PdM lowers steel O&M costs

Reducing Unscheduled Downtime in Steel Factories

The hidden costs of unscheduled downtime can prevent manufacturers from reaching their target revenues. The solution? Prevent unscheduled downtime from occurring in the first place. One Steel manufacturer did just that with SKF Enlight AI's predictive maintenance solution.

Metals and Mining Case Study

Background Information

Industry: Steel Manufacturing

Location: South America

Machine Type(s): Continuous Steel Casters

Components: Bending Roll, Breakout, Pinch Roll

At-A-Glance

A major South American steel manufacturer was underperforming its revenue targets because of unscheduled plant downtime. SKF Enlight AI’s AI-driven Industrial Analytics solution triggered alerts up to 8 days before failure occurrence. The failure prediction rate of 93% has helped the manufacturer improve yield rates and reduce maintenance and overhead costs.

Business Problem

Lost production / revenue due to unscheduled downtime High O&M costs from overtime labor expenditures

Results

Prediction Rate: 93% Time to Failure: 8 days

Business Impact / ROI

30% reduction in unplanned downtime

15% reduction in operating costs

Methodology

Data generated by over 400 sensors was streamed to SKF Enlight AI’s cloud. Enlight AI applies advanced Machine Learning algorithms (AutoML) to the data. Based on detection of anomalous behavioral patterns, the solution provided predictions of evolving failure and indications of failure root cause.

What is Automated Machine Learning?

Within the Machine Learning discipline, there are multiple manual processes that are dependent on data scientists. AutoML applies advanced algorithms which automate manual Machine Learning processes, thereby reducing the need for human labor.

With AutoML industrial plants can scale Predictive Maintenance solutions across multiple plants within a few days.

Detailed Results

PdM Lowers Mining O&M Costs-Table

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