Solution
Predictive Maintenance · RAM
Combine MTBF/MTTR, reliability R(t), and maintainability metrics with AI anomaly detection to predict failures and optimize maintenance timing and crews.
Maintenance after a failure is a cost; maintenance before one is a strategy. PlantPulse® brings an always-on anomaly pipeline and reliability-engineering metrics onto one screen — answering which asset to maintain, when, and why, with data.
A Challenges we solve
Availability loss and delivery risk from sudden failures
Calendar-based over-maintenance and blind-spot under-maintenance
No single view of equipment health
Real failure precursors buried under alarm floods
B Key capabilities
Reliability metrics (RAM)
Compute MTBF, MTTR, availability, and reliability R(t)=exp(-λt) automatically from operating data.
AI anomaly & forecast
Industrial time-series models learn per-tag baselines to catch failure precursors early.
Equipment health index
Index equipment health to set maintenance priorities with data.
Response workflow
Manage detection → severity triage → acknowledge → response report as a pipeline, so nothing slips through.
Maintenance briefings
AI compiles daily and weekly briefings of core issues and priority actions — start maintenance meetings from data.
Alarm noise suppression
Band, boolean, and CEP stream alarms filter repetitive noise so teams focus on real precursors.
C How it works
Collect
Edge continuously ingests high-frequency sensors — vibration, temperature, current — alongside PLC signals.
Learn baselines
Time-series models learn each tag's normal behavior, building per-asset baselines.
Detect & diagnose
The always-on pipeline flags deviations, triages severity, and the AI proposes root-cause candidates.
Act
Track responses with acknowledgments and reports; outcomes feed the next baselines and briefings.
30–50%
less unplanned downtime (estimate)
24/7
always-on anomaly pipeline
MTBF·MTTR
reliability metrics, automated
R(t)
reliability-function forecasting
D Where it applies
Turbine health across remote sites was known only through patrol inspections, so sudden stops were handled reactively.
Continuous remote monitoring · early precursor detection
Sudden crane and welding-equipment failures shook entire dock schedules.
Health-index-driven maintenance priorities for critical assets
Subtle degradation in rotating machinery never tripped rule-based alarms.
Early-warning system built on learned baselines
C Outcomes
- 30–50% less unplanned downtime (estimate)
- Better maintenance efficiency, less over-maintenance
- Extended equipment life
D Related products
D FAQ
How much data do we need to start?
Existing history feeds training directly; without it, several weeks to months of collection builds per-tag baselines. Reliability metrics (MTBF/MTTR) compute from day one.
What about false positives?
Severity triage and the acknowledge workflow accumulate verified outcomes, and thresholds and sensitivity are tuned per tag. CEP and band alarms pre-filter repetitive noise.
Is the cloud required?
No. Both the anomaly pipeline and the LLM can run fully on-premises, so data never leaves the site.
See it live on real operating screens
A 30-minute demo walks you from ingest to AI. Check the fit for your plant with an expert, right away.