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Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization

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  • Mustufa Haider Abidi

    (Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia)

  • Usama Umer

    (Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia)

  • Muneer Khan Mohammed

    (Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia)

  • Mohamed K. Aboudaif

    (Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia)

  • Hisham Alkhalefah

    (Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

Data classification has been considered extensively in different fields, such as machine learning, artificial intelligence, pattern recognition, and data mining, and the expansion of classification has yielded immense achievements. The automatic classification of maintenance data has been investigated over the past few decades owing to its usefulness in construction and facility management. To utilize automated data classification in the maintenance field, a data classification model is implemented in this study based on the analysis of different mechanical maintenance data. The developed model involves four main steps: (a) data acquisition, (b) feature extraction, (c) feature selection, and (d) classification. During data acquisition, four types of dataset are collected from the benchmark Google datasets. The attributes of each dataset are further processed for classification. Principal component analysis and first-order and second-order statistical features are computed during the feature extraction process. To reduce the dimensions of the features for error-free classification, feature selection was performed. The hybridization of two algorithms, the Whale Optimization Algorithm (WOA) and Spotted Hyena Optimization (SHO), tends to produce a new algorithm—i.e., a Spotted Hyena-based Whale Optimization Algorithm (SH-WOA), which is adopted for performing feature selection. The selected features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). To enhance the efficiency of conventional RNNs, the number of hidden neurons in an RNN is optimized using the developed SH-WOA. Finally, the efficacy of the proposed model is verified utilizing the entire dataset. Experimental results show that the developed model can effectively solve uncertain data classification, which minimizes the execution time and enhances efficiency.

Suggested Citation

  • Mustufa Haider Abidi & Usama Umer & Muneer Khan Mohammed & Mohamed K. Aboudaif & Hisham Alkhalefah, 2020. "Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization," Mathematics, MDPI, vol. 8(11), pages 1-33, November.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2008-:d:443146
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    References listed on IDEAS

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    1. Akyol, Sinem & Alatas, Bilal, 2020. "Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    2. Pier Francesco Orrù & Andrea Zoccheddu & Lorenzo Sassu & Carmine Mattia & Riccardo Cozza & Simone Arena, 2020. "Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
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    Cited by:

    1. Mohammed Alkahtani, 2022. "Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    2. Mohammed Alkahtani & Mustufa Haider Abidi & Hamoud S. Bin Obaid & Osama Alotaik, 2023. "Modified Gannet Optimization Algorithm for Reducing System Operation Cost in Engine Parts Industry with Pooling Management and Transport Optimization," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    3. Mustufa Haider Abidi & Muneer Khan Mohammed & Hisham Alkhalefah, 2022. "Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing," Sustainability, MDPI, vol. 14(6), pages 1-27, March.

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