From Chaos to Control.
Case Study: Printing & Converting Equipment Manufacturer
At a Glance:
Industry:
Machinery Manufacturing
Use Case:
Prevention of costly blow outs through early detection
Technologies Used:
- Machine Learning
- Anomaly Detection
- Edge Computing
- Alerting Engines
Impact:
Reduce part failures by 74%
Lower warranty costs
Better customer experience
The Challenge
Operators faced a recurring and costly issue: sudden “blow outs” within high-speed printing machines. These incidents occurred when a deteriorated guard rail allowed ink to escape—spoiling the print job, contaminating the machine, and occasionally damaging the facility. The aftermath involved extensive cleaning, unexpected downtime, and mounting operational costs. Despite the machines generating over 200 continuous data streams, none directly measured the degradation leading to these failures.
Our Approach
We turned to historical data—years of process logs and records of past blow outs—to search for early warning signals. Using machine learning algorithms, we analyzed this large dataset to uncover hidden patterns preceding the incidents. Unlike controlled lab environments, real-world data is noisy and inconsistent. Our models were trained to recognize not just exact matches, but similar patterns with tolerable variations—mimicking how human intuition might work, but at scale.
What we uncovered
Amid thousands of correlations, a clear signal emerged: just three out of the 200+ variables consistently showed increased variability ahead of a blow out. These three were previously overlooked, but their combined behavior proved to be a reliable early indicator. We validated this pattern across multiple incidents and passed it through expert review for operational relevance.
The Results
Implementation was fast and non-invasive. A lightweight statistical process was set up to monitor live data from those three variables. When all three showed elevated variability, an alert was triggered—providing enough lead time to slow down or halt the machine, avoiding a blow out entirely. This proactive measure now prevents damage and minimizes costly downtime.
What’s Next?
The machine builder is exploring how similar methods can be scaled across other failure modes. By continuously mining operational data for subtle but actionable patterns, we’re building smarter safeguards that keep performance high—and surprises low.