Topic
Predictive analytics
AI-Driven Predictive Analytics in Equipment Management
Topic
Challenge
A mining company faced challenges with frequent equipment failures in both its fixed plant and mobile fleet. This resulted in unscheduled downtime, high maintenance costs, and operational inefficiencies. The traditional reactive maintenance approach was proving to be costly and ineffective.
Approach
To address these challenges, we implemented AI algorithms for predictive maintenance to analyse historical and real-time data from equipment sensors. These analytics helped in predicting potential equipment failures before they occurred. Utilizing the insights gained from predictive analytics, the company could schedule maintenance activities proactively, avoiding unexpected breakdowns.
Results
The company saw a drastic decrease in the frequency of equipment failures, thanks to the early detection and pre-emptive maintenance actions. By shifting to a predictive maintenance model, the company was able to reduce the costs associated with emergency repairs, parts replacement, and unplanned downtime. The reduction in downtime and more efficient maintenance scheduling led to a noticeable improvement in overall operational efficiency.