Paper Contents
Abstract
The industrial sector is facing new challenges and increased competition nowadays, customer satisfaction depends on products, it can be achieved through efficient error control, as well as possible customization. To reach these objectives, one of the main components is efficient productivity, meaning machine availability needs to be maxed and not impacted by unplanned breakdowns, which mitigates wastage of money and time, and possibly quality issues on parts produced during the deteriorating phase of the machine. The industry will play an essential role, as it comes with new digital tools to improve productivity through real-time interactions from the digital world to the physical world. It is especially true with the maintenance policies, which are changing from corrective to planned ones from predictions of machine failures. We use the Condition-Based Maintenance (CBM) algorithm in these cases; they are based on data analysis to propose a health assessment of critical components, to predict future issues. In recent years, many types of research focused on these topics; however, few of them deal with the full scope of implementing practically this strategy in the industry, particularly in the automotive sector. Thus, this paper aims at predicting the fault that occurs in an industrial motor by using a condition monitoring algorithm and how it can be overcome efficiently.
Copyright
Copyright © 2023 M.Gokila. This is an open access article distributed under the Creative Commons Attribution License.
