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The purpose of this paper is to simulate and analyze accurately the multi-scale characteristics, variation periods and trends of the annual streamflow series in the Haihe River Basin (HRB) using the Hilbert-Huang Transform (HHT).

The Empirical Mode Decomposition (EMD) approach is adopted to decompose the original signal into intrinsic mode functions (IMFs) in multi-scales. The Hilbert spectrum is applied to each IMF component and the localized time-frequency-energy distribution. The monotonic residues obtained by EMD can be treated as the trend of the original sequence.

The authors apply HHT to 14 hydrological stations in the HRB. The annual streamflow series are decomposed into four IMFs and a residual component, which exhibits the multi-scale characteristics. After the Hilbert transform, the instantaneous frequency, center frequency and mean period of the IMFs are obtained. Common multi-scale periods of the 14 series exist, e.g. 3.3a, 4∼7a, 8∼10a, 11-14a, 24∼25a and 43∼45a. The residues indicate that the annual streamflow series has exhibited a decreasing trend over the past 50 years.

The HHT method is still in its early stages of application in hydrology and needs to be further tested.

It is helpful for the study of the complex features of streamflow.

This paper will contribute to the sustainable utilization of water resources.

This study represents the first use of the HHT method to analyze the multi-scale characteristics of the streamflow series in the HRB. This paper provides an important theoretical support for water resources management.

Despite the wide dissemination and application of current signature analysis (CSA) in general industry, CSA is not commonly used in the wind industry, where the use of vibration signals predominates. Therefore, the purpose of this paper is to review the use of generator CSA (GCSA) in the online fault detection and diagnosis of wind turbines (WTs).

This is a bibliographical investigation in which the use of GCSA for the maintenance of WTs is analyzed. A section is dedicated to each of the main components, including the theoretical foundations on which GCSA is based and the methodology, mathematical models and signal processing techniques used by the proposals that exist on this topic.

The lack of appropriate technology and mathematical models, as well as the difficulty involved in performing actual studies in the field and the lack of research projects, has prevented the expansion of the use of GCSA for fault detection of other WT components. This research area has yet to be explored, and the existing investigations mainly focus on the gearbox and the doubly fed induction generator; however, modern signal treatment and artificial intelligence techniques could offer new opportunities in this field.

Although literature on the use of GCSA for the detection and diagnosis of faults in WTs has been published, these papers address specific applications for each of the WT components, especially gearboxes and generators. For this reason, the main contribution of this study is providing a comprehensive vision for the use of GCSA in the maintenance of WTs.

This research paper adopts the fundamental tenets of advanced technologies in industry 4.0 to monitor the structural health of concrete beam members using cost-effective non-destructive technologies. In so doing, the work illustrates how a coalescence of low-cost digital technologies can seamlessly integrate to solve practical construction problems.

A mixed philosophies epistemological design is adopted to implement the empirical quantitative analysis of “real-time” data collected via sensor-based technologies streamed through a Raspberry Pi and uploaded onto a cloud-based system. Data was analysed using a hybrid approach that combined both vibration-characteristic-based method and linear variable differential transducers (LVDT).

The research utilises a novel digital research approach for accurately detecting and recording the localisation of structural cracks in concrete beams. This non-destructive low-cost approach was shown to perform with a high degree of accuracy and precision, as verified by the LVDT measurements. This research is testament to the fact that as technological advancements progress at an exponential rate, the cost of implementation continues to reduce to produce higher-accuracy “mass-market” solutions for industry practitioners.

Accurate structural health monitoring of concrete structures necessitates expensive equipment, complex signal processing and skilled operator. The concrete industry is in dire need of a simple but reliable technique that can reduce the testing time, cost and complexity of maintenance of structures. This was the first experiment of its kind that seeks to develop an unconventional approach to solve the maintenance problem associated with concrete structures. This study merges industry 4.0 digital technologies with a novel low-cost and automated hybrid analysis for real-time structural health monitoring of concrete beams by fusing several multidisciplinary approaches into one integral technological configuration.

The purpose of this paper is to present the application of Hilbert–Huang transform (HHT) for fatigue damage feature characterisation in the time–frequency domain based on strain signals obtained from the automotive coil springs.

