Preventive, Predictive, or Condition-Based Maintenance: Which Model for French Industry in 2026?

· 9 min read
9 min read
Eziwan Team
Eziwan Team
IoT Infrastructure

The terminology surrounding industrial maintenance is often a source of confusion: predictive maintenance, condition-based maintenance, preventive maintenance, corrective maintenance—these terms are frequently used interchangeably, but they refer to fundamentally different approaches with very different ROIs.

This article clarifies the definitions, compares approaches based on concrete criteria, and helps you choose the strategy best suited to your situation in 2026—whether it’s a small factory with 20 pieces of equipment or a large industrial site with 500+ assets.

The 4 Maintenance Strategies: Precise Definitions​

1. Corrective Maintenance​

Definition: We step in after a breakdown. When the equipment breaks down, we fix it.

Variants:

  • Deferred corrective action: The failure is detected, but the repair is scheduled (non-critical equipment)
  • Emergency corrective action: Critical failure; immediate action required

Typical cost: 4 to 5 times higher than the equivalent preventive maintenance (express parts, overtime, lost production).

When it is acceptable: Non-critical equipment that is easily replaceable, where the cost of monitoring exceeds the cost of failure.

2. Systematic Preventive Maintenance​

Definition: Items are replaced or overhauled according to a fixed schedule (based on time or production intervals), regardless of their actual condition.

Example: Change the oil in a gearbox every 2,000 hours, even if it is still in good condition.

Main problem: 30 to 40% of the parts replaced during preventive maintenance visits are still usable. This results in a waste of both resources AND components.

Typical cost: 2 to 3 times higher than well-calibrated condition-based maintenance.

When applicable: Equipment with well-known and predictable degradation patterns over time, in a regulatory context that requires periodic inspections.

3. Condition-Based Maintenance (CBM)​

Definition: Action is taken when the actual condition of the equipment warrants it, based on continuous physical measurements (vibration, temperature, current, acoustic levels, etc.).

Principle: Every piece of equipment "speaks" through its data. We listen to it.

Example: The bearing is replaced when the vibration exceeds a critical threshold established during commissioning, not on a fixed schedule.

Documented ROI: A 25–55% reduction in unplanned downtime; 10–30% savings on total maintenance costs.

What's needed: Sensors installed on equipment, real-time data collection, configured alert thresholds.

4. Predictive Maintenance (PdM)​

Definition: The date of a future failure is predicted using a mathematical model (statistical or machine learning) fed with historical and real-time data.

Difference from the conditional: The CBM says, "The equipment is in poor condition; take action soon." The PdM says, "The equipment will break down in 3 to 5 days with an 87% probability."

What's needed: At least 12–18 months of historical data per piece of equipment, expertise in modeling, and teams capable of acting on the predictions.

The Reality on the Ground in 2026: True predictive maintenance (machine learning + physical models) is now accessible to large companies and mid-sized enterprises with dedicated data teams. For small and medium-sized businesses, condition-based maintenance offers 80% of the benefits with only 20% of the complexity.

Comparison Based on 7 Criteria​

CriterionCorrectivePreventiveConditionalPredictive
Implementation costVery lowLowMediumHigh
Operating costVery highMediumLowMedium
Technical complexityNoneLowMediumHigh
Data requiredNoneScheduledReal-timeHistorical + RT
ROI timeframeImmediate12–18 months8–14 months18–36 months
PredictabilityNoneGoodGoodExcellent
Suitable for SMEsYesYesYesSometimes

Which Sensors Are Best for Condition-Based Maintenance?​

Condition-based maintenance relies on physical measurements. Here are the most commonly used sensors, ranked by ROI:

1. Vibration (accelerometer)​

What we detect: Rotational imbalance, shaft misalignment, bearing damage, pump cavitation, gear wear.

Applications: Electric motors, centrifugal pumps, compressors, fans, conveyors.

Early warning signs: Increase in the overall vibration level (RMS), appearance of characteristic harmonic frequencies, increase in kurtosis (impulsivity).

Sensor cost: €50 (basic industrial model) to €500 (broadband + ICP).

Time to Failure: Detection 2 to 12 weeks before bearing failure.

2. Infrared/Thermal Temperature​

What is detected: Abnormal overheating of motors, transformers, electrical connections, bearings, and brakes.

Applications: Electrical panels, motors, bearings, cooling systems.

Warning signs: Operating temperature rises above the established reference value.

Sensor cost: €20 (PT100 contact probe) to €2,000 (fixed thermal imaging camera).

3. Electric Current (Current Clamp)​

What we detect: Load imbalance, insulation degradation, abnormal harmonics, difficult startup.

Applications: Induction motors, servo motors, drive systems.

Advantage: No mechanical installation required—the measurement is non-intrusive. Ideal for equipment that is difficult to access.

4. Acoustics / Ultrasound​

What we detect: Compressed gas leaks, electric arcs, bearing degradation under low load, lubrication issues.

Applications: Compressed air systems, high-voltage electrical cabinets, low-speed bearings.

Typical ROI: An ultrasonic compressed air audit typically identifies 15 to 30% of leaks—resulting in immediate savings on compressor electricity consumption.

5. Oil Analysis (In-Line)​

What is detected: Particle contamination, viscosity degradation, presence of water, wear particles.

Applications: Gear reducers, gearboxes, turbines, compressors.

In-line sensors: Enable continuous monitoring without sampling, with automatic alerts for particles or dielectric substances.

Practical Implementation: How to Get Started?​

Step 1: Identify Critical Equipment (Simplified FMEA)​

List your equipment and evaluate each item based on:

  • Criticality (impact of a failure: production shutdown, safety, environment)
  • Failure Frequency (CMMS history, if available)
  • Detectability (Does the failure give advance warning, or is it sudden?)

Focus CBM sensors on equipment with high criticality and progressive failures.

Step 2: Define Key Metrics by Equipment​

For each piece of critical equipment, define which parameters to measure. Example for a centrifugal pump:

  • Vibration (accelerometer on the motor-side and pump-side bearings)
  • Bearing temperature (PT100 or infrared)
  • Motor current (current clamp)
  • Differential pressure (if pressure is critical)

Step 3: Install and Connect the Sensors​

The sensors connect to the Eziwan gateway via RS-485/Modbus RTU for standard industrial sensors, or via 4–20 mA outputs for process transmitters. The gateway transmits all measurements to the cloud platform in real time.

Step 4: Establish Baseline Values​

During commissioning, record the reference values for each parameter on new equipment or equipment in good condition. These reference values are used to:

  • Set alarm thresholds (alarm at +20%, emergency at +50% of the reference)
  • Detect a gradual drift from the baseline

Step 5: Calibrate the alarm thresholds​

Too sensitive = false alarms that discourage teams. Not sensitive enough = missed failures. Calibrating the thresholds requires 2 to 4 weeks of observation under normal conditions to fine-tune them.

Eziwan allows you to adjust thresholds from the cloud platform without on-site intervention.

Step 6: Integrate with the CMMS​

A CBM alarm is only useful if it triggers a planned action in the CMMS. The Eziwan REST API allows you to automatically create a work order in your CMMS (SAP PM, Infor EAM, Hive Maintenix) as soon as an alarm is triggered.

Feedback from the Field​

Cement Plant — Main Grinder Gearbox​

Background: 800-kW gearbox, replacement cost €180,000, delivery time 6 weeks.

Before CBM: 2 catastrophic failures in 4 years. Each failure = 6 weeks of partial downtime = €420,000 in lost production.

After CBM (vibration + temperature + oil analysis): Detection of gradual deterioration 8 weeks before failure. Repair scheduled during a planned shutdown. Cost of preventive repair: €28,000 vs. €180,000 for catastrophic replacement.

ROI: 9 months.

Food processing plant — packaging line​

Background: 12 gearmotors on a critical production line. Preventive maintenance at 3,000 hours (routine oil change + inspection).

After CBM (current + vibration): Of the 12 gearmotors monitored, CBM made it possible to:

  • Prevent 3 unplanned shutdowns
  • Reduce unnecessary preventive maintenance by 40% (oil was still clean according to analysis)
  • Detect 2 alignment issues that were imperceptible to the naked eye

ROI: 11 months.

Conclusion: What Strategy Should We Adopt?​

For an industrial SME (fewer than 50 pieces of equipment): Start with condition-based maintenance on your 5 to 10 most critical pieces of equipment. The investment is affordable, the ROI is quick, and the complexity is manageable even without a dedicated data team.

For a mid-sized or large company: Condition-based maintenance remains the foundation. For the most critical equipment with sufficient historical data, supplement this with predictive models (starting with 2 to 3 priority pieces of equipment).

What we see in 2026: The majority of French manufacturers that have truly improved their maintenance performance have done so using IoT-based condition-based maintenance—not through complex ML projects. The ROI is measurable in less than a year.

FAQ​

What is the difference between predictive maintenance and condition-based maintenance (CBM)? Condition-based maintenance triggers an action when a threshold is exceeded (e.g., temperature > 85°C → immediate alert). Predictive maintenance analyzes trends over time to anticipate deterioration before the critical threshold is reached (e.g., temperature has been rising by 2°C per week for the past 3 weeks → schedule an intervention in 2 weeks). Condition-based maintenance is easier to implement, while predictive maintenance offers a longer lead time for intervention.

What type of sensor is most useful for implementing condition-based maintenance? For motors and pumps: the current sensor (current transformer on the power cable) is the most versatile—it detects overloads, hard starts, and mechanical degradation without requiring any modifications to the equipment. For mechanical transmissions (gearboxes, bearings): the accelerometer vibration sensor. These two types of sensors cover 80% of preventable failures in industry.

Further Reading​

Additional Resources​