A parameter-adaptive ACMD method based on particle swarm optimization algorithm for rolling bearing fault diagnosis under variable speed

Ma, Zengqiang; Lu, Feiyu; Liu, Suyan; Li, Xin

doi:10.1007/s12206-021-0405-7

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…e discoverer is responsible for guiding the population to forage, and the follower follows the discoverers to obtain food. Meanwhile, a certain proportion of individuals in the population are selected as scouters for detection and early warning, which keep alert to environmental threats and warn the sparrow population to move to closer to safe areas [45].…”

Section: Sparrow Search Algorithm Sparrow Search Algorithmmentioning

confidence: 99%

An Integrated Approach Fusing CEEMD Energy Entropy and Sparrow Search Algorithm-Based PNN for Fault Diagnosis of Rolling Bearings

Xiao

Zeng

Deng

et al. 2022

Computational Intelligence and Neuroscience

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This paper solves the problem of difficulty in achieving satisfactory results with traditional methods of bearing fault diagnosis, which can effectively extract the fault information and improve the fault diagnosis accuracy. This paper proposes a novel artiﬁcial intelligence fault diagnosis method by integrating complementary ensemble empirical mode decomposition (CEEMD), energy entropy (EE), and probabilistic neural network (PNN) optimized by a sparrow search algorithm (SSA). The vibration signal of rolling bear was firstly decomposed by CEEMD into a set of intrinsic mode functions (IMFs) at different time scales. Then, the correlation coefficient was used as a selection criterion to determine the effective IMFs, and the signal features were extracted by EE as the input of the diagnosis model to suppress the influence of the redundant information and maximize the retention of the original signal features. Afterwards, SSA was used to optimize the smoothing factor parameter of PNN to reduce the influence of human factors on the neural network and improve the performance of the fault diagnosis model. Finally, the proposed CEEMD-EE-SSA-PNN method was veriﬁed and evaluated by experiments. The experimental results indicate that the presented method can accurately identify different fault states of rolling bearings and achieve better classification performance of fault states compared with other methods.

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Section: Sparrow Search Algorithm Sparrow Search Algorithmmentioning

confidence: 99%

An Integrated Approach Fusing CEEMD Energy Entropy and Sparrow Search Algorithm-Based PNN for Fault Diagnosis of Rolling Bearings

Xiao

Zeng

Deng

et al. 2022

Computational Intelligence and Neuroscience

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“…Particle swarm optimization (PSO) is a group optimization algorithm that simulates the social behavior of birds [9]. Compared with traditional optimization algorithms such as genetic algorithm, ant colony algorithm and firefly algorithm, PSO has the characteristics of easy understanding, easy implementation, strong global search ability and no human interference.…”

Section: Vwpsomentioning

confidence: 99%

“…, and the optimal position of the particle is Pbest, the global optimal position of the population is Gbest. The particle position and velocity are updated as shown in equation [9] 1 1 2 2…”

Section: Vwpsomentioning

confidence: 99%

Early Fault Diagnosis of Gearbox Based on VWPSO-VMD

Cao

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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Aiming at the problem that the characteristic of the early weak fault of the gearbox is submerged by the background noise under the low-speed operating condition, this paper proposes a gearbox fault diagnosis method based on the variable weighted particle swarm optimization variational mode decomposition (VWPSO-VMD). First, a variable weight particle swarm optimization algorithm (VWPSO) is proposed, and the effectiveness of the VWPSO is verified by the optimization calculation of test functions. Secondly, using fuzzy entropy as the fitness function, the proposed VWPSO is used to optimize the modal number K and the penalty factor α of the variational mode decomposition (VMD). Then, based on the grey relational analysis method (GRA), the optimal intrinsic mode function (IMF) is selected for envelope demodulation analysis. Finally, VWPSO-VMD and empirical mode decomposition (EMD) are used to diagnose the early crack and broken tooth fault signals of the gearbox. The results show that, compared with EMD, VWPSO-VMD can more accurately extract the early fault characteristic frequency of the gearbox. This provides a new method for the extraction of early fault features of the gearbox.

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“…Chen et al (Chen et al 2019) used ACMD for fault diagnosis of the rotor-stator system and achieved good results. Ma et al (Ma et al 2021) used the particle swarm optimization algorithm and ACMD to perform fault diagnosis of variable speed rolling bearings, and the results showed that the method is practical. yao et al (yao et al 2022) used the ACMD algorithm with the sparrow search algorithm to optimize the BiLSTM network and improved the accuracy of short-term load prediction.…”

Section: Introductionmentioning

confidence: 99%

Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD

2023

Gospodarka Surowcami Mineralnymi - Mineral Resources Management

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A parameter-adaptive ACMD method based on particle swarm optimization algorithm for rolling bearing fault diagnosis under variable speed

Cited by 14 publications

References 23 publications

An Integrated Approach Fusing CEEMD Energy Entropy and Sparrow Search Algorithm-Based PNN for Fault Diagnosis of Rolling Bearings

An Integrated Approach Fusing CEEMD Energy Entropy and Sparrow Search Algorithm-Based PNN for Fault Diagnosis of Rolling Bearings

Early Fault Diagnosis of Gearbox Based on VWPSO-VMD

Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD

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