Compressor Predictive Maintenance

Diagnose specific compressor fault modes. Forecast remaining useful life from the data you already collect.

TruPrognostics™ AI combines physics-based models, machine learning, and contextual AI to diagnose fault modes by name, forecast remaining useful life, and recommend the next inspection or maintenance action grounded in relevant operating manuals.

15-page technical guide. Reciprocating & centrifugal compressors. Built for reliability and process engineers.
Live deployment
Compressor predictive maintenance dashboard showing loss of efficiency detected 40+ days before OEM alarm
40+ days lead timevs. the OEM built-in alarm on a U.S. supermajor's reciprocating compressor.
>90%
Diagnostic true positive rate
40+ days
Earlier than OEM alarm (live deployment)
1 platform
PI & waveform analytics
The Compressor Predictive Maintenance Gap

Most compressor predictive maintenance still relies on fixed thresholds. Fixed thresholds can't tell what's normal.

A discharge temperature that's high at full load may be normal. The same reading at half load may indicate intercooler fouling. Most compressor condition monitoring still treats each measurement in isolation, ignoring how operating conditions change what "normal" looks like.

The result is familiar: nuisance alarms during load swings, missed signals during real degradation, and maintenance driven by surprise. Effective compressor predictive maintenance starts with a model that understands the machine, not just its measurements.

Step 1
Physics Model
Predicts Expected State
A machine-specific physics model predicts the expected value of every monitored tag under current operating conditions and compressor configuration (load step, clearance volume, etc.).
Step 2
Measured State
Deviates from Prediction
The difference between measured and predicted values becomes a residual for each signal. Healthy operation keeps residuals near zero.
Step 3
Pattern and Magnitude
Identify the Fault
The pattern of residuals is classified by model-based diagnostics: the measured pattern is compared against known fault patterns and a list of the most likely diagnoses is produced.
How a residual is formed
Single channel shown: discharge pressure
Discharge pressure (bar)Absolute signal spaceMeasuredPhysics-model predictionHealthy envelope (dynamic)Subtract predictionResidualResidual space (measured − predicted)time →
What this shows. The physics model predicts the expected value of each tag from the current operating condition, so the "expected" line moves with the machine. The healthy envelope around it expands and contracts with operating point: wider during transients, tighter at steady load. The residual below is what remains after the model's expectation has been subtracted. The healthy band in the residual grid below looks flat, but it is not a threshold on the raw signal.
Machine State Suction
Pressure
Discharge
Pressure
Discharge
Temperature
Interstage
Temperature
Healthy Baseline
All residuals hover near zero within the healthy band.
Fault A: Valve Leak
Coordinated drift across pressure and temperature residuals.
Fault B: Intercooler Fouling
Strong drift in interstage and discharge temperature residuals. Pressures stay in band.
Residual (measured − predicted)
Healthy band
Zero residual
Reading the figure. Each row is one machine state; each column is a process-domain residual channel. The shaded band marks healthy variation. A valve leak drives coordinated drift across pressure and discharge temperature residuals. Intercooler fouling drives a distinct thermal signature: interstage and discharge temperatures drift positive while pressures stay in band. TruPrognostics AI recognizes each fault by its full multivariate signature, not by thresholds on any single channel.
How Physics-Based Compressor Predictive Maintenance Works

Physics-based residuals. Specific fault names. A time-to-action window.

TruPrognostics AI builds a physics-based model of each compressor's expected behavior under its actual operating conditions: load, speed, ambient temperature, gas composition, configuration. The residual (what the machine is doing minus what the model expects) isolates degradation from normal variation.

From there, the platform matches multi-signal patterns against a library of fault models, names the specific fault mode, tracks progression, and projects a probabilistic remaining useful life estimate.

