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Predictive Maintenance.

From sensor reading to verified outcome. Not dashboards. Not alerts. Governed decisions with traceable results.

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Predictive Maintenance

The Only Predictive Maintenance Platform That Closes the Loop

Most predictive maintenance tools tell you something might fail. AssetStack tells you what will fail, when, why, what to do about it, and whether the fix worked, with every prediction classified by evidence quality and every outcome fed back into the model.

  • 30 to 50%Downtime reduction with integrated predictive maintenance
  • 8 to 12%Cost savings of predictive over preventive maintenance
  • VerifiedEvery prediction traced to evidence and outcome

The AssetStack Predictive Loop

  1. Detect
  2. Diagnose
  3. Predict
  4. Decide
  5. Execute
  6. Verify

Each stage produces the evidence the next stage depends on. Verification feeds confirmed outcomes back into the models. The loop never breaks.

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From raw sensor data to a scheduled intervention, in one click.

AI-powered failure prediction and remaining useful life estimation, as it runs in the platform. Simulated data on a council pool pump; illustrative values.

Predictive Analytics

AI-powered failure prediction and remaining useful life estimation

  • 0Predictions Made
  • 0At Risk Assets
  • 200Monitored Assets
  • 82%Avg Fleet Health

AI Failure Prediction

Analyse sensor data and maintenance history

Demo complete. From raw sensor data to a scheduled intervention in one click.

No Predictions Yet

Run AI analysis to predict potential failures across all assets

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Select Equipment

Choose equipment to analyse

Pool Circulation Pump 2centrifugal pump
DegradedDemo asset

Pool Circulation Pump 2

centrifugal pump · South West Sports Centre · Plant Room

42
  • 47Days RUL
  • 58%Failure Risk
  • 31,240Op. Hours
  • HighRisk Level

AI Prediction Engine

Advanced failure prediction analysis

Analysing…
  • 68%Failure Probability
  • 41Days RUL
  • 91%Confidence

Risk Level

High Risk

Primary Failure Mode

Bearing Wear

Analysis Summary

Advanced ML analysis using 4/4 model agreement. Health index: 38%. Vibration and bearing temperature trends indicate accelerating mechanical degradation over the last 21 days.

Anomaly Score

74.2/100

Significant Deviation

RUL Confidence Interval (90%)

Lower Bound
29 days
Upper Bound
58 days

Likely Failure Modes

  • Bearing Wear62%
  • Seal Leakage21%
  • Impeller Cavitation11%
  • Motor Winding Fault6%

Estimated Failure Cost

$184K

If unaddressed

Risk Factors

  • Vibration RMS up 38% over 3-week baseline
  • Bearing temperature trending +6.4°C above seasonal norm
  • Operating hours exceed 85% of rated service interval
  • Two unplanned stoppages logged in the last 90 days

Optimal Maintenance Window

High Priority

Schedule between 14 to 28 days

Model Performance Metrics

Accuracy
94.2%
Precision
91.8%
Recall
89.5%
F1 Score
90.6%

Recommended Actions

  • Schedule bearing replacement within the 14 to 28 day window
  • Inspect mechanical seal and replace if scoring is present
  • Raise a work order and reserve pump downtime with aquatic operations
  • Increase vibration sampling to 15-minute intervals until rectified

The Problem

Most "Predictive Maintenance" Is Just Scheduled Maintenance with a Dashboard

The market is flooded with tools that claim predictive capabilities but deliver little more than threshold alerts, calendar reminders and pretty charts. They collect sensor data. They show you graphs. They send you an email when a reading crosses a line. That is not predictive maintenance. That is monitoring.

What Most Vendors Call Predictive Maintenance

  • IoT sensors that send threshold alerts when readings cross a static limit
  • Calendar-based maintenance schedules repackaged as "AI-optimised"
  • Dashboards that show you what already happened, not what's coming
  • Black-box algorithms with hidden assumptions engineers cannot review
  • No connection between the prediction and the work order that follows
  • No verification of whether the intervention actually worked
  • Models that never learn from outcomes. Same predictions, cycle after cycle
  • Generic benchmarks presented as site-specific limits

What AssetStack Delivers

  • Multi-source evidence fusion: sensors, inspections, history, operational context
  • Remaining useful life calculated per asset, adjusted for actual condition
  • Prescriptive alerts: diagnosis, procedure, parts and labour checked before the alert fires
  • Model inputs exposed as reviewable assumptions, never hidden constants
  • Auto-generated work orders linked directly to the prediction that created them
  • Post-work verification compares condition before and after
  • Verified outcomes feed back into models. Predictions get more accurate over time
  • Your own operational data, your own limits, your own margin economics

What Real Predictive Maintenance Requires

Five Capabilities No Other Platform Delivers Together

Predictive maintenance is not a feature. It is a system. AssetStack is the only platform that connects all five capabilities into one continuous, governed, self-improving loop.

  1. Evidence Classification

    Every result is classified: measured, derived or modelled. Users can always distinguish source evidence from calculated indicators and predictive outputs. The confidence placed in a conclusion depends on the quality of the evidence behind it.

  2. Multi-Source Data Fusion

    Sensors alone are not enough. AssetStack ingests geometry vehicle readings, inspection records, sensor values, survey results, laser wear profiles, work orders, environmental exposure and operational context, then fuses them into a single coherent picture.

