Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm

Gai, Jingbo; Zhong, Kunyu; Du, Xuejiao; Yan, Ke; Shen, Jun‐Xian

doi:10.1016/j.measurement.2021.110079

Cited by 70 publications

(35 citation statements)

References 28 publications

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“…Wang et al conducted a research on economic optimization of microgrid cluster based on chaos SSA [ 53 ]. Gai et al proposed a parameter-optimized deep belief network (DBN) based on SSA for gear fault severity detection [ 11 ]. Kathiroli et al presented an efficient cluster-based routing model using Sparrow search algorithm derives a fitness function to perform clustering process [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Threshold image segmentation based on improved sparrow search algorithm

Yuan

2022

Multimed Tools Appl

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Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants’ update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows’ update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.

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Section: Introductionmentioning

confidence: 99%

Threshold image segmentation based on improved sparrow search algorithm

Yuan

2022

Multimed Tools Appl

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“…Although GA is an influential global optimization algorithm, the encoding and decoding process is complex, and PSO has the disadvantage of low accuracy at later iterations. Recently, as an excellent optimization method, the sparrow search algorithm (SSA) with fast convergence speed and high accuracy has been proposed [41], which performs better than the traditional GA and PSO approaches [42,43]. To the best of our knowledge, the SSA has also rarely been applied to optimize the RVM.…”

Section: Introductionmentioning

confidence: 99%

PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type

Qiu

Sun

et al. 2022

Energies

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Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.

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“…Compared with other common algorithms, SSA demonstrates the advantages of high precision, fast convergence, enhanced stability and robustness [ 34 ]. SSA was used for the optimisation of the model for detection [ 35 , 36 , 37 ]. SSA was used to optimise BP combined with hyperspectral means to detect protein content in milk rapidly; the results showed that its performance was superior to that of other methods [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

Fan

Zhu

et al. 2022

Foods

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The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an RP2 of 0.9304 and an RMSEP of 0.0458 g·g−1. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

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Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm

Cited by 70 publications

References 28 publications

Threshold image segmentation based on improved sparrow search algorithm

Threshold image segmentation based on improved sparrow search algorithm

PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

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