Optimizing the Performance of the Maintenance Planning Department in Petroleum Plants by Use of Artificial Intelligence

Abstract

This study shows how artificial intelligence helps fix maintenance issues in oil and gas factories, tackling problems like frequent breakdowns, wasted time, or rising repair bills. Instead of guessing when machines fail, it uses smart systems - like random forest models and combined learning methods - that learn from past records, live signals from IoT gadgets, plus advice from makers to plan fixes just in time. In harsh areas where gear such as spinning pumps or heat changers runs nonstop under heavy stress, the tech checks movement shifts, heat changes, rust levels - linking these clues to predict malfunctions correctly more than 85 times out of 100, according to field tests. Results include slashing surprise halts by nearly one-seventh while boosting output efficiency by a fifth, seen at firms like Shell after they adopted AI tools that cut unexpected stoppages by a full fifth. This method cuts upkeep costs by using resources smarter - maybe saving big sites millions every year - while boosting dependability, cutting pollution risks from leaks, yet staying aligned with tough rules like OSHA’s and EPA’s. Its ability to fit different oil-based setups shows it could spread widely - even helping industries run tougher, smoother.