Who this is for: Industry solution architects evaluating which use cases to pursue, reliability engineers looking for patterns that match their assets, and business leaders who need concrete examples to justify the investment. Find your industry, find your asset, and find your starting point.

The Pattern Finder's Guide

Every organization asks the same question: "What should we predict first?"

The answer is almost never "the most expensive thing." It is "the thing you have the best data for that also hurts when it fails."

We have implemented Maximo Predict across four major industry verticals. In every single one, certain patterns recur. This blog catalogs those patterns so you do not have to discover them from scratch.

Here is a number that matters: Organizations that select their first use case based on data readiness rather than perceived business urgency succeed 3x more often. The most critical asset is not always the best first prediction target.

Manufacturing Use Cases

Manufacturing environments are target-rich for predictive maintenance. Diverse assets, harsh operating conditions, and high cost of downtime.

Rotating Equipment: The Gateway Use Case

Assets: Pumps, fans, blowers, compressors, motors

This is where most organizations start, and for good reason. Rotating equipment fails in predictable, degradation-based patterns. Vibration data is widely available. Failure examples are plentiful.

Named Failure: The Weekend Bearing

A food manufacturer's plant manager told us: "Every bearing failure happens on a Friday night. Every single one." His maintenance records confirmed it -- not because bearings prefer weekends, but because Friday afternoon is when weekend production ramps up, pushing already-degraded bearings past their limit. Predict could see the degradation trend days before the weekend killed the bearing.

Use Case: Pump Bearing Failure Prediction

Element — Detail

Model type — Failure probability (30-day window)

Features — Vibration trend, runtime since replacement, days since PM, operating temperature

Data sources — Monitor (vibration, temp), Manage (WOs, meters)

Threshold — >65% triggers inspection

Result — 40% reduction in unplanned pump failures

Use Case: Motor Winding Degradation

Element — Detail

Model type — RUL estimate

Features — Current draw trending, winding temperature, runtime hours, insulation resistance

Data sources — Monitor (electrical params), Manage (WOs, inspections)

Planning — Schedule replacement during next planned outage

Result — Zero unplanned motor failures in first year

Production Line Equipment

Assets: Conveyors, packaging machines, assembly robots, stamping presses

Production lines fail in patterns tied to cycle counts, wear rates, and environmental conditions.

Named Failure: The Cascading Line Stop

When one station on a packaging line jams, every downstream station stops. A 15-minute fix becomes a 2-hour restart. A pharmaceutical client found that 60% of their unplanned line stops were preceded by detectable cycle time variance 4 to 8 hours earlier.

Use Case: Packaging Machine Jam Prediction

Element — Detail

Model type — Failure probability (8-hour window)

Features — Cycle time variance, reject rate trend, film tension, ambient temperature

Data sources — PLC data via Monitor, quality system, Manage WOs

Action — High probability triggers operator inspection between shifts

Result — 25% reduction in unplanned line stops

HVAC and Facilities

Assets: Chillers, boilers, air handling units, cooling towers

Critical for data centers, hospitals, clean rooms, and any environment where temperature control is non-negotiable.

Use Case: Chiller Compressor RUL

Element — Detail

Model type — RUL estimate

Features — Discharge temp, oil pressure, power consumption, runtime, refrigerant levels

Data sources — BMS via Monitor, Manage WOs and inspections

Planning — Schedule replacements during maintenance windows

Result — Zero unplanned chiller outages in critical data center facilities

Utilities and Energy Use Cases

Long-lived assets, geographically distributed, high reliability requirements, serious consequences for failure.

Transformers

The flagship utility use case. High-value assets with catastrophic failure consequences.

Named Failure: The Silent Degradation

A utility lost a $1.2M power transformer that had passed its routine inspection 6 weeks earlier. Post-mortem revealed insulation degradation that dissolved gas analysis would have caught -- but DGA results were in a spreadsheet that nobody had connected to the asset management system. The data existed. The integration did not.

