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Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第19期235卷 4458-4474页

作者： Mo, Zhenling Zhang, Heng Wang, Jinglin Wang, Jianyu Fu, Hongyong Miao, Qiang Sichuan Univ Sch Aeronaut & Astronaut Ctr Aerosp Informat Proc & Applicat Chengdu Sichuan Peoples R China Aviat Key Lab Sci & Technol Fault Diag & Hlth Man Shanghai Peoples R China Technol & Engn Ctr Space Utilizat Chinese Acad Sci Key Lab Space Utilizat Beijing Peoples R China Sichuan Univ Coll Elect Engn Chengdu 610065 Sichuan Peoples R China

Meyer wavelet filters are the key building blocks of empirical wavelet transform. In mechanical fault diagnosis, however, the boundaries of Meyer wavelet filters are usually defined empirically. In order to solve the problems, this paper proposes a new index called harmonic infinite-taxicab norm to guide grasshopper optimization algorithm to primarily optimize a band-pass filter and thus, concurrently and secondarily optimize a low-pass filter and a high-pass filter of Meyer wavelet. The proposed index is inspired by spectral Lp/Lq norm and it is closely related to fault characteristic frequency of rotating machinery. In addition, only three Meyer wavelet filters are demanded in each iteration of optimization. The effectiveness of the proposed method is validated by comparing with fast kurtogram method on analyzing faulty bearing data and gearbox data.

关键词： Wavelet filter general Lp norm optimization algorithm rotating machinery fault diagnosis

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Theory of Machine Learning Assisted Structural optimization algorithm and Its Application

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AIAA JOURNAL 2023年第10期61卷 4664-4680页

作者： Xing, Yi Tong, Liyong Univ Sydney Sch Aerosp Mech & Mechatron Engn Sydney NSW 2006 Australia

The machine learning assisted structural optimization (MLASO) algorithm has recently been proposed to expedite topology optimization. In the MLASO algorithm, the machine learning model learns and predicts the update of the chosen optimization quantity in routine and prediction iterations. The routine and prediction iterations are activated with a predefined learning and predicting scheme;and in the prediction iterations, the design variable can be updated using the predicted quantity without running a finite element analysis and sensitivity analysis, and thus the computational time can be saved. Based on the MLASO algorithm, this work first proposes a novel generic criterion-driven learning and predicting (CDLP) scheme that allows the algorithm to autonomously activate prediction iterations in the solution procedure. Second, this work presents the convergence analysis and the computational efficiency analysis of the MLASO algorithm with the CDLP scheme. The MLASO algorithm is then embedded within the solid isotropic material with penalization topology optimization method to solve two-dimensional and three-dimensional problems. Numerical examples and results demonstrate the prediction accuracy and the computational efficiency of the MLASO algorithm, and that the CDLP scheme can remarkably improve the computational efficiency of the MLASO algorithm.

关键词： Mathematical optimization optimization algorithm Machine Learning Structural Design and Development Generative Adversarial Network Lagrange Multipliers Topology optimization Aircraft Structures Structural optimization Aircraft Engine Mounts

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Black-Box Function Aerodynamic Topology optimization algorithm via Machine Learning Technologies

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AIAA JOURNAL 2021年第12期59卷 5174-5185页

作者： Ban, Naohiko Yamazaki, Wataru Nagaoka Univ Technol Dept Energy & Environm Sci Niigata 9402188 Japan Nagaoka Univ Technol Dept Mech Engn Niigata 9402188 Japan

The objective of this research is to construct an efficient global topology optimization method using machine learning technologies. In the conventional design process of mechanical design, the conceptual design is the earliest stage of the design process;and it is carried out based on the designer's own idea/experience. Therefore, it is difficult to obtain innovative and high-quality concepts overwhelming the designer's knowledge since the earliest stage of the design process has the largest impact. In this research, therefore, the black-box function aerodynamic topology optimization algorithm via machine learning technologies (FANTOM) is developed to overcome the problem. In the FANTOM approach, topology optimization problems are solved using/combining two efficient global optimization methods developed by the authors: the efficient global optimization method for discontinuous optimization problems with infeasible regions using classification method, and the efficient global optimization method via clustering/classification methods and exploration strategy. In the present approach, topological optimal designs can be obtained only by setting an objective function and constraint conditions. The validity of the FANTOM approach is demonstrated in an inviscid drag minimization problem at a two-dimensional supersonic flow condition, which provides an optimal topology as the Busemann biplane airfoil. Executing topology optimizations with the variation in freestream Mach number, it is also demonstrated that the FANTOM approach can explore topological optimal designs robustly.

