Predictive Maintenance: How to Reduce Machine Downtime

A machine that stops without warning almost always does so at the worst time: orders are piling up, spare parts are not in stock, and technicians have to work overtime. The loss is not just the repair bill, but also lost output, late deliveries, and people waiting for the machine to run again.

IoT-based predictive maintenance changes that pattern. Instead of waiting for failure or replacing parts by the calendar, maintenance follows the machine's real condition. This article covers how it works, which parameters to watch, and how to start.

Three maintenance approaches

• Reactive: fix after failure. Looks cheap at first, but expensive when breakdowns arrive unplanned.
• Preventive: scheduled by time or running hours. Better, but often replaces parts that are still healthy, and failures can still slip through between schedules.
• Predictive: maintenance happens when data shows early signs of failure, just before a part gives out.

How predictive maintenance works

Sensors monitor machine parameters continuously. The system learns each machine's normal pattern, then detects small deviations people often miss, such as bearing vibration creeping up over several weeks. When a deviation crosses a set limit, the system sends a warning so the team can schedule inspection and repair when it disrupts production least.

Commonly monitored parameters

• Vibration, an early sign of bearing problems, imbalance, or looseness in rotating machines.
• Temperature of motors, bearings, or panels, which rises with friction or poor cooling.
• Electrical current, which changes when machine load is abnormal.
• Running hours, as the basis for more accurate service schedules.

For rotating machines, standards such as ISO 10816 are often used as the reference for vibration limits by machine class.

Steps to start

1. Pick the most critical machines: those that stop a whole line when they fail, or that fail most often.
2. Install vibration and temperature sensors at the right points, such as near bearings.
3. Collect normal data for a few weeks as a reference.
4. Set warning limits and who receives them.
5. Record every finding and repair, so models and limits get sharper over time.

INCLUDE's field experience

Predictive maintenance is not new to INCLUDE. InAsset's predecessor, InCrane, was built to monitor port container cranes and won 1st place at IdenTIK 2022 from the Ministry of Communication. The story is in our IdenTIK 2022 press release.

Measuring success

Measure a few things before and after rollout: sudden breakdowns per month, total downtime, and emergency spare-part costs. The comparison shows whether predictive maintenance truly reduces disruption, and helps decide which machines to monitor next.

Mistakes to avoid

• Fitting sensors to every machine at once before the team is used to responding to warnings.
• Limits that are too sensitive, so warnings fire too often and end up ignored.
• Not following up warnings with a physical check and a record of the result.

Reading vibration trends

A single high vibration reading does not necessarily mean a problem; the machine may have just started or its load may be changing. The trend matters more. Vibration rising slowly but steadily over several weeks is the classic sign of a part starting to wear. A sudden jump that persists, on the other hand, can mean damage has already happened and needs checking right away.

Comparing similar machines helps too. If one pump vibrates far more than its twin under the same conditions, it should be checked first.

Technicians still matter

Data does not replace technicians; it points them to the right place. System warnings show which machine needs checking; the technician confirms the cause and makes the repair. Records of those checks then make the system sharper, as limits and patterns are tuned to real plant conditions.

Planning repairs from data

When a warning appears, the maintenance team does not have to stop the machine immediately. The data trend helps estimate how long the machine can still run safely, so the repair can join a planned stop such as a changeover or a weekend. Spare parts can be ordered in advance too, without stocking large amounts all year.

Frequently asked questions

What is the difference between preventive and predictive maintenance?

Preventive follows a time or running-hour schedule. Predictive follows the real condition of the machine from sensor data.

Which machines are best to start with?

Critical rotating machines, such as motors, pumps, compressors, and fans, whose failure stops production.

Do we need AI?

Simple limits already deliver a lot. AI helps catch subtler patterns once there is enough history.

Predictive maintenance with InAsset

InAsset on the INCLUDE platform monitors each machine's vibration, temperature, and running hours, predicts service needs, and uses vibration limits based on ISO 10816. More technical detail is in rotating machine vibration and temperature monitoring and anomaly detection with machine learning.