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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 analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

The purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.

The generalized Stockwell transform (GST) and the singular value ratio spectrum (SVRS) methods are combined. A time-frequency distribution measurement criterion named the energy concentration measurement (ECM) is initially used to determine the parameter of the optimal GST method. Then, the optimal GST is applied to conduct a time-frequency transformation for a raw signal. Subsequently, the two-dimensional time-frequency matrix is obtained. Finally, the improved singular value decomposition (SVD) analysis is used to conduct a noise reduction of the time-frequency matrix. The SVRS is proposed to select the effective singular values. Furthermore, the time-domain feature of the impact signal is obtained by taking the inverse GST transform.

The simulated and experimental signals are used to verify the superiority of the proposed method over conventional methods. The obtained results show that the proposed method can effectively extract fault features of the rolling element bearing.

This paper mainly discusses the application of GST and SVRS methods to analyze the weak fault feature extraction problem. The next research direction is to explore the application of the Hilbert Huang transform (HHT) and variational modal decomposition (VMD) in the impact feature extraction of rolling bearing.

In the present study, a new SVRS method is proposed to select the number of effective singular values. This paper proposed an effective way to obtain the fault feature in monitoring of rotating machinery.

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.

This research unfolds a holistic association between economic policy uncertainty (EPU) and three important markets (oil, stock and gold) in the Indian context. To do same, the current study uses the monthly dataset of each variable spanning from November 2005 to March 2022.

The authors have portrayed the wavelet-based coherence, correlation and covariance plots to explore the interaction between EPU and markets' behavior. Then, a wavelet-based quantile on quantile regression model and wavelet-based Granger causality has been applied to examine the cause-and-effect relation and causality between the EPU and markets.

The authors’ findings report that the Indian crude oil buyers do not need to consider Indian EPU while negotiating the oil deals in the short term and medium term. However, in case of the long-term persistence of uncertainty, it becomes difficult for a buyer to negotiate oil deals at cheap rates. EPU causes unfavorable fluctuation in the stock market because macroeconomic decisions have a substantial impact on it. The authors have also found that gold is a gauge for economic imbalances and an accurate observer of inflation resulting from uncertainty, showing a safe haven attribute.

The authors’ work is original in two aspects. First, their study solely focused on the Indian economy to investigate the impact and causal power of Indian EPU on three major components of the Indian economy: oil, stock and gold. Second, they will provide their findings after analyzing data at a very microlevel using a wavelet-based quantile on quantile and wavelet-based Granger causality.

Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to predict retail sales exhibiting these patterns. Due to economic instability, recent retail sales time-series data show a higher degree of variability and nonlinearity, which makes the ARIMA model less accurate. This chapter demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) in forecasting aggregate retail sales. The hybrid forecasting method of integrating EMD and neural network (EMD-NN) models was applied to two real data sets from two different time periods. The one-period ahead forecasts for both time periods show that EMD-NN outperforms the classical NN model and seasonal ARIMA. In addition, the findings also indicate that EMD-NN can significantly improve forecasting performance during the periods in which macroeconomic conditions are more volatile.

This study aims to include the diagnosis of an old concrete deck steel truss rural road bridge in the damaged and retrofitted state through vibration response signals.

The analysis of the vibration response signals is performed in time and time-frequency domains using statistical features-root mean square, impulse factor, crest factor, kurtosis, peak2peak and Stockwell transform. The proposed methodology uses the Hilbert transform in combination with spectral kurtosis and bandpass filtering technique for obtaining robust outcomes of modal frequencies.

The absence or low amplitude of considered mode shape frequencies is observed both before and after retrofitting of bridge indicates the deficient nodes. The kurtosis feature among all statistical approaches is able to reflect significant variation in the amplitude of different nodes of the bridge. The Stockwell transform showed better resolution of present modal frequencies but due to the yield of additional frequency peaks in the vicinity of the first three analytical modal frequencies no decisive conclusions are achieved. The methodology shows promising outcomes in eliminating noise and visualizing distinct modal frequencies of a steel truss bridge.

The findings of the present study help in analyzing noisy vibration signals obtained from various structures (civil or mechanical) and determine vulnerable locations of the structure using mode shape frequencies.

The literature review gave an insight into few experimental investigations related to the combined application of Hilbert transform with spectral kurtosis and bandpass filtering technique in determining mode frequencies of a steel truss bridge.

The purpose of this article is to determine the parameters of the preparation process for devulcanized and pyrolytic crumb rubber modified asphalt (DCRMA) and then study the rheological and microscopic properties of DCRMA through experiments.

In this study, a new preparation process for DCRMA was developed, then the penetration, softening point and viscosity tests were employed to determine the parameters of the preparation process. The crumb rubber (CR) solubility, Fluorescence microscopy (FM), Fourier Transform Infrared (FTIR) spectroscopy and thermogravimetric analysis tests were conducted to verify the devulcanized and pyrolytic effectiveness of the preparation process. Furthermore, dynamic shear rheometer and bending beam rheometer were used to characterize the high and low-temperature rheological properties of DCRMA.

The results showed that the penetration balanced the CR degradation and the virgin asphalt aging well and thus could be used as a main parameters control indicator. The CR solubility, FM and FTIR tests proved that the CR has been fully devulcanized and pyrolytic via the preparation process. The DCRMA exhibited better low-temperature and fatigue performance and lower rutting performance than the conventional crumb rubber modified asphalt (CRMA) with the same CR content. Finally, the time–temperature superposition principle could be employed for all binders in this study.

A new preparation process for DCRMA was developed.

We investigate how different cognitive conceptualizations of reference point and tax withholdings jointly influence aggressive tax filing. We utilize a field study with responses captured from actual taxpayers immediately after filing their returns. Consistent with both prospect theory and mental accounting perspectives, we hypothesize and find evidence that more aggressive filing decisions depend on mental categorization of whether taxpayers expect a tax refund or owe additional taxes relative to their expected asset position (EAP). We find a joint and additive impact of EAP with a cognitive link made between taxes and the categorization of amounts owed. Our findings suggest that more aggressive filing behavior is found in taxpayers in a tax loss position relative to their EAP and in those that do not separately categorize taxes owing from their own resources. By highlighting the importance of EAP and the cognitive separation of taxes owed, we provide insight for revenue agencies to use cognitive framing strategies to mitigate aggressive taxpayer behavior. The cognitive framing of EAP may be influenced by the use of installment payments and tax withholdings, but also may be affected by communications that alter taxpayers' expectations of taxes owed.