The synchrosqueezing transform (SST) is a novel approach for time-frequency (T-F) representation of non-stationary signals. By synchrosqueezing and reassigning the T-F spectrum of the wavelet transform (WT) or the short time Fourier transform (STFT) of a signal, the SST can obtain a high-resolution T-F spectrum. In the light of the superiority of S-transform (ST) over the WT and the STFT, especially, in representing a high-frequency weak-amplitude signal on its T-F spectrum, we propose a synchrosqueezing S-transform (SSST) which is realized by synchrosqueezing the spectrum of the ST. The formulas for the SSST and its inverse transform are derived. Synthetic examples show that the SSST has obviously higher resolution than the ST, and is superior to the SST like the ST to the WT. We then applied the SSST to perform the spectral decomposition of a marine seismic data for natural gas hydrate exploration. The results illustrate that the SSST can be used to well detect frequency spectral anomalies correlated with the gas hydrate and free-gas accumulations. We can also conclude that the SSST is a good potential technique to assist seismic interpretation.

Synchrosqueezing S-Transform and Its Application in Seismic Spectral Decomposition

Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis

Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances

This paper presents a new time–frequency (TF) analysis method called matching synchrosqueezing wavelet transform (MSWT) to signals with fast varying instantaneous frequency (IF). The original synchrosqueezing wavelet transform (SWT) can effectively improve the readability of TF representation (TFR) of signals with slowly varying IF. However, SWT still suffers from TF blurs for signals with fast varying IF. Moreover, the variable operating conditions of the aeroengine always make the vibration a signal with fast varying IF, especially when it comes to significant speed changes, which results in the obscure TFR for aeroengine vibration monitoring. In this paper, the MSWT introduces a chirp rate estimation into a comprehensive IF estimation to match the TF structure of the signals with fast varying IF and thus to achieve a highly concentrated TFR as the standard TF reassignment methods. Most importantly, the MSWT retains the reconstruction benefit like the SWT. The proposed MSWT is validated by both numerical simulation and applications in a bat echolocation signal analysis. Finally, a case study of a dual-rotor turbofan engine is given to illustrate the effectiveness of the proposed method for aeroengine vibration monitoring.

Matching Synchrosqueezing Wavelet Transform and Application to Aeroengine Vibration Monitoring

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

/pdf/basic-research-on-machinery-fault-diagnostics-past-present-2pjq0tijdu.pdf

Basic research on machinery fault diagnostics: Past, present, and future trends

Nonlinear squeezing time-frequency transform for weak signal detection

Vigorously developing new energy sources such as wind power and photovoltaics will promote the low-carbon transformation of the power system. With the increase of the penetration rate of distributed energy in the distribution network, many complex and changeable power quality disturbance have been generated, which will seriously affect the stability of the distribution network. This paper presents a new method for effectively identifying and classifying the complex and variable PQDs, which is based on proposed improved adaptive S-transform (IAST) and random forest (RF). The IAST first employs proposed iterative loop filter envelope extremum algorithm which can effectively detect the main frequency points of PQDs, followed by proposed time-frequency resolution optimization improvement method that optimally adjusts the standard deviation σ to adaptively control the Gaussian window width D. In addition, a parameter F is used to make IAST more flexible. Through IAST, various PQDs features can be extracted, and then which will be classified using Random Forest (RF). To demonstrate the effectiveness of the proposed method, extensive tests are conducted on the diverse simulation PQDs and the actual data obtained from the practical power systems. The work in this paper can provide a good choice for the design and development of an intelligent monitoring and analysis system for distribution network disturbances. 

A new method for recognition and classification of power quality disturbances based on IAST and RF

We introduce a novel algorithm, called nonlinear squeezing transform, to characterize the time-frequency pattern of nonstationary multi-component signals, especially for weak component detection. As a generally known, conventional time-frequency analysis (TFA) methods can characterize the time-frequency pattern of nonstationary signal. However, it is difficult to detect the weak component hidden in complex signals because the time-frequency information is influenced by the amplitude of the signal. Inspired by the calculation procedure of instantaneous frequency in the synchro-squeezing transform, nonlinear squeezing transform is presented, which is only relevant to the phase but is independent of the signal amplitude. The experiment on real signal shows that the method can effectively detect the weak component in complex signals, and the comparison study with some TFA methods also shows the advantages in weak signal detection.

Nonlinear squeezing transform for weak signal detection

Abstract A novel detection method based on multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm (MEVMDTFI–IRVM) is presented for fault detection of gearbox. The time–frequency images are constructed by multivariate extended variational mode decomposition. Compared with single-variable modal decomposition method, multivariate extended variational mode decomposition not only has an accurate mathematical framework, but also has good robustness to non-stationary multi-channel signals with low signal-to-noise ratio. The incremental RVM algorithm is presented for fault detection of gearbox based on the time–frequency images constructed by multivariate extended variational mode decomposition. The testing results demonstrate that the detection results of MEVMDTFI–IRVM for gearbox are stable, in addition, the detection results of MEVMDTFI–IRVM for gearbox are better than those of variational mode decomposition-based time–frequency images and incremental RVM algorithm (VMDTFI–IRVM), variational mode decomposition–RVM algorithm (VMD–RVM), and traditional RVM algorithm. 

/pdf/fault-detection-of-gearbox-by-multivariate-extended-2uefobf4.pdf

Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm

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Papers

Nonlinear squeezing time-frequency transform for weak signal detection

A new method for recognition and classification of power quality disturbances based on IAST and RF

Nonlinear squeezing transform for weak signal detection

Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm