Time-frequency analysis (TFA) is considered as a useful tool to extract the time-variant features of the nonstationary signal. In this paper, a new method called demodulated high-order ...synchrosqueezing transform (DHST) is proposed. The DHST introduces a two-step algorithm, namely, demodulated transform and high-order synchrosqueezing method to achieve a compact time-frequency representation (TFR) while enabling the reconstruction of the signal from TFR. The performance of the proposed DHST method in this paper is validated by both the simulated and experimental signals including bat echolocation and a vibration signal. The results show that the proposed TFA method is more effective in processing the nonstationary signals with fast varying instantaneous frequency.
Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or ...failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Time-frequency analysis (TFA) is an effective tool to identify the signal frequency components and to reveal their time variant features. In this paper, a new instantaneous frequency (IF) estimation ...method is proposed for signals with heavy noise, which is based on a polynomial chirplet transform and a ridge curve extraction scheme. Based on this method, an iterative stepwise refinement algorithm is developed to generate a time-frequency distribution (TFD) with satisfactory energy concentration. Both simulated signals and experimental vibration signals are used to validate the performance of the proposed methods. The results demonstrate that the proposed TFA method is more effective in processing the nonstationary signals with heavy noise. Further, it can perform an accurate evaluation of the IF and obtain a clear TFD.
Time-frequency analysis is recognized as a dynamic tool to analyze the nonstationary signal. The synchrosqueezing transform is usually applied as a post-processing method to further improve the ...readability of the time-frequency representation. Synchrosqueezing transform is related to the reassignment method and can be performed in two directions, namely time direction and frequency direction. Frequency-direction reassignment helps to squeeze the slowly changing ridge. However, the time-direction reassignment is efficient to process the signal with rapid variation in instantaneous frequency. Thus, there exists a conflict in most of the time-frequency analysis methods while dealing with a signal containing both of these two components. In this study, a new method called demodulated time-direction synchrosqueezing transform is introduced, which is not only capable of achieving a higher compact TFR but also allow reconstructing the mode. In order to explain demodulated time-direction synchrosqueezing transform, a signal model is established in frequency domain. Then, a demodulated procedure is implemented to eliminate time-frequency analysis diffusion. Finally, time-direction reassignment is carried out to further enhance the energy concentration of the time-frequency analysis. The proposed demodulated time-direction synchrosqueezing transform method is evaluated by both simulation and experimental research. The results reveal that the performance of demodulated time-direction synchrosqueezing transform is better than the conventional time-frequency analysis methods, and it can be applied to the fault diagnosis in a machine.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Bearings are widely used in rotating machinery, such as aircraft engines and wind turbines. In this paper, we proposed a new data-driven method called frozen convolution and activated memory network ...(FCAMN) for bearing remaining useful life (RUL) estimation based on the deep neural network. The proposed method is composed of two parts: the multi-scale convolutional neural network is carried out to pre-train the raw data to directly obtain the global and local features, and the second step is accomplished by the convolutional-memory neural network, which enables to connect the convolutional layer with the long-short-time-memory layer together to predict the continuous bearing RUL. Compared with the traditional networks, the proposed network can additionally extract both the global and local information on the vertical feature axis and the associated context information on the horizontal time axis. The experiments are conducted to prove that the proposed method requires fewer training samples and outperforms other methods in RUL estimation.
•The HSWT method can effectively improve the TF energy concentration and obtain a high accuracy reconstruction for signals with fast varying IF.•The performance of the HSWT method has been ...theoretically proved.•The effectiveness of the HSWT method has been verified by a simulated signal and experimental signals.
The synchrosqueezing transform (SST) is a powerful tool for time-frequency analysis of signals with slowly varying instantaneous frequency (IF). However, the SST and its extensions provide poor time-frequency resolution for signals with wide frequency range and fast varying IF. In this paper, a new SST method called high-order synchrosqueezing wavelet transform is proposed to achieve a highly energy-concentrated time-frequency representation (TFR) for nonstationary signals with wide frequency range and fast varying IF. This method uses high-order group delay and chirp rate operators to obtain the accurate estimation of instantaneous frequency. The proposed method can effectively improve the energy concentration of the TFR and remain invertible simultaneously. The numerical simulations investigate the performance and noise robustness of the proposed method when analyzing a typical amplitude-modulated and frequency-modulated (AM-FM) multicomponent signal. Finally, the application of planetary gearbox fault diagnosis in the variable operating condition verifies the effectiveness of the proposed method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper presents a novel approach to detect the milling chatter based on energy entropy. By using variational mode decomposition and wavelet packet decomposition, the cutting force signal is ...decomposed into two group of sub-signals respectively, and each component has limited bandwidth in spectral domain. Since milling chatter is characterized by the change of frequency and energy distribution. Therefore the energy features extracted from the two group of sub-signals are considered and the energy entropies are obtained, which can be utilized to demonstrate the condition of the milling system synthetically. Several milling tests are conducted and the results show that the proposed method can effectively detect the chatter at an early stage.
•A new approach is presented to detect milling chatter in peripheral milling.•The non-stationary chatter signal is decomposed based on the VMD and WPD.•Energy entropy are utilized to demonstrate the condition of milling system.•The method has an excellent performance for chatter identification.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The rolling element bearing is easy to be malfunctioning due to the harsh operation. When a fault exists in the bearing, it can generate the periodical or quasi-periodical impulses, which are ...important features for the bearing fault detection. These impulses may be submerged in the background noise and interferences of other unrelated components. The spectral kurtosis, and its fast realization, fast kurtogram, have been widely used for the bearing fault diagnosis by extracting the impulsive feature. However, the performance is weakened due to its fixed decomposition scheme and prior information of the bearing faults. A new and adaptive spectral kurtosis method is proposed in this paper. This method is free from parameter selection. Different from the fast kurtogram, the decomposition scheme of the proposed method is flexible and adaptive. The effectiveness of the proposed method is verified by the simulation and the experiment. Both results show that the proposed method can effectively extract the bearing fault features.
The order tracking method based on time-frequency representation is regarded as an effective tool for fault detection of bearings with varying rotating speeds. In the traditional order tracking ...methods, a tachometer is required to obtain the instantaneous speed which is hardly satisfied in practice due to the technical and economical limitations. Some tacholess order tracking methods have been developed in recent years. In these methods, the instantaneous frequency ridge extraction is one of the most important parts. However, the current ridge extraction methods are sensitive to noise and may easily get trapped in a local optimum. Due to the presence of noise and other unrelated components of the signal, bearing fault features are difficult to be detected from the envelope spectrum or envelope order spectrum. To overcome the abovementioned drawbacks, an adaptive and tacholess order analysis method is proposed in this paper. In this method, a novel ridge extraction algorithm based on dynamic path optimization is adopted to estimate the instantaneous frequency. This algorithm can overcome the shortcomings of the current ridge extraction algorithms. Meanwhile, the enhanced empirical wavelet transform (EEWT) algorithm is applied to extract the bearing fault features. Both simulated and experimental results demonstrate that the proposed method is robust to noise and effective for bearing fault detection under variable speed conditions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Synchrosqueezing transform (SST) is a currently proposed novel postprocessing time-frequency (TF) analysis tool. It has been widely shown that SST is able to improve TF representation. However, so ...far, how to improve the TF resolution while ensuring the accuracy of signal reconstruction is still an open question, particularly for the vibration signal with time-varying instantaneous frequency (IF) characteristics, due to the fact that the vibration signals of mechanical equipment usually contain many types of noise generated by harsh operating conditions, and the SST will mix these noise into the real signal. Our first contribution is using the Gaussian modulated linear chirp (GMLC) signal model to represent the general nonstationary signals. The GMLC signal model can more accurately represent the time-varying nonstationary signal, compared with the SST signal model composed of linear phase function and constant amplitude. Our second contribution in this work is proposing a method to improve the TF resolution and reconstruction accuracy for nonstationary signals with time-varying IF, which we coined the second-order synchroextracting transform (SET2). In SET2, we apply the GMLC to deduce the nonstationary signal model and then only use the energy at the IF to characterize the TF distribution, which improves the TF while reducing the impact of noise on the real signal.
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