关键词： optimization algorithm Support Vector Machine Black Box Aerodynamic Shape optimization Topology optimization Supersonic Airfoils Supersonic Flow Numerical Simulation Aerodynamic Performance Surrogate Model

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Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

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WIRELESS NETWORKS 2015年第3期21卷 841-857页

作者： Liu, Ning Plets, David Goudos, Sotirios K. Martens, Luc Joseph, Wout Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Univ Ghent iMinds WiCa Dept Informat Technol B-9050 Ghent Belgium Aristotle Univ Thessaloniki Dept Phys Radiocommun Lab Thessaloniki 54124 Greece

Due to the huge popularity of wireless networks, future designs will not only consider the provided capacity, but also the induced exposure, the corresponding power consumption, and the economic cost. As these requirements are contradictory, it is not straightforward to design optimal wireless networks. Those contradicting demands have to satisfy certain requirements in practice. In this paper, a combination of two algorithms, a genetic algorithm and a quasi-particle swarm optimization, is developed, yielding a novel hybrid algorithm that generates further optimizations of indoor wireless network planning solutions, which is named hybrid indoor genetic optimization algorithm. The algorithm is compared with a heuristic network planner and composite differential evolution algorithm for three scenarios and two different environments. Results show that our hybrid-algorithm is effective for optimization of wireless networks which satisfy four demands: maximum coverage for a user-defined capacity, minimum power consumption, minimal cost, and minimal human exposure.

关键词： Indoor wireless network planning optimization algorithm WiFi (IEEE 802.11n) Coverage Power consumption Exposure

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Designing a hybrid PV/FC/wind for economic study using modified ruddy turnstone optimization algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第82期48卷 32077-32092页

作者： Yue, Ye Zhao, Yinghui Druzhinin, Zumrat Shijiazhuang Univ Sch Sci Shijiazhuang 050035 Hebei Peoples R China Tajik Tech Univ Dushanbe Tajikistan

This paper outlines the optimal design of a hybrid system that includes wind turbines, solar panels, and a fuel cell to successfully meet the load's requirements. All cost components of the system, including load loss and expenditure for operation and maintenance, have been taken into consideration. A modified version of Ruddy Turnstone optimization algorithm is applied to determine the ideal equipment capacity for the hybrid system. Comparison and analysis are done between the optimization results and the outcomes obtained through evolutionary techniques. Moreover, the proposed technique is tested on a case study from Egypt and a comparison is made with existing Hybrid Firefly/Harmony Search method from literature. According to the results, both modified Ruddy Turnstone optimization and Hybrid Firefly/Harmony Search methods have produced satisfactory outcomes. While the production cost slightly increased using the Hybrid Firefly/Harmony Search methodology, the system's dependability level has improved significantly, thereby ensuring reliable load supply. The results of a loss of power supply possibility comparison using 0.5%, 1%, and 2% of the modified Turnstone optimization algorithm versus Hybrid Firefly/Harmony Search method approach yields a minimum cost of energy of 0.0798, 0.0739, and 0.0573 $/kWh, respectively.& COPY;2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Economic study Hybrid system Photovoltaic Wind fuel cell Improved ruddy turnstone optimization algorithm

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A parameter free Continuous Ant Colony optimization algorithm for the optimal design of storm sewer networks: Constrained and unconstrained approach

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ADVANCES IN ENGINEERING SOFTWARE 2010年第2期41卷 188-195页

作者： Afshar, M. H. Iran Univ Sci & Technol Tehran Iran

This paper describes the application of the newly introduced Continuous Ant Colony optimization algorithm (CACOA) to optimal design of sewer networks. Two alternative approaches to implement the algorithm is presented and applied to a storm sewer network in which the nodal elevations of the network are considered as the decision variables of the optimization problem. In the first and unconstrained approach, a Gaussian probability density function is used to represent the pheromone concentration over the allowable range of each decision variable. The pheromone concentration function is used by each ant to randomly sample the nodal elevations of the trial networks. This method, however, will lead to solutions which may be infeasible regarding some or all of the constraints of the problem and in particular the minimum slope constraint. In the second and constrained approach, known value of the elevation at downstream node of a pipe is used to define new bounds on the elevation of the upstream node satisfying the explicit constraints on the pipe slopes. Two alternative formulations of the constrained algorithm are used to solve a test example and the results are presented and compared with those of unconstrained approach. The methods are shown to be very effective in locating the optimal solution and efficient in terms of the convergence characteristics of the resulting algorithms. The proposed algorithms are also found to be relatively insensitive to the initial colony and size of the colony used compared to the original algorithm. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Constrained Continuous Ant Colony optimization algorithm Explicit constraints Optimal design Storm water network

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A Novel Social Insect optimization algorithm for the Optimal Design of an Interior Permanent Magnet Synchronous Machine

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IEEE TRANSACTIONS ON MAGNETICS 2018年第12期54卷 1-6页

作者： Kwon, Hyuk-Sung Ro, Jong-Suk Jung, Hyun-Kyo Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea

optimization of an electric machine is a nonlinear multi-variable problem. For optimization of the nonlinear multi-variable problem, many function evaluations are required, which in turn requires much time. To address this problem, we propose a novel optimization algorithm of which the convergence speed, accuracy, and reliability are superior compared to those of widely used conventional algorithms. The performance of the proposed algorithm is verified through mathematical test functions and applied to a practical optimization scenario of cogging torque minimization for an interior permanent magnet synchronous machine.

