Research on Flow Field Perception Based on Artificial Lateral Line Sensor System

Liu, Guijie; Wang, Mengmeng; Wang, Anyi; Wang, Shirui; Yang, Tingting; Malekian, Reza; Li, Zhixiong

doi:10.3390/s18030838

Cited by 29 publications

(19 citation statements)

References 19 publications

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“…The AUV-shaped carrier was designed with a length of 378 mm and a diameter of 160 mm as shown in Figure 1. Since the static and dynamic pressures were substantially the same in the cross-section perpendicular to the carrier axis [27], the carrier could be simplified as a two-dimensional model. The horizontal plane of the carrier is shown in Figure 1, and the sensors are distributed along the horizontal plane.…”

Section: Model Establishmentmentioning

confidence: 99%

Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System

Liu

Hao

Yang

et al. 2020

Sensors

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At present, autonomous underwater vehicles (AUVs) cannot perceive local environments in complex marine environments, where fish can obtain hydrodynamic information about the surrounding environment through a lateral line. Inspired by this biological function, an artificial lateral line system (ALLS) was built on a moving bionic carrier using the pressure sensor in this paper. When the carrier operated with different speeds in the flow field, the pressure distribution characteristics surrounding the carrier were analyzed by numerical simulation, where the effect of the flow angle between the fluid velocity direction and the carrier navigation direction was considered. The flume experiment was carried out in accordance with the simulation conditions, and the analysis results of the experiment were consistent with those in the simulation. The relationship between pressure and fluid velocity was established by a fitting method. Subsequently, the pressure difference method was investigated to establish a relationship model between the pressure difference on both sides of the carrier and the flow angle. Finally, a back propagation neural network model was used to predict the fluid velocity, flow angle, and carrier speed successfully in the unknown fluid environment. The local fluid environment perception by moving carrier carrying ALLS was studied which may promote the engineering application of the artificial lateral line in the local perception, positioning, and navigation on AUVs.

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Section: Model Establishmentmentioning

confidence: 99%

Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System

Liu

Hao

Yang

et al. 2020

Sensors

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“…A simple failure of the machine will cause severe safety accidents and serious economic losses. Therefore, monitoring the health condition of mining machines is crucial to ensuring mining safety [2][3][4][5]. The vibration, noise, temperature and pressure of mining machines can be used to detect machine faults.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis

et al. 2019

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Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind source separation (BSS) algorithm to separate the critical independent sources associated with the transmission fault vibrations. For this reason, a new method based on wavelet de-noising and nonlinear independent component analysis (ICA) is presented in this paper to tackle the nonlinear BSS problem with additive noise. The wavelet de-noising approach was first employed to eliminate the influence of the additive noise in the BSS procedure. Then, the radial basis function (RBF) neural network combined with the linear ICA was applied to the de-noised vibration signals. Vibration sources involved with the machine faults were separated. Subsequently, wavelet package decomposition (WPD) was used to extract distinct fault features from the source signals. Lastly, an RBF classifier was used to recognize the fault patterns. Field data acquired from a mining machine was used to evaluate and validate the proposed diagnostic method. The experimental analysis results show that critical fault vibration source component can be separated by the proposed method, and the fault detection rate is superior to the linear ICA based approaches.

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“…Via a set of features and a MLP classifier, they were able to discern these three locations based on the hydrodynamic environment. In [82] two differently shaped objects (a circle and square) were moved along an artificial lateral line. From the measured velocity profiles, they were able to discern between the two.…”

Section: Neural Network For Hydrodynamic Imagingmentioning

confidence: 99%

“…Some ALL systems focus on estimating fluid flow parameters such as the flow speed, direction and vorticity, either in natural environments [101] or in confined flow tanks. In the latter case, an object is usually placed upstream with respect to a sensor array [82]. At constant flow rates, this object can shed Kármán vortex streets that encode object characteristics in the spatial and periodic properties of the vortices [19,25,70,111].…”

Section: Hydrodynamic Signaturesmentioning

confidence: 99%

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Hydrodynamic Imaging with Artificial Intelligence: detecting submerged objects at a distance using a 2D-sensitive flow sensor array and neural networks

Wolf

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Background 2 1.1.1 The biological lateral line 2 1.1.2 What is hydrodynamic imaging? 4 1.1.3 Artificial neural networks in practice 8 1.2 Research motivation 10 1.2.1 Scalability 10 1.2.2 Neural-network based signal processing 11 1.2.3 2D-sensitive sensing 11 1.3 Thesis outline 11 Graphical abstract 13 i sensing 2 design and validation of an all-optical 2d flow sensor 17 2.1 Introduction 18 2.2 Sensor and fluid model 20 2.2.1 Sensor design 20 2.2.2 FBG sensing 20 2.2.3 Isotropic sensor mechanics 21 2.2.4 Dynamic properties 24 2.2.5 Design optimization 25 2.3 Methods 26 2.4 Results 29 2.4.1 Linearity 29 2.4.2 Dynamic response 29 2.5 Discussion 31 2.5.1 Sensitivity and dynamic range 31 2.5.2 Two-dimensional fluid flow sensing 32 2.6 Conclusion 34 ii simulation studies 3 simulated 1d-sensitive localization 37 3.1 Introduction 38 3.2 Methods 40 v vi contents 3.2.1 Data generation 40 3.2.2 Neural network algorithms 44 3.3 Results 48 3.3.1 Parameter settings 48 3.3.2 Overall performance on high signal to noise input 49 3.3.3 MLP overfitting 50 3.3.4 Noise robustness 50 3.4 Discussion 53 3.4.1 Practical implementation neural networks 53 3.4.2 Overall performance on x and y coordinates 55 3.4.3 Noise robustness 55 3.5 Conclusion 56 3.5.1 Practical implementation neural networks 56 3.5.2 Further research on single source localization 56 4 simulated 1d-sensitive multi-source localization 59 4.1 Introduction 60 4.2 Background 61 4.2.1 Source location encoding by the lateral line organ 61 4.2.2 Object localization using artificial lateral lines 63 4.2.3 Convolutional neural networks 64 4.3 Methods 66 4.3.1 Location encoding in 3D 66 4.3.2 Data synthesis 69 4.3.3 CNN implementation 70 4.3.4 CNN optimization 73 4.3.5 Location decoding in 3D 75 4.4 Results 77 4.4.1 Probability grid reconstruction 77 4.4.2 3D position reconstruction 79 4.5 Discussion 79 4.5.1 The processing pipeline 80 4.5.2 CNN design variations 80 4.5.3 Iterative source detection 82 4.5.4 Detection limits and future research 83 4.6 Conclusion 84 Supplementary information 84 5 simulated 2d-sensitive localization 87 5.1 Introduction 88 5.1.1 State of the art 88 5.1.2 Aims of this study 89 contents vii 5.

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Research on Flow Field Perception Based on Artificial Lateral Line Sensor System

Cited by 29 publications

References 19 publications

Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System

Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System

Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis

Hydrodynamic Imaging with Artificial Intelligence: detecting submerged objects at a distance using a 2D-sensitive flow sensor array and neural networks

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