Research on consumption prediction of spare parts based on fuzzy C-means clustering algorithm and fractional order model

Chen, Yukun; Gao, Qi; XiaoBo, Su; Yu, Chenchen

doi:10.21595/vp.2017.19465

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Guo et al 84 proposed the maintenance equipment support demand prediction steps based on I'SO-BPNN. Chen et al 85 considered the noise in consumption series, proposed a prediction method combining fuzzy C-means clustering algorithm and fractional order model, the result showed that the relative error and average relative error of the proposed method are less than EWT-BPNN.…”

Section: Machine Learning Of Spare Parts Predictionmentioning

confidence: 99%

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Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Feng

Chen

et al. 2021

Advances in Mechanical Engineering

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Spare parts are treated as the basis to guarantee the safe and economic operation of civil aircraft, its scientific prediction and reasonable configuration play an important role in perfecting integrated logistics support (ILS) and achieving win-win situation of stakeholders (e.g. manufacturers, operators, maintenance providers). This paper studies the existing spare parts prediction and configuration methods of civil aircraft, and discusses future development trend of spare parts from the perspective of prediction and configuration, respectively. The current development status of civil aircraft spare parts prediction techniques are firstly introduced, according to demand characteristics of spare parts; the present research status of civil aircraft spare parts configuration methods are then elaborated, based on the traditional methods and machine learning methods; the development trend of civil aircraft spare parts prediction and configuration methods is finally analyzed, combined with the advantages and disadvantages of the existing methods of civil aircraft spare parts management methods. The efforts of this work provide a reference for the comprehensive management of civil aircraft spare parts and improve the perfection degree for the ILS of civil aircraft.

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Section: Machine Learning Of Spare Parts Predictionmentioning

confidence: 99%

“…Artificial neural network [76][77][78] Back-propagation neural network [81][82][83][84][85] demand patterns. It should be noted that the above method is limited by the assumptions of METRIC theory.…”

Section: Nonlinear Systemmentioning

confidence: 99%

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Feng

Chen

et al. 2021

Advances in Mechanical Engineering

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Effect of GO on the formation and differentiation of tobacco leaf callus and its antioxidant capacity

Huang

Peng

Chen

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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Fractional Derivative to Symmetrically Extend the Memory of Fuzzy C-Means

Safouan,

El Moutaouakil,

Patriciu

2024

Symmetry

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The fuzzy C-means (FCM) clustering algorithm is a widely used unsupervised learning method known for its ability to identify natural groupings within datasets. While effective in many cases, FCM faces challenges such as sensitivity to initial cluster assignments, slow convergence, and difficulty in handling non-linear and overlapping clusters. Aimed at these limitations, this paper introduces a novel fractional fuzzy C-means (Frac-FCM) algorithm, which incorporates fractional derivatives into the FCM framework. By capturing non-local dependencies and long memory effects, fractional derivatives offer a more flexible and precise representation of data relationships, making the method more suitable for complex datasets. Additionally, a genetic algorithm (GA) is employed to optimize a new least-squares objective function that emphasizes the geometric properties of clusters, particularly focusing on the Fukuyama–Sugeno and Xie–Beni indices, thereby enhancing the balance between cluster compactness and separation. Furthermore, the Frac-FCM algorithm is evaluated on several benchmark datasets, including Iris, Seed, and Statlog, and compared against traditional methods like K-means, SOM, GMM, and FCM. The results indicate that Frac-FCM consistently outperforms these methods in terms of the Silhouette and Dunn indices. For instance, Frac-FCM achieves higher Silhouette scores of most cases, indicating more distinct and well-separated clusters. Dunn’s index further shows that Frac-FCM generates clusters that are better separated, surpassing the performance of traditional methods. These findings highlight the robustness and superior clustering performance of Frac-FCM. The Friedman test was employed to enhance and validate the effectiveness of Frac-FCM.

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Research on consumption prediction of spare parts based on fuzzy C-means clustering algorithm and fractional order model

Cited by 3 publications

References 5 publications

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Effect of GO on the formation and differentiation of tobacco leaf callus and its antioxidant capacity

Fractional Derivative to Symmetrically Extend the Memory of Fuzzy C-Means

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