Overview Typical application areas and the added value of predictive maintenance
You are responsible for …
the maintenance of several (complex) machines or systems and are interested in
- replacing defective parts at an early stage,
- reliably determining the remaining service life of parts,
- avoiding unplanned production downtimes, and
- thus saving costs and reliably serving customers?
Then Predictive Maintenance could be the tool of choice to achieve your goals. Or simply find out more about this exciting data science use case and its applications on this page.
Where predictive maintenance delivers ROI fastest in manufacturing
- Rotating components (pumps, motors, gearboxes)
- Machine tools and special-purpose machines (high downtime costs)
- Plants with many sensors/signals (multivariate monitoring)
Starting point Concrete example: A typical predictive maintenance challenge
A large system is equipped with a variety of sensors that record measurements at short intervals. These measurements are expected to provide early information if an unusual condition occurs in the system. Such fault conditions should be detected and addressed early. It is important to note that sensor values often depend strongly on external conditions and on each other. What maintenance work is necessary and when should it ideally be carried out?
What to do Solution: Multivariate Monitoring of Sensors
Multivariate monitoring of sensors supports the detection of fault conditions. This means, in particular, that not only the behavior and progression of individual sensors are decisive, but also how different sensors behave in relation to each other. In the event of unexpected sensor behavior, for example when certain thresholds are exceeded, warning signals can be issued automatically. A major advantage is that such critical thresholds can be determined using data analytics and do not have to be specified by experts. This is particularly advantageous in the case of complex interactions between sensors.
Through these predictive analyses, maintenance work can be planned early. A user dashboard enables the display of signals as well as the tracing of the cause or location of the fault. By providing feedback on the reported signals, users enable the underlying artificial intelligence methods to continuously learn and become even more reliable over time in recognizing and reporting unusual behavior in the system.
If faults occur frequently, machine learning methods can help to identify the patterns and correlations that lead to the respective faults. Not infrequently, such correlations become visible for the first time in this way and can be taken into account in the future. This means that action only needs to be taken when the wear and tear of components or machine parts actually becomes acute and not, for example, just because a certain period of time has passed since the last replacement. This saves time and effort.
Further information Video Material on the Topic of 'Predictive Maintenance'
A deep dive into the data science use case “Planning Maintenance Activities with Data Analytics” using the example of wind turbine monitoring is provided by the video "Planning Maintenance Activities with Data Analytics".
FAQ
How much data do I need to get started?
In many cases, you can get initial, useful results with a few weeks to a few months of sensor data – as long as it contains representative “normal operation” (different loads, speeds, ambient conditions, shifts, etc.). With more history, the model becomes better at recognizing longer-term trends and seasonal patterns and distinguishing true anomalies from normal variation.
If you have little or no labeled failure history, that’s usually still fine: many Predictive Maintenance projects start with anomaly detection that learns the normal behavior (including how sensors behave together) and flags deviations early. That multivariate view is especially important in complex machines where signals influence each other.
How long does a pilot take?
A pilot is typically measured in weeks, not quarters. A common structure looks like this:
- Week 1–2: Scope & success metrics (what counts as an actionable signal?) and a quick data check
- Week 2–4: Data integration + baseline monitoring (dashboards, signal health, first anomaly signals)
- Week 4–8+: Validation with your experts/operators, tuning alert logic, and turning insights into recommendations (what to inspect, when to schedule maintenance)
The exact timing mainly depends on data access/integration (how quickly you can reach the sensor streams) and how fast you can validate alerts with domain knowledge. We are happy to look more closely into your case and give an estimate.
Edge or cloud – what would you recommend in Predictive Maintenance use cases?
In practice, the best results often come from a hybrid approach:
- Edge is great when you need low latency (near-real-time reactions), limited connectivity, or want to keep raw data local. It’s often used for data acquisition and lightweight scoring/monitoring close to the machine.
- Cloud is ideal for model training, fleet-wide learning, deeper analytics, and cross-asset comparisons, especially when you want to scale across many machines or sites.
If your goal is early warning and operational simplicity, start with a setup that’s easy to run: edge for stable data collection, cloud (or a central server) for continuous improvement and reporting. The right choice depends on your IT/security requirements, connectivity, and the criticality of response times. Let’s find out together what the best solution is in your particular case.
What if sensors are ‘difficult’?
“Difficult” sensors are common: noisy signals, missing values, drifting baselines, inconsistent sampling rates, or strong dependency on external conditions (temperature, load, product mix). The good news: these challenges are exactly why multivariate monitoring is so valuable. Instead of judging one sensor in isolation, the system learns how signals behave in relation to each other, which often makes anomaly detection more robust in complex settings.
A practical way to start is:
- begin with a subset of the most reliable or informative sensors,
- perform a data quality pass (gaps, outliers, resampling, calibration events), and
- iterate using operator feedback to reduce false alarms and improve relevance.
Even when sensors are imperfect, you can often still achieve meaningful improvements because the objective is not “perfect measurement,” but earlier, actionable indication that something is moving away from normal behavior.
Blog Articles on the Topic of ‘Predictive Maintenance’
AI in Mechanical Engineering: 5 Use Cases for Production and Service
Five practical AI use cases that measurably improve production and service – from visual quality inspection to an AI maintenance assistant.
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We explain how the modern theory of copulas can be applied to allow for better multivariate monitoring of processes.
discover now >Statistical Monitoring of Wind Turbines
The condition of wind turbines can be monitored based on a statistical framework to allow for accurate and timely detection of abnormal behavior.
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