Predictive Machine Learning Maintenance of VRLA Batteries based Uninterruptible Power Supply Systems

This thesis evaluates standard statistical and machine learning models for early fault detection for Valve Regulated Lead-Acid (VRLA) batteries in uninterruptible power supply (UPS) units. Unexpected battery failures in emergency support systems throughout CERN can endanger working personnel. Thus the minimization of downtime of such battery...

Scalability Analysis of Predictive Maintenance Using Machine Learning in Oil Refineries

Modern refineries typically use a high number of sensors that generate an enormous amount of data about the condition of the plants. This generated data can be used to perform predictive maintenance, an approach to predict impending failures and mitigate downtime in refineries. This research analyzes the scalability of machine learning methods...

Machine Learning for Predictive Maintenance: A Boeing 747 Bleed Air Valves case study

The newest generation of aircraft has seen a strong increase in sensor data generated on-board. The available data has the potential to indicate the health state of individual components based on which their maintenance requirements can be determined, a maintenance strategy called Condition Based Maintenance. Predictive Maintenance is a specific...

The potential of advanced data analytics using machine learning to increase overall equipment effectiveness in an underground mining operation

Changing market dynamics make that mining companies need to increase their equipment utilization and use their equipment more effectively. The growing availability of data from mining equipment and mining operations enables the use of data analytics to achieve that goal. This thesis explores the potential of data analytics and machine learning...