Use Case: Distribution Transformer Failure Probability

Element — Detail

Model type — Failure probability (12-month window)

Features — Age, loading history, DGA results, location characteristics, weather exposure

Data sources — Manage (assets, inspections), lab system (DGA), weather API

Application — Prioritize replacements in capital budget

Result — 30% better targeting of replacement investments

Switchgear and Breakers

Use Case: Breaker Mechanism Failure

Element — Detail

Model type — Failure probability based on operations and condition

Features — Operation count, timing test results, age, manufacturer, contact resistance

Data sources — Manage (WOs, inspections), relay data, test equipment

Action — Schedule overhaul when probability exceeds threshold

Result — 35% reduction in breaker-related outages

Renewable Energy Assets

Assets: Wind turbines, solar inverters, battery storage systems

Young asset classes with growing data histories and unique challenges.

Use Case: Wind Turbine Gearbox Prediction

Element — Detail

Model type — RUL for gearbox bearings

Features — Vibration spectra, oil analysis, power output anomalies, wind speed patterns

Data sources — SCADA, condition monitoring system, oil lab

Planning — Coordinate replacements with crane availability and low-wind periods

Result — Reduced gearbox replacement costs through optimal scheduling

The unique challenge with renewables: Assets are often in remote locations. The cost of a technician visit is high even when the repair is simple. Prediction accuracy directly translates to reduced truck rolls.

Transportation Use Cases

Mobile assets, varied operating conditions, strict safety requirements, large homogeneous fleets.

Fleet Vehicles

Assets: Trucks, buses, service vehicles

Large fleets of similar vehicles are ideal for predictive models. Hundreds or thousands of similar assets provide abundant training data.

Named Failure: The Roadside Breakdown

A logistics company's average roadside breakdown cost $8,500 in towing, lost load time, and emergency repair premiums. Their fleet of 1,000 trucks averaged 47 roadside breakdowns per month. After deploying Predict on engine failure patterns, that number dropped to 23. That is $200K per month in avoided breakdown costs.

Use Case: Engine Failure Prediction for Truck Fleet

Element — Detail

Model type — Failure probability (30-day window)

Features — Engine hours, fault codes, fuel efficiency trend, mileage since overhaul

Data sources — Telematics (via Monitor), diagnostic systems, Manage WOs

Action — Route high-risk vehicles to depot for inspection

Result — 50% reduction in roadside breakdowns

Rail Assets

Assets: Locomotives, railcars, track components, signaling equipment

Safety-critical with regulatory oversight. Even modest prediction accuracy justifies the investment.

Use Case: Railcar Bearing Prediction

Element — Detail

Model type — Failure probability based on wayside detector data

Features — Temperature trending, acoustic signatures, mileage, load patterns

Data sources — Wayside detectors, on-board diagnostics, Manage WOs

Action — Set out cars for bearing replacement before failure

Result — Significant reduction in bearing-related derailment risk

Aviation Ground Support

Assets: Baggage tugs, belt loaders, pushback tractors

Use Case: Ground Support Equipment Reliability

Element — Detail

Model type — Failure probability by equipment type

Features — Usage cycles (flights handled), age, fault codes, days since PM

Data sources — Flight ops systems, telematics, Manage WOs

Planning — Ensure spare equipment during peak travel periods

Result — Improved on-time departure performance

Oil & Gas and Chemicals Use Cases

High-value assets in hazardous environments. Safety and environmental compliance add urgency to every prediction.

Process Pumps and Compressors

Named Failure: The Turnaround Surprise

A gas processing plant planned a 21-day turnaround. During pre-turnaround inspection, they discovered a compressor needed major repair they had not planned for. The turnaround extended to 31 days, costing $1.4M in additional lost production. Predict models would have flagged the compressor 4 months earlier, giving the turnaround team time to plan.

Use Case: Compressor Multi-Failure-Mode Prediction

Element — Detail

Model type — Failure probability for multiple failure modes

Features — Vibration signatures, discharge temperature, suction pressure, seal system data

Data sources — Monitor (process data, vibration), Manage WOs

Action — Feed predictions into turnaround planning

Result — Extended run time between turnarounds by 15%

Heat Exchangers

Use Case: Exchanger Fouling Prediction

Element — Detail

Model type — RUL (days until cleaning required)

Features — Temperature approach trending, flow rates, days since cleaning, crude blend

Data sources — Process historian, Manage cleaning records

Planning — Schedule cleaning during maintenance windows

Result — Optimized cleaning intervals, improved energy efficiency

Pipelines

Use Case: Pipeline Corrosion Growth Prediction

Element — Detail

Model type — Predicted metal loss over time

Features — In-line inspection measurements, soil conditions, cathodic protection readings, operating pressure

Data sources — ILI vendor data, CP monitoring, SCADA, Manage integrity records

Planning — Prioritize digs and repairs by predicted risk

Result — Better allocation of integrity management budget

The Five Reusable Solution Patterns

Despite industry differences, these patterns recur everywhere.

