Energy Distribution Of Vibration Signals Research Articles

Adaptive cyclic content ratiogram: a new signal decomposition method for bearing concurrent fault diagnosis

Fast kurtogram (FK) has been proven to be an effective tool for resonance frequency band detection, which is widely used in bearing fault diagnosis. However, FK is not robust to impulsive noise, and its frequency band segmentation rule is fixed, which leads to over-decomposition or under-decomposition of the fault resonance frequency band in its signal decomposition results. Therefore, an adaptive cyclic content ratiogram is proposed in this paper. Firstly, based on the energy distribution of vibration signals on different frequency components, the frequency spectral segmentation is performed adaptively, and multiple sub-signals containing different frequency components are obtained. Secondly, the ratio of cyclic content (RCC), which cannot only more accurately characterize the cyclostationarity of bearing fault impacts but also be insensitive to impulsive noise, is applied to evaluate the fault feature information contained in each sub-signal separately. In the meanwhile, considering that fault characteristic frequency information is required in the process of RCC evaluation, the proposed method performs adaptive fault characteristic frequency detection for each sub-signal based on the envelope spectrum. The RCC maximization is used to locate the fault resonance frequency band. Also, combined with the estimated fault characteristic frequencies, the proposed method can achieve the extraction of concurrent fault features. Simulation and experimental data verify the effectiveness of the proposed method.

Evaluation of Vibration Effect Caused by Carbon Dioxide Phase-Transition Fracturing Based on the Hilbert–Huang Transform

To evaluate the vibration effect caused by carbon dioxide phase-transition fracturing, the Hilbert–Huang transform was used to study the change of vibration energy with distance in different frequency bands. The results are drawn as follows: (1) The peak particle velocity (PPV) decreases as a power function with an increase in distance and has fallen below 25 mm/s at 2.8 m. (2) The energy of vibration signals induced by carbon dioxide phase-transition fracturing is mainly distributed at the frequency band of 10–50 Hz. With the increase in distance, the energy distribution of vibration signals falls in four phases: Propagation to high frequency (0–13.9 m); a rapid high-frequency energy decrease (20–30 m); energy fluctuation (30–47.2 m) and a stable energy distribution (larger than 50 m). (3) The proportion of the low-frequency vibration energy (0–10 Hz) increases as a result of the increase in distance (less than 8.7 m), which should be paid more attention.

Building safety monitoring based on extreme gradient boosting in distributed optical fiber sensing

Abstract Aiming at the problem of the safety monitoring of the surrounding environment of the glass window on the building, a scheme of distributed optical fiber acoustic sensors based on the phase sensitive time-domain reflection is proposed. Firstly, Wigner bispectrum is used to analyze the time–frequency energy distribution of vibration signal, then the axially integrated bispectrum algorithm is used to extract the time–frequency characteristic components, and the method of sub-band division is used to avoid information loss and decrease the cost of computing. The eigenvectors of different vibration events are recognized by extreme gradient boosting tree algorithm. Based on the diversity of acoustic signal types in the environment, there are eight kinds of events including wind blowing, knocking, window opening, watering, hammering, dog barking, aircraft sound and no disturbance to be selected for vibration experiments. The experimental results show that the scheme can get better recognition effect, and the average recognition rate of eight kinds of events reaches 93.3%.

Feature Extraction of Vibration Signal Based on an Improved Local Wave Analysis

In order to reduce the random noise influence on local wave decomposition (LWD), the original data is adaptively denoised by lifting wavelet transform to strain mode aliasing in LWD, avoid the pseudo mode functions in LWD and improve the quality of LWD. Local wave marginal spectrum is employed to analyze vibration signal for extracting the characteristics information. Intrinsic mode functions obtained by LWD, the instantaneous frequency and amplitude can be calculated by the Hilbert transform. The improved local wave analysis can offer a measure of the energy distribution of vibration signal from its instantaneous frequency. Analysis and experimental result demonstrate the validity of the proposed method.