Visplore connects to your IBA, AVEVA PI, or other IoT historians and gives maintenance engineers the tools to find root causes fast and move from reactive to preventive maintenance.
THE CHALLENGE
Maintenance teams in metal processing collect enormous amounts of process data — but translating that data into root causes and preventive action remains slow and labor-intensive.
“We‘ve replaced the roll four times this quarter. What’s actually driving early wear — and why always the same shift?”
“We replaced the valve, but the pressure spikes in the arc furnace are back. What are we missing in our analysis?“
“Second servo motor failure this month. Six months of sensor data — can we find the drift pattern before the next one?”
WHY VISPLORE
Visplore gives engineers the means to investigate directly in their IoT data — fast, interactive, without IT support — and to build on those insights to move maintenance from reactive to preventive.
USE CASES
Root Cause / Predictive
Detect bearing wear from vibration frequency drift weeks before alarm threshold. Cross-correlate with lubrication cycles and load profiles.
Root Cause Analysis
Recurring pressure anomalies traced to valve seat erosion patterns only visible when overlaying temperature, flow, and electrode position simultaneously.
Predictive Maintenance
Current draw and temperature drift signatures up to 3 weeks before failure. Build a failure fingerprint from past events and monitor live deviations.
Condition Monitoring
Flow rate degradation curves reveal filter clogging and impeller wear. Optimize cleaning intervals based on actual performance data, not fixed schedules.
RETURN ON INVESTMENT
recurring failures
Teams resolving root causes report fewer repeat failure events within 6 months
faster root cause
What took days or weeks of manual data work takes minutes in Visplore
earlier warnings
Drift-based predictive signals surface days to weeks before threshold alarms fire
time-to-insight
From connecting to your historian to actionable findings on a real investigation
TRUSTED BY
SYSTEM INTEGRATION
CUSTOMER VOICES
“A quality issue had plagued us for two years. We replaced components, consulted vendors — nothing held. With Visplore, one engineer found the root cause in an afternoon: a coolant temperature interaction nobody had visualized before.”
Head of Maintenance Engineering
Flat Steel Producer · Central West Europe
“Our IBA data was mostly used for post-incident reconstruction. Visplore changed that — we now use it proactively. We caught a bearing degradation pattern 11 days before an unplanned stop would have occurred.”
Reliability Engineer, Hot Rolling
Long Steel Products · Automotive Supplier Segment · Germany
“Our engineers are metallurgists, not data scientists. Within three months of deploying Visplore, they were running root cause analyses independently and confidently sharing proven templates and best practices between shifts.“
Plant Maintenance Manager
Aluminium Processing Facility · Southern Europe
FEATURED USE CASE
Example: Visplore helps identify coil grip failure events during hundreds of coil changes in a rolling process, and a clamping force issue as potential explanation. The changes are extracted and compared automatically, and impact factors are sorted by relevance.
ROOT CAUSE ANALYSIS
Problem
A rolling mill experienced recurring coil grip failures during coil changes — causing production stops and quality rejects. The events appeared random and were investigated manually, one by one.
Approach
Connected to the plant’s IBA historian, Visplore automatically extracted all 847 coil change events from months of data and compared them side-by-side. Impact factors were ranked by statistical relevance to grip failure.
READY?
Book a 45-minute live demo with your own process data — or a realistic production dataset from your industry. No slides, no pitch. Just Visplore, your signals, and an engineer who speaks maintenance.
