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Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing

Author

Listed:
  • Mustufa Haider Abidi

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box-800, Riyadh 11421, Saudi Arabia)

  • Muneer Khan Mohammed

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box-800, Riyadh 11421, Saudi Arabia)

  • Hisham Alkhalefah

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box-800, Riyadh 11421, Saudi Arabia)

Abstract

With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities’ lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3387-:d:770630
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    References listed on IDEAS

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    1. Rupa Ch & Thippa Reddy Gadekallu & Mustufa Haider Abidi & Abdulrahman Al-Ahmari, 2020. "Computational System to Classify Cyber Crime Offenses using Machine Learning," Sustainability, MDPI, vol. 12(10), pages 1-16, May.
    2. 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.
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    Cited by:

    1. 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.

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