scholarly journals Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Grzegorz Żak ◽  
Agnieszka Wyłomańska ◽  
Radosław Zimroz
Keyword(s):  
Stable Distribution ◽  
Signal To Noise Ratio ◽  
Mining Industry ◽  
Low Frequency ◽  
Real Data ◽  
Vibration Signal ◽  
High Energy ◽  
Data Driven ◽  
Time Frequency ◽  
Vibration Data

The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, α-stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.

Parametric Time-Frequency Map and its Processing for Local Damage Detection in Rotating Machinery

2013 ◽  
Vol 588 ◽  
pp. 214-222 ◽  
Author(s):  
Ryszard Makowski ◽  
Radoslaw Zimroz
Keyword(s):  
Damage Detection ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Time Varying ◽  
Local Damage ◽  
Time Frequency ◽  
Vibration Data ◽  
Novel Approach ◽  
Vibration Signal Analysis

The detection of local damage in rotating machinery (gears, bearings) via vibration signal analysis is one of the most powerful techniques in condition monitoring. However, in some cases, especially in heavy industrial machinery, it is difficult to detect damage because of the poor signal-to-noise ratio of the measured vibration. Therefore it is necessary to use unconventional advanced techniques to enhance the signal. In this paper, a novel approach based on parametric time-frequency analysis and further processing for: i) time-varying spectral content modelling, ii) the identification of informative frequency bands by statistical analysis, iii) local damage detection and iv) cycle identification via cepstral analysis, is presented. The proposed procedure is validated using real vibration data from bearings and gearboxes. It is worth noting that this methodology can be also successfully used in time-varying speed conditions (with limited fluctuation).

scholarly journals Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Teng Wang ◽  
Zheng Liu ◽  
Guoliang Lu
Keyword(s):  
Condition Monitoring ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Data Driven ◽  
Time Frequency ◽  
Vibration Data ◽  
Bearing Condition Monitoring ◽  
Condition Monitoring Systems ◽  
Early Fault Detection ◽  
Condition Indicator

Most condition monitoring systems rely on system-driven generation of indicators or features for early fault detection. However, this strategy requires the prior knowledge on the system kinematics and/or exact structure parameters of monitored system. To address this problem, this paper presents a novel condition monitoring framework where the condition indicator is generated via data-driven method. In this framework, the time-frequency periodogram is extracted from raw vibration signal first. Then, the acquired time-frequency periodogram is mapped by pseudo Perron vector, which is learned from vibration data, to generate the condition indicator. Finally, the bearing can be monitored via analyzing this indicator using gaussian based control chart. Based on experimental results on a publicly-available database, we show the effectiveness of presented framework for early fault detectionin the continuous operation of rolling bearing, indicating its great potentials in real engineering applications.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

Sparse spectral attributes to detect hydrocarbons: A case study from deep dolomite reservoirs

2019 ◽  
Vol 7 (3) ◽  
pp. T701-T711
Author(s):  
Jianhu Gao ◽  
Bingyang Liu ◽  
Shengjun Li ◽  
Hongqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Spectral Amplitude ◽  
Data Set ◽  
Frequency Spectra ◽  
Hydrocarbon Production ◽  
Hydrocarbon Detection ◽  
Time Frequency ◽  
Gas Bearing

Hydrocarbon detection is always one of the most critical sections in geophysical exploration, which plays an important role in subsequent hydrocarbon production. However, due to the low signal-to-noise ratio and weak reflection amplitude of deep seismic data, some conventional methods do not always provide favorable hydrocarbon prediction results. The interesting dolomite reservoirs in Central Sichuan are buried over an average depth of 4500 m, and the dolomite rocks have a low porosity below approximately 4%, which is measured by well-logging data. Furthermore, the dominant system of pores and fractures as well as strong heterogeneity along the lateral and vertical directions lead to some difficulties in describing the reservoir distribution. Spectral decomposition (SD) has become successful in illuminating subsurface features and can also be used to identify potential hydrocarbon reservoirs by detecting low-frequency shadows. However, the current applications for hydrocarbon detection always suffer from low resolution for thin reservoirs, probably due to the influence of the window function and without a prior constraint. To address this issue, we developed sparse inverse SD (SISD) based on the wavelet transform, which involves a sparse constraint of time-frequency spectra. We focus on investigating the applications of sparse spectral attributes derived from SISD to deep marine dolomite hydrocarbon detection from a 3D real seismic data set with an area of approximately [Formula: see text]. We predict and evaluate gas-bearing zones in two target reservoir segments by analyzing and comparing the spectral amplitude responses of relatively high- and low-frequency components. The predicted results indicate that most favorable gas-bearing areas are located near the northeast fault zone in the upper reservoir segment and at the relatively high structural positions in the lower reservoir segment, which are in good agreement with the gas-testing results of three wells in the study area.

