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Analytics Case Study Series: 1. Mining

The demand for energy is on the rise and resources being harder to locate, analytics will be imperative to meet demand.

Key Areas of application:

  • Reducing the time taken to locate natural resources
  • Reducing the costs associated with locating natural resources
  • Increasing the efficiency of both new and old deposit

The Case Details:

Organisation: Absolutdata

Client: A Mining major

Geography: US

Category: Predictive

The Situation:

  • Identifying failure risk by predicting engine failure
  • Changing engine oil at the ‘optimum time’ using predictive maintenance
  • Decreasing false alarm rates for operational efficiencies

The Solution:

  • Used terabytes of truck sensor’s data along with truck’s operations and alarms data
  • Merged data sources using Big Data, applied machine learning algorithms such as gradient boosted regression and cluster analysis
  • Used sentiment analysis and word cloud inputs to further refine the models

Results:

  • Alarms categorised into 5 categories, with critical alarms forming only 6% of the alarms as against 24%
  • 3-5 days of savings on every oil change leading to engine oil savings
  • Engine replacement costs down by 50%

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