关键词： Electric machine interior permanent magnet synchronous machine (IPMSM) multivariable problem nature-inspired optimization algorithms optimization algorithm

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Direct Torque Control With Variable Flux for an SRM Based on Hybrid optimization algorithm

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2022年第6期37卷 6688-6697页

作者： Feng, Liyun Sun, Xiaodong Tian, Xiang Diao, Kaikai Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China

This article presents an optimal direct torque control (DTC) strategy with variable flux for a switched reluctance motor using the improved linear active disturbance rejection control (LADRC) plus the hybrid optimization algorithm (HOA). First, the constant flux amplitude is substituted by the variable flux DTC (VF-DTC) to reduce the torque ripple. Then, the LADRC with the improved extended state observer applied in speed controller is utilized instead of the conventional PI control to improve the speed of the observer, antidisturbance ability, and robustness. Moreover, the HOA is employed to search for the optimal control parameters and acquire satisfactory dynamic performances. Finally, the optimal VF-DTC system is implemented on a 12/8 SRM. Simulation and experimental results are carried out to compare the performances of the conventional DTC, the VF-DTC with LADRC, the VF-DTC with PI using HOA, and the proposed optimal VF-DTC using HOA. The results show that the proposed control method has a faster speed response, superior antidisturbance ability, and lower torque ripples.

关键词： Torque Reluctance motors optimization Control systems Couplings Observers Tuning Active disturbance rejection control (ADRC) direct torque control (DTC) optimization algorithm switched reluctance motor (SRM) variable flux

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An efficient optimization algorithm for waste Heat Integration using a heat recovery loop between two plants

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APPLIED THERMAL ENGINEERING 2016年 105卷 799-806页

作者： Chang, Chenglin Chen, Xiaolu Wang, Yufei Feng, Xiao China Univ Petr State Key Lab Heavy Oil Proc Beijing 102249 Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Peoples R China

Heat recovery loop (HRL) is an indirect approach for waste Heat Integration between plants. The mathematical model for HRL design is a complex and nonlinear problem, which results in a non-convex Mixed Integer Nonlinear Programming (MINLP) model. Solving the problem without any strategy is difficult since computational results can easily be trapped in the local optimum solutions. This is mainly due to the reasons that operation cost, capital cost of heat exchanger networks, piping and pumping cost are considered simultaneously. To overcome this limitation, an efficient optimization algorithm is proposed for the complex problem. With application of convex reformulation and piecewise wise relaxation, the problem can be reformulated as a convex MINLP model, in which the objective function is convex and all constraints are linear. The computational efforts are reduced largely and better solutions can be obtained for the HRL design. As this work concentrates on low grade heat recovery, hot water is selected as the intermediate fluid to achieve waste Heat Integration between plants. An industrial case is demonstrated to illustrate the effectiveness of the proposed algorithm. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Heat Integration Heat recovery loop MINLP model optimization algorithm

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An efficient optimization algorithm for nonlinear 2D fractional optimal control problems

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JOURNAL OF SUPERCOMPUTING 2024年第6期80卷 7906-7930页

作者： Moradikashkooli, A. Javadi, H. Haj Seyyed Jabbehdari, S. Islamic Azad Univ Dept Comp Engn North Tehran Branch Tehran Iran Shahed Univ Dept Comp Engn Tehran Iran

In this research article, we present an optimization algorithm aimed at finding the optimal solution for nonlinear 2-dimensional fractional optimal control problems that arise from nonlinear fractional dynamical systems governed by Caputo derivatives under Goursat-Darboux conditions. The system dynamics are described by equations such as the Klein-Gordon, convection-diffusion, and diffusion-wave equations. Our algorithm utilizes a novel class of basis functions called generalized Laguerre polynomials (GLPs), which are an extension of the traditional Laguerre polynomials. To begin, we introduce the GLPs and their properties, and we develop several new operational matrices specifically tailored for these basis functions. Next, we expand the state and control functions using the GLPs, with the coefficients and control parameters remaining unknown. This expansion allows us to transform the original problem into an algebraic system of equations. To facilitate this transformation, we employ operational matrices of Caputo derivatives, the rule of 2D Gauss-Legendre quadrature, and the method of Lagrange multipliers.

关键词： Nonlinear 2D fractional optimal control problems Generalized Laguerre polynomials optimization algorithm Operational matrix Coefficients and parameters

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