Predictive Maintenance

What is Predictive Maintenance?

Predictive maintenance (PdM) is a maintenance strategy that uses sensor data, trend analysis and often machine learning to forecast when a machine component will fail, so that maintenance is carried out exactly in time: neither too early nor too late. The European standard EN 13306 describes PdM as a form of condition-based maintenance that acts on a forecast of degradation, not just on the current measured value. As a result, you get fewer unplanned failures, spare parts can be ordered in good time and assets last longer.

Predictive maintenance combines real-time and periodic measurements, historical data and analytical models to predict future failures before they affect production.

🧠 How does predictive maintenance work?

A PdM system always runs through the same cycle. The standard ISO 13374 divides it into six functional blocks: data acquisition, data manipulation (signal processing), state detection, health assessment, prognostic assessment and advisory generation.

1. Data collection

• Continuously via permanently mounted sensors: vibration, temperature, pressure, current, acoustics
• Periodically via measurement routes with handheld vibration meters, thermal imaging cameras or oil samples
• Historical and contextual data from SCADA, MES, the CMMS or a historian

2. Analysis and models

• Thresholds, trends and anomalies
• Advanced algorithms or AI models for pattern recognition

3. Prediction

• When is the failure likely to occur (remaining useful life)?
• Which component and which failure mode are involved?
• How much time is left before action is needed?

4. Action and planning

• Automatically creating a work order in the CMMS
• Showing a warning on the HMI or in SCADA
• Ordering parts and scheduling the work in a planned shutdown

🔄 What is the difference between maintenance strategies?

Predictive maintenance is one step on the maintenance maturity ladder. Each strategy has its own trigger:

This makes the difference between condition monitoring and PdM clear: condition monitoring is the measurement technique, condition-based maintenance responds to a crossed limit, and predictive maintenance calculates when that limit will be reached. Reactive maintenance remains sensible for cheap, non-critical or redundant components.

📉 What is the P-F curve?

The P-F curve shows how the condition of a component deteriorates over time. Point P (potential failure) is the moment an incipient defect becomes measurable. Point F (functional failure) is the moment the component no longer performs its function. The time in between, the P-F interval, is the window you have to intervene.

The more sensitive the measurement technique, the earlier P is detected. In an oil-lubricated rolling-element bearing, a defect typically shows up first in oil analysis and ultrasound, then in the vibration spectrum, later in temperature and finally as audible noise or heat you can feel by hand. As a rule of thumb, choose a measurement interval well below the P-F interval, often half or less, so that you see the defect at least once before it becomes critical.

🔧 Which measurement techniques does predictive maintenance use?

Vibration analysis is the most widely used technique for rotating machinery. ISO 20816-3 (2022), which replaced ISO 10816-3 and ISO 7919-3, evaluates industrial machines above 15 kW with speeds between 120 and 30,000 r/min based on RMS vibration velocity in mm/s, measured on the bearing housing:

These zone boundaries are a general guideline. For predictive maintenance, the trend relative to the machine’s own baseline matters more than the absolute value: a doubling within zone B is already a signal. You recognise the bearing defect itself in the spectrum by characteristic frequencies that follow from the bearing geometry. Many modern variable frequency drives and smart transmitters also provide their own diagnostic information.

🤖 How do you use data, AI and machine learning?

Not every prediction requires industrial AI. The range of methods increases in complexity:

• Rules and thresholds - fixed or adaptive alarm limits per measurement point; simple and easy to explain
• Statistical trending - extrapolating a rising trend to the alarm limit gives a first estimate of remaining useful life
• Anomaly detection - machine learning models learn normal behaviour and flag deviations, even without examples of failures
• Prognostic models - regression and deep learning models estimate the Remaining Useful Life (RUL); ISO 13381-1 sets out the principles for this
• Physics-based models and digital twins - a digital twin combines engineering knowledge with measured data and also works when there are few historical failures

The biggest pitfall is data, not the algorithm. Real failures are rare, labels in the CMMS are often incomplete and sensor data lacks context such as product, load or speed. So store measurements with timestamps and context in a time series database or Unified Namespace, and link every prediction to the work order that follows. That way the model learns from the outcome.

🏭 Where is predictive maintenance used?

• Rotating equipment - motors, pumps, fans, bearings, gearboxes and drives
• Process industry - preventing failures of valves, compressors and sensors
• Energy, chemicals and pharmaceuticals - sectors where maximum availability is required and a shutdown quickly costs a fortune
• Infrastructure - pumping stations, locks and water treatment plants with geographically distributed installations
• Architecture - analysis on edge computing platforms close to the machine, or in the cloud for fleet-wide models

Predictive maintenance is a core element of Smart Industry and Industry 4.0. Less unplanned downtime directly raises the availability factor of OEE (Overall Equipment Effectiveness) and improves metrics such as MTBF and MTTR.

🔐 How do you secure the data flows between OT and IT?

PdM takes data from the OT environment to analytics platforms in IT or the cloud. That creates a new path across the OT convergence boundary, and that path needs the same security as any other connection. Sensor and system data can also be sensitive, because they reveal production volumes and process recipes.

• One-way traffic - let data flow from OT to IT, never the other way; for high requirements use a data diode or the second-channel principle of NAMUR Open Architecture
• Segmentation - separate networks or VLANs and a DMZ between the levels of the Purdue model
• Access Control and encryption - encrypted connections, for example via OPC UA or MQTT over TLS
• Authentication - strong authentication for cloud and edge platforms and for suppliers with remote access
• Monitoring - SIEM monitoring and log capture for gateways and edge devices

Two European rules come into play. Organisations within the scope of NIS2 must also manage supply chain risks, including suppliers that analyse machine data remotely. And since 12 September 2025 the Data Act has given users of connected machines the right to access the data their equipment generates and to share it with a third party. That makes independent PdM services alongside those of the machine builder more feasible.

🧭 How do you implement predictive maintenance step by step?

1. Identify the critical assets - rank installations by the consequences of failure using a criticality analysis or FMEA. Start with the most critical installations, for example the top ten per cent.
2. Choose the technique per failure mode - vibration for bearings and alignment, oil analysis for gearboxes, thermography for switchgear.
3. Record a baseline - capture each machine’s normal values at different loads.
4. Run a pilot - one line or one pump type, with measurable targets for downtime and maintenance costs.
5. Integrate with the CMMS - every warning leads to a work order, and every finding is fed back.
6. Build expertise - train analysts; ISO 18436-2 defines four categories for vibration analysts.
7. Scale up and measure the result - evaluate every quarter and expand step by step.

On returns, the best-known figures come from the O&M Best Practices Guide of the US Department of Energy. For a well-functioning PdM programme it cites average reductions of 25 to 30 per cent in maintenance costs, 70 to 75 per cent in breakdowns and 35 to 45 per cent in downtime, plus savings of 8 to 12 per cent compared with preventive maintenance alone. These are indicative averages; your actual result depends heavily on your starting point.

❓ Frequently asked questions

What is the difference between preventive and predictive maintenance?

Preventive maintenance takes place at fixed moments, for example every 2,000 operating hours, regardless of the machine’s actual condition. Predictive maintenance uses measurements and analysis to determine when a component really needs replacing. As a result, parts are not replaced too early and failures between two intervals are prevented.

Does predictive maintenance always require AI?

Predictive maintenance also works without AI, for example with trend analysis of vibration values against a baseline. Machine learning adds value with many measurement points, complex patterns or estimating remaining useful life. Start with simple, explainable methods and add AI where it demonstrably delivers better predictions.

Which machines are suitable for predictive maintenance?

Predictive maintenance pays off mainly for critical, expensive or hard-to-replace machines with a measurable failure mode, such as pumps, compressors, motors and gearboxes. For cheap, redundant or non-critical components, reactive or preventive maintenance is often more cost-effective. A criticality analysis determines which assets qualify first.

What is remaining useful life (RUL)?

Remaining Useful Life (RUL) is the estimated time until a component no longer performs its function. Predictive maintenance models calculate RUL from the trend in measured values, often with a confidence interval. Based on the RUL, you schedule maintenance in a suitable production stop.

How does predictive maintenance relate to condition monitoring?

Condition monitoring is the continuous or periodic measurement of a machine’s condition, for example with vibration or temperature sensors. Predictive maintenance uses those measurements to forecast when a failure will occur. Without condition monitoring, predictive maintenance is therefore not possible.

What security risks does predictive maintenance introduce?

Predictive maintenance creates new connections between the OT network and IT or cloud platforms, often to suppliers as well. A poorly secured gateway or remote access connection can open an attack path to the control system. So restrict the data flow to one-way traffic, segment the network and monitor every connection.

📌 In summary

Predictive maintenance uses sensor data, trend analysis and machine learning to forecast when a component will fail, so you schedule maintenance exactly in time rather than too early or too late. With the right measurement techniques, a clear P-F approach, integration with the CMMS and well-secured data flows between OT and IT, predictive maintenance is a strong element of modern, data-driven OT strategies.