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

Optimal Sensor Placement of the Artificial Lateral Line for Flow Parametric Identification

The multi-sensor artificial lateral line system (ALLS) can identify the flow-field’s parameters to realize the closed-loop control of the underwater robotic fish. An inappropriate sensor placement of ALLS may result in inaccurate flow-field parametric identification. Therefore, this paper proposes a method to optimize the sensor placement configuration of the ALLS, which mainly included three algorithms, the feature importance algorithm based on mean and variance (MVF), the feature importance algorithm based on distance evaluation (DF), and the information redundancy (IR) algorithm. The optimal sensor placement performance selected by this method is verified by simulation. In addition, further experimental verification was conducted using the ALLS. Compared with the uniform sensor placement configuration mentioned in recent studies, the experimental results suggest that the optimal sensor placement method can achieve a more effective prediction of the flow-field parameters, therefore strengthening the underwater robotic fish’s perception and control function.

Vibration sensing the mammalian way: an artificial Pacinian corpuscle

A vibration sensor is presented mimicking the structure of the Pacinian corpuscle. A multi-step casting process is used to create a 5 mm diameter sensor with a liquid metal core, elastomer dielectric, and graphite counter electrode creating a spherical capacitive sensing element with sensitivities on the order of 10 Δ pF/mm^-1. A model for the capacitance change of the spherical capacitor as it is formed is developed and its findings support the sensitivities observed. Various elastomer dielectric compositions with integrated barium titanate nanoparticles are tested to increase the dielectric constant. The biological acoustic filter within the corpuscle is mimicked using alternating cast layers of oligomers and elastomers around the spherical sensor element. Vibration sensing is characterized over the low frequency range of 10-300 Hz and the minimum detectable sensitivity is found to be 1 µm with a low power requirement of 7 mW. The artificial Pacinian corpuscle has potential applications in tactile sensing and seismic monitoring devices.

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

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.

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

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.

Flow field perception based on the fish lateral line system

Fish are able to perceive the surrounding weak flow and pressure variations with their mechanosensory lateral line system, which consists of a superficial lateral line for flow velocity detection and a canal lateral line for flow pressure gradient perception. Achieving a better understanding of the flow field perception algorithms of the lateral line can contribute not only to the design of highly sensitive flow sensors, but also to the development of underwater smart skin with good hydrodynamic imaging properties. In this review, we discuss highly sensitive flow-sensing mechanisms for superficial and canal neuromasts and flow field perception algorithms. Artificial lateral line systems with different transduction mechanisms are then described with special emphasis on the recent innovations in the field of polymer-based artificial flow sensors. Finally, we discuss our perspective of the technological challenges faced while improving flow sensitivity, durability, and sensing fusion schemes.