A packaging manufacturer with frequent stoppages needed earlier fault detection across multiple lines. Their team used Protonest Master to collect device-level health signals and automate maintenance alerts.
Challenge: too many reactive interventions
Operators spent too much time responding to late-stage failures. Troubleshooting usually started after throughput dropped.
Main blockers
- No centralized event correlation
- Manual checks done at inconsistent intervals
- Limited trend visibility per line
- Escalation depended on individual experience
Implementation: health scoring and threshold alerts
The team defined signal thresholds for temperature variance, cycle-time drift, and retry spikes. Protonest Master generated health scores and routed alerts by severity.
Outcome: less downtime and faster decisions
Within weeks, maintenance moved from reactive response to scheduled intervention windows. Teams resolved anomalies before line stoppage in most cases.
Key takeaway
Predictive maintenance requires consistent signal quality and clear ownership paths. Operational value comes from fast, actionable routing, not dashboard complexity.
Frequently asked questions
Which signals should teams start with?
Start with a small set tied to known failure modes, then expand as confidence grows.
Do all lines need identical thresholds?
No. Baseline by equipment profile, then tune thresholds per line behavior.
How often should alert rules be reviewed?
Review weekly during rollout, then move to monthly once alert quality stabilizes.