Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm

Zhu, Xiaojin; Xiong, Jiang; Liang, Qiong

doi:10.1109/access.2018.2789933

Cited by 83 publications

(47 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Compared with numeric and binary representations, individuals represented with Q-bits have better diversity. Due to the diversity and the global convergence of the QGA, it has been adopted as an effective method to solve combinatorial problem in recent years [29]- [32].…”

Section: A Related Workmentioning

confidence: 99%

Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation

Nie

2020

IEEE Access

View full text Add to dashboard Cite

Compressed sensing (CS) technologies have been widely adopted to pilot-assisted orthogonal frequency division multiplexing (OFDM) sparse channel estimation. However, only few works have focused on the location optimization of the pilot pattern. This paper investigates the pilot location optimization for the measurement matrix construction based on the minimum mutual coherence (MC) rule. We consider the design of the deterministic OFDM pilot pattern for solving a combinatorial optimization problem. The proposed approach utilizes the advantages of the Q-bit to update the location of the OFDM pilot pattern. The obtained results show that the proposed approach can form measurement matrix with a smaller MC and the estimated performance can be essentially improved compared with the standard genetic algorithms or random search method. INDEX TERMS channel estimation, genetic algorithm, Q-bit, pilot pattern, mutual coherence.

show abstract

Section: A Related Workmentioning

confidence: 99%

Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation

Nie

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The genetic algorithm (GA) [43] is a kind of method to deal with complex optimization problems by simulating the rules of survival of the fittest and the mechanism of chromosome information exchange within the population. The quantum genetic algorithm (QGA) [44][45][46][47] is based on the state vector representation of quantum. It refers the probability amplitude representation of quantum bits to the coding of chromosomes, so that a chromosome can express the superposition of multiple states, and uses quantum revolving gate and quantum non-gate to realize the finer operation of chromosomes, thus achieving the optimal solution of the goal.…”

Section: Quantum Evolutionary Algorithmmentioning

confidence: 99%

Optimal Allocation of Intermittent Distributed Generation under Active Management

et al. 2018

View full text Add to dashboard Cite

In recent years, distributed generation (DG) has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. At present, most distributed generators follow the principle of “installation is forgetting” after they are connected to a distribution network. This principle limits the popularization and benefit of distributed generation to a great extent. In order to solve these problems, this paper presents a two-tier model for optimal allocation of distributed power sources in active distribution networks (ADN). The objective of upper level planning is to minimize the annual comprehensive cost of distribution networks, and the objective of lower level planning is to minimize the active power cut-off of distributed generation through active management mode. Taking into account the time series characteristics of load and distributed power output, the improved K-means clustering method is used to cluster wind power and the photovoltaic output in different scenarios to get the daily curves in typical scenarios, and a bilevel programming model of distributed generation based on multiscenario analysis is established under active management mode. The upper level programming model is solved by Quantum genetic algorithm (QGA), and the lower level programming model is solved by the primal dual interior point method (PDIPM). The rationality of the model and the effectiveness of the algorithm are verified by simulation and analysis of a 33-bus distribution network.

show abstract

“…Based on SVM, sparse representation was jointly employed to conduct fault diagnosis [24]. With the optimization method of quantum genetic algorithm, Zhu et al used SVM to diagnose fault of rotary machinery [25]. To overcome multiple failures problems, an improved SVM was investigated by Deng et al [26].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

View full text Add to dashboard Cite

Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

show abstract

Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm

Cited by 83 publications

References 8 publications

Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation

Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation

Optimal Allocation of Intermittent Distributed Generation under Active Management

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Contact Info

Product

Resources

About