Predicting alarms in manufacturing industries
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This paper introduces a predictive algorithm designed for managing alarms in manufacturing industries. By gathering data from various sources, such as machine sensors and maintenance records, the algorithm can anticipate potential alarms and notify operators ahead of time. Unforeseen downtime can have substantial negative consequences for businesses, including revenue loss, decreased productivity, and diminished customer satisfaction. As reported by the Association for Manufacturing Technology, the average cost of unexpected downtime for a manufacturing facility amounts to $22,000 per minute, which can rapidly accumulate and significantly affect a company's profitability. This study aims to evaluate the performance of multiple machine learning algorithms, such as decision trees and random forests, in predicting alarms and identifying potential issues. The insights gained from this research will shed light on the potential advantages and limitations of employing machine learning algorithms in the manufacturing sector.