Graph Partition Based on Dimensionless Similarity and Its Application to Fault Diagnosis

Zheng, Bo; Gao, Huiying; Ma, Xin; Zhang, Xiaoqiang

doi:10.1109/access.2021.3059757

Cited by 5 publications

(9 citation statements)

References 18 publications

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“…For the traditional and various PSO variants, the position update is realized on the basis of velocity update. As shown in formula (1), the position update needs to be superimposed with a velocity vector. Moreover, due to the inherent premature convergence problem caused by trapping into the local optimal region, the optimization performance of PSO is significantly reduced in the face of complex, high-dimensional and multimodal optimization problems.…”

Section: Direct Position Updating-based Trying Mutation Psomentioning

confidence: 99%

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Zheng

Zhang

et al. 2023

Quality & Reliability Eng

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In order to improve the global optimization of particle swarm optimization (PSO), enhance the performance of PSO in dealing with these complex, highdimensional, multimodal optimization problems, and furthermore, promote the optimization effect in reliability optimization applications, a direct position updating-based trying-mutation PSO (DTPSO) was proposed. In this algorithm, the direct position updating strategy and trying-mutation strategy were designed, which could effectively maintain the population diversity, balance the exploitation and exploration, and increase the probability of obtaining global optimal solution. After being verified and compared by nine complex test functions, the rationality of algorithm design and the excellent global optimization performances were proved. In the reliability optimization, the reliability redundancy allocation and reliability allocation were optimized by different advanced improved algorithms, the comparison results proved the stability and optimization performance of DTPSO. Due to its stable and efficient global optimization performance, it was concluded the proposed DTPSO could provide effective technical support for practical applications such as reliability optimization.

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Section: Direct Position Updating-based Trying Mutation Psomentioning

confidence: 99%

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Zheng

Zhang

et al. 2023

Quality & Reliability Eng

Self Cite

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“…The Gauss radial basis function can approach the train samples better when dealing with nonlinear problems [9], which is conducive to data prediction. Therefore, the Gauss radial basis function is taken as a kernel function in Equation ( 5):…”

Section: Svr Prediction Modelmentioning

confidence: 99%

“…where x is the samples to be predicted, and x i is the ith support vector. Thus, the five testing samples can be brought into Equation (9). Meanwhile, the influence of randomness on the PSO is discussed in details in [20].…”

Section: Multi-step Predictionmentioning

confidence: 99%

Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO

Zheng

Gao

et al. 2021

Applied Sciences

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In order to predict aeroengine wear accurately and automatically, as a predictor, support vector regression (SVR) was optimized by means of particle swarm optimization (PSO). The guided mutation strategy of PSO (GMPSO) is presented herein to determine the proper structure parameters of an SVR, as well as the embedding dimensions of the training samples. The guided mutation strategy was able to increase the diversity of particles and improve the probability of finding the global extremum. Furthermore, single-step and multi-step prediction methods were designed to meet different accuracy requirements. A prediction comparison study on spectral analysis data was carried out, and the contrast experiments show that compared with SVR optimized by means of a traditional PSO, a neural network and an auto regressive moving average (ARMA) prediction model, the SVR optimized by means of the GMPSO approach produced prediction results not only with higher accuracy, but also with better consistency.

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“…us, the air transportation industry can be suitable for long-term development, and the future air transportation will undergo a period of rapid global scientific and information technology development [2]. To fully implement the digital, intelligent, and high-quality development of air transportation, new requirements are put forward [3].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multisource Dynamic Model in TBO

Xia

Yan

Zhang

2022

Mathematical Problems in Engineering

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When extracting flight data from airport terminal area, there are matters such as large volume, unclear features, and similar trend in time series. In order to deal with the related issues and to optimize the description, by combining with the TBO (Trajectory-Based Operation), an application proposed by the ICAO (International Civil Aviation Organization) in ASBU (Aviation System Block Upgrade), using multisource dynamic model to establish 4DDW (4D dynamic warping) algorithm, the multisource modeling integrated with evaluation system is proposed to realize the flight path optimization with time series characteristics and accord with the interval concept. The calculation results show that 4DDW can obtain the optimal solution for multiprofile calculation of TBO by comparing the composite trajectory deviation values and time dimension planning using the buffer and threshold values recommended by ICAO in airspace planning and flight procedure design. The results meet the requirements of high accuracy and convergence features of spatial waypoints and can improve the airport operation standards and terminal area capacity.

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Graph Partition Based on Dimensionless Similarity and Its Application to Fault Diagnosis

Cited by 5 publications

References 18 publications

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO

Optimization of Multisource Dynamic Model in TBO

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