Learning from your machines rather than machine learning makes a difference to machine builders

IXON says that the insight gained by learning from a machine, especially from parts that never fail, allows components to be downgraded to make more profit.

The industrial service solution company believes there is a good chance that many machine parts are too good and advises engineers to tap into this new business model and learn from their machines in the field to make major savings. What about learning from your machines to create new revenue streams, it asks.

New machines are like a new car, they have failures in the beginning and need to be optimised. By learning from your machines in the field you can eventually prevent parts from breaking down or discover other interesting insights. Most machine builders have very effective quality assurance systems and based on root cause analysis the failures and weak points on the equipment are found and corrected.

Machine builders have systems in place to improve the quality of their machines through their lifetime based on failures and corrective measures. These are also passed on to their R&D department, so similar faults on future equipment can be avoided. But this method doesn’t take account of discovering that some parts never fail or that they are running well below expected design criteria.

It’s often the case that design criteria and safety margins are too high, as users try to avoid failures in the field. However, this feedback cannot be received without looking into any machine data.

With this insight, companies can downgrade the machine parts that are ‘too’ good and lower design criteria’s or safety margins in any redesign. This can lead to major savings without running any risk of quality loss.

IXON invites machine builders to discover if they can save on certain parts and has put together some pointers about how to build a ‘machine learning’ strategy. Learn from historical data from machines in the field in a practical and effective way with very limited investment and fast ROI and build your own strategy from this new business opportunity.

The following list could help:

1) Starting point - current equipment performance - needed data

The first phase is to analyse the current part practices from your machine park. Determine which parts are replaced regularly and which aren’t. Establish if any data, with a clear statistical relation between the given value and the condition of the part in question, is being captured.

Some data will have a clear correlation between standard PLC variables and parts, others might need additional variables being defined and collected, or even additional sensors being used. Via an edge gateway the PLC data can be transmitted in bulk from machines in the field to the cloud.

2) Prototyping - testing

Next step is to find out what the minimum and maximum values are for your safety margins. Analyse the performance of your machine parts and determine which parts need improvement and which parts can be downgraded.

Carry out in-house tests on the parts in question to strengthen or refute the hypothesis. Use these insights in the design of your next machine.

3) Business model roll-out

Define how to lower your safety margins and implement this in your next redesign to make major savings.

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