Shandong Mining Reduces Mechanical Failure Rates with Predictive Maintenance Technology

Shandong Mining needed to improve competitiveness in the fiercely competitive mining industry by preventing unplanned downtime from critical asset failures that can cost billions of dollars per year. Their reactive maintenance approach left them vulnerable to unexpected failures.

Predictive maintenance technology was implemented using FactoryTalk Analytics with machine learning to monitor critical assets, analyze data from connected sensors and control systems, and build predictive models to identify normal operations and predict future failures.

Scheduled maintenance time and costs were significantly reduced. Mechanical failure rates and response times were reduced through early detection. The solution earned recognition as a Smart Industry IIoT Pioneer award winner.

• Machine learning-based predictive maintenance transforms reactive break-fix culture into proactive reliability management • Critical asset monitoring in mining must be continuous — even brief unplanned downtime carries outsized financial impact • Prescriptive analytics that recommend specific actions reduce dependence on expert interpretation of raw sensor data