An Improved Random Decrement Algorithm and Applications

The free response function is the foundation of mode analysis and recognition of vibration signal, and random decrement algorithm is the commonly used classical algorithm of extracting the free response function. But under the restriction of engineering conditions, it may be impossible for long-time signal acquisition, which makes the number of sample points fail to meet the requirements of the random decrement algorithm, causing the extracted free response signals to contain strong noise and other influencing factors. Aiming at the shortcomings of the existing random decrement technique, this paper proposes an improved random decrement algorithm based on multi-secant method, which can get satisfactory free response signals with short vibration response signals to provide excellent basis of analysis for the vibration mode recognition algorithm of various time-frequency domains. Actual engineering tests confirm that the improved algorithm greatly improves the precision of extracting free response signals while basically keeping the computation speed unchanged, it has high application value.

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Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm

Journal of Electrical and Computer Engineering ◽

10.1155/2020/5720695 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Bernard Marie Tabi Fouda ◽

Dezhi Han ◽

Bowen An ◽

Xiangzhi Chen

Keyword(s):

Optical Fiber ◽

Monitoring System ◽

Time Domain ◽

Signal Transmission ◽

Vibration Signal ◽

Recognition Algorithm ◽

Time Domain Reflectometry ◽

Experimental Simulation ◽

Time Frequency ◽

Security Monitoring System

Distributed optical fiber vibration signal plays a significant role in the communication and safety of any perimeter security monitoring system. It uses light as an information carrier and optical fiber as a means of signal transmission and communication. Phase-sensitive optical time-domain reflectometry (Φ-OTDR) is used to detect the signals generated during events (intrusions or nonintrusion). This paper proposes the time-frequency characteristic (TFC) method for the recognition of the fiber vibration signal and designs and implements the corresponding software function module. The combination of time-domain features and time-frequency-domain features is called TFC; and it is based on the Hilbert transform and on the empirical mode decomposition (EMD) of time-frequency entropy and center-of-gravity frequency that is described. A feature vector is formed, and multiple types of probabilistic neural networks (PNNs) are performed on it to determine whether intrusion events occur. The experimental simulation results show that the monitoring system software can intelligently display the data collected in real time, which demonstrates that the proposed method is effective and reliable in identifying and classifying accurately the types of events. The data processing time is less than 2 s, and the accuracy of the system identification can reach 99%, which ensures the system’s validity.

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Failure Diagnosis System for a Ball-Screw by Using Vibration Signals

Shock and Vibration ◽

10.1155/2015/435870 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Won Gi Lee ◽

Jin Woo Lee ◽

Min Sung Hong ◽

Sung-Ho Nam ◽

YongHo Jeon ◽

...

Keyword(s):

Manufacturing Systems ◽

Vibration Signal ◽

Failure Diagnosis ◽

Ball Screw ◽

Signal Acquisition ◽

Frequency Range ◽

Monitoring Method ◽

Diagnosis System ◽

Time Frequency ◽

Defect Position

Recently, in order to reduce high maintenance costs and to increase operating ratio in manufacturing systems, condition-based maintenance (CBM) has been developed. CBM is carried out with indicators, which show equipment’s faults and performance deterioration. In this study, indicator signal acquisition and condition monitoring are applied to a ball-screw-driven stage. Although ball-screw is a typical linearly reciprocating part and is widely used in industry, it has not gained attention to be diagnosed compared to rotating parts such as motor, pump, and bearing. First, the vibration-based monitoring method, which uses vibration signal to monitor the condition of a machine, is proposed. Second, Wavelet transform is used to analyze the defect signals in time-frequency domain. Finally, the failure diagnosis system is developed using the analysis, and then its performance is evaluated. Using the system, we estimated the severity of failure and detect the defect position. The low defect frequency (≈58.7 Hz) is spread all over the time in the Wavelet-filtered signal with low frequency range. Its amplitude reflects the progress of defect. The defect position was found in the signal with high frequency range (768~1,536 Hz). It was detected from the interval between abrupt changes of signal.

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Recognition of Impulse of Love at First Sight Based On Photoplethysmography Signal

Sensors ◽

10.3390/s20226572 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6572

Author(s):

Huan Lu ◽

Guangjie Yuan ◽

Jin Zhang ◽

Guangyuan Liu

Keyword(s):

Feature Selection ◽

Pulse Signal ◽

Recognition Algorithm ◽

Machine Learning Techniques ◽

Support Vector ◽

Signal Acquisition ◽

Interesting Phenomenon ◽

Linear Discriminant ◽

Time Frequency ◽

Gradient Enhancement

Love at first sight is a well-known and interesting phenomenon, and denotes the strong attraction to a person of the opposite sex when first meeting. As far as we know, there are no studies on the changes in physiological signals between the opposite sexes when this phenomenon occurs. Although privacy is involved, knowing how attractive a partner is may be beneficial to building a future relationship in an open society where both men and women accept each other. Therefore, this study adopts the photoplethysmography (PPG) signal acquisition method (already applied in wearable devices) to collect signals that are beneficial for utilizing the results of the analysis. In particular, this study proposes a love pulse signal recognition algorithm based on a PPG signal. First, given the high correlation between the impulse signals of love at first sight and those for physical attractiveness, photos of people with different levels of attractiveness are used to induce real emotions. Then, the PPG signal is analyzed in the time, frequency, and nonlinear domains, respectively, in order to extract its physiological characteristics. Finally, we propose the use of a variety of machine learning techniques (support vector machine (SVM), random forest (RF), linear discriminant analysis (LDA), and extreme gradient enhancement (XGBoost)) for identifying the impulsive states of love, with or without feature selection. The results show that the XGBoost classifier has the highest classification accuracy (71.09%) when using the feature selection.