scholarly journals A SFTD Algorithm for Optimizing the Performance of the Readout Strategy of Residence Time Difference Fluxgate

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3985 ◽  
Author(s):  
Siyu Chen ◽  
Yanzhang Wang ◽  
Jun Lin
Keyword(s):  
Residence Time ◽  
Time Domain ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Transformation Method ◽  
Time Difference ◽  
Single Frequency ◽  
Time Frequency ◽  
Frequency Magnetic Field ◽  
The Time Domain

Residence time difference (RTD) fluxgate sensor is a potential device to measure the DC or low-frequency magnetic field in the time domain. Nevertheless, jitter noise and magnetic noise severely affect the detection result. A novel post-processing algorithm for jitter noise reduction of RTD fluxgate output strategy based on the single-frequency time difference (SFTD) method is proposed in this study to boost the performance of the RTD system. This algorithm extracts the signal that has a fixed frequency and preserves its time-domain information via a time–frequency transformation method. Thereby, the single-frequency signal without jitter noise, which still contains the ambient field information in its time difference, is yielded. Consequently, compared with the traditional comparator RTD method (CRTD), the stability of the RTD estimation (in other words, the signal-to-noise ratio of residence time difference) has been significantly boosted with sensitivity of 4.3 μs/nT. Furthermore, the experimental results reveal that the RTD fluxgate is comparable to harmonic fluxgate sensors, in terms of noise floor.

Applied Research on Segmented-Hilbert-Huang Transform Algorithm in Fault Feature Extraction of Main Reducer

2013 ◽  
Vol 823 ◽  
pp. 417-421 ◽  
Author(s):  
Feng Yun Huang ◽  
Huan Huan Sun ◽  
Hao Pan ◽  
Wei Ru Zhang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Vibration Signal ◽  
Model Decomposition ◽  
Frequency Dictionary ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
Scale Characteristics ◽  
Frequency Impulse

For the multi-time scale characteristics of vibration signal, a composite multi-frequency dictionary combining the low-frequency Fourier dictionary and the high-frequency impulse time-frequency dictionary is constituted, to decompose multi-component vibration signal into the combination of several one-component signals. The use of empirical model decomposition (EDM) in high-frequency impulse Component signal including feature information is to realize segmented Hilbert-Huang transform of signal and to acquire the time-frequency representation of every one-component signal, which is the process of fault information extraction of vibration signal. The application of the method in main reducer fault diagnosis verifies the engineering practicability and validity of the new algorithm.

Signal de-noising method for vibration signal of flood discharge structure based on combined wavelet and EMD

2015 ◽  
Vol 23 (15) ◽  
pp. 2401-2417 ◽  
Author(s):  
Jianwei Zhang ◽  
Qi Jiang ◽  
Bin Ma ◽  
Yu Zhao ◽  
Lianghuan Zhu
Keyword(s):  
White Noise ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Vibration Signal ◽  
Arch Dam ◽  
Elastic Model ◽  
Flood Discharge ◽  
Reconstructed Signal ◽  
Discharge Structure ◽  
Filtering Effect

A new de-noising method combining Wavelet threshold and empirical mode decomposition (EMD) (WTEMD for short) is proposed to improve the precision of de-noising performance for vibration signal of flood discharge structure in low signal to noise ratio (SNR). White noise is partially filtered out by decomposing the vibration signal with wavelet. Then conducting the further EMD on wavelet reconstructed signal to obtain Intrinsic Mode Function (IMF), through analyzing spectrum diagram of every IMF component, low-frequency waterflow noise and the rest of high-frequency white noise are filtered out, regarding SNR and root mean square error (RMSE) as evaluation index for noise reduction effect. The novelty of this method is that it can reduce the endpoint effect of EMD. By comparing the filtering effect of WTEMD with other methods on simulation signals, study shows that, WTEMD has a higher precision and a better de-noising effect. The dominant vibration information of dam structure is achieved by using WTEMD in Laxiwa arch dam hydro-elastic model and Three Gorges Dam, which can provide the basis for safe operation and on-line monitoring of the dam structure. This method can effectively solve the problem of dominant information extraction for large flood discharge structure.

