Mitigation of low-frequency underwater noise generated by rotating machinery on a mobile work barge using large tethered encapsulated bubbles

2012 ◽  
Vol 131 (4) ◽  
pp. 3507-3507 ◽  
Author(s):  
Kevin M. Lee ◽  
Mark S. Wochner ◽  
Preston S. Wilson
Keyword(s):  
Low Frequency ◽  
Rotating Machinery ◽  
Underwater Noise ◽  
Mobile Work

scholarly journals In Design of an Ocean Bottom Seismometer Sensor: Minimize Vibration Experienced by Underwater Low-Frequency Noise

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3446 ◽  
Author(s):  
Xiaohan Wang ◽  
Shangchun Piao ◽  
Yahui Lei ◽  
Nansong Li
Keyword(s):  
Seismic Waves ◽  
Low Frequency ◽  
Sound Field ◽  
Average Density ◽  
Vibration Velocity ◽  
Frequency Noise ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Underwater Noise ◽  
The Impact

Ocean Bottom Seismometers (OBS) placed on the seafloor surface are utilized for measuring the ocean bottom seismic waves. The vibration of OBS excited by underwater noise on its surface may interfere with its measured results of seismic waves. In this particular study, an OBS was placed on the seabed, while ray acoustic theory was used to deduce the sound field distribution around the OBS. Then using this information, the analytical expression for the OBS vibration velocity was obtained in order to find various factors affecting its amplitude. The finite element computing software COMSOL Multiphysics® (COMSOL) was used to obtain the vibration response model of the OBS which was exposed to underwater noise. The vibration velocity for the OBS calculated by COMSOL agreed with the theoretical result. Moreover, the vibration velocity of OBS with different densities, shapes, and characters were investigated as well. An OBS with hemispherical shape, consistent average density as that of the seafloor, and a physical structure of double tank has displayed minimum amplitude of vibration velocity. The proposed COMSOL model predicted the impact of underwater noise while detecting the ocean bottom seismic waves with the OBS. In addition, it provides significant help for the design and optimization of an appropriate OBS.

scholarly journals Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity

2020 ◽  
Vol 10 (12) ◽  
pp. 4221 ◽  
Author(s):  
Bing Han ◽  
Shun Wang ◽  
Qingqi Zhu ◽  
Xiaohui Yang ◽  
Yongbo Li
Keyword(s):  
Fault Diagnosis ◽  
Multiple Scales ◽  
Complexity Analysis ◽  
Low Frequency ◽  
Health Condition ◽  
Rotating Machinery ◽  
Vibrational Signal ◽  
Single Scale ◽  
Frequency Components ◽  
Fault Information

The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.

scholarly journals Underwater noise mitigation in the Santa Barbara Channel through incentive-based vessel speed reduction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vanessa M. ZoBell ◽  
Kaitlin E. Frasier ◽  
Jessica A. Morten ◽  
Sean P. Hastings ◽  
Lindsey E. Peavey Reeves ◽  
...  
Keyword(s):  
Low Frequency ◽  
Frequency Noise ◽  
Underwater Noise ◽  
Santa Barbara Channel ◽  
Noise Mitigation ◽  
Santa Barbara ◽  
Speed Reduction ◽  
Sound Exposure ◽  
Exposure Levels ◽  
Vessel Speed

AbstractCommercial shipping is the dominant source of low-frequency noise in the ocean. It has been shown that the noise radiated by an individual vessel depends upon the vessel’s speed. This study quantified the reduction in source levels (SLs) and sound exposure levels (SELs) for ships participating in two variations of a vessel speed reduction (VSR) program. SLs and SELs of individual ships participating in the program between 2014 and 2017 were statistically lower than non-participating ships (p < 0.001). In the 2018 fleet-based program, there were statistical differences between the SLs and SELs of fleets that participated with varying degrees of cooperation. Significant reductions in SL and SEL relied on cooperation of 25% or more in slowing vessel speed. This analysis highlights how slowing vessel speed to 10 knots or less is an effective method in reducing underwater noise emitted from commercial ships.