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A new real-time signal processing approach for frequency-varying machinery

Journal of Vibration and Control ◽

10.1177/1077546316687923 ◽

2017 ◽

Vol 24 (11) ◽

pp. 2359-2368 ◽

Cited By ~ 2

Author(s):

Jie Zhang ◽

Hongli Gao ◽

Qiyue Liu ◽

Christopher Grebe

Keyword(s):

Condition Monitoring ◽

Vibration Signal ◽

Time Signal ◽

Detailed Knowledge ◽

Mixing Processes ◽

Time Frequency ◽

Analysis Methods ◽

Power Spectral ◽

New Processing ◽

The Stability

Development of condition monitoring approaches has played a key role in the stability and safety of frequency-varying machinery operations. Conventional time–frequency analysis methods suffer problems such as analysis results being too complex to realize highly intelligent and automated condition monitoring systems. Blind source separation is an attractive tool due to its excellent performance in separating defect source signals from their mixtures without detailed knowledge of sources and mixing processes; however, it can only be applied under some strict conditions. In this paper, a nonuniform sampling model is built and a new processing algorithm of frequency-varying signal is proposed. The relationship between the power spectral density (PSD) of the vibration signal of frequency-varying machinery and frequencies at different rotational speeds is derived. The proposed method can adaptively eliminate the influence of the varying rotational speed in the revised PSD. Some classical signal analysis methods are implemented to compare with the proposed approach by simulations. An experiment has been conducted by using a JD-1 wheel/rail simulation facility to illustrate the effectiveness of the proposed method.

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Study on Remote Condition Monitoring and Fault Diagnosis System for Rotating Machinery Based on LabVIEW

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.430-432.1939 ◽

2012 ◽

Vol 430-432 ◽

pp. 1939-1942 ◽

Cited By ~ 1

Author(s):

Chuan Hui Wu ◽

Yan Gao ◽

Yu Guo

Keyword(s):

Fault Diagnosis ◽

Frequency Domain ◽

Condition Monitoring ◽

Vibration Signal ◽

Signal Acquisition ◽

Diagnosis System ◽

Time Frequency ◽

Machinery Condition Monitoring ◽

Temperature Signal ◽

Fault Diagnosis System

In order to suit the demand of monitoring and fault diagnosis of modern small and medium machinery devices better, this paper discusses the development of machinery condition monitoring and fault diagnosis system of good universality and strong expansibility using LabVIEW. Mainly illuminates vibration signal, temperature signal and electric current signal acquisition module using NI data acquisition hardware; signal analysis module in time domain, frequency domain and joint time–frequency domain using signal processing technology. DataSocket, database and fuzzy diagnosis technique have been utilized enabling this system to monitor and diagnose machinery fault remotely.

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Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽

10.3390/electronics10111248 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1248

Author(s):

Rafia Nishat Toma ◽

Cheol-Hong Kim ◽

Jong-Myon Kim

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Empirical Mode Decomposition ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Fault Classification ◽

Original Signal ◽

Time Frequency ◽

Mode Decomposition ◽

Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

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A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.650 ◽

2013 ◽

Vol 333-335 ◽

pp. 650-655

Author(s):

Peng Hui Niu ◽

Yin Lei Qin ◽

Shun Ping Qu ◽

Yang Lou

Keyword(s):

Signal Processing ◽

Real Time ◽

Phase Difference ◽

Time Signal ◽

Time Interval ◽

Time Varying ◽

Signal Model ◽

Time Frequency ◽

Mass Flowmeter ◽

Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

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Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

Mathematical Problems in Engineering ◽

10.1155/2016/7616393 ◽

2016 ◽

Vol 2016 ◽

pp. 1-16 ◽

Cited By ~ 19

Author(s):

Irena Orović ◽

Vladan Papić ◽

Cornel Ioana ◽

Xiumei Li ◽

Srdjan Stanković

Keyword(s):

Signal Processing ◽

Fourier Transform ◽

Compressive Sensing ◽

Signal Reconstruction ◽

Signal Acquisition ◽

Transform Domain ◽

Hermite Transform ◽

Time Frequency ◽

Reconstruction Methods ◽

Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

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A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery

Scientific Reports ◽

10.1038/s41598-021-96343-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kai Wei ◽

Xuwen Jing ◽

Bingqiang Li ◽

Chao Kang ◽

Zhenhuan Dou ◽

...

Keyword(s):

Vibration Signal ◽

Rotating Machinery ◽

Second Order ◽

Variable Speed ◽

Short Time Fourier Transform ◽

Stationary Characteristic ◽

Nonstationary Signals ◽

Time Frequency ◽

Effective Technology ◽

Short Time

AbstractIn recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.

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