Reducing Downtime and Improving Equipment Maintenance

Industry: Mining

 

Challenge

A large mining company was struggling with unplanned equipment downtime, which was negatively impacting production schedules and increasing operational costs. Equipment data was fragmented across different systems, and the lack of a unified view made it difficult to predict maintenance needs and optimise equipment scheduling.

 

Solution

The team first integrated data from various sources, including IoT sensors on equipment, maintenance logs, and operational systems, using a comprehensive data integration approach. By creating clear data models, the team was able to represent the relationships between equipment, operational schedules, and maintenance records. This provided a structured understanding of how data flowed through the company’s operations.

Data storage solutions were implemented, utilising a combination of data lakes for unstructured IoT sensor data and data warehouses for structured maintenance records. This allowed easy access to both real-time and historical data, supporting predictive maintenance efforts. Data governance frameworks ensured that all equipment data adhered to standards for accuracy and security, allowing the company to trust the data it was using for decision-making.

Interactive dashboards were developed to visualise equipment performance, downtime trends, and maintenance schedules. This gave the operations team real-time insights into equipment health, enabling them to plan maintenance more effectively and reduce costly unplanned downtime.

 

Results

Within six months, the mining company saw a 25% reduction in equipment downtime, leading to significant improvements in production schedules. Maintenance costs were reduced by 15%, as predictive models helped the company shift from reactive to proactive maintenance. Additionally, the integration of data from IoT sensors and maintenance logs improved overall equipment lifespan by 10%.

 

Key Takeaways

  • Data integration allowed for real-time access to equipment data from IoT sensors and maintenance systems.
  • Data models provided a structured view of equipment relationships, helping optimise maintenance schedules.
  • Data governance ensured the accuracy and security of equipment data, improving decision-making.
  • Data storage solutions enabled the company to store and retrieve both real-time and historical data, facilitating predictive maintenance.

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