Reconstruction of stress corrosion cracks using high-dimension hybrid optimization algorithm from eddy current signals

A scheme to apply signals of pulsed eddy current testing (PECT) to reconstruct a deep stress corrosion crack (SCC) is proposed on the basis of a multi-layer and multi-frequency reconstruction strategy. First, a numerical method is introduced to extract conventional eddy current testing (ECT) signals of different frequencies from the PECT responses at different scanning points, which are necessary for multi-frequency ECT inversion. Second, the conventional fast forward solver for ECT signal simulation is upgraded to calculate the single-frequency pickup signal of a magnetic field by introducing a strategy that employs a tiny search coil. Using the multiple-frequency ECT signals and the upgraded fast signal simulator, we reconstructed the shape profiles and conductivity of an SCC at different depths layer-by-layer with a hybrid inversion scheme of the conjugate gradient and particle swarm optimisation. Several modelled SCCs of rectangular or stepwise shape in an SUS304 plate are reconstructed from simulated PECT signals with artificial noise. The reconstruction results show better precision in crack depth than the conventional ECT inversion method, which demonstrates the validity and efficiency of the proposed PECT inversion scheme.

In Fast Breeder Reactors (FBR) which are sodium cooled, the steam generator (SG) heat exchanger tubes separate the low pressure sodium flowing in the SG vessel with the high pressure water-steam in tubes. During In-Service Inspection (ISI), sodium is first drained and then SG tubes are cooled down to the room temperature. After sodium draining, due to the high temperature (more than 500 °C), sodium adheres to SG tubes and structures around (SG support plates, welds) in a thin layer, filling eventually the gaps between SG support plates and tubes. During ISI, SG tubes are inspected for cracks and corrosions using differential eddy currents (EC) probes. Due to the high electrical conductivity of sodium adhering to the outer SG tube surface, the eddy current testing (ECT) signal modifies, in accord with sodium layer thickness or sodium deposits located on the outer SG tube surface. The sodium wetting properties depends on several factors as: material surface, temperature and sodium wetting time. The effect of sodium adhering to the outer SG tube on ECT signals were measured using a small mock-up tank (2 m high and 0.7 m in diameter) in which were introduced two SG tubes similar with the ones used in the Monju FBR (one tube is ferromagnetic and made of 2.25Cr–1Mo alloy, while the other one is made of SUS321 and is austenitic). Defects, SG support plates (on both helical and straight part of the tube) and welds were added to tubes and the ECT signal was measured before and after sodium draining. Variations in the sodium layer thickness and consequently its effect on ECT signals were measured by filling and draining the tank three times in order to recreate each time new layers of sodium. The paper describes the experimental conditions and the ECT results for both types of SG tubes by comparing the defects, SG support plates and weld signals before and after draining of sodium. Additionally, sodium structures were examined visually using a VideoScope camera, confirming the recorded ECT signals. The paper also presents details about sodium layer thickness measurements in several parts of SG tubes (near defect, SP, weld, bend, helical tube, straight tube) by scratching and collecting the sodium on a small area of 20mm×20mm. The volume of sodium drops is also estimated. The measurement results showed that there are significant differences in the sodium layer thickness depending on the SG tube material.

This paper is concerned with deep-lying effects in fatigue cracking from eddy current testing (ECT) signals. The fatigue crack often remains tightly closed, and thus difficult to find by visual inspection. It is important to estimate the direction of crack growth because of evaluating the common mode of failure in structural and mechanical engineering components. Our study is aimed at developing a practical method for identifying deep-lying profiles from surface image obtained by scanning ECT probe. A various type of fatigue cracks are made from compact tension (CT) specimen of aluminum alloy samples. ECT signals are collected for those test samples using transmit-receive (TR) probe. We discuss the features of ECT profiles of fatigue cracking which are different from electric discharge machining (EDM) crack signals.

