Complexity Analysis of Time‐Frequency Features for Vibration Signals of Rolling Bearings Based on Local Frequency

Tang, Youfu; Feng, Lin; Zou, Qian

doi:10.1155/2019/7190568

Cited by 5 publications

(7 citation statements)

References 40 publications

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“…From equations (2) and 3, h and g are filtered coefficients, p is the number of samples, and u is shifting parameter. e extracted features are used for feature selection viz.…”

Section: Methodsmentioning

confidence: 99%

“…It emphasizes the importance of both time and frequency domain analysis for nonstationary signals. Tang et al [2] have proposed an adaptive waveform decomposition method of the waveform to extract timefrequency features of nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

“…In an overview of the literature study, the feature extraction-based condition monitoring technique is discussed for various faults other than misalignment [1][2][3][4][5][6][7][8]. e various fault analysis technique based on vibration, temperature, stress analysis, and some of these in combination with ANN are considered for fault identification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Umbrajkaar

Krishnamoorthy

Dhumale

2020

Shock and Vibration

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The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions.

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“…From equations (2) and 3, h and g are filtered coefficients, p is the number of samples, and u is shifting parameter. e extracted features are used for feature selection viz.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Umbrajkaar

Krishnamoorthy

Dhumale

2020

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Although the above methods have achieved good detection results, there are also a small amount of detection errors, so the extraction of a more effective feature that is beneficial to leakage identification needs further study. For non-stationary signals, the Lempel-Ziv index was calculated to characterize the rate of generating new patterns when the signals changed, then measured the complexity of time series (Tang et al, 2019). The results showed that the Lempel-Ziv index can quantitatively measure the complexity of nonlinear signals and effectively distinguish different signals.…”

Section: Introductionmentioning

confidence: 99%

Leakage detection method of natural gas pipeline combining improved variational mode decomposition and Lempel–Ziv complexity analysis

Zhu

Wang

Yue

et al. 2022

Transactions of the Institute of Measurement and Control

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With the continuous development of pipeline transportation industry, pipeline leakage often occurs, posing a great threat to people’s lives and property safety. In order to improve the detection accuracy of natural gas pipeline leakage, a pipeline leakage detection method based on improved variational mode decomposition algorithm and Lempel–Ziv complexity analysis is proposed. In this work, the normalized mutual information is used to determine the decomposition level K of variational mode decomposition, and the Lempel–Ziv complexity analysis algorithm is used to extract pipeline signal feature. The results show that the proposed leakage detection method has higher classification accuracy than other methods, which verifies the effectiveness of this method in the process of pipeline leakage detection.

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“…In terms of nonstationary signal analysis, according to the different technical means adopted, it can be roughly divided into two categories: nonparametric method and parametric method. In addition, according to the different analysis domain of each method, the above two categories are divided into time-domain methods, frequency domain methods, and time-frequency-domain methods [8][9][10], respectively.…”

Section: Introductionmentioning

confidence: 99%

Signal Nonstationary Degree Evaluation Method Based on Moving Statistics Theory

Cheng

Zhang

2021

Shock and Vibration

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Nonstationary signal refers to the signal whose statistics change with time, and its nonstationary degree evaluation can provide effective support for the evaluation of the operating state of the signal source. This paper introduced a variety of typical signal global and local nonstationary degree evaluation methods and compared the applicable scope of different evaluation methods. In view of the limitations of the existing evaluation methods in the scope of application, considering the influence of adjacent signal points, this paper proposed the concepts and calculation methods of the moving mean, moving standard deviation, moving variation coefficient, and moving Hurst exponent based on the theory of moving statistics. According to different nonstationary degree evaluation methods, three different fields of signals (sinusoidal signal, mechanical fault signal, and ECG signal) are analyzed. The results show that, compared with the existing nonstationary degree evaluation methods, the signal nonstationary degree evaluation method proposed in this paper can reveal the time-varying details of the nonstationary signals, with high precision and strong stability, and has unique advantages in nonstationary signal processing.

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Complexity Analysis of Time‐Frequency Features for Vibration Signals of Rolling Bearings Based on Local Frequency

Cited by 5 publications

References 40 publications

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Leakage detection method of natural gas pipeline combining improved variational mode decomposition and Lempel–Ziv complexity analysis

Signal Nonstationary Degree Evaluation Method Based on Moving Statistics Theory

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