Ask an engineer on a factory floor how they decide when to replace a bearing, and you will usually get one of three answers. The third one is where a lot of the actual machine learning work in German industry happens, and almost nobody explains it in plain language before you land the job.
Three ways to decide when to fix something
Every maintenance strategy sits somewhere on this scale.
Run to failure. You use the part until it breaks, then you replace it. This is fine for something cheap and non-critical, like a light bulb. It is a bad idea on a production line, because an unplanned stop costs far more than the part itself: the whole line waits, orders slip, and someone has to explain why.
Fixed schedule. Also called preventive maintenance. You service or replace a part on a calendar or a usage counter (every six months, every ten thousand hours) regardless of its actual condition. This is easy to plan around, and it is what most factories still do. Its weakness is that it ignores the real state of the machine: you replace plenty of parts that had life left in them, and you can still get a failure in the gap between two scheduled services.
Predict from condition. This is predictive maintenance, sometimes called condition-based maintenance. Instead of a calendar, you use live data from the machine, things like vibration, temperature and electrical current, to estimate how worn a part actually is, and flag it before it fails. Done well, this gives a maintenance team enough warning to schedule the fix during planned downtime instead of an emergency stop.
That third strategy is where a machine learning model earns its place. But the model is the easy part of the job.
What the sensor data actually looks like
Three signals show up again and again in industrial predictive maintenance.
Vibration, captured by an accelerometer bolted to a bearing housing or a motor casing. As a bearing wears, the pattern of vibration changes in ways a trained model can pick up long before the fault becomes audible.
Temperature, read from a thermocouple or an infrared sensor. A motor or a bearing running hotter than its usual baseline is often the first sign that lubrication has broken down or that something is under abnormal load.
Current, read straight off the motor's electrical supply. This one surprises people: the electrical signal carries mechanical information, because a labouring motor draws current differently to a healthy one. It is also the cheapest signal to install, since it needs no sensor anywhere near the moving part itself.
All three arrive as a time series, often sampled thousands of times a second for vibration. That means the raw files are enormous, and most of the real engineering work is turning that raw signal into a much smaller set of numbers a model can actually use, things like how much energy sits in the signal, or where its strongest frequency lands. This step is called feature extraction, and it is where knowing what a worn bearing's vibration looks like matters as much as being able to write code.
The hard part is not the model
Fitting a model to labelled data is the part every online course teaches well. In a real factory, three problems show up long before you get anywhere near that step.
Labels are scarce. Failures are rare, which is good news for the factory and bad news for anyone trying to train a model on examples of failure. Maintenance logs are often messy, and a part that gets swapped out pre-emptively during other work never generates a clean failure label at all.
False alarms cost real money. A model tuned to catch every possible fault will also flag plenty of machines that are actually fine. Each false alarm means an unnecessary teardown or an unnecessary stop, and after enough of those, the maintenance team starts ignoring the model altogether. Balancing missed faults against false alarms, not building the model itself, is usually the actual job.
Normal operation swamps everything else. A machine spends most of its life running fine, so a model can score well on a simple accuracy check while still missing every fault that actually matters. Judging a fault-detection model properly takes more care than reading one headline number off a dashboard.
Where this shows up in an entry-level role
You do not need years of experience to work in this area. Reliability and maintenance engineers increasingly work alongside data teams rather than instead of them. Junior analysts on an industrial IoT team spend real time cleaning and labelling sensor logs before anyone touches a model. Quality engineers now read model output as part of their normal inspection routine rather than relying only on manual checks.
This sits right at the overlap between classical mechanical engineering and data work, exactly the kind of overlap German manufacturing keeps hiring for. For a wider view of where data roles show up across German industry, our guide to data science jobs in Germany is worth a look.
The fastest way to understand any of this is to work with real sensor data yourself before you touch a line of model code. Look at what a healthy trace looks like next to a faulty one, decide what you would actually flag, and only then build the model. For more on what makes a project like this worth showing to an employer, see our piece on machine learning portfolio projects.