From physics model to time-to-action
Shown: discharge temperature model on a reciprocating compressor throw
Operating Inputs
Suction temperature
Compression ratio
From tags already in your historian
Physics Model
Thermodynamic model of the compression process
Estimated Parameter
Polytropic efficiency
per throw
Informed by engineering parameters
(load step, clearance volume, etc.)
Residual
Deviation from expected efficiency
Diagnostic Output
Valve leakage Piston ring wear Packing wear Efficiency loss
residual trajectory identified fault mode
Fault Severity
How severe?
Deviation magnitude mapped to a scale specific to the fault mode in question
Prognosis Model
Fault progression
Projected forward using operating conditions, damage trajectory, and typical fault progression
Output
Probabilistic
Remaining Useful Life
A time-to-action window for planning maintenance around production, not around surprise
One model of many. TruPrognostics AI runs multiple models concurrently on each monitored machine. Each model estimates a different performance parameter from the available operating data. The discharge temperature model shown here is one example. Separate models for stage capacity, interstage pressures, stage gas horsepower, and other parameters provide additional residuals that support differential diagnosis and broader fault coverage.

The flow above shows the physics layer. Machine learning calibrates these models to your specific fleet and projects remaining useful life from observed degradation rates. Contextual AI converts the diagnosis into an inspection or maintenance recommendation grounded in relevant OEM and operating manuals. Read how the full TruPrognostics AI stack works →

Start With The Data You Have

Three tiers, defined by data.

All Novity compressor predictive maintenance models are pre-built to support all three tiers. A site starting at Base can unlock Plus or Premium by adding sensors. No new software, no new integration, no redeployment.

Tier Data Required What It Enables
Base PI / time-series process data (pressures, temperatures, flows, speed) already collected by your SCADA / historian system. Process-domain diagnostics and prognostics. Detects efficiency loss, compression changes, and some bearing conditions. Works with data you already have.
Plus Base data + 1 high-frequency vibration sensor (raw waveform data) on select machines. Precise mechanical and bearing diagnostics on critical machines. Valve leakage diagnosis on reciprocating compressors. Oil whip / whirl detection on journal bearings.
Premium Multiple high-frequency sensors and modalities (e.g., vibration + high-frequency cylinder pressure + crosshead / piston rod sensors). Maximum diagnostic precision: individual valve identification, crosshead wear, rider band wear, piston ring leakage, and more precise efficiency diagnostics.

Capability scales with instrumentation. Day-one value comes from data already in the historian. Investment in additional sensors is targeted at the assets where the diagnostic uplift earns it.

Transparent Compressor Predictive Maintenance

AI that shows its work.

Every TruPrognostics AI model is documented by data tier with explicit capability statements: which fault modes it detects, which it diagnoses, which it forecasts a remaining useful life for, and what data it needs to do each. Reliability and process engineers know what they are getting before deployment begins, not after.

The full fault coverage matrices for reciprocating and centrifugal compressors, mapped to data tier and fault mode, are included in the technical guide.

Field Evidence

40+ days earlier than the OEM alarm.

40+ days
of additional lead time before the OEM alarm.
CustomerU.S. oil & gas supermajor
MachineAriel reciprocating compressor
OutcomePlanned maintenance, no unplanned shutdown

TruPrognostics AI detected a loss of efficiency more than 40 days before the OEM's built-in alarm system would have flagged it. The model returned three plausible fault modes (suction valve leak, discharge valve leak, excessive valve power losses), each with supporting signal evidence and confidence scores.

The machine was serviced during the next scheduled maintenance event, with no unplanned shutdown required.

Compressor predictive maintenance dashboard showing fault diagnosis with multiple ranked fault modes, confidence scores, and projected remaining useful life
What's In The Guide

15 pages. Specific. Technical. Built for reliability and process engineers.

  • How physics-based residuals isolate degradation from operating variation.
  • The detection, diagnostics, and prognostics pipeline with annotated schematics.
  • Process-domain and frequency-domain analytics, side by side.
  • Complete fault coverage matrices for reciprocating and centrifugal compressors, mapped to data tier.
  • The Novity data tier model and exactly what each tier enables.
  • Two field cases, including the 40+ day lead time deployment.
Download the guide

Compressor Predictive Maintenance: A Technical Guide

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