Predictive maintenance software that explains degradation before failure.
TwinEdge connects live condition, physics models, anomaly evidence, and remaining-life context to the asset record. Teams can review what is degrading, why it matters, and a scoped work draft before approving the maintenance handoff.
Predictive maintenance software
Explain degradation, estimate risk, and route reviewed findings into maintenance work.
TwinEdge predictive maintenance software combines connected condition signals, physics models, anomaly detection, failure history, and remaining-useful-life estimates. It keeps the evidence, assumptions, confidence, and work handoff attached to the asset record for engineering and planner review.
- Condition and anomaly context
- Evaluate trends, operating envelopes, anomalies, and asset-specific state instead of treating an alert as an isolated number.
- Physics and remaining-life models
- Use supported degradation, performance, and remaining-life models with named inputs, limits, and confidence context.
- Explainable recommendations
- Show contributing signals, source evidence, model context, likely impact, limitations, and the next recommended check.
- Approved work handoff
- Draft maintenance scope, timing, parts, procedures, and field tasks for review before work enters execution.
Best fit
- Reliability and maintenance teams with critical assets, usable condition data, and a defined response workflow.
- Organizations that need predictions connected to asset history, engineering context, human review, and completed-work evidence.
Scope and boundaries
- A prediction is an engineering decision input, not a guarantee of failure timing or an equipment safety clearance.
- Model suitability, sensors, thresholds, validation, and response policy must match the asset and operating environment.
Buyer questions
Frequently asked questions
What is predictive maintenance software?
Predictive maintenance software uses condition data, operating context, models, and history to identify degradation or failure risk early enough for a team to review and plan an appropriate response.
How does TwinEdge connect a prediction to a work order?
TwinEdge keeps the asset, source signals, model output, assumptions, limitations, and recommended response together, then drafts work scope for configured planner or engineering approval.
Does TwinEdge require cloud connectivity?
No single deployment model is required. TwinEdge OS can collect, buffer, and run supported inference locally, while TwinEdge Platform coordinates fleet, model, work, and evidence workflows when connectivity and policy allow.
Can predictive maintenance eliminate physical inspections?
No. Connected evidence can reduce unnecessary checks and focus inspections, but physical verification remains necessary where telemetry, model confidence, safety policy, or the failure mode requires it.
The Real Cost of Reactive Maintenance
When equipment fails without warning, the cost extends far beyond the repair bill. Every hour of unplanned downtime triggers a cascade of hidden expenses.
Direct Costs
- Emergency labor (overtime, call-backs)
- Rush-shipped replacement parts
- Expedited vendor service contracts
- Damaged secondary equipment
Indirect Costs
- Lost production / throughput
- Missed delivery commitments
- Customer penalties and SLA breaches
- Regulatory non-compliance fines
Hidden Costs
- Shortened equipment lifespan
- Technician burnout and turnover
- Insurance premium increases
- Reputation and customer trust erosion
Why Calendar-Based PM Falls Short
Preventive maintenance was a step forward from reactive -- but it creates its own waste. The P-F curve shows why time-based intervals miss most failures.
The Over-Maintenance Problem
The Missed Failure Problem
How Predictive Changes the Math
This operating-model comparison is qualitative, not a universal ROI claim. Validate costs, warning intervals, failure consequences, and savings with your own asset data.
| Metric | Reactive | Preventive | Predictive (TwinEdge) |
|---|---|---|---|
| Failure response | After failure | At fixed intervals | From condition evidence |
| Parts planning | Rush procurement | Schedule-based stock | Risk-window planning |
| Work priority | Emergency | Calendar priority | Condition and consequence |
| Equipment intervention | Corrective repair | Planned regardless of condition | Evidence-supported timing |
| Technician context | Failure symptoms | Checklist and history | Condition, physics, history, and parts |
| Operational handoff | Immediate dispatch | Scheduled work | Governed draft and approval |
What You Are Monitoring
Every asset class has different degradation signatures. Edge models evaluate local condition, cloud analytics can find cross-site patterns, and AssetOps routes evidence-backed work drafts for review.
| Asset | Edge (Real-time) | Cloud (Cross-Site) | OpsIntel (Action) |
|---|---|---|---|
| Centrifugal Pump | Vibration FFT, cavitation index, seal temp | Cross-site failure clustering, RUL curves | Bearing-work draft for review |
| Screw Compressor | Discharge temp, oil quality, vibration | Cross-site efficiency benchmarks | Parts recommendation on degradation |
| Induction Motor | Current signature, winding temp, bearing vib | Motor population aging model | Condition-based schedule proposal |
| Battery Bank | Cell voltage drift, internal resistance | Capacity fade prediction | Cell-replacement proposal |
| Heat Exchanger | Fouling factor, approach temp, dP | Cleaning interval optimization | Cleaning work draft at review threshold |
Switching from another platform?
We'll migrate your data from ANY CMMS or analytics platform — free. Assets, work orders, maintenance history, sensor configurations — everything.
See the ROI for Your Operation
Tell us your asset count, failure rates, and downtime costs. We will show you exactly what predictive maintenance saves -- before you commit to anything.