What Is Predictive Maintenance?


Post By: Ryan King On: 13-12-2019 - Industry 4.0 - Industry Trends - Manufacturing


Monitoring performance in any environment is necessary, but on the factory floor it becomes an issue of far greater importance. Machines inevitably develop faults, and this can lead to knock-on effects including safety hazards, unscheduled downtime and loss of production.

In the past, performance has been monitored and measured in many aspects of production, including the effectiveness of the equipment, output rates and overall productivity. Some of these processes eventually became automated, making them faster, more efficient and more accurate. In recent years, with the rise of Industry 4.0, automation of performance tracking has seen its capabilities expand from simply monitoring and reacting to maintenance issues. With advanced AI and data analysis, many automated factory facilities are able to predict in advance when maintenance will be required.

What Is Predictive Maintenance?

Predictive Maintenance

Predictive maintenance is a strategy which takes a proactive rather than a reactive approach to maintenance. Using an array of sensors, data analysis and CMMS (computerised maintenance management systems), the aim is to predict the point at which a component or item of equipment is likely to fail, in order to schedule maintenance work before such a failure occurs. A successful predictive maintenance programme should reduce unscheