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Research on equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization

The Journal of China Universities of Posts and Telecommunications 2020年第4期27卷 17-25,42页

作者： Yang Jianjian Zhang Qiang Wang Xiaolin Du Yibo Wang Chao Wu Miao Department of Mechanical Electrical and Information EngineeringChina University of Mining and Technology(Beijing)Beijing 100083China Tian Di Science&Technology Co. LtdBeijing 100013China

The traditional fault diagnosis method of industrial equipment has low accuracy and poor applicability. This paper proposes a equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization(RSApso). The entire model is validated by using the data of healthy bearings collected by Case Western Reserve University. Different gradient descent algorithms and standard particle swarm optimization(pso) algorithms in a back propagation(BP) network are compared experimentally. The results show that the RSApso algorithm has a higher accuracy of weight threshold updating than the gradient descent algorithm and does not easily fall into a local optimum. Compared with pso, it has a faster optimization speed and higher accuracy. Finally, the RSApso algorithm is validated with the data of bearings collected from the laboratory rotating machinery test bench and motor data collected from the tower reflux pump. The average recognition rate of the four kinds of bearing data constructed is 97.5%, and the average recognition rate of the two kinds of motor data reaches 100%, which prove the universality of the method.

关键词： fault diagnosis BP network gradient descent pso algorithm

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Multisensor information-based adaptive control method for cutting head speed of roadheader

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第11期235卷 1941-1955页

作者： Wang, Pengjiang Shen, Yang Li, Rui Zong, Kai Fu, Shichen Wu, Miao China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China

An adaptive control method to improve the cutting head speed of roadheaders using multisensor information is proposed, so as to solve the problems of low cutting efficiency and low intelligence of roadheaders during underground tunnelling. The operation of a roadheader is analysed, and a control strategy for its cutting head speed is proposed. In addition, the cutting head speed is categorised into five gears according to the multisensor information of different cutting states. The controller for speed estimation is designed using a back propagation neural network optimised using an improved particle swarm optimisation algorithm. A control system is established in MATLAB to analyse the effectiveness of the method. The simulation results show that an Ipso-BP controller has the best control effect and can attain the target speed. The response time was lower than those of fuzzy logic controllers and traditional PI controllers by 46% and 68%, respectively, and the overshoot decreased by 4.69% and 12.19%, respectively. Furthermore, experimental research verified the effectiveness of this method. This method can adaptively adjust the cutting head speed of a roadheader using multisensor information and is important (both theoretical and practically) for extending the service life of roadheaders and improving tunnelling efficiency.

关键词： roadheader BP neural network pso algorithm cutting head speed adaptive control

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Performance analysis and optimization for coverage enhancement strategy of Narrow-band Internet of Things

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 101卷 434-443页

作者： Wang, Xiangming Yang, Dong Jian, Xin Chen, Min Guna, Joze Chongqing Univ Coll Microelect & Commun Engn Chongqing 400030 Peoples R China China Elect Technol Grp Corp 26th Inst Chongqing 400060 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Univ Ljubljana Lab Multimedia Fac Elect Engn Trzaska Cesta 25 Ljubljana 1000 Slovenia

Narrowband Internet of Things (NB-IoT) is a clean-slate wireless protocol proposed by the 3rd Generation Partnership Project intending for massive machine type communications. The general objectives of the NB-IoT include supporting massive connections, enhanced coverage, reduced cost and complexity, ultra-low power consumption, and flexible delay characteristics. To achieve the objective of 20dB enhanced coverage of NB-IoT, the concept of narrow-band modulation, coverage classes updating and adaptive repetition were introduced. To evaluate the performance of these new techniques, a Markov chain model with coverage classes as state variables was proposed to describe the dynamics of the coverage classes updating mechanism of NB-IoT. In addition, an optimization model minimizing average probability of access failure as well as average power consumption was formulated, with which the effects of preamble repetition number, system load and global maximum transmission number on the optimal configuration of maximum transmission number of each coverage class was analyzed. To solve the aforementioned optimization model, two algorithms, namely exhaustive search method combining constraint transform and particle swarm optimization (pso) algorithm with self-adaptive stochastic inertia weight were proposed. Numerical results show that the maximum transmission number of normal coverage and extended coverage have a great influence on the system performance and their value ranges should be set within [3,10] and 11,61 respectively;while the maximum transmission number of extreme coverage has little influence on the system performance, and its recommended value is 1 for smaller power consumption. The average power consumption of coverage classes updating mechanism with coverage classes rollback is about 61% lower than that of 3GPP proposed model. All these researches together provide good reference for scale deployment of NB-IoT. (C) 2019 Elsevier B.V. All rights reserved.

关键词： NB-IoT Random access Coverage enhancement Coverage classes Markov chain pso algorithm

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Optimal PFC corrector of single stage power converter using BC tuned PID controller

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2016年第6期30卷 3155-3166页

作者： Umamaheswari, K. Venkatachalam, V. VMKV Engn Coll Fac Elect & Instrumentat Engn Salem Tamil Nadu India Kavery Engn Coll Mecheri Salem India

In this paper, Bee Colony (BC) algorithm based tuning of the proportional, integral and derivative (PID) controller is proposed. The novelty of the proposed method is enhanced searching ability, reliability and suitable for complex problems. Here, the proposed method is used to optimize the proportional, integral and derivative gains of the PID controller based on the output power variation from the input power. With the optimized gains, PID controller determines the control output to reduce the error between the actual and reference value of the instantaneous input power. This instantaneous input reference is computed with the aid of Radial Basis Function Network (RBFN). In the presented work, the topology of the AC to DC power converter is modelled and controlled with BC optimized PID controller. The performance of the proposed BC-PID controller for AC to DC converter is analyzed in terms of power factor correction and efficiency. The validation of the proposed control algorithm is performed on MATLAB/Simulink working platform and the effectiveness of the proposed method is analyzed by using the comparison analysis with the existing techniques.

关键词： Power factor correction AC to DC Converter single stage efficiency pso algorithm BC algorithm PID control optimal control

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Non-linear parametric vibration of the laminated composite shallow shells including primary and 1:2 internal resonances

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第15期31卷 3444-3475页

作者： Foroutan, Kamran Dai, Liming Zhao, Haixing Qinghai Normal Univ Sino Canada Res Ctr Computat & Math Xining Qinghai Peoples R China Univ Regina Ind Syst Engn Regina SK Canada

This research aims to study the non-linear parametric vibration of laminated composite shallow (LCS) shells with the optimal fiber angles exposed to external and parametric excitations, including primary and 1:2 internal resonances. In this regard, optimal fiber angles are found with implementations of the P-T method for the objective functions and utilization of the particle swarm optimization (pso). Also, the non-linear model of the shallow shells is established based on the stress function and the first-order shear deformation theory (FSDT). According to FSDT, Hooke's law, von-Karman equation, Hamilton's principle, and Galerkin method, two-degree-of-freedom non-linear ordinary differential governing equations are discretized.

关键词： Laminated composite shallow shells non-linear parametric vibration chaos primary and internal resonances pso algorithm P-T method multiple scales method

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Directional Signage Location Optimization of Subway Station Based on Big Data

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IEEE ACCESS 2020年 8卷 12354-12363页

作者： Tu, Qun Zhang, Zhenji Zhang, Qianqian Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China

The imperfection of the guide signs in the subway will lead to many difficulties for passengers, which directly affects the operation efficiency of subway stations. In this paper, we use big data to analyze the problem of signages in Beijing subway, and propose the optimization model of signages in subway stations based on particle swarm optimization algorithm. The experimental results of Dongzhimen subway station in Beijing show that the model has strong robustness in optimization, and the global best position can be found 100%.

关键词： pso algorithm signage location optimization subway station big data

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Optimization of Construction Material Cost through Logistics Planning Model of Dragonfly algorithm - Particle Swarm Optimization

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KSCE JOURNAL OF CIVIL ENGINEERING 2021年第7期25卷 2350-2359页

作者： Pham Vu Hong Son Nguyen Huynh Chi Duy Pham Ton Dat Ho Chi Minh City Univ Technol Vietnam Natl Univ Bach Khoa Univ Construct Engn & Management Dep Ho Chi Minh City 700000 Vietnam

Managing a construction project is challenging because of cost, time, safety, and quality considerations. In the most projects, the cost of construction is one of the most critical aspect because material cost alone accounts for significant ratio of the total project. Therefore, the cost of construction materials should be controlled. In this study, we proposed the use of material requirements planning (MRP) to control the cost of construction materials. After determining the demand for the materials required for construction, we estimated both the quantity of materials required and time taken to deliver the materials to the construction site. Although economic order quantity models have been applied to analyze construction material costs, they do not accurately reflect concerns related to material cost. Therefore, we used the material supply chain model (construction logistics planning) to analyze material costs. To optimize MRP according to the current progress of a project, a novel approach combining the dragonfly algorithm (DA) and particle swarm optimization algorithm (pso) was proposed. To verify the advanced searchability of the DA-pso algorithm, the algorithm was compared with the gray wolf and the genetic algorithms.

关键词： Construction logistics planning Optimizing material Inventory control Dragonfly algorithm pso algorithm Optimize delivery schedules Time to import construction materials

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Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2017年第6期14卷 1040-1063页

作者： Navabi, M. Mirzaei, H. Shahid Beheshti Univ Fac New Technol Engn Tehran *** Iran

This paper focuses on robust optimal adaptive control strategy to deal with tracking problem of a quadrotor unmanned aerial vehicle (UAV) in presence of parametric uncertainties, actuator amplitude constraints, and unknown time-varying external disturbances. First, Lyapunov-based indirect adaptive controller optimized by particle swarm optimization (pso) is developed for multi-input multi-output (MIMO) nonlinear quadrotor to prevent input constraints violation, and then disturbance observer-based control (DOBC) technique is aggregated with the control system to attenuate the effects of disturbance generated by an exogenous system. The performance of synthesis control method is evaluated by a new performance index function in time-domain, and the stability analysis is carried out using Lyapunov theory. Finally, illustrative numerical simulations are conducted to demonstrate the effectiveness of the presented approach in altitude and attitude tracking under several conditions, including large time-varying uncertainty, exogenous disturbance, and control input constraints.

关键词： Quadrotor Adaptive Control Nonlinear Disturbance Observer pso algorithm

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Optimisation of channel capacity for multiple-input multiple-output smart antenna using a particle swarm optimiser

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IET COMMUNICATIONS 2012年第16期6卷 2645-2653页

作者： Ho, M. -H. Chiu, C. -C. Liao, S. -H. Tamkang Univ Dept Elect Engn New Taipei City Taiwan

In this study, the channel capacity of indoor multiple-input multiple-output ultra-wide band (MIMO-UWB) transmission for a smart antenna is described. A particle swarm optimiser (pso) is used to synthesise the radiation pattern of the directional circular arc array to maximise the capacity performance in an indoor MIMO-UWB communication system. The UWB impulse responses of the indoor channel for any transmitter-receiver location are computed by applying shooting and bouncing ray/image techniques, inverse fast Fourier transform and Hermitian processing. By using the calculated frequency response, the capacity performance of the synthesised antenna pattern on an MIMO-UWB system can be computed. Based on the topography of the circular antenna array and the capacity formula, the array pattern synthesis problem can be reformulated into an optimisation problem and solved by the pso algorithm. pso optimisation is applied to a high-order non-linear optimisation problem. The novelties of our approach are not only choosing capacity as the cost function instead of the sidelobe level of the antenna pattern, but also considering the antenna feed length effect of each array element. The cost function for the problem is non-smooth and discontinuous with respect to the antenna pattern. It is difficult to solve by gradient methods, since the derivative is hard to derive. The strong point of the pso algorithm is that it can find out the solution even if the performance index cannot be formulated by simple equations. The simulation results show that the synthesised antenna array pattern is effective to focus maximum gain on the line-of-sight path which scales as the number of array elements.

关键词： particle swarm optimisation directional circular arc array Optimisation techniques nonlinear programming antenna radiation pattern ultra wideband communication performance index topography antenna feed length effect sidelobe level high-order nonlinear optimisation problem array element channel capacity optimisation antenna array pattern synthesis adaptive antenna arrays indoor channel frequency response cost function transmitter-receiver location indoor multiple-input multiple-output ultrawide band transmission directive antennas fast Fourier transforms impulse responses pso algorithm shooting ray-image techniques Antenna arrays smart antenna Integral transforms line-of-sight path antenna radiation patterns transient response Radio links and equipment Hermitian processing antenna feeds inverse fast Fourier transform indoor MIMO-UWB communication system inverse transforms bouncing ray-image techniques MIMO communication particle swarm optimiser

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Adaptive filtering noisy transcranial Doppler signal by using artificial bee colony algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第2期26卷 677-684页

作者： Karaboga, Nurhan Latifoglu, Fatma Erciyes Univ Fac Engn Elect & Elect Eng Dept Kayseri Turkey Erciyes Univ Biomed Eng Dept Fac Engn Kayseri Turkey

Computerized processes are supportive in the new age of medical treatment. Biomedical signals which are collected from the human body supply or important useful data that are related with the biological actions of human body organs. However, these signals may also contain some noise. Heart waves are commonly classified as biomedical signals and are non-stationary due to their statistical specifications. The probability distributions of the noise are very different, and for this reason there is no common method to remove the noise. In this study, adaptive filters are used for noise elimination and the transcranial Doppler signal is analyzed. The artificial bee colony algorithm was employed to design the adaptive IIR filters for noise elimination on the transcranial Doppler signal and the results were compared to those obtained by the methods based on popular and recently introduced evolutionary algorithms and conventional methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Artificial bee colony algorithm pso algorithm DE algorithm Adaptive IIR filter Noise elimination Transcranial Doppler signal

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