HHT was employed to detect the temporary changes in frequency characteristics of the vibration response of the signals. The extraction successfully reduced the length of the original signal to 40 per cent, whereas the fatigue damage was retained. The analysis process for this work is divided into three stages: signal characterisation with the application of fatigue data editing (FDE) for fatigue life assessment, empirical mode decomposition with Hilbert transform, an energy–time–frequency distribution analysis of each intrinsic mode function (IMF).

The edited signal had a time length of 72.5 s, which was 40 per cent lower than the original signal. Both signals were retained statistically with close mean, root-mean-square and kurtosis value. FDE improved the fatigue life, and the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential. HHT helped to remove unnecessary noise in the recorded signals. EMD produced sets of IMFs that indicated the differences between the original signal and mean of the signal to produce new components. The low-frequency energy was expected to cause large damage, whereas the high-frequency energy will cause small damage.

HHT and EMD can be used in the strain data signal analysis of the automotive component of a suspension system. This is to improve the fatigue life, where the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential.

This paper aims to present a new method for identification of some flight modes, including natural and non-standard modes, and extraction of their characteristics, directly from measurements of flight parameters in the time domain.

The Hilbert-Huang transform (HHT), as a novel prevailing tool in the signal analysis field, is used to attain the purpose. The study shows that the HHT has superior potential capabilities to improve the airplane flying quality analysis and to conquer some drawbacks of the classical method in flight dynamics.

The proposed method reveals the existence of some non-standard modes with small damping ratios at non-linear flight regions and obtains their characteristics.

The paper examines only airplane longitudinal dynamics. Further research is needed regarding lateral-directional dynamic modes and coupling effects of the longitudinal and lateral modes.

Application of the proposed method to the flight test data may result in real-time flying quality analysis, especially at the non-linear flight regions.

First, to utilize the empirical mode decomposition (EMD) capabilities in real time, a local-online algorithm is introduced which estimates the signal trend by the Savitzky-Golay sifting process and eliminates it from the signal in the EMD algorithm. Second, based on the local-online EMD algorithm, a systematic method is proposed to identify flight modes from flight parameters in the time domain.

This study aims to investigate the relationship between structural damage and sensitivity indices using the Hilbert‐Huang transform (HHT) method.

The relationship between structural damage and the sensitivity indices is obtained by using the HHT method. Three sensitivity indices are proposed: the ratio of rotation (RR), the ratio of shifting value (SV) and the ratio of bandwidth (RB). The nonlinear single degree of freedom and multiple degree of freedom models with various predominant frequencies are constructed using the SAP2000 program. Adjusted PGA El Centro and Chi‐Chi (TCU068) earthquake data are used as the excitations. Next, the sensitivity indices obtained using the HHT and the fast Fourier transform (FFT) methods are evaluated separately based on the acceleration responses of the roof structures to earthquakes.

Simulation results indicate that, when RR < 1, the structural response is in the elastic region, and neither the RB nor SV in the HHT and FFT spectra change. When the structural response is nonlinear, i.e. RR1, a positive trend of change occurs in RB and RR, while in the HHT spectra, SV increases with an increasing RR. Moreover, the FFT spectra reveal that SV changes only when the RR is sufficiently large. No steady relationship between the RB and the RR can be found.

The paper demonstrates the effectiveness of the HHT method.

Continuous on-line monitoring of structural integrity are in priority in many engineering fields such as aerospace, automotive, civilian structures, and industrial applications. Of all these possible applications, the aerospace industry has one of the highest payoffs. Possible damage can lead to catastrophic failures and costly inspections. On the other hand, processing a signal consists of important feature from sensors measurements to reach the considered target. Typically, the sensors translate a physical phenomenon from one or many sources in temporal variations or in spatial variations. The purpose of this paper is to investigate damages, in terms of suddenly screw removal or in a small cut, detection in vibrating (clamped-free) aluminum beam structures using the empirical mode decomposition (EMD) method along with the Hilbert-Huang transformation (HHT). The perspective is to identify very small defects in real aircraft structures.

