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The purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.
Abstract
Purpose
The purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
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Provides an introduction to the field of time‐frequency analysis by reviewing four important and popular used time‐frequency analysis methods with focus on the principles and…
Abstract
Provides an introduction to the field of time‐frequency analysis by reviewing four important and popular used time‐frequency analysis methods with focus on the principles and implementation. The basic idea of time‐frequency analysis is to understand and describe situations where the frequency content of a signal is changing in time. Although time‐frequency analysis had its origin almost 50 years ago, significant advances have occurred in the past 15 years or so. Recently, the time‐frequency representation has received considerable attention as a powerful tool for analysing a variety of signals and systems.
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The studies on international housing markets have not modeled frequency domain and focused only on the time domain. The purpose of the present research is to fill this gap by…
Abstract
Purpose
The studies on international housing markets have not modeled frequency domain and focused only on the time domain. The purpose of the present research is to fill this gap by using the state-of-the-art econometric technique of wavelets to understand how differences in the horizon of analysis across time impact international housing markets’ relationship with some of the key macroeconomic variables. The purpose is to also analyze the direction of causation in the relationships.
Design/methodology/approach
The author uses the novel time–frequency analysis of international housing markets’ linkages to the macroeconomic drivers. Unlike conventional approaches that do not distinguish between time and frequency domain, the author uses wavelets to study house prices’ relationship with its drivers in the time–frequency space. The novelty of the approach also allows gaining insights into the debates that deal with the direction of causation between house price changes and macroeconomic variables.
Findings
Results show that the relationship between house prices and key macroeconomic indicators varies significantly across countries, time, frequencies and the direction of causation. House prices are most related to interest rates at the higher frequencies (short-run) and per capita income growth at the lower frequencies (long-run). The role of industrial production and income growth has switched over time at lower frequencies, particularly, in Finland, France, Sweden and Japan. The stock market’s nexus with the housing market is significant mainly at high to medium frequencies around the recent financial crisis.
Research limitations/implications
The present research implies that in contrast to the existing approaches that are limited to the only time domain, the frequency considerations are equally, if not more, important.
Practical implications
Results show that interested researchers and analysts of international housing markets need to account for the both horizon and time under consideration. Because the factors that drive high-frequency movements in housing market are very different from low-frequency movements. Furthermore, these roles vary over time.
Social implications
The insights from the present study suggest policymakers interested in bringing social change in the housing markets need to account for the time–frequency dynamics found in this study.
Originality/value
The paper is novel on at least two dimensions. First, to the best of the author’s knowledge, this study is the first to propose the use of a time–frequency approach in modeling international housing market dynamics. Second, unlike present studies, it is the first to uncover the direction of causation between house prices and economic variables for each frequency at every point of time.
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Rui Zhang, Na Zhao, Liuhu Fu, Lihu Pan, Xiaolu Bai and Renwang Song
This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic…
Abstract
Purpose
This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters.
Design/methodology/approach
To express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model.
Findings
The experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications.
Originality/value
This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.
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This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method.
Abstract
Purpose
This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method.
Design/methodology/approach
By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal.
Findings
The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method.
Originality/value
Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.
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Amine Ben Amar, Mondher Bouattour and Jean-Etienne Carlotti
This study aims to investigate the time-frequency comovement between wheat futures traded on three US markets (Chicago Board of Trade (CBOT), Kansas City Board of Trade (KCBOT…
Abstract
Purpose
This study aims to investigate the time-frequency comovement between wheat futures traded on three US markets (Chicago Board of Trade (CBOT), Kansas City Board of Trade (KCBOT) and Minneapolis Grain Exchange (MGE)) at different maturities and a global equity index.
Design/methodology/approach
As they allow to trace transitional shifts over time and across different frequency bands, this paper relies on continuous wavelet tools to investigate the time-frequency comovement among wheat and global stock markets.
Findings
The results show an increase in wheat futures prices at all maturities and a weak integration level within each wheat market during the subprime crisis. Moreover, the wavelet power spectra maps show high wheat and equity price volatility at different time scales and for various subperiods. Furthermore, the continuous wavelet coherence highlights time-frequency-varying comovements between the markets considered, which become particularly high during times of crisis.
Practical implications
The results provide market participants with a better understanding of the nature as well as the magnitude of the relationship between the global financial market and different wheat markets at different maturities and during tranquil and crisis periods. Indeed, from investors' perspective it is important to understand how markets are segmented or integrated during tranquil and crisis periods in order to better assess risks, diversify portfolios and implement more effective hedging strategies. As for regulators, a better understanding of the level of integration of different markets would further help refine macroprudential policies, and thus strengthen financial stability and resilience.
Originality/value
This paper enriches the existing literature by investigating the time-frequency comovement between wheat and a global equity market. Indeed, the dynamics between stock and wheat markets across different nearest to maturities have not been widely explored by previous studies.
