Predictive Maintenance
Evaluate sensor and machine data with AI, identify emerging failures early, and make maintenance plannable — turning unplanned downtime into scheduled maintenance windows.
What is Predictive Maintenance?
Predictive Maintenance is a condition-based maintenance approach that continuously monitors machines based on their operating data. Sensors and IoT provide time series such as vibration, temperature, and current, supplemented by signals from PLCs and controls as well as job data from the MES. Models learn normal behavior and identify deviations indicating wear or emerging failures. This allows maintenance to be scheduled at the right time — rather than on fixed intervals or only after a breakdown.
Unplanned downtime is expensive
In production, every unplanned machine failure causes follow-up costs — from production loss to emergency orders and customer delays.
Reactive maintenance
Waiting until after a failure to act creates downtime, follow-up damage, and rushed repairs under time pressure.
Fixed maintenance intervals
Set intervals either waste effort on healthy equipment or arrive too late for degrading components.
Unused data
Controls and sensors continuously generate data — but without analysis, warning signals before failure go unnoticed.
From data signal to planned maintenance
Predictive Maintenance combines existing machine data with pattern recognition — turning raw data into concrete action recommendations for maintenance.
Collect data
Sensor and machine data like vibration, temperature, pressure, or current are gathered — supplemented with historical maintenance and failure records.
Recognize patterns
Models learn the system's normal behavior and identify deviations indicating wear or emerging failures.
Warn early
Anomalies are reported before they cause failure — indicating affected component and urgency.
Plan maintenance
Maintenance is scheduled into a planned window, spare parts prepared, and the intervention coordinated — rather than reacting after the fact.
Less downtime, plannable maintenance
- Unplanned downtime and costly emergency repairs are reduced
- Maintenance is scheduled into planned windows rather than triggered reactively
- Follow-up damage from delayed intervention is prevented
- Spare parts and personnel can be planned proactively
- Equipment availability and production planning become more reliable
- Maintenance effort aligns with actual equipment condition
- Existing machine data finally becomes actionable
- Maintenance expertise is supported by data
Connected to sensors, PLCs, MES, and ERP
Predictive Maintenance begins at the OT and control level: sensors and IoT modules provide measurements, PLCs and controls — such as Siemens TIA or Beckhoff — provide machine and status signals, a historian collects time series data, and the MES provides operational context. Only the bridge from this level into the ERP and maintenance system turns a warning into a scheduled maintenance action with spare parts allocation. Given the diversity of control and ERP systems on the market — SAP, Microsoft Dynamics, proALPHA, Infor, abas, and others — there is no standard off-the-shelf solution.
That is why for the project phase we integrate an experienced Interim Manager directly into your organization to safely manage sensor, control, and historian integration through to ERP and maintain momentum.
How integration works- Integration with sensors, IoT, and PLCs/controls (e.g. Siemens TIA, Beckhoff)
- Tapping into historian and time series data
- Linking with MES and maintenance planning
- Handoff to spare parts and master data in ERP
- Start with critical systems, then scale
Answers about Predictive Maintenance
What is Predictive Maintenance?
Predictive Maintenance evaluates sensor and machine data continuously to identify wear and emerging failures early. Instead of maintaining on fixed intervals or only after a breakdown, maintenance is scheduled at the right time.
What sensor data is needed?
Typical sensor data includes vibration, temperature, pressure, current, or speed, supplemented by machine and control signals from the PLC and historical maintenance and failure records. Time series data — the progression of these values over time — is especially valuable. Which signals are relevant is determined for each system together with maintenance.
Can existing PLCs and controls be integrated?
Usually yes. Controls such as Siemens TIA or Beckhoff already provide many status and process signals that can be read. These are often collected in a historian as time series and supplemented with MES data. Which interfaces and approach makes sense depends on your OT environment and is coordinated in the project with maintenance and IT/OT.
Do I need new sensor hardware?
Often existing controls and sensors already provide usable data. Where gaps exist, targeted retrofitting with additional sensors or IoT modules is possible. It makes sense to start with selected critical systems before rolling out more broadly.
How are historian and time series data used?
Predictive Maintenance lives from the temporal progression of measured values. A historian stores signals from sensors and controls as time series. Models learn a system's normal behavior and identify deviations that indicate wear or emerging failures — often well before a classical limit triggers.
How does this reduce downtime?
By identifying emerging problems before they cause failure, maintenance can be scheduled into planned windows. Unplanned downtime, follow-up damage, and emergency spare parts orders are reduced.
Does the solution actively intervene in machine control?
No. Predictive Maintenance is designed for detection and warning: it reads data and reports anomalies with indication of affected component and urgency. Decisions about intervention remain with maintenance and production — maintenance is scheduled into a planned window.
What does implementation cost?
Flat-rate pricing cannot be responsibly quoted because effort depends heavily on your system and OT environment — such as data availability and quality, control types, existing historian, and MES/ERP integration. Starting with a few critical systems makes sense. In the initial consultation, we define the scope and estimate effort for the first phase.
Which systems deserve priority?
In a free initial consultation, we identify critical systems with the greatest impact and assess what data is already available. Our ROI Calculator also provides initial guidance.
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