Application of variational mode decomposition optimized with improved whale optimization algorithm in bearing failure diagnosis

Wang, Hailun; Wu, Fei; Zhang, Lu

doi:10.1016/j.aej.2021.03.034

Cited by 37 publications

(16 citation statements)

References 15 publications

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“…Z et al [140] applied a WOA based upon a forward-looking sonar to realize two-dimensional optimal path planning for an AUV. In [141], a failure diagnosis method based on variational mode decomposition and optimized via an IWOA was proposed, and the effectiveness of the method was verified via a failure experiment on a test stand. Anitha et al [142] proposed a modified WOA (MWOA) to optimize the threshold selection of multi-level color image thresholds.…”

Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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“…The switching timing is determined by the switching logic. In the sensorless technique, voltage and current values are measured, and the actual rotor speed is estimated by the Kalman filter algorithm 28 . Figure 10 shows the functional blocks of the FDD for sensorless speed control of the BLDC motor.…”

Section: Fdd‐based Speed Control Of Sensorless Bldc Motor Drive Using...mentioning

confidence: 99%

An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances

Vanchinathan¹,

Valluvan²,

Chinnaraj

et al. 2021

Int Trans Electr Energ Syst

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In general, unexpected failures in sensorless brushless DC (BLDC) motors can result in production downtime, costly repairs, and safety concerns. BLDC motors are commonly used in home appliances, the medical sector, aerospace, small-scale, and large-scale industries under uncertain operating conditions. Therefore, the fault detection and diagnosis (FDD) of BLDC motor drives can play a very important role in increasing their performance, reliability, robustness control, and operational safety under uncertain operating conditions in critical real-time applications. To satisfy these issues of hall effect sensor, misplacement of a hall-effect sensor, inverter IGBT open-switch fault diagnosis, failure of hall effect sensor, lack of robustness speed control of BLDC motor, which has received substantial interest in academic and industry sectors to establish the proposed work optimization techniques approach FDD strategy for speed control of sensorless BLDC motor under uncertain operating conditions. The proposed optimization techniques such as Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) approach FDD strategies for BLDC motor drives. These FDD strategies simulated by the above optimization techniques on a sensorless BLDC motor with numerical Matlab/Simulink 2020a simulation results are verified. From the simulation results, out of three optimization techniques, the WOA-based FDD strategies are very effective for both bearing and stator winding faults detection and diagnosis in sensorless BLDC motor drives.

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“…Li et al [ 20 ] constructed a new objective function, the maximum average envelope kurtosis, to reduce the impact of random noise and determine the mode number and quadratic penalty term adaptively through an intelligent optimization algorithm. Wang et al [ 21 ] applied cross-correlation theory into the determination of the penalty factor, but ignored the influence of the penalty factor.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis of Rolling Bearings Based on Variational Mode Decomposition Improved by the Niche Genetic Algorithm

Shi

Wang

et al. 2022

Entropy

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Due to the influence of signal-to-noise ratio in the early failure stage of rolling bearings in rotating machinery, it is difficult to effectively extract feature information. Variational Mode Decomposition (VMD) has been widely used to decompose vibration signals which can reflect more fault omens. In order to improve the efficiency and accuracy, a method to optimize VMD by using the Niche Genetic Algorithm (NGA) is proposed in this paper. In this method, the optimal Shannon entropy of modal components in a VMD algorithm is taken as the optimization objective, by using the NGA to constantly update and optimize the combination of influencing parameters composed of α and K so as to minimize the local minimum entropy. According to the obtained optimization results, the optimal input parameters of the VMD algorithm were set. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a rolling bearing. The decomposition process of the rolling-bearing fault signal was transferred to the variational frame by the NGA-VMD algorithm, and several eigenmode function components were obtained. The energy feature extracted from the modal component containing the main fault information was used as the input vector of a particle swarm optimized support vector machine (PSO-SVM) and used to identify the fault type of the rolling bearing. The analysis results of the simulation signal and measured signal show that: the NGA-VMD algorithm can decompose the vibration signal of a rolling bearing accurately and has a better robust performance and correct recognition rate than the VMD algorithm. It can highlight the local characteristics of the original sample data and reduce the interference of the parameters selected artificially in the VMD algorithm on the processing results, improving the fault-diagnosis efficiency of rolling bearings.

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Application of variational mode decomposition optimized with improved whale optimization algorithm in bearing failure diagnosis

Cited by 37 publications

References 15 publications

A survey on several new popular swarm intelligence optimization algorithms

A survey on several new popular swarm intelligence optimization algorithms

An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances

Research on Fault Diagnosis of Rolling Bearings Based on Variational Mode Decomposition Improved by the Niche Genetic Algorithm

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