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This paper sets out to detect and characterize electric fields in the ground (such as stray current fields) using a tandem time/frequency method of signal analysis.
Abstract
Purpose
This paper sets out to detect and characterize electric fields in the ground (such as stray current fields) using a tandem time/frequency method of signal analysis.
Design/methodology/approach
Results were obtained from investigations performed in the presence of a generated electric field with controlled variable characteristics, and in the presence of an electric field generated by a tramline. The analysis of measurement registers was performed using Short‐Time Fourier Transformation. The results were presented in the form of spectrograms, which illustrate changes in the spectral power density of the measured signal versus time.
Findings
Tandem time/frequency analysis reveals the random or deterministic character of the electric field, enabling its complete time/frequency characteristics to be obtained. Such information is inaccessible using exclusively the frequency analysis methods that utilize classical Fourier transformations. Moreover, an analysis of the spectral power density distribution of the signals in three directions on the ground surface makes it possible to define the localization of the field source.
Practical implications
Analysis methods for electric fields in the ground should be adapted to the evaluation of non‐stationary signals because the stray currents are of this type. Such a possibility is given by combined analysis in the domains of time and frequency. This method can be used as complementary to applied measurement techniques of stray current interference.
Originality/value
The method of electric field detection and characterization, as related to stray currents, previously has not been presented in the literature. This method of signal analysis may be adopted for other investigations that are reliant on the registration of voltages or potentials characterized by arbitrary frequencies.
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Shuxue Ding, Andrzej Cichocki, Jie Huang and Daming Wei
We present an approach for blind separation of acoustic sources produced from multiple speakers mixed in realistic room environments. We first transform recorded signals into the…
Abstract
We present an approach for blind separation of acoustic sources produced from multiple speakers mixed in realistic room environments. We first transform recorded signals into the time‐frequency domain to make mixing become instantaneous. We then separate the sources in each frequency bin based on an independent component analysis (ICA) algorithm. For the present paper, we choose the complex version of fixedpoint iteration (CFPI), i.e. the complex version of FastICA, as the algorithm. From the separated signals in the time‐frequency domain, we reconstruct output‐separated signals in the time domain. To solve the so‐called permutation problem due to the indeterminacy of permutation in the standard ICA, we propose a method that applies a special property of the CFPI cost function. Generally, the cost function has several optimal points that correspond to the different permutations of the outputs. These optimal points are isolated by some non‐optimal regions of the cost function. In different but neighboring bins, optimal points with the same permutation are at almost the same position in the space of separation parameters. Based on this property, if an initial separation matrix for a learning process in a frequency bin is chosen equal to the final separation matrix of the learning process in the neighboring frequency bin, the learning process automatically leads us to separated signals with the same permutation as that of the neighbor frequency bin. In each bin, but except the starting one, by chosen the initial separation matrix in such a way, the permutation problem in the time domain reconstruction can be avoided. We present the results of some simulations and experiments on both artificially synthesized speech data and real‐world speech data, which show the effectiveness of our approach.
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The purpose of this paper is to detect the source of stray current interference on underground pipelines in urban areas using a joint time/frequency method of signal analysis.
Abstract
Purpose
The purpose of this paper is to detect the source of stray current interference on underground pipelines in urban areas using a joint time/frequency method of signal analysis.
Design/methodology/approach
Investigations are performed on an underground pipeline located in the vicinity of the two direct current tractions: a tramway line and a train line. The results of the analysis are presented in the form of spectrograms, which illustrate changes in the spectral power density of the potential of the rails and of the potential of the pipe in the joint domain time‐frequency.
Findings
The comparison of the spectrograms can be used to evaluate if and which stray current source has influence on the investigated metal construction.
Originality/value
The combined analysis in the domain of time and frequency can be used as a supplementary one providing new information useful in the evaluation of stray current corrosion hazard. In the presence of several electric field sources in urban areas, this method reveals the complete time‐frequency characteristic of each stray current source and its interference on the investigated construction.
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P.I.J. Keeton and F.S. Schlindwein
Provides an introduction into wavelets and illustrates their application with two examples. The wavelet transform provides the analyst with a scaleable time‐frequency…
Abstract
Provides an introduction into wavelets and illustrates their application with two examples. The wavelet transform provides the analyst with a scaleable time‐frequency representation of the signal, which may uncover details not evidenced by conventional signal processing techniques. The signals used in this paper are Doppler ultrasound recordings of blood flow velocity taken from the internal carotid artery and the femoral artery. Shows how wavelets can be used as an alternative signal processing tool to the short time Fourier transform for the extraction of the time‐frequency distribution of Doppler ultrasound signals. Implements wavelet‐based adaptive filtering for the extraction of maximum blood velocity envelopes in the post processing of Doppler signals.
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