Journal of Soft Computing Paradigm
IRO Journals
Volume 2 — Issue 4 — December 2020

Machine Learning Approach to Predictive Maintenance in Manufacturing Industry - A Comparative Study

https://doi.org/10.36548/jscp.2020.4.006
P. Karuppusamy
Professor, Department of EEE, Shree Venkateshwara Hi-Tech Engineering College, Erode
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Karuppusamy, P. 2021. “Machine Learning Approach to Predictive Maintenance in Manufacturing Industry - A Comparative Study”. Journal of Soft Computing Paradigm 2 (4): 246-55. https://doi.org/10.36548/jscp.2020.4.006.
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Keywords

— Industry 40
— Analytic Models
— Machine Learning
— IIoT Sensors
Published: 27-01-2021

Abstract

Predictive maintenance is the way to improve asset management in every manufacturing industry. While handling advance costlier machinery in the industry, the predictive maintenance knowledge will be essential to protect the machinery before gets degradation performance. Recently, the emergence of business in manufacturing industry deals with good systems, regular intervals maintenance process, predictive maintenance (PdM), machine learning (ML) approaches are extensively applied for handling the health standing of business instrumentation. Now the digital transformation towards I4.0, data techniques, processed management and communication networks; it��s doable to gather huge amounts of operational and processes conditions information generated type many items of kit and harvest information for creating an automatic fault detection and diagnosing with the aim to attenuate period of time and increase utilization rate of the parts and increase their remaining helpful lives. The predictive maintenance is inevitable for property good producing in I40. This paper aims to provide a comprehensive review of the recent advancements of metric capacity unit techniques wide applied to PdM for good producing in I4.0 by classifying the analysis consistent with metric capacity unit algorithms, ML class, machinery and instrumentation used device employed in information acquisition, classification of knowledge size and kind, and highlight the key contributions of the researchers and so offers pointers and foundation for additional analysis. In this research paper we constructed a Random Forest model to predict the failure of the various machine in manufacturing industry. It compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.

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