It is not personal but business data !

Preventative Maintenance__#3_   Preventative Maintenance__#2_

Business data is being used in our factories and services industry to decrease maintenance costs. There are two types of maintenance: ‘condition based’ and ‘predictive’. The former tends to be reactive, repairing faults & failures as they occur over time, whereas predictive maintenance is pro-active and relies on performance data.

Predictive maintenance works on real-time anomaly detection algorithms that can pick up tell tale signs of a fault by processing huge amounts of historic and real-time machine behavioural data.

To achieve the optimal balance in maintenance, parts need to be changed at just the right time, before a fault or failure occurs, to avoid further damage and limit the impact of systems downtime and loss of production. Preventative repairs can be scheduled to optimise production or machine output.

Today we have access to so much data, via machine sensors & instrumentation and the ‘internet of things’, which once utilised correctly can be used by the predictive algorithms. This utilisation comes under the banner of ‘data science’, which interprets & processes the data to develop predictive models.

In a nutshell Pedictive Maintenance is a pro-active approach to ensure high machine efficiency & reliability that ultimately saves industry money by the utilisation of performance data.

Hope you have a great month

All the best


Quotation corner:

“I haven’t failed. I’ve just found 10,000 ways that won’t work.”

– Thomas Edison

“No man goes before his time – unless the boss leaves early”

– Groucho Marx


ps  If you have enjoyed the blog please forward to those who might be interested, many thanks in advance, Mike



About Mike Osborn CEng

A Chartered Engineer and member of the Institution of Mechanical Engineers living in the UK with a passion for engineering design, technology and innovation, peppered with a little humour. Presently running a small engineering design & CAD drawing solutions business, serving mainly London and home counties.
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