Process Automation · Industrial IoT
Predictive Maintenance: Practical Use in Companies
Machines serviced on a fixed schedule still fail without warning, and healthy parts get replaced anyway. Predictive maintenance uses sensor data and machine learning to service equipment when its condition calls for it. Here is how it works, when it pays off and what the first steps look like.
Key takeaways
- Predictive maintenance schedules service based on the measured condition of a machine instead of a failure or a fixed interval.
- Machine learning detects anomalies, classifies operating states and estimates how much time is left before service is needed.
- The hard part is rarely the model: sensor choice, missing labels, unreliable connectivity and integration decide whether it works.
- A focused assessment of machines, data and infrastructure is the cheapest way to find out whether and where it pays off.
Why maintenance is a business question
Maintenance costs appear twice. Once as spare parts, labour and service contracts, and once as the damage when a machine fails: a stopped line, a faulty batch, a missed delivery, an unhappy customer. How much each weighs depends on the plant, the product and how production is organised, so there is no general figure that fits every company.
What most manufacturers share is the question behind it: how do we keep machines running reliably without paying for service they do not need?
Three ways to maintain machines
Predictive maintenance (PdM) is a condition-driven, proactive approach. It uses sensor data and machine learning to assess the risk of a failure and to schedule service before it happens. It sits next to two older approaches, and each has its place.
| Run-to-failure | Preventive | Predictive | |
|---|---|---|---|
| Type | reactive | proactive, time-driven | proactive, condition-driven |
| What triggers service | the breakdown | elapsed time or operating hours | early signs of a failure in the machine’s data |
| Key metrics | none | mean time to failure (MTTF), mean time between failures (MTBF) | current condition estimated from all available data |
| Main costs | downtime, overtime, large spare parts stock | frequent service, parts replaced while still working | sensors, data architecture and models |
| Spare parts | stock of all critical parts or fast vendor delivery | planned from the service calendar, gaps when something breaks early | ordered according to actual need, can be linked to procurement |
| Fits | cheap, redundant equipment where a failure does not affect quality | operations that can accept occasional interruptions | critical or expensive assets, continuous production, large fleets |
Most companies combine all three. The useful question is which assets justify the effort of predictive maintenance, and the answer is usually the ones where a failure is expensive or dangerous.
How predictive maintenance works
- Collect sensor data: sensors on the machine log vibration, temperature, pressure or other signals.
- Evaluate the readings: a machine learning model trained on historical data checks new readings, flags anomalies and estimates when service is due.
- Alert the right people: when a failure is likely, the maintenance team gets a notification with the affected machine and the reason.
- Store and learn: readings and results go into a database, and the model is retrained on new data so it keeps up with changing conditions.
- Show the trends: a dashboard shows the state of each machine and how it develops over time.
The decision to send a technician stays with people. The system makes sure they decide based on the machine’s actual condition.
Which techniques are used
Different questions need different models. The most common machine learning tasks in predictive maintenance are:
- Anomaly detection: is this machine behaving differently from its normal pattern?
- Classification: which operating state is the machine in, or will it fail within a given period?
- Regression: how much useful life is left?
- Survival analysis: how does the probability of failure develop over time?
Depending on the data, these tasks are solved with logistic regression, decision trees and random forests, clustering or neural networks. The signals come from established condition monitoring methods: vibration, temperature, infrared thermography, oil analysis, pressure and ultrasound. Each can be analysed on its own. The best predictions often come from combining several sensors on the same machine, because the model then finds patterns that no single measurement shows.
What projects show in practice
The textbook workflow assumes clean, labelled data and a stable connection. Real projects rarely start there.
In practice: elevator manufacturer
An elevator manufacturer wanted to send service only when a unit needs it. The first step was knowing where each elevator is and what it is doing. Three constraints ruled out the obvious approach: the shaft blocks wireless signals, the sensor set had to stay minimal to be affordable across thousands of units, and there was no labelled data. With an IoT partner we built clustering models with custom domain logic that detect floor and operating state from raw readings and keep working offline, syncing once the connection returns. This is the foundation for predictive maintenance at fleet scale.
In practice: HVAC manufacturer
A global manufacturer of heating and air conditioning systems had more than 100 devices in the field, each with 50 or more sensors. The data was collected, but nobody looked at it, and failing sensors went unnoticed. We built the data architecture on AWS and the machine learning pipelines that detect anomalies, classify operating modes and find sensors that have stopped telling the truth.
Both projects began with the data layer before any prediction: what to measure, how to handle gaps, and how to learn normal behaviour without labels.
What companies gain
- More uptime: fewer unplanned breakdowns and production stops.
- Longer asset life: problems are addressed before they cause damage.
- Fewer unnecessary visits: healthy machines are not serviced just because the calendar says so.
- Leaner spare parts stock: parts are ordered when they are actually needed.
- Better quality and safety: a machine drifting out of tolerance is noticed before it produces scrap or puts people at risk.
How to get started
- Pick the assets: start where a failure is most expensive or most frequent.
- Check the data: which sensors exist, how often they report, whether failure history is recorded, and where the data ends up.
- Clarify the environment: cloud, on-premise or on the device, and what happens when the connection drops.
- Plan the integration: alerts are only useful if they reach the maintenance system and the people who act on them.
- Prove it on a small scope: one line or one machine type first, then scale what works.
An automotive manufacturer took this route with us: multi-day workshops assessed its Azure and on-premise infrastructure, evaluated which technologies fitted its machines and produced a solution architecture and roadmap for predictive maintenance. Our AI Discovery Workshop follows the same idea, and the energy and IoT page shows more applications for sensor data.
Considering predictive maintenance?
We look at your machines, data and infrastructure with you and work out where predictive maintenance pays off and what a first step looks like.
Request a process analysisFrequently asked questions
A maintenance approach that uses sensor data and machine learning to assess the condition of a machine and schedule service before a failure, instead of waiting for a breakdown or servicing at fixed intervals.
Preventive maintenance services machines after a set time or number of operating hours. Predictive maintenance services them when their measured condition shows that a failure is becoming likely.
Sensor data such as vibration, temperature or pressure, ideally with a history of past failures and service. Where no labelled failures exist, models can learn normal behaviour and flag deviations.
Yes, if it is designed for it. Models can run on the device, store results locally and sync when the connection returns.
With the assets where failures are most expensive, an honest check of the available data and infrastructure, and a small pilot before scaling.