Development of a very fast simulator for pulsed eddy current testing signals of local wall thinning

This paper proposes a novel algorithm for the reconstruction of stress corrosion cracking from eddy current testing signals, and reconstructs natural stress corrosion cracking on 8.7 mm thick austenitic stainless piping that provided coolant water for a nuclear reactor. The cracks, which were caused by vinyl chloride labels attached to the outer surface of the piping, are modeled as a conductive region, and both the boundary profile and internal conductivity are reconstructed with the algorithm. The reconstructed profiles agree well with the true ones revealed by destructive testing, which confirms the validity of the algorithm and proves that the applicability of the eddy current testing to the sizing of real cracks not only in thin structures such as steam generator tubes but also general structures.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

The numerical schemes based on neural networks with a shifting aperture method to reconstruct a crack shape from eddy current test (ECT) signals are presented. Aiming at the final goal of ECT study and the reconstruction of the cracks that occur in the actual steam generator tubes of pressurized water reactor power plants, the cracks considered are not electrodischarge machined notches but the natural ones with distributed conductivity. The data are obtained from numerical simulations using an FEM-BEM coupling method based on the A-/spl phi/ formulation. A database approach, a kind of fast forward solver, is also applied to save computational resources. The results obtained show that the inverse scheme proposed in this paper can give quite an accurate reconstruction and is robust against noises.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

In order to solve the problem of interaction between multiple evaluation indexes of seed metering performance under multiple factors of centralized seed feeding device, a multi-objective optimization of structure based on particle swarm optimization (PSO) algorithm was proposed in this paper. The wheat centralized seed feeding device was taken as the research object, and the experimental factors were cone angle of type hole, working speed and seed filling gap. The working process of wheat centralized seed feeding device was simulated by discrete element method (DEM). The average seed number of type hole, the variation coefficient of the average seed number of type hole, and the maximum tangential force between seed and seed feeding mechanism were selected as the evaluation indexes. Through the variance analysis of the evaluation indexes by the experimental factors, the optimization objective function was constructed. Using PSO algorithm, the multi-objective optimization was carried out for the wheat centralized seed feeding device. The optimization results show that the best structural combination parameters of the wheat centralized seed feeding device are the hole cone angle of 31.6° and the seed filling gap of 4.6 mm. The validity of the method was verified by simulation and field test. The results show that the PSO algorithm multi-objective optimization method proposed in this paper can provide a reference for the structural improvement and optimal design of the centralized seed feeding device. Keywords: centralized seed feeding device, multi-objective, optimization, PSO algorithm DOI: 10.25165/j.ijabe.20201306.5665 Citation: Wang Q Q, Li Z D, Wang W W, Zhang C L, Chen L Q, Wan L. Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm. Int J Agric & Biol Eng, 2020; 13(6): 76–84.

This paper proposes a novel method for identifying the number and positions of cracks and reconstructing crack shapes. It assumes that two-dimensional scanned eddy-current testing (ECT) signals obtained from a steam generator tube are a picture image, then a template matching method with the help of genetic algorithms predicts the number and positions of cracks. The present method employs the superposition of crack signals and a nonlinear scaling technique of a signal profile on the crack length that are verified by numerical simulation. The number and positions of the cracks are satisfactorily predicted. The crack-shape reconstructions from the predicted positions with the help of inverse analysis are achieved with a satisfactory degree of accuracy.

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

The study discusses a proper numerical modeling of stress corrosion crack (SCC) in finite element simulations, mainly from the viewpoint of the distribution of conductivity along the surface of crack. Two SCCs introduced into austenitic stainless steel plates are studied. SCCs are modeled as a region with a constant width, and non-uniform conductivity along surface of crack in the direction of depth of the SCCs, on the basis of the true profile revealed by destructive tests. Eddy current signals gathered by a differential probe with several frequencies are analyzed. The results show that a more appropriate model of SCC can be obtained by considering non-uniform conductivity.

In this paper, we present a concept of a transistor level implementation of the Particle Swarm Optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN that operates in accordance with the PSO algorithm is composed of a set of cooperating particles (agents) that explore an input data space and communicate information on the best found solution to other particles. The PSO algorithm is usually implemented in software. We in our investigations focus on its transistor level realization. Such an approach enables parallel data processing, in which the overall data rate only moderately depends on the number of particles. Most of the operations and components of such implemented PSO algorithm may be reused considering our former CMOS realizations of other self-organizing learning algorithms. This allowed us to assess main parameters of the PSO.