ANTICIPATING MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ML ALGORITHM

Abstract

Developing predictive models for early detection of machine failures in automated industries using
machine learning algorithms is a sophisticated approach aimed at anticipating and mitigating potential
breakdowns before they occur. This involves leveraging advanced computational techniques to analyze
historical data, identify patterns, and create models capable of forecasting potential failures. By employing
machine learning algorithms, these models can continuously learn and adapt to changing conditions, providing a
proactive and data-driven solution for maintenance planning and minimizing downtime in automated industrial
settings. Monitoring the performance and predicting failures of industrial equipment is crucial for ensuring the
quality of manufactured materials and optimizing time and cost in maintenance. This project aims to examine
the ongoing research, development, and progress in employing AI/ML techniques for predicting equipment
faults in various industries. The surveyed topics in this paper encompass ML algorithms, use cases, and concepts
relevant to the application of this technology across diverse industries such as software and hardware.