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Intelligent bearing fault diagnosis using swarm decomposition method and new hybrid particle swarm optimization algorithm

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SOFT COMPUTING 2022年第3期26卷 1475-1497页

作者： Chegini, Saeed Nezamivand Amini, Pouriya Ahmadi, Bahman Bagheri, Ahmad Amirmostofian, Illia Univ Guilan Fac Mech Engn Dept Dynam Control & Vibrat Rasht Iran Tech Univ Dresden Inst Acoust & Speech Commun Dresden Germany Univ Kurdistan Fac Engn Dept Mech Engn Sanandaj Iran

The quality of information extracted from the vibration signals, and the accuracy of the bearing status detection depend on the methods used to process the signal and select the informative features. In this paper, a new hybrid approach is introduced in which the relatively new swarm decomposition (SWD) method and the optimized compensation distance evaluation technique (OCDET) are used to enhance the signal processing stage and to improve the optimal features selection process, respectively. Firstly, the vibration signals are decomposed into their Oscillatory Components (OCs) using the SWD. The feature matrix is constructed by computing the time-domain features for the OCs. The CDET method is consequently utilized to select the most sensitive features corresponding to the bearing status. On the other hand, The CDET approach contains a parameter called threshold which affects the number of the selected features. In this way, the hybrid optimization algorithm, which is a combination of the particle swarm optimization (PSO) algorithm with the Sine-Cosine algorithm (SCA) and the Levy flight distribution, has been used to select the optimal CDET threshold and improve the support vector machine (SVM) classifier. The proposed technique ability is evaluated by vibration signals corresponding to different bearing defects and various speeds. The results indicate the capability of the proposed fault diagnosis method in identifying the very small-size defects under various bearing conditions. Finally, the presented method shows better performance in comparison with other well-known methods in the most of the case studies.

关键词： Bearing fault diagnosis swarm decomposition Optimized compensation distance evaluation hybrid particle swarm optimization algorithm Support vector machine

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Mechanism Isomorphism Identification Based on Decision Tree algorithm and hybrid particle swarm optimization algorithm

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NEURAL PROCESSING LETTERS 2024年第6期56卷 249页

作者： Yu, Luchuan Zhou, Shunqing Wang, Hongbin Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China

Mechanism isomorphism identification is a typical quadratic assignment problem similar to traveling salesman and job-shop scheduling. For the complex mechanism with more components, common methods of isomorphism identification may fail due to low solving efficiency and reliability. Based on the decision tree algorithm and hybrid particle swarm optimization (HPSO) algorithm, the global-local search method is proposed to identify isomorphism of mechanisms. More precisely, based on the intrinsic relationship between links and vertices in the mechanism, the decision tree algorithm globally searches the characteristic path with mapping properties of different mechanisms. On this basis, HPSO algorithm combines genetic algorithm with particle swarm optimization algorithm to find the exact global optimal solution instead of local optimal solution. Some complex cases such as 14-link kinematic chains, 18-vertex topological graphs, and 8-vertex planetary gear trains are used to evaluate the efficiency and reliability of the proposed method. Results show that the proposed method can accurately identify isomorphism of mechanisms in a relatively short time. It can improve the solving efficiency of isomorphism identification in structural synthesis.

关键词： Isomorphism identification Adjacency matrix hybrid particle swarm optimization algorithm Decision tree algorithm Kinematic chain

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Voltage Sag State Estimation Based on hybrid particle swarm optimization algorithm

Voltage Sag State Estimation Based on Hybrid Particle Swarm ...

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10th IEEE Conference on Industrial Electronics and Applications

作者： Fan Di Tian Lijun Cui Yu Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China Jiangsu Elect Power Co State Grid Corp China Nanjing 210024 Jiangsu Peoples R China

ISBN: (纸本)9781479983896

This paper presents an approach applying hybrid particle swarm optimization algorithm to voltage sag frequency state estimation, based on the monitoring system determined by optimum allocation method of voltage sag monitoring nodes. Voltage sag frequency state estimation is interpreted as the estimation of the number of voltage sags occurring at non-monitored buses from the recorded voltage sags occurrence number at the monitored buses. In this study, a hybrid particle swarm optimization algorithm is proposed to solve the problem of voltage sag state estimation by the use of round-off method. The effectiveness and accuracy of the proposed approach are verified by the case study of the IEEE-39 standard test system.

关键词： power quality voltage sag state estimation hybrid particle swarm optimization algorithm optimal allocation round-off method

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Compensation Module Design for Overlapping Band in Band-Interleaved Data Acquisition Systems Based on hybrid particle swarm optimization algorithm

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IEEE ACCESS 2020年 8卷 178835-178848页

作者： Zhao, Yu Ye, Peng Meng, Jie Yang, Kuojun Gao, Jian Pan, Zhixiang Huang, Wuhuang Song, Jinpeng Dai, Xuefeng Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Uni Trend Technol China Dept Res & Dev Dongguan 523000 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

The band interleaved data acquisition system (BI-DAS) is an attractive structure to improve the bandwidth of the acquisition system. However, the non-ideal characteristic of the spectrum analysis filter in BI-DAS results in an overlapping frequency band between two adjacent frequency sub-bands. Phase misalignment (PM) between two sub-bands in the overlapping band may cause those signals canceled or partially canceled to each other when sub-bands' signal are merged. In this paper, a compensation module with a digital all-pass filter (APF) is proposed for the PM of the overlapping bands in BI-DASs. Based on this compensation module, a hybrid particle swarm optimization (PSO) algorithm, along with the Levenberg-Marquardt (LM) algorithm is proposed to design coefficients of the compensation module. The compensation module and corresponding method proposed in this paper are verified in a BI-DAS with 20Gsps sampling rate and 5.5GHz bandwidth. The experimental results show that the proposed compensation module can effectively compensate the PM between the sub-bands in the overlapping band. The proposed hybrid PSO-LM (HPSOLM) algorithm combines the flexibility and reliability inherent in the PSO with the fast convergence and precision of the LM algorithm. It can effectively design the compensation module with stable APF while consuming less time and obtaining better compensation results than the conventional PSO method.

关键词： optimization Bandwidth Convergence Band-pass filters Data acquisition particle swarm optimization Genetic algorithms Band interleaved data acquisition system overlapping band phase compensation digital all-pass filters hybrid particle swarm optimization algorithm Levenberg-Marquardt algorithm

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Application of improved particle swarm optimization algorithm in solving camera extrinsic parameters

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JOURNAL OF MODERN OPTICS 2019年第18期66卷 1827-1835页

作者： Li, Weimin Zhong, Kun Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei Anhui Peoples R China

A hybrid particle swarm optimization algorithm for determining extrinsic parameters of the camera, with the intrinsic parameters known, is presented in this paper. An adaptive inertia weighting strategy is designed, and improved crossover and mutation operation are performed. Taking the result of the particle swarm optimization algorithm as the initial value, the extrinsic parameters of the camera are optimized by Gauss?Newton method. The simulation results demonstrate that, when the standard deviation of image noise is 0.1 pixels, the mean relative error of rotation angle is less than 0.04844% and the mean relative error of translation vector is less than 0.05583%. The experimental results show that the mean reprojection error of the proposed algorithm is no more than 0.06045 pixels. The algorithm can achieve high precision and robustness in practical applications. Besides, the algorithm is computational efficient.

关键词： hybrid particle swarm optimization algorithm adaptive inertia weighting strategy crossover mutation extrinsic parameters Gauss?Newton method high precision

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A novel adaptive wavelet threshold estimation based on hybrid particle swarm optimization for partial discharge signal denoising

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OPTIK 2019年 181卷 175-184页

作者： Si, Wen Qin, Bingyang Li, Qingquan Liu, Hongshun Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Shandong Peoples R China State Grid Zhejiang Elect Power Co Hangzhou Power Supply Co Hangzhou 271000 Zhejiang Peoples R China Shandong Univ Sch Elect Engn Shandong Prov Key Lab UHV Transmiss Technol & Equ Jinan 250061 Shandong Peoples R China

Partial discharge (PD) of high voltage equipment detected by online monitoring is regarded as a key indicator of insulation status. Threshold estimation exerts an important influence on the denoising effects of PD signals after wavelet-based denoising. In order to improve the adaptive performance of wavelet-based denoising and reduce the distortion of denoised signal, an approach of hybrid particle swarm optimization (HPSO) adaptive wavelet threshold estimation (HPSOTE) is presented in this paper. To obtain the global optimum thresholds, a novel hybrid particle swarm optimization algorithm merging crossover mutation and chaos is proposed in HPSOTE which could solve the premature convergence problem of common threshold selection methods. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are adopted to compare the effects with HPSOTE. The denoising results of simulated and actual PD signals show that HPSOTE has a faster convergence rate and more effective global optimization ability than the others and significantly improves the credibility and calculation speed. It gives better mean square error (MSE) and amplitude error performance. This HPSOTE can remove the noise effectively and has comparatively high application value in PD online monitoring for smart grid based on big data.

关键词： Partial discharge Online monitoring Wavelet-based denoising Adaptive thresholding hybrid particle swarm optimization algorithm

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An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2020年 219卷 347-359页

作者： Mohammadi, S. Al-e-Hashem, S. M. J. Mirzapour Rekik, Y. Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Rennes Sch Business 2 Rue Robert dArbrissel F-35065 Rennes France EMLYON Business Sch DISP Lab 23 Ave Guy Collongue F-69130 Ecully France

Recently, many modern industries have adopted joint scheduling of production and distribution decisions. Such coordination is necessary in make-to-order (MTO) businesses, where it is challenging to achieve timely delivery at minimum total cost and meet the requirements for high customization. To deal with these challenges, a practical production configuration and delivery method is required, in addition to a closer link between production and distribution schedules. Hence, in this study, we address an integrated production scheduling-vehicle routing problem with a time window, where it is assumed that production is performed in a flexible job-shop system. Our framework is modeled as a novel bi-objective mixed integer problem, in which the first objective function aims to minimize a sum of the production and distribution scheduling costs, and the second objective function tries to minimize a weighted sum of delivery earliness and tardiness. To practically validate the application of our framework, a case study from a furniture manufacturing company producing customized goods is considered, and experimental data are derived. Based on the real data, the model is first optimally solved by an epsilon-constraint method, and then a hybrid particle swarm optimization (HPSO) algorithm is developed to solve the model for medium- and large-sized problems in a reasonable time. We discuss the benefits of integration by comparing the results of the proposed model with that of the separate approach. The results show that the company can establish a proper rational balance between cost and customer concerns, and they can use the integration policy as a lever to improve customer satisfaction without the system experiencing a significant increase in total operational cost.

关键词： Supply chain scheduling Multi-objective optimization Integrated production-distribution epsilon-constraint method hybrid particle swarm optimization algorithm

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Stability Assessment and Damping optimization Control of Multiple Grid-connected Virtual Synchronous Generators

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2021年第4期36卷 3555-3567页

作者： Sun, Peng Yao, Jun Zhao, Yang Fang, Xin Cao, Junying Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China

The virtual synchronous generator (VSG) control has been widely used in grid-connected inverter to effectively increase the equivalent inertia of power system. However, the active power oscillation similar to electromechanical transient characteristics of conventional synchronous generator (SG) is introduced to power grid, especially under the condition of multi-virtual synchronizer. In this paper, the dynamic stability of the multiple grid-connected voltage source converters (VSC) controlled by VSG is studied. Firstly, the system linearization model is established to analyze the oscillation risk. In order to improve the damping characteristics of multi-VSG grid-connected systems, an additional power system stabilizer (PSS) method is proposed. Then considering the influence of controller installation location and control parameters on the dynamic stability of the system, the design methods of multi-VSG damping controller based on residue index and hybrid particle swarm optimization algorithm are proposed respectively. Finally, the effectiveness of the damping controller design method is verified through the simulation and test cases of the multi-VSG grid-connected system.

关键词： Power system stability Damping Stability criteria Oscillators Voltage control Power system dynamics Synchronous generators Damping controller hybrid particle swarm optimization algorithm residue index small signal stability virtual synchronous generator (VSG)

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hybrid particle swarm algorithm for Assembly Line Balancing Problem in Complicated Products

Hybrid Particle Swarm Algorithm for Assembly Line Balancing ...

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2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Changyi Liu Haijun Wen Changyi Liu School of Mechanical and Automobile Engineering Hefei University of Technology College of Management Engineering Anhui Polytechnic University

At present,the assembly line balancing problem mainly lies in the fact that it is proceeded from the perspective of assembly time to conduct the study in time balance,which is difficult to cope with the dynamic changes occurring in the actual *** paper,therefore,comes up with the optimized objective to minimize the assembly complexity relationship differentiation through the research into complexity of the ***,when combined with the optimization index multi-objective assembly line balancing research,it also puts forward the method of hybrid particle swarm algorithm to *** algorithm adopts topological sorting encoding based on operating elements of priority diagram,applies sorting and the number of niche to evaluate individuals,and it forms a new fitness function based on ***,it introduces the thought of Simulated Annealing to expand the choice for Global Best to the entire procedure;the result of some cases can demonstrate the superiority of the algorithm.

关键词： assembly line balancing manufacturing complexity multi-objective hybrid particle swarm optimization algorithm Pareto sorting

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Cooperative Game Theory-Based Collaborative Task Planning Method for Multiple Heterogeneous Agile Satellites 24

Cooperative Game Theory-Based Collaborative Task Planning Me...

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International Conference on Modeling, Natural Language Processing and Machine Learning (CMNM)

作者： Long, Guangwei Chen, Yingguo Chen, Dongdong Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

ISBN: (纸本)9798400709760

As aerospace technology continues to develop, more and more companies and institutions are launching their own satellite constellations, forming heterogeneous satellite networks in space controlled by different users. Due to the satellites being heterogeneous and controlled by different entities, it is difficult to establish a mechanism for collaborative mission planning, leading to a waste of satellite resources. Addressing this issue, this paper begins with the interests of different controlling users as a starting point. Firstly, a cooperative game theory model with controlling users as participants is constructed. Taking into account the characteristics and constraints of the multiple satellite collaborative mission planning problem, a characteristic function for the cooperative game model is designed. Subsequently, a solution framework for the cooperative game model based on the core and a hybrid particle swarm optimization algorithm is proposed. A hybrid particle swarm algorithm that integrates a genetic algorithm crossover operator and hill-climbing strategy is designed. Moreover, to address the issue that coalitions may not always have a core, an algorithm for finding coalitions that do have a core is devised. Finally, a series of experiments are designed to verify the effectiveness of the model and algorithm. Simulation results show that the cooperative game model can allocate tasks and profits effectively, thereby establishing a robust collaborative mission planning mechanism among multiple heterogeneous agile satellites. Furthermore, by comparing with the traditional particle swarm optimization algorithm, the effectiveness of the proposed hybrid particle swarm algorithm is demonstrated.

关键词： Multi-heterogeneous agile satellites Cooperative game Collaborative Task Planning Coalition with a core hybrid particle swarm optimization algorithm

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