A GOA-MSVM based strategy to achieve high fault identification accuracy for rotating machinery under different load conditions

Zhang, Jianqun; Zhang, Jun; Zhong, Min; Zheng, Jinde; Yao, Ligang

doi:10.1016/j.measurement.2020.108067

Cited by 36 publications

(25 citation statements)

References 28 publications

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“…Because rotating machinery may work under variable load conditions, it is difficult to meet the assumption. 18–21 Zhang et al. 21 seemed to be aware of this problem.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

Zhang

Qin

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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To identify rolling bearing faults under variable load conditions, a method named DISA-KNN is proposed in this paper, which is based on the strategy of feature extraction-domain adaptation-classification. To be specific, the time-domain and frequency-domain indicators are used for feature extraction. Discriminative and domain invariant subspace alignment (DISA) is used to minimize the data distributions’ discrepancies between the training data (source domain) and testing data (target domain). K-nearest neighbor (KNN) is applied to identify rolling bearing faults. DISA-KNN’s validation is proved by the experimental signal collected under different load conditions. The identification accuracies obtained by the DISA-KNN method are more than 90% on four datasets, including one dataset with 99.5% accuracy. The strength of the proposed method is further highlighted by comparisons with the other 8 methods. These results reveal that the proposed method is promising for the rolling bearing fault diagnosis in real rotating machinery.

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“…Because rotating machinery may work under variable load conditions, it is difficult to meet the assumption. 18–21 Zhang et al. 21 seemed to be aware of this problem.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

Zhang

Qin

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

Self Cite

View full text Add to dashboard Cite

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“…Content may change prior to final publication. [122] hybrid-GOA-GA [123] GOA-jDE [124] DE-GOA-KELM [125] HAGOA [126] GWGHA [127] BGOA [128] HGOA [129] SM-GNCSOA [130] GOALO [131] HAGOA [132] TLGOA [133] IWGOA [128] GOA-SVR [134] GOA-SVM [72] GOA-SVM [73] GOA-SVM [135] GOA-ImLSTM [136] GOA-MSVM [137] GOA-SVM [138] GOA was proposed by hybridizing GOA with GWO for tackling the text feature selection problem. GWO-GOA was assessed using eight datasets taking into account five metrics (i.e accuracy, sensitivity, specificity, precision, recall, and F-measure).…”

Section: ) Hybridization With Grey Wolf Optimizermentioning

confidence: 99%

“…Zhang et al [137] suggested a novel model (GOA-MSVM) by combining GOA with SVM for fault identification accuracy of rotating machinery. GOA-MSVM was assessed in two case studies: rolling bearing faults and gear faults.…”

Section: ) Hybridization With Invasive Weed Optimization Algorithmmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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“…VMD has been widely studied and applied in the denoising of vibration signals and the fault feature extraction of rotating machinery [24][25][26]. In this paper, VMD is introduced into the research of the NPW signal denoising and inflection point extraction, and the effectiveness of the proposed method is verified by simulation and actual experiments.…”

Section: Introductionmentioning

confidence: 99%

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

2021

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Leakage problems are common in the water supply pipeline system, which will threaten the health of residents and cause economic losses. Negative pressure wave (NPW) technology calculates the time difference through the inflection point to locate the leak. However, due to the nonlinear and nonstationary characteristics of the pressure signal, it is difficult to obtain an accurate inflection point of the NPW by the traditional method. Therefore, the advantages of applying variational mode decomposition (VMD) in NPW technology are explored. Firstly, the correlation coefficient and permutation entropy (PE) are used for effective intrinsic mode function (IMF) component selection and parameter optimization. Thus, an adaptive denoising method based on VMD (AD-VMD) is presented. Then, to effectively separate the detail features of the NPW, a novel inflection point extraction method based on VMD (IPE-VMD) is proposed. Simulation and experimental results show that AD-VMD can effectively suppress noise interference and conserve the mutation characteristic of the leakage. IPE-VMD can obtain a distinct maximum peak at the inflection point and has good robustness to noise interference. This method can calculate the time difference precisely and stably. In addition, the accuracy of the leak location is verified. The average relative positioning error is 5.13%.

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A GOA-MSVM based strategy to achieve high fault identification accuracy for rotating machinery under different load conditions

Cited by 36 publications

References 28 publications

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

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