An intelligent computing technique to analyze the vibrational dynamics of rotating electrical machine

Raja, Muhammad Asif Zahoor; Niazi, Shahab Ahmad; Butt, Saeed Ahmad

doi:10.1016/j.neucom.2016.09.032

Cited by 56 publications

(9 citation statements)

References 31 publications

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“…The number of illustrative applications of these solvers based on back propagation (BP) neural networks and genetic algorithms is seen in the literature, such as nonlinear optics problems [14], nonlinear nanofluidic systems of Jeffery-Hamel flow [15], the dynamics of nonlinear singular heat conduction model of the human head [16], nonlinear Painlev'e II systems in applications of random matrix theory [17], hermal analysis of porous fin model [18], Nonlinear Singular Thomas-Fermi Systems [19], credit evaluation for listed companies [20], vibration dynamics of rotating electrical machines [21], environmental quality assessment [22], grid fault diagnosis [23], wind speed soft sensor [24], prediction of postgraduate entrance examination [25], fault section locating in distribution net-work with DG [26], crude oil production prediction [27], prediction of junction temperature for high power LED [28].…”

Section: B Innovation Contributionmentioning

confidence: 99%

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

Jiang

Huang²,

Huang³

et al. 2019

IEEE Access

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The artificial intelligence calculation method can effectively solve various nonlinear mapping relationships. The strength of these nonlinear solvers is exploited for the evaluation of power grid investment risk using back propagation (BP) neural network optimized by genetic algorithm. The mathematical model of the problem is constructed by selecting the transfer function of the neural network and defining the fitness function of genetic algorithm. BP neural network has good ability of self-learning, self-adaptation and generalization, which can overcome the drawbacks of traditional evaluation methods relying on experts' experience. For the characteristics of genetic algorithm global optimization, the genetic algorithm is used to optimize the weight and threshold of BP neural network, and BP neural network is trained to obtain the optimal evaluation model. The model fully exploits the local search ability of BP neural network and the global search ability of genetic algorithm. It has obtained good evaluation accuracy for the processing of multi-dimensional influence factor problem. And the model can be adapted to different power grids by changing the training data. However, the method cannot describe the specific relationship between each impact factor and the investment risk of the grid. The case study shows that the method can accurately and effectively evaluate power grid investment risk and improve the fault tolerance of the power grid investment risk evaluation.INDEX TERMS Power grid investment risk, risk evaluation, BP neural network, genetic algorithm.

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Section: B Innovation Contributionmentioning

confidence: 99%

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

Jiang

Huang²,

Huang³

et al. 2019

IEEE Access

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show abstract

“…In these intelligence computing schemes, the strength of artificial neural networks (ANN) and optimization with local and global search algorithms are exploited [ 28 ]. The spectrum of ANN modeling is spreading extensively in various real-life studies such as electromagnetism [ 29 ], nonlinear optics [ 30 ], nanotechnology [ 31 ], bioinformatics [ 32 ], mathematical equations [ 33 ], meteorology [ 34 ], fluid dynamics [ 35 ], thermodynamics [ 36 ], rotating electrical machine [ 37 ], electric motors [ 38 ], atomic physics [ 39 ], plasma physics [ 40 ] and astrophysics [ 41 ]. ANNs show outstanding and significant performance in real-life applications and emerged as one of the dominating models [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

Ali

Khan

Raja

et al. 2021

Entropy

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In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.

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“…In artificial intelligence computing methodologies, the strength of optimization algorithms and robustness of neural networks are exploited for solving different complex real-life problems [ 25 , 26 ]. Neural intelligence computing schemes are finding their extensive applications in several branches of practical sciences like astrophysics [ 27 ], plasma physics [ 28 ], atomic physics [ 29 ], thermodynamics [ 30 ], fluid dynamics [ 31 ], electric motors [ 32 ], rotating electrical machine [ 33 ], electromagnetic [ 34 ], meteorology [ 35 ], nonlinear optics [ 36 ], mathematical equations [ 37 ], bioinformatics [ 38 ], and nanotechnology [ 39 ]. These soft computing schemes performed exceptionally well in the above-mentioned applications and took place of all conventional methodologies [ 40 ], while, in correspondence with other traditional techniques, neural networks have produced much better efficiency and convergence rate in all related studies.…”

Section: Introductionmentioning

confidence: 99%

State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

Ali

Raja

et al. 2021

Entropy

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In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.

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An intelligent computing technique to analyze the vibrational dynamics of rotating electrical machine

Cited by 56 publications

References 31 publications

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

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