quality disturbances or events by adaptive NFS classifier

In power distributed generation system, analysis of power quality is a major issue and hence it is essential to implement an efficient power quality system. To address and end this issue, the proposed work is mainly focused on the detection and classification of power quality disturbances or events by applying Hilbert–Huang transform (HHT) technique. HHT technique is a novel signal processing algorithm that comprises two processes. First is the empirical mode decomposition (EMD), which is an iterative process where N number of power quality signals are decomposed into intrinsic mode functions (IMFs). The second process, Hilbert transform, is applied to each IMF component. The advantage of using these two processes makes it an attractive power tool for quality event analysis. Once the IMF is obtained, it is easy to construct a time–frequency representation model. Then, the significant features are extracted from amplitude, phase and frequency instantly, which are the contour of IMFs of each power quality disturbance. For classification, adaptive neuro-fuzzy system (NFS) classifier is used. It is an intelligent system used for the combination of neural network and fuzzy logic. Power quality features are given as input to the NFS system. The experimental simulation is conducted in a MATLAB environment, and proposed HHT has higher efficiency compared to existing methods.

Analyzing groundwater level anomalies in a fault zone in Korea caused by local and offshore earthquakes

This study analyzed groundwater level changes in a monitoring well located in Korea’s Yangsan fault zone, induced by local (Gyeongju and Pohang) earthquakes in Korea and offshore earthquakes around the world. Among offshore earthquakes, the MW 7.3 Honshu earthquake changed raw groundwater level of a maximum of 10.3 cm. The groundwater level data were analyzed by using the Hilbert-Huang transform (HHT), modified moving average (MMA), and simple subtraction (SS) methods. In the first stage, earthquake-induced groundwater level changes were identified using the MMA and SS methods because the SS and MMA methods may excellently be used for identifying earthquake-induced groundwater level anomalies as a convenient way to identify abnormal fluctuations. Then, the HHT technique was applied to the 2016–2017 groundwater level data for comparison with the MMA and SS methods. The HHT method has been proven to be the most accurate method for detecting the effects of earthquakes on groundwater levels. The differences of water levels analyzed by the HHT method and the other two methods resulted in between 0.4 and 1.9 cm, showing closer levels between the HHT and SS methods.

Generate tri-directional spectra-compatible time histories using HHT method

This paper proposes two algorithms to generate spectrum-compatible time histories based on two approaches recommended by USNRC Standard Review Plan 3.7.1. Hilbert–Huang Transform technique is used to analyze frequency contents and amplitudes of seed motions. Through adjusting the frequency contents and amplitudes of seed motions, spectrum-compatible time histories are obtained. The first algorithm is to generate tri-directional time histories compatible with multi-damping target design spectra (ground response spectra or floor response spectra). The second algorithm is to generate tri-directional time histories compatible with single-damping target design spectra. Examples are presented to demonstrate versatility of these two proposed algorithms to generate spectra-compatible time histories.

Fault detection in analog and mixed-signal circuits by using Hilbert–Huang transform and coherence analysis

Using Hilbert–Huang transform (HHT) and coherence analysis, a signature extraction method for testing analog and mixed-signal circuits is proposed in this paper. The instantaneous time–frequency signatures extracted with HHT technique from the measured signal of circuits under test (CUT) are used for faults detection that is implemented through comparing the signatures of faulty circuits with that of the fault-free circuit. The coherence functions of the instantaneous time–frequency signatures and its integral help to test faults in the faulty dictionary according to the minimum distance criterion. The superior capability of HHT-based technique, compared to traditional linear techniques such as the wavelet transform and the fast Fourier transform, is to obtain the subtle time-varying signatures, i.e., the instantaneous time–frequency signatures, and is demonstrated by applying to Leapfrog filter, a benchmark circuit for analog and mixed-signal testing, with 100% of F.D.R (fault detection rate) in the best cases and with the least 24.2% of F.L.R. (fault localization rate) with one signature.

Damage detection in structures under traveling loads by Hilbert–Huang transform

Hilbert–Huang transform (HHT) is an innovative data-processing technique for analyzing nonstationary and nonlinear signals. A novel HHT-based method for damage detection of bridge structures under a traveling load is proposed. The technique uses a single point measurement and is able to identify the presence and the location of the damage along the beam. The measured data are processed by the HHT technique, and none a priori information is needed about the response of the undamaged structure. Damage location is revealed by direct inspection of the first instantaneous frequency, which presents a sharp crest in correspondence of the damaged section. The identification capabilities of the proposed technique are studied varying the damage locations, crack depths and velocity of the moving load. The effect of ambient noise is also taken into account. Theoretical as well as numerical results show the identification is rather accurate, results are not very sensitive to the crack depth and ambient noise, while they are sensibly affected by the damage location and by the speed of the moving load as well. Theoretical analysis identifies a characteristic load velocity interval, depending both on the first natural frequency of the bridge and the damage location, within which the HHT can be successfully applied.

Damage identification of a benchmark building for structural health monitoring

An important objective of health monitoring systems for civil infrastructures is toidentify the state of the structure and to evaluate its possible damage. Recently, anIASC–ASCE benchmark problem for structural health monitoring has been developed inorder to facilitate the comparison of various analysis techniques for the damageidentification of structures on a common basis. The technique of Hilbert–Huangtransform (HHT) has been shown to be a possible system identification methodfor linear structures. This paper presents the application of HHT to the phaseI IASC–ASCE benchmark building for the complete identification of stiffnessand damping coefficients. The cases analyzed involve the damage assessmentin the weak direction of the benchmark building using 12-DOF and 120-DOFmodels. In this benchmark problem, the structural parameters, including thestiffness and damping, before and after damage are identified first, and then thelocation and severity of the damage are assessed by a comparison. The effect ofmeasurement noise has been taken into account. Simulation results demonstrate that theaccuracy of the HHT technique presented in identifying the structural parameters isquite plausible, and it represents a possible damage detection technique for linearstructures.