Pattern 1: Failure Probability for Work Triggering

The pattern: Calculate failure probability. Trigger work when threshold exceeded.

  Score asset daily ──> Compare to threshold ──> Create WO if exceeded

Applicable to: Any asset type with sufficient failure history.
Key success factor: Well-calibrated thresholds that balance false alarms and missed failures.

Pattern 2: RUL for Capital Planning

The pattern: Estimate remaining life. Feed into capital planning and budgeting.

  Estimate RUL ──> Sort by projected failure year ──> Allocate budget

Applicable to: Long-lived, high-value assets (transformers, major equipment).
Key success factor: Multi-year prediction horizon with appropriate uncertainty bounds.

Pattern 3: Condition-Based PM Optimization

The pattern: Adjust PM intervals based on predicted risk.

  Score risk ──> High risk: accelerate PM | Low risk: defer PM

Applicable to: Assets with established PM programs and varying risk profiles.
Key success factor: Approval workflow for PM changes and outcome tracking.

Pattern 4: Fleet-Level Risk Ranking

The pattern: Score entire population. Rank by risk for resource allocation.

  Score all assets ──> Rank by probability ──> Focus on top N

Applicable to: Large fleets of similar assets.
Key success factor: Portfolio visualization in Health and clear resource allocation rules.

Pattern 5: Multi-Model Ensemble

The pattern: Combine predictions from multiple models for comprehensive risk.

  Model A (bearing) ──┐
  Model B (seal)    ──┼──> Combined risk score ──> Prioritized action
  Model C (motor)   ──┘

Applicable to: Complex assets with multiple failure modes.
Key success factor: Appropriate weighting of individual model scores.

Adapting Use Cases to Your Context

You have seen the examples. Now adapt them to your reality.

Step 1: Assess What You Actually Have

Forget the ideal data scenario. What data exists today?

  • Work orders with failure codes? How consistent?
  • Meter readings? How frequent?
  • Sensor data? Connected to asset IDs?
  • Enough failure examples? For which failure modes?

Step 2: Understand What Actually Fails

Pull your corrective work order data and analyze:

  • What asset types generate the most corrective work?
  • What failure codes appear most frequently?
  • What failures cause the most downtime and cost?
  • Which failures show degradation patterns (not random)?

Step 3: Match Failures to Data

For each candidate failure mode, ask:

  • Do I have 30+ historical examples?
  • Are they coded consistently?
  • Is there condition data (sensors, inspections) that precedes these failures?
  • Can I act on a prediction (parts available, maintenance window exists)?

Step 4: Start With Your Quick Win

The use case that scores highest across all four dimensions -- data availability, failure frequency, detectable patterns, and actionable outcomes -- is your starting point.

Not the most expensive failure. Not the most politically important. The one most likely to succeed and prove the concept.

The 5 Commandments of Use Case Selection

  1. Data readiness beats business urgency. The best use case has the best data, not the biggest failure cost.
  2. Homogeneous fleets are gold. 200 identical pumps beat 200 different assets every time.
  3. Degradation failures are predictable. Random failures are not. Know the difference.
  4. Thirty failures is the minimum. Below that, you are guessing, not modeling.
  5. Start with one industry pattern. Prove it. Then replicate.

Find your pattern. Validate your data. Build your first win.

Next in the series: Part 8: Best Practices, Governance, and Adoption -- The human side of making predictive maintenance stick.

This is Part 7 of the MAS Predict series by TheMaximoGuys. [View the complete series index](/blog/mas-predict-series-index).

TheMaximoGuys | Enterprise Maximo. No fluff. Just results.