The proposed method deals with a new time-frequency signal processing analysis tool, for damages detection in a vibrating plate. An experimental test ring is used in order to excite a clamped-free aluminum plate. Two types of excitations are used. The first one is a harmonic excitation and the second one is a random excitation provided by an impact hammer. A hole and its filled by a screw with mass of 0.2 g, and a small cut is created, simulating a cut creation, are produced afterword, and the HHT is used in order to arise the developed oscillations, and to reveal hidden reflections in the data and to provide a high-resolution energy-time frequency spectrum.

The major finding was the clear amplitude increment either for screw removal or for cut creation, using the EMD process with the HHT, giving the possibility to detect them.

The use of the HHT to detect, using an experimental procedure, two different defects: a suddenly screw removal and a cut creation, in a clamped-free beam, excited by non-stationary and non-linear signals.

An electrical power system is expected to deliver undistorted sinusoidal, rated voltage and current continuously to the end-users. The problem of power quality (PQ) occurs when there is (are) deviation(s) in voltage and/or current which cause(s) failure or mal-operation of the customer's equipments. Various methods are suggested to detect and classify single PQ event in a power system, the performance of such methods to classify composite PQ events is limited. The purpose of this paper is the classification of composite PQ events in emerging power systems.

This paper proposes an effective method to classify composite PQ events using Hilbert Huang transform (HHT). The performance of probabilistic neural network (PNN) classifier and support vector machine (SVM) classifier to efficiently classify composite PQ events is compared.

The features extracted from HHT are simple yet effective. SVMs and PNN classifiers are used for PQ classification. It is found that PNN classifier outperforms SVM with the classification accuracy of 100 percent.

Different PQ signals used for analysis are generated by simulating a practical distribution system of an Indian academic institution.

Most systems have a non-linear (NL) behavior and measured signals reflect this non-linearity such that in general they are composed with more than one sinusoidal component. NL analysis methods represent an option for analyzing such signals, however these methods have been developed for single frequency signals, this forces to implement a components separation procedure before performing the signal analysis. The purpose of this paper is to present a new method for analyzing multi-component signals that allows calculating amplitude, frequency and damping constants of the contained sinusoidal components. The method is able to simultaneously identify the different components within a detection bandwidth without previous separation of mono-components, as needed for most methods in used today.

The method proposed in this work characterizes sinusoidal signals determining their amplitude, frequency and damping constant. This method is based on transforming from the time domain to the z-domain an oscillatory signal that may or may not possess damping. Since frequency and damping of a signal can be determined knowing its z-domain poles, using the signal in z-transform domain an equations system to find the signal poles can be written.

From the results it can be concluded that the proposed method is reliable and consistent. One quality of the method is its short delay, when the procedure starts there is a delay equal to the time needed to accumulate four samples for each detectable frequency in order to perform the first calculation, after this, the algorithm can deliver a result at each sampling instant. This short delay and the low complexity of the algorithm can permit using the method in real time applications.

The proposed method is able to determine frequencies, damping constants and amplitudes of the components of a signal without a previous separation of mono-components, in contrast with other methods that require filter banks tuned using a previous knowledge of the signal. Moreover unlike techniques such as the Hilbert-Huang Transform the proposed method can be applied to signals with components having very close frequencies.

Surface texturing has emerged in the past two decades as a viable option of surface engineering, resulting in significant improvement in wear resistance and friction coefficient. The purpose of this study is to find the appropriate surface texture to reduce vibration and improve the stability of journal bearings.

Micro-dimples, evenly distributed in a square array, were selected as the texture pattern and formed on the lower surface of bush by the laser surface texturing technique. Experiments were carried out to evaluate the effects of micro-dimples under different depths, densities and distributions.

The results are summarized in the form of shaft center orbits, waterfall illustrations and Hilbert-Huang transforms. In the entire test, it was found that an optimum geometric and distributive range of micro-dimples exists, where vibration acceleration can be decreased at least 3dB and stability can be greatly improved.

A majority of researchers devoted to studying on static characteristics, such as friction coefficient, load carrying capacity, pressure distribution and cavitation model. Besides, the influence of surface texture on stability of rotor-journal bearing system was rarely investigated and the recent examples can be found in Refs. (Ausas et al. 2007). However, a complete study of textured journal bearings has not been undertaken in the dynamic properties. Therefore, the purpose of this paper is to experimentally investigate the comprehensive effects of density, depth and distribution of micro-dimples on bearing vibration and stability.