Machine Learning-Based Predictive Maintenance of Industrial Machines
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© 2023 by IJCTT Journal
Volume-71 Issue-3
Year of Publication : 2023
Authors : Chaitali R. Patil, Sanika K. Jadhav, Asmeeta L. Bardiya, Ankita P. Davande, Mahee P. Raverkar
DOI :  10.14445/22312803/IJCTT-V71I3P108

How to Cite?

Chaitali R. Patil, Sanika K. Jadhav, Asmeeta L. Bardiya, Ankita P. Davande, Mahee P. Raverkar, "Machine Learning-Based Predictive Maintenance of Industrial Machines," International Journal of Computer Trends and Technology, vol. 71, no. 3, pp. 50-56, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I3P108

Abstract
IoT (IIoT) enables machines, people, cloud computing and analytics to function together, improving the performance and productivity of industrial processes. Earlier maintenance of industrial machines used methods such as periodic maintenance (PM) or condition-based maintenance (CBM). PM often leads to a waste of personnel and material since, in many cases, maintenance is unnecessary and could be postponed. CBM requires a high level of expert knowledge to define the threshold values. In contrast to these approaches, predictive maintenance (PdM) is a more efficient and effective maintenance approach involving monitoring the state and health of industrial machines to identify potential failures and threats before they create a mishap leading to severe property loss, production, life, etc. Prediction requires machine learning models based on large amounts of data for each system component. The proposed system acquires data about machine health through a standard input mechanism. The system will collect, store and send audio data for processing in the edge devices as well as the cloud. It will use sensor data visualization tool to analyse system health. Machine health reports will be periodically generated, and tools to forecast machine failures will be used. The system generates alarms and sends notifications to the concerned officials of the industry. The proposed methodology will achieve real-time computing prediction of failures of industrial machines. Industry 4.0 focuses on process optimization, reducing costs and increasing efficiency, which imbibes a major motivation.

Keywords
Audio Data, Data Visualization, Industrial IoT (IIoT), Machine Learning, Predictive Maintenance (PdM), Sensors.

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