Solutions · Predictive Maintenance

Stop reacting. Start predicting.

Combine the Digital Twin's alarm engine, telemetry trending, and MTBF/MTTR tracking to shift from calendar-based maintenance to condition-based — and eventually predictive — maintenance.

Maintenance Maturity

Where are you today?

Most manufacturers are stuck between reactive and preventive. DFI gets you to condition-based in weeks and predictive within months.

Reactive

Fix it when it breaks. No data collection.

Preventive

Calendar-based schedules. Over-maintain or under-maintain.

Condition-Based

Monitor live telemetry. Act on actual asset health signals.

DFI starts here

Predictive

Trend analysis + alarm correlation predicts failure windows.

Capabilities

Four building blocks of predictive maintenance

Configurable Alarm Engine

Set thresholds per asset, per parameter — with dead-band, delay, and severity levels. Escalation rules route critical alarms to the right technician via SMS, email, or webhook. Bulk acknowledge and annotate from the alarm console.

  • Dead-band and time-delay to eliminate nuisance alarms
  • Severity tiers: Info, Warning, Critical, Emergency
  • Escalation chains with configurable timeout
  • Alarm shelving for planned maintenance windows

Telemetry Trending & Anomaly Detection

Overlay any parameter against time, shift, or production batch. Built-in statistical process control detects drift before it crosses alarm thresholds. Annotate trends with maintenance events to correlate cause and effect.

  • Multi-parameter overlay on a single time axis
  • Moving average, standard deviation, and Cpk auto-calculation
  • Annotations linked to maintenance work orders
  • 90-day hot storage, 2-year warm tier, unlimited cold archive

MTBF / MTTR Tracking

Automatically calculate Mean Time Between Failures and Mean Time To Repair per asset, per failure mode. Identify your worst-performing assets and most time-consuming repair categories to prioritize maintenance investment.

  • Auto-calculated from alarm-to-resolution timestamps
  • Breakdown by asset, failure mode, shift, and technician
  • Pareto of top downtime contributors
  • Target setting with red/amber/green status indicators

Automated Work Order Triggers

When a condition threshold is crossed, DFI can auto-generate a maintenance work order in your CMMS/EAM system — or in the built-in work order module. Include asset context, recent telemetry, and suggested parts.

  • Trigger on alarm, on threshold, or on MTBF degradation
  • Pre-populated work orders with asset context and recent readings
  • Integration with SAP PM, Maximo, UpKeep, and Fiix via API
  • Built-in lightweight work order tracker for teams without a CMMS

Results

Before and after DFI

Aggregate results from three manufacturing sites after 6 months of condition-based maintenance with DFI.

MetricBeforeAfter DFIImprovement
Unplanned downtime12.4 hrs/month4.1 hrs/month67% reduction
Maintenance cost per asset$2,840/yr$1,920/yr32% reduction
Alarm noise (false positives)340/week45/week87% reduction
MTTR (avg)4.2 hrs1.8 hrs57% faster

Connects to your existing stack

DFI ingests telemetry from any protocol and pushes work orders to your CMMS.

OPC UAMQTTModbus TCP/RTUSiemens S7EtherNet/IPSAP PMIBM MaximoUpKeepFiixREST API

How It Works

From sensor to work order in four steps

1

Ingest telemetry

DFI Edge Gateway connects to PLCs, sensors, and SCADA via OPC UA, MQTT, or Modbus. Data flows to the twin engine in sub-second intervals.

2

Evaluate thresholds

The alarm engine evaluates every reading against configurable thresholds with dead-band and delay. Only real anomalies trigger alerts.

3

Trend and correlate

Trending overlays show parameter drift over time. Statistical rules detect patterns before they breach alarm limits.

4

Trigger action

When conditions warrant intervention, DFI auto-generates a work order with asset context, recent telemetry, and suggested spare parts — sent to your CMMS or managed in the built-in tracker.

Cut unplanned downtime by 60% or more

Start with your highest-downtime line. We'll show measurable results in a 6-week pilot.