Measurement of propeller-induced cavitation noise for ship identification

Widjiati, Endang; Djatmiko, Eko Budi; Wardhana, Wisnu; Wirawan, Wirawan

doi:10.1121/1.4709183

Cited by 5 publications

(4 citation statements)

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“…Input layer Hidden layer Output layer Equations (10) and (11) are mutual information gap standards and mutual information quotient standards. In the process of optimization, the incremental optimization algorithm is used to get the optimal subset of the fault features, thus realizing the feature optimization.…”

Section: Feature Selection Based On Mrmrmentioning

confidence: 99%

“…When there is need for a new feature to be selected, after some features have been selected, the remaining features need to be recalculated according to equations (10) and (11). And then, the feature which satisfied the two conditions of equations (10) and (11) can be selected as the next feature.…”

Section: Feature Selection Based On Mrmrmentioning

confidence: 99%

“…The conventional power spectrum analysis of an acoustical measurement is normally employed for an inception criterion. 9,10 It was realized that the presence of increase within a certain frequency band of the power spectrum in comparison with a non-cavitation condition can be a sign when the cavitation happened. Lee et al employed the short-time Fourier transform analysis and the Detection of Envelope Modulation On Noise spectrum analysis, both of which are appropriate in finding such a repeating frequency.…”

Section: Introductionmentioning

confidence: 99%

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Stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

Kang

Feng

Wan

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Cavitation is a phenomenon that occurs during the continuous operation of a hydro turbine that directly affects the efficiency and working capacity of the unit. This paper proposes an innovative classification paradigm that uses deep learning-based methodologies in order to identify both cavitation noise signal and non-cavitation noise signal that will help prevent the damage or breakage in the earliest possible time to avoid more irreversible and irreparable damage to the hydro turbine. The stacked sparse autoencoder (SSA) nuclear framework is utilized to learn more abstract and invariant high-level features from the multiple feature sets. Then, the method on minimum redundancy maximum relevance (mRMR) selection is used to evaluate and sort out all the characteristics found by the stacked sparse autoencoder. Finally, the random forest (RF) classifier algorithm is employed to perform supervised fine-tuning and classification. The traditional supervised learning models such as support vector machine, logistic regression, and sparse representation classification are chosen to be used as contrast algorithms. SSA-mRMR-RF generally produces a better performance than the support vector machine, logistic regression and sparse representation classification when used in the same set of features. The SSA-mRMR-RF produced the highest overall average accuracy since it reached 93.18%. The SSA-mRMR-RF offers a 3.85% higher overall accuracy than the support vector machine. It also offers a 2.44% higher overall accuracy than logistic regression. In addition, it also offers a 20.30% higher overall accuracy than the sparse representation classification on the average. When the signals are divided into three categories, the highest overall accuracy decreased to 71.88%, and the classification accuracy of incipient noise signals is very low. Therefore, this paper proposes the models of power spectral-SSA-mRMR-RF and fast Fourier transform-SSA-mRMR-RF to be used, and these discoveries are based on the frequency characteristics of the cavitation noise signal. The highest overall accuracy rebound went back to 88.97% based on the power spectrum, and 96.05% based on fast Fourier transform. The corresponding experimental results found on the four data sets of the different operating conditions demonstrate that the proposed methods for discovering the extent of cavitation outperforms these traditional alternatives.

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Section: Feature Selection Based On Mrmrmentioning

confidence: 99%

Section: Feature Selection Based On Mrmrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

Kang

Feng

Wan

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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show abstract

“…For various cavity patterns, this method will be practical if the acoustical signature is pre-identified, but it is not in most cases. Furthermore, it will be difficult to detect the cavitation phenomenon 17 when the working environment and equipment structure becomes more and more complex. Noise classification is an issue of great significance in cavitation noise detection.…”

Section: Introductionmentioning

confidence: 99%

Cavitation Noise Signal Classification of Hydroturbine Based on Improved Multi-Scale Symbol Dynamic Entropy

Kang¹,

Liu²,

Guo³

et al. 2022

The International Journal of Acoustics and Vibration

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Cavitation is a phenomenon in the operation of hydroturbine, which is related to the operation efficiency and service life of the turbine. To identify both the cavitation noise signal and the non-cavitation noise signal, prevent damage as soon as possible, and avoid irreversible damage to the hydroturbine, a new paradigm based on multi-scale information entropy is proposed in this paper. The new proposed classification model combines improved multi-scale symbol dynamic entropy (IMSDE) and least square support vector machine (LSSVM). Improved multi-scale symbol dynamic entropy is utilized to learn features from the cavitation noise signal, and then the classifier of the least square support vector machine is used to classification. Multi-scale sample entropy (MSE), multi-scale permutation entropy (MPE) and multi-scale symbol dynamic entropy (MSDE) are selected as the contrast algorithms. According to the experimental results of four different operating conditions, IMSDE has the highest recognition rate. The average recognition rate of IMSDE is higher than that of MSDE, MSE and MPE. There is no significant difference in computational efficiency of IMSDE, MSDE and MPE. In conclusion, the IMSDE method proposed in this paper is superior to MSDE, MSE and MPE, for meeting the requirements of cavitation noise signal classification.

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Flow Noise Characteristics Analysis of Underwater High-Speed Vehicle Based on LES/FW-H Coupling Model

Jiangfeng

Gan

et al. 2019

Acoust Aust

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Measurement of propeller-induced cavitation noise for ship identification

Cited by 5 publications

References 0 publications

Stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

Stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

Cavitation Noise Signal Classification of Hydroturbine Based on Improved Multi-Scale Symbol Dynamic Entropy

Flow Noise Characteristics Analysis of Underwater High-Speed Vehicle Based on LES/FW-H Coupling Model

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