Detection of Life Characteristic Signals Based on High Order Statistics

2012 ◽  
Vol 239-240 ◽  
pp. 807-810
Author(s):  
Jian Jun Li ◽  
Jian Feng Zhao
Keyword(s):  
Order Statistics ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Time Estimation ◽  
High Order ◽  
Signal To Noise ◽  
Time Frequency ◽  
Noise Ratio ◽  
Short Time ◽  
High Order Statistics

Life parameters signal has characteristics of extremely low frequency, low signal-to-noise ratio, and the easy submerged in strong clutter noises. The method for detecting life signal based on filter bank and high order statistics is presented, in which neither the Gaussian supposition of the observed signal, nor a prior information about the waveform and arrival time of the observed signal is necessary. The principle of method is to separate the spectrum of input signal into many narrow frequency bands, whose Sub-band signal is followed by a short-time estimation of higher-order statistics so as to suppress Gaussian noises. Simulated results show that the method not only can completely descript life signals in the time-frequency domain, but improve the signal-to-noise ratio and the ability of detecting algorithm. Moreover, the method is effective and practical.

scholarly journals In search for the most optimal EEG method: A practical evaluation of a water-based electrode EEG system

2021 ◽  
Author(s):  
M. Topor ◽  
B. Opitz ◽  
P. J. A. Dean
Keyword(s):  
Signal To Noise Ratio ◽  
Flanker Task ◽  
Low Frequency ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Specific System ◽  
Time Frequency ◽  
Beta Power ◽  
Water Based ◽  
Related Potentials

AbstractThe study assessed a new mobile electroencephalography (EEG) system with water-based electrodes for its applicability in time-frequency and event related potential research. It was compared to a standard gel-based wired system. EEG was recorded on two occasions as participants completed the flanker task, first with the gel-based system followed by the water-based system. Technical and practical considerations for the application of the new water-based system are reported based on the participant and experimenter experiences. Empirical comparisons focused on EEG data noise levels, frequency power across four bands including theta, alpha, low beta and high beta and P300 and ERN event related potential components. The water-based system registered more noise compared to the gel-based system which resulted in increased loss of data during artefact rejection. Signal to noise ratio was significantly lower for the water-based system in the parietal channels which impacted the observed parietal beta power. It also led to a shift in topography of the maximal P300 activity from parietal to frontal regions. It is also evident, that the water-based system may be prone to slow drift noise which may affect the reliability and consistency of low frequency band analyses. Considerations for the use of this specific system for time-frequency and event related potentials are discussed.

The Fault Diagnosis Method for Roller Bearing with Harmonic Wavelet Packet and DT-SVM

2015 ◽  
Vol 724 ◽  
pp. 238-241
Author(s):  
Rui Pan ◽  
Tao Xu ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Support Vector ◽  
Time Frequency ◽  
Vibration Data ◽  
Electric Engineering ◽  
Harmonic Wavelet ◽  
Diagnosis Method

This paper studies the roller bearing fault diagnosis method with harmonic wavelet packet and Decision Tree-Support Vector Machine (DT-SVM). The harmonic wavelet packet possesses better performances for its box-shaped spectrum and unlimited subdivision compared with the conventional time-frequency feature exaction method. Firstly, the proposed method decomposes the roller bearing vibration signal with harmonic wavelet packet and extracts the feature energy with coefficients of each spectrum. After the feature energy is normalized, feature vector are available. Based on multi-level binary tree, this paper designs the multi-classification SVM model due to its superior nonlinear mapping capability. Three two-classifications are incorporated to diagnosis the roller bearing faults. Finally, the proposed method is illustrated with the vibration data from the roller bearing stand of electric engineering lab in case western reserve university. Experimental results illustrate the higher accuracy of the proposed method compared with conventional method.

Sign in / Sign up

Export Citation Format

Share Document