The Application of Parametric Acoustic Receiving Array to Vehicle Radiated Underwater Noise Measurement

2013 ◽  
Vol 475-476 ◽  
pp. 107-110
Author(s):  
Song Wen Li
Keyword(s):  
Low Frequency ◽  
Noise Measurement ◽  
Far Field ◽  
Underwater Noise ◽  
Radiated Noise ◽  
Target Vehicle ◽  
Sensor Arrangement

Parametric acoustic receiving array has the advantage of achieving high directional index for low frequency signals with very simple sensor arrangement. This characteristic is suitable for vehicle radiated noise measurement in costal area and in the far field of the target vehicle. This paper gives a design for such application of parametric acoustic receiving array with the calculation of possible ability that can be achieved.

scholarly journals Evidence that ship noise increases stress in right whales

2012 ◽  
Vol 279 (1737) ◽  
pp. 2363-2368 ◽  
Author(s):  
Rosalind M. Rolland ◽  
Susan E. Parks ◽  
Kathleen E. Hunt ◽  
Manuel Castellote ◽  
Peter J. Corkeron ◽  
...  
Keyword(s):  
Low Frequency ◽  
Acoustic Signals ◽  
Frequency Noise ◽  
Underwater Noise ◽  
Baleen Whales ◽  
Ship Traffic ◽  
Right Whale ◽  
Right Whales ◽  
North Atlantic Right Whales ◽  
Shipping Traffic

Baleen whales ( Mysticeti ) communicate using low-frequency acoustic signals. These long-wavelength sounds can be detected over hundreds of kilometres, potentially allowing contact over large distances. Low-frequency noise from large ships (20–200 Hz) overlaps acoustic signals used by baleen whales, and increased levels of underwater noise have been documented in areas with high shipping traffic. Reported responses of whales to increased noise include: habitat displacement, behavioural changes and alterations in the intensity, frequency and intervals of calls. However, it has been unclear whether exposure to noise results in physiological responses that may lead to significant consequences for individuals or populations. Here, we show that reduced ship traffic in the Bay of Fundy, Canada, following the events of 11 September 2001, resulted in a 6 dB decrease in underwater noise with a significant reduction below 150 Hz. This noise reduction was associated with decreased baseline levels of stress-related faecal hormone metabolites (glucocorticoids) in North Atlantic right whales ( Eubalaena glacialis ). This is the first evidence that exposure to low-frequency ship noise may be associated with chronic stress in whales, and has implications for all baleen whales in heavy ship traffic areas, and for recovery of this endangered right whale population.

scholarly journals A key to quieter seas: half of ship noise comes from 15% of the fleet

2018 ◽  
Author(s):  
Scott Veirs ◽  
Val Veirs ◽  
Rob Williams ◽  
Michael Jasny ◽  
Jason Wood
Keyword(s):  
Low Frequency ◽  
Noise Pollution ◽  
Marine Species ◽  
International Maritime Organization ◽  
Total Power ◽  
Underwater Noise ◽  
Noise Levels ◽  
Management Options ◽  
Ocean Noise ◽  
Wide Range

Underwater noise pollution from ships is a chronic, global stressor impacting a wide range of marine species. Ambient ocean noise levels nearly doubled each decade from 1963-2007 in low-frequency bands attributed to shipping, inspiring a pledge from the International Maritime Organization to reduce ship noise and a call from the International Whaling Commission for member nations to halve ship noise within a decade. Our analysis of data from 1,582 ships reveals that half of the total power radiated by a modern fleet comes from just 15% of the ships, namely those with source levels above 179 dB re 1 μPa @ 1 m. We present a range of management options for reducing ship noise efficiently, including incentive-based programs, without necessarily regulating the entire fleet.

scholarly journals Fault Diagnosis Method for Rotating Machinery Based on Hierarchical Amplitude-Aware Permutation Entropy and Pairwise Feature Proximity

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ling Shu ◽  
Jinxing Shen ◽  
Xiaoming Liu
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Low Frequency ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Support Vector ◽  
Feature Vectors ◽  
Fault Features ◽  
Diagnosis Method ◽  
Frequency Features

With a view to solving the defect that multiscale amplitude-aware permutation entropy (MAAPE) can only quantify the low-frequency features of time series and ignore the high-frequency features which are equally important, a novel nonlinear time series feature extraction method, hierarchical amplitude-aware permutation entropy (HAAPE), is proposed. By constructing high and low-frequency operators, this method can extract the features of different frequency bands of time series simultaneously, so as to avoid the issue of information loss. In view of its advantages, HAAPE is introduced into the field of fault diagnosis to extract fault features from vibration signals of rotating machinery. Combined with the pairwise feature proximity (PWFP) feature selection method and gray wolf algorithm optimization support vector machine (GWO-SVM), a new intelligent fault diagnosis method for rotating machinery is proposed. In our method, firstly, HAPPE is adopted to extract the original high and low-frequency fault features of rotating machinery. After that, PWFP is used to sort the original features, and the important features are filtered to obtain low-dimensional sensitive feature vectors. Finally, the sensitive feature vectors are input into GWO-SVM for training and testing, so as to realize the fault identification of rotating machinery. The performance of the proposed method is verified using two data sets of bearing and gearbox. The results show that the proposed method enjoys obvious advantages over the existing methods, and the identification accuracy reaches 100%.

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