  3. Consequence-Ranked Prioritisation

    Not every predicted failure matters equally. AssetStack ranks interventions by failure consequence, production exposure, safety risk and intervention readiness, so the work that protects the most margin or prevents the worst outcome is done first.

  4. Governed Decision Lifecycle

    Every material recommendation moves through proposal → approval → implementation → verification. Evidence basis, intervention window, consequence of deferral and acceptance criteria are retained. No black box. No hidden constants.

  5. Verified Outcome Feedback

    The verified outcome becomes new evidence for future predictions. Models improve from confirmed results, not self-attested savings. Your auditor can trace every dollar from prediction to outcome, independently verified.

  6. The Result

    A predictive maintenance system that gets more accurate over time, where every prediction is traceable to evidence, every decision is governable, and every outcome is verifiable. Not a dashboard. A decision operating system.

The Melbourne skyline at dusk, its towers rendered in the blue two-tone dither against a natural sky.

How It Works

Detect. Diagnose. Predict. Decide. Execute. Verify.

One connected process, not six disconnected systems. Each stage produces the evidence the next stage depends on, and verification feeds confirmed outcomes back into the models.

Detect

Ingest and validate evidence from every source

  • Sensor Telemetry

    Vibration, temperature, pressure, current, lubrication, fuel burn, operating time, ingested in real time and mapped to the correct asset and network position.

  • Inspection Evidence

    Geometry vehicle readings, condition surveys, visual inspections, drone imagery, LiDAR scans, ground penetrating radar, all validated and positioned.

  • Operational Context

    Loading history, environmental exposure, maintenance records, work orders, possession schedules and asset configuration. The context that makes raw data meaningful.

Diagnose

Identify the failure mechanism and contributing factors

  • Failure Mode Analysis

    Combine current condition with inspection history, deterioration rates, environmental exposure and operational context to identify the likely failure mechanism, contributing factors and affected assets.

  • Confidence Grading

    Every diagnosis carries a confidence level based on the quality, quantity and recency of the underlying evidence. Where evidence is insufficient, the result is flagged as uncertain, never presented as fact.

  • Cascade Modelling

    Predict which deferred defect triggers a bigger failure downstream. An auxiliary pump that stops a production line is flagged with the same severity as the primary turbine it supports.

Predict

Forecast failure probability and remaining useful life

  • Remaining Useful Life (RUL)

    Every component carries a modelled remaining useful life, adjusted for actual condition, level of service and criticality. Not a generic benchmark. Your asset, your operating conditions, your deterioration curve.

  • Failure Probability Curves

    Probabilistic forecasts with confidence bounds, not single-point guesses. Where forecast evidence is insufficient, the platform identifies the result as uncertain rather than presenting false precision.

  • Prescriptive Alerts

    Not "elevated readings on Motor 3". The alert arrives as a diagnosis: outer race bearing defect, severity high, estimated time to failure two weeks. The work order, repair procedure and part availability are already checked.

Decide

Evaluate options and govern the recommendation

  • Consequence Evaluation

    Evaluate severity, consequence, urgency and available intervention options. Compare similar assets, model future condition and show the likely impact of acting now, delaying work or taking no action.

  • Scenario Modelling

    Model Premium, Balanced and Must-Do funding scenarios side by side. See exactly what each service level costs and what deferral does to your renewal backlog, escalated to future dollars.

  • Governed Approval

    Material decisions move through engineering review, approval and governance. Evidence basis, intervention window, consequence of deferral, accountable role and acceptance criteria are retained in one record.

Execute

Convert decisions into action with readiness controls

  • Auto-Generated Work Orders

    Approved decisions become work orders, maintenance tasks, possession requirements, material reservations and field instructions. Linked directly to the prediction and evidence that created them.

  • Readiness Controls

    Check whether required access, resources, equipment, materials and evidence are available before work begins. No work released without confirmation that it can be completed.

  • Integrated Scheduling

    AI scheduler optimises timing across trades, access windows, possession schedules and amenity closures. Shutdown maintenance coordinated in a single integrated schedule.

Verify

Measure outcomes and feed learning back into the models

  • Before / After Comparison

    Compare condition before and after the intervention. Record whether the work was effective, partially effective or ineffective. The verified outcome becomes new evidence for future predictions.

  • Verified Savings Ledger

    Every prediction tied to an intervention, attached evidence and a verified outcome. Independently verified, not self-attested. Your auditor can trace every dollar from prediction to outcome.

  • Model Retraining

    Learning deltas are recorded and fed back into the predictive models. The system gets more accurate over time because it learns from what actually happened, not what was assumed.

Proof

The Gap Between Monitoring and Predictive Maintenance Is Measurable

Organisations running disconnected monitoring tools versus those running integrated predictive maintenance systems show stark differences in downtime, cost and safety outcomes.

  • 30 to 50%Reduction in unplanned downtime with integrated digital twin and predictive maintenance
  • 8 to 12%Cost savings of predictive over preventive maintenance programs
  • 40%Reduction in reactive maintenance achievable in under one year
  • 99.5%Reliability achieved by operators using closed-loop predictive systems

See what real predictive maintenance looks like

Book a demo and we'll model your assets using your actual sensor, inspection and operational data, with predictions, prescriptive alerts and a verified savings ledger you can take to your board.

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