Preventing Equipment Failure
Preventing Process Disruption and Costly Repairs
Unplanned downtime happens for multiple reasons – a component breaks down from operating under harsh conditions, a networking change impacts automated processes, or a cyber incident brings the entire business to a halt.
Here’s an example of how equipment failure can impact operations in multiple ways: The hub of each $60K tire on a $5M haul truck contains a small orbital motor. Operating under 4,000 PSIs of constant pressure can cause the fluid in the motor to overheat, damaging the hydraulic system and reducing the component’s life expectancy.
In the asset-intensive mining industry, equipment maintenance and repairs can hit productivity and margins hard. According to Gartner, production downtime cost adds up to somewhere between $300k – $500k an hour.
Imagine the benefits of proactively identifying temperature and pressure anomalies, or other preventative maintenance issues before they bring operations to a halt and hurt your bottom line.
Using Anomaly Detection to Identify At-Risk Equipment Before It Fails
The Nozomi Networks solution tackles preventative maintenance head on with passive network monitoring and anomaly detection that identifies normal behavior and alerts you to deviations.
In the initial Dynamic Learning™ phase, the solution uses machine learning and artificial intelligence to observe your mine network traffic and create asset and process baselines. It models behavior and correlates multiple types of data, including information about similar assets within the mine, to determine what normal activity looks like.
In the second phase, monitoring, the solution automatically detects when a specific component or process is deviating from its baseline, and moving towards a state that could disrupt your mining operation. It uses advanced correlation and operational context to deliver a simple, consolidated view of what’s happening in your network, and proactively alert you that remediation may be necessary.
Anomaly detection significantly reduces troubleshooting efforts and enables you to take action before a component or process failure incident occurs.
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