Automated Application of Full Matrix Capture to Assess the Structural Integrity of Mooring Chains

Dissanayake, Mahesh; Carswell, David; Corsar, Michael; Sattar, TP; Sutcliffe, Mark; Lowe, Premesh Shehan; Gan, Tat-Hean

doi:10.1109/access.2018.2883378

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…There are numerous work done related to mooring operations, for example, [20]- [22]. However, they are focused on manual operation operations and how the structural integrity can be maintained, the effect of mooring angle on mooring operations and so forth.…”

Section: Related Workmentioning

confidence: 99%

Bollard Segmentation and Position Estimation From Lidar Point Cloud for Autonomous Mooring

Jindal

Jha

Cenkeramaddi

2022

IEEE Trans. Geosci. Remote Sensing

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This paper presents a computer-aided object detection and localization method from lidar 3D point cloud data. This topic of interest is in the framework of autonomous mooring, where the ship is tied to the rigid structure onshore (bollard) for autonomous maritime navigation. Using shape and features priors, unlike matching the whole object template to the experimental 3D point cloud representation of scene, two customized algorithms -(a) 3D feature matching (3DFM) and (b) Mixed feature-correspondence matching (MFCM) are presented. The proposed algorithms discriminate and extract the 3D points corresponding to the non-cooperative bollard's surface from background thus capable of classification, localization, and representing it using a unique co-ordinate in the 3D world. The proposed algorithms are tested and validated by implementing upon an experimental data set of 105 scenes where the bollard is at different positions and orientations with respect to lidar mounted on robotic arm. Statistical and probabilistic based approaches are taken into account to determine the performance of proposed algorithms. Model parameters estimation imply that errors resulting from 3DFM algorithm follow homoscedastic bimodal Gaussian distribution with individual Gaussian components having mean 0.03 m, and 0.09 m and both have equal standard deviation of 0.01 m. Further, posteriori component assignment probability is used to identify and cluster the scenes that contribute to relatively larger errors. Finally, an improved algorithm, MFCM is proposed whose errors follow unimodal Gaussian distribution with mean and standard deviation of 0.03 m and 0.01 m respectively thus mitigating the shortcomings of the former.

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Section: Related Workmentioning

confidence: 99%

Bollard Segmentation and Position Estimation From Lidar Point Cloud for Autonomous Mooring

Jindal

Jha

Cenkeramaddi

2022

IEEE Trans. Geosci. Remote Sensing

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“…The contrast resolution and spatial resolution can be characterized by the maximum sidelobe level (MSL) and the -6 dB width of the mainlobe of the directivity diagram of a sparse array, respectively [12]. The sparse array directivity diagram can be obtained by (8). Since the mainlobe width (MLW) and MSL cannot be perfect optimized simultaneously perfectly in the directivity diagram, we define the fitness function as follows…”

Section: Optimization Problem Formulationmentioning

confidence: 99%

“…The total focusing method (TFM) uses full array acquisition, which means that a single array element excites all array elements to receive, and each array element sequentially fires until all the combination of excitation and reception is used to obtain complete detection information. In the post-processing phase, various phased sound beams are reconstructed through flexible delay settings, to achieve focused imaging at any position in the inspection area [8]. However, to obtain a smaller main beam width, a modern high-resolution ultrasonic phased array detection system requires a larger aperture of the transducer array.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Sparse Array for Ultrasonic Total Focusing Method by Binary Particle Swarm Optimization

et al. 2020

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Ultrasonic phased array technology is used in various fields. Traditional full phased arrays place elements in every position of a uniform lattice with half-wavelength spacing between the lattice points, so the hardware cost is very high. This paper introduces an automatically method to sparsify the full array method with well-controlled sidelobes and the main lobe. By calculating one-dimensional phased array patterns that can reflect phased array performance, the binary particle swarm optimization (BPSO) algorithm is used to optimize the array layout. The method initialized form full array and decreased several elements step by step, then, a sparse array with comprehensive acoustic performance close to the reference full array is obtained. By applying the proposed method to the sparse array design of total focusing method (TFM), the simulation results indicate that the proposed sparse total focusing method can greatly increase computational efficiency while providing significantly higher image quality. The BPSO can provide effective optimization design for sparse arrays. INDEX TERMS Binary particle swarm optimization, sparse array, total focusing method, ultrasonic phased array.

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“…The equivalent permeability theory is used to simplify other reluctances except magnetic core in (1). µ 1 1, µ 4 1, and µ γ 2 1. The order of magnitude in these three is 10 3 , so they are approximated to µ r [15].…”

Section: Model Construction and Application A Model Constructionmentioning

confidence: 99%

“…For thinner objects, the accuracy of ultrasonic testing is reduced or even missed. The magnetic particle testing has the highest defect resolution, but it has higher requirements than other detection techniques, such as smooth surfaces, etc [2]- [4]. Therefore, the above testing methods are not suitable for the production line detection of workpieces with large production capacity, a large number of welding processes and a small weld size.…”

Section: Introductionmentioning

confidence: 99%

Research on Detection Method of Small Size Weld Bead Defects Based on Reluctance Measurement

Wang

2019

IEEE Access

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To achieve non-destructive testing for small size weld beads of metal workpieces, we developed a new testing method of closed magnetic circuit reluctance measurement. According to the shape and volume of the small size weld defects of the metal workpiece, a reluctance detection model was established in the detection environment of alternating magnetic field. The relationship between different weld defects and closed magnetic circuit reluctance was quantitatively analyzed to achieve the identification of weld defects. A particle swarm optimization algorithm (PSO) was applied to optimize the cost-sensitive support vector machine (CS-SVM), which effectively reduced the coupling errors caused by the limitation of the workpiece coupling size. This new method was used to verify the weld bead detection of representative carbide saw blades. Compared with the basic support vector machine, the improved cost-sensitive support vector machine has better performance in the classification of unbalanced samples. The experimental results showed this new method can detect the weld bead of carbide saw blade with the correct rate to 98.2%. It reduced the interference of coupling error effectively. The improved cost-sensitive support vector machine not only improved the detection accuracy, but also avoided the possibility that the defective weld workpiece samples are misclassified into qualified workpieces. This study provides a guarantee for safe production and has great significance in engineering applications. The new method provides an effective solution for the application of reluctance testing technology in small size weld bead detection. INDEX TERMS Closed magnetic circuit, cost-sensitive support vector machine, coupling error, particle swarm optimization, reluctance measurement, small size weld bead.

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Automated Application of Full Matrix Capture to Assess the Structural Integrity of Mooring Chains

Cited by 4 publications

References 13 publications

Bollard Segmentation and Position Estimation From Lidar Point Cloud for Autonomous Mooring

Bollard Segmentation and Position Estimation From Lidar Point Cloud for Autonomous Mooring

Optimal Design of Sparse Array for Ultrasonic Total Focusing Method by Binary Particle Swarm Optimization

Research on Detection Method of Small Size Weld Bead Defects Based on Reluctance Measurement

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