PredicTwin™ watches bearing temperature, vibration, motor signature, and lubrication parameters on the equipment that matters. Failure pattern recognition trained on Indian plant data. Spares pre-positioned. The line doesn't stop because nothing it depends on is allowed to surprise you.
Bearing temperature, vibration (RMS, peak, kurtosis), motor current signature, lubrication, alignment. Per critical asset, continuously.
Failure-mode pattern recognition trained on Indian plant data. Confidence intervals. Time-to-failure estimates with bands.
Auto-suggests which spare to position, when. Linked to AssetTwin BOM and your spares inventory. Lead times factored.
Most Indian plants run reactive maintenance — the breakdown happens, the spare isn't on shelf, the line is down for 14 hours, the shift is lost. PredicTwin shifts that to predictive: alert 13 days out, spare staged in 7, planned changeover during scheduled downtime, zero unplanned hours.
// THE PAIN
Reactive maintenance costs us 38% in unplanned downtime. Bearings fail without warning because we're sampling vibration quarterly with a handheld unit. Spares are over-ordered because we don't trust the failure data. The same critical equipment fails twice a year and we never catch the early signal.
// SUCCESS METRICS
Continuous sampling, not quarterly walks. Wireless sensors retrofit non-invasively on existing equipment. Edge processing.
Models trained on your specific equipment over the first 8-12 weeks. Generic models start strong; tuned models get sharper.
Not just "alarm." A predicted time-to-failure with confidence interval and a recommended action. Plant Heads can plan.
Linked to AssetTwin BOM. Auto-suggested spare orders. Vendor lead times factored. Zero "the spare wasn't on shelf."
RMS, peak, kurtosis, crest factor, envelope. Per axis. Continuous sampling, not quarterly walks.
±0.1°C precision. Thermal-camera enabled for non-contact assets. Trend analysis vs ambient.
MCSA — broken rotor bar, stator imbalance, eccentricity detection from current waveform.
Oil pressure, particle count via in-line sensors. Lube degradation predicted from wear-particle trends.
Per-asset MTBF and MTTR from work-order history in AssetTwin. Reliability dashboards.
1,200+ failure patterns documented across Indian plant deployments. Continuously expanding.
Not just "alarm." Predicted hours-to-failure with confidence intervals.
Auto-orders spares based on prediction + vendor lead time. Linked to AssetTwin BOM.
SAP PM, Maximo, in-house CMMS — auto-generates preventive work orders.
Cross-asset alarm correlation. Suppresses noise. Routes to right responder.
Maintenance team mobile view. Add inspection notes, photos, custom readings.
Why does Plant A's identical motor fail twice as often as Plant B's? PredicTwin tells you.
Source: Wistwin internal benchmark across active deployments, January 2024 – April 2026.
PredicTwin reads asset BOM from AssetTwin to know which spares to stage. No AssetTwin, no auto-staging.
SmarTwin's downtime-tagged data trains PredicTwin's failure pattern library over time. The two get smarter together.
Predicted-and-prevented failures create the CAPA records TrusTwin uses for IATF / Schedule M audit evidence.
Take the DMM Check™. Get a personalised readiness score, a benchmark against 200+ Indian plants in your sector, and the top three priorities PredicTwin™ should activate first.
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