An interdigitated impedance microsensor for detection of moisture content in engine oil

To address the need for the on-site measurement of aging oil, in this paper, we propose an impedance-based microsensor for analyzing the moisture content in engine oil. Using a microfabrication process, we fabricated an interdigitated microelectrode and integrated it with a 3 D-printed microcontainer to produce a microsensor that can detect changes in the permittivity of oil. When the moisture content in oil increases, this sensor can detect the resulting change in the oil impedance, which is related to its permittivity, and then determine the degree to which the oil has aged. The test results show that the proposed microsensor has the advantages of being small and having high sensitivity, good accuracy, and the ability to be combined with hand-held instruments.The proposed method is expected to be used for the rapid, low cost, on-site determination of oil aging.

Research on polarization of oil spill and detection

The SAR（Synthetic Aperture Radar） has the capabilities for all-weather day and night use. In the case of determining the effects of oil spill dumping, the oil spills areas are shown as dark spots in the SAR images.Therefore, using SAR data to detect oil spills is becoming progressively popular in operational monitoring, which is useful for oceanic environmental protection and hazard reduction. Research has been conducted on the polarization decomposition and scattering characteristics of oil spills from a scattering matrix using allpolarization of the SAR data, calculation of the polarization parameters, and utilization of the CPD（Co-polarized Phase Difference） of the oil and the sea, in order to extract the oil spill information. This method proves to be effective by combining polarization parameters with the characteristics of oil spill. The results show that when using Bragg, the oil spill backscattering machine with Enopy and a mean scatter α parameter. The oil spill can be successfully identified. However, the parameter mechanism of the oil spill remains unclear. The use of CPD can easily extract oil spill information from the ocean, and the polarization research provides a base for oil spill remote sensing detection.

A new oil spill detection algorithm based on Dempster-Shafer evidence theory

Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these features.Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental factors.To distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed.The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model.The Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution features.The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory.The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image samples.The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm.The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.

A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

A method for coastal oil tank detection in polarimetric SAR images based on recognition of T-shaped harbor

To automatically detect oil tanks in polarimetric synthetic aperture radar（SAR） images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest（ROI） of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter（SP） of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.

Common Transceiver LIF⁃Lidar Based on Y⁃Type Optical Fiber for Marine Oil Spill Detection

This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver for this LIF⁃Lidar system.This LIF⁃Lidar system was further applied to measure the excitation spectra from floating oil slicks with different thicknesses on top of seawater at different distances.The system presents several advantages such as compact structure,stable optical path,and convenient operation,which offers a wide application prospect in ocean exploration.

The polarimetric features of oil spills in full polarimetric synthetic aperture radar images

Compared with single-polarized synthetic aperture radar （SAR） images, full polarimetric SAIl images contain not only geometrical and backward scattering characteristics, but also the polarization features of the scattering targets. Therefore, the polarimetric SAR has more advantages for oil spill detection on the sea surface. As a crucial step in the oil spill detection, a feature extraction directly influences the accuracy of oil spill discrimination. The polarimetric features of sea oil spills, such as polarimetric entropy, average scatter angle, in the full polarimetric SAR images are analyzed firstly. And a new polarimetric parameter P which reflects the proportion between Bragg and specular scattering signals is proposed. In order to investigate the capability of the polarimetric features for observing an oil spill, systematic comparisons and analyses of the multipolarization features are provided on the basis of the full polarimetric SAR images acquired by SIR-C/X-SAR and Radarsat-2. The experiment results show that in C-band SAR images the oil spills can be detected more easily than in L-band SAR images under low to moderate wind speed conditions. Moreover, it also finds that the new polarimetric parameter is sensitive to the sea surface scattering mechanisms. And the experiment results demonstrate that the new polarimetric parameter and pedestal height perform better than other polarimetric parameters for the oil spill detection in the C-band SAR images.

DISTRIBUTED OPTICAL FIBER SENSOR FOR LONG-DISTANCE OIL PIPELINE HEALTH

A fully distributed optical fiber sensor （DOFS） for monitoring long-distance oil pipeline health is proposed based on optical time domain reflectometry （OTDR）. A smart and sensitive optical fiber cable is installed along the pipeline acting as a sensor, The experiments show that the cable swells when exposed to oil and induced additional bending losses inside the fiber, and the optical attenuation of the fiber coated by a thin skin with periodical hardness is sensitive to deformation and vibration caused by oil leakage, tampering, or mechanical impact. The region where the additional attenuation occurred is detected and located by DOFS based on OTDR, the types of pipeline accidents are identified according to the characteristics of transmitted optical power received by an optical power meter, Another prototype of DOFS based on a forward traveling frequency-modulated continuous-wave （FMCW） is also proposed to monitor pipeline. The advantages and disadvantages of DOFSs based on OTDR and FMCW are discussed. The experiments show that DOFSs are capable of detecting and locating distant oil pipeline leakages and damages in real time with an estimated precision of ten meters over tens of kilometers.

TEST STUDY OF DETECTING OIL AND GAS BY USING REMOTE SENSING TECHNIQUE

Ⅰ The Indexes of Detecting Oil and Gas Resources The deeply buried reservoir which in a dynamic equilibrium state has a great pressure inside, and between it and earth surface there is a great difference of pressure. Therefore the hydrocarbon must spread and move vertically to the surface along the pressure gradient orientation. Hydrocarbons in the reservoir along some small rifts, cracks, joints and cleavages penetrate the overlying strata and seepage onto the surface. Thus the hydrocarbons become unvisble oil and gas signs. This process is called the phenomena of hydrocarbon microseepage of reservoir. Hydrocarbons microseepage in the process

Bionic Optimization Design of Electronic Nose Chamber for Oil and Gas Detection

In this paper, a miniaturized bionic electronic nose system is developed in order to solve the problems arising in oil and gas detection for large size and inflexible operation in downhole. The bionic electronic nose chamber is designed by mimicking human nasal turbinate structure, V-groove structure on shark skin surface and flow field distribution around skin surface. The sensitivity of the bionic electronic nose system is investigated through experimentation. Radial Basis Function （RBF） and Support Vector Machines （SVM） of 10-fold cross validation are used to compare the recognition performance of the bionic electronic nose system and common one. The results show that the sensitivity of the bionic electronic nose system with bionic composite chamber （chamber B） is significantly improved compared with that with common chamber （chamber A）. The recognition rate of chamber B is 4.27% higher than that of chamber A for the RBF algorithm, while for the SVM algorithm, the recognition rate of chamber B is 5.69% higher than that of chamber A. The three-dimensional simulation model of the chamber is built and verified by Computational Fluid Dynamics （CFD） simulation analysis. The number of vortices in chamber B is fewer than that in chamber A. The airflow velocity near the sensors inside chamber B is slower than that inside chamber A. The vortex intensity near the sensors in chamber B is 2.27 times as much as that in chamber A, which facilitates gas molecules to fully contact with the sensor surface and increases the intensity of sensor signal, and the contact strength and time between odorant molecules and sensor surface. Based on the theoretical investigation and test validation, it is believed that the proposed bionic electronic nose system with bionic composite chamber has potential for oil and gas detection in downhole.

Self-supervised learning-based oil spill detection of hyperspectral images

Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training set.To solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection network.First,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model.Then,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features.Finally,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method.Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.

Bionic Layout Optimization of Sensor Array in Electronic Nose for Oil Shale Pyrolysis Process Detection

In order to meet the requirements for miniaturization detection of oil shale pyrolysis process and solve the problem of low sensitivity of oil and gas detection devices,a small bionic electronic nose system was designed.Inspired by the working mode of the olfactory receptors in the mouse nasal cavity,the bionic spatial arrangement strategy of the sensor array in the electronic nose chamber was proposed and realized for the first time,the sensor array was used to simulate the distribution of mouse olfactory cells.Using 3D printing technology,a solid model of the electronic nose chamber was manufactured and a comparative test of oil shale pyrolysis gas detection was carried out.The results showed that the proposed spatial arrangement strategy of sensor array inside electronic nose chamber can realize the miniaturization of the electronic nose system,strengthen the detection sensitivity and weaken the mutual interference error.Moreover,it can enhance the recognition rate of the bionic spatial strategy layout,which is higher than the planar layout and spatial comparison layout.This bionic spatial strategy layout combining naive bayes algorithm achieves the highest recognition rate,which is 94.4%.Results obtained from the Computational Fluid Dynamics(CFD)analysis also indicate that the bionic spatial strategy layout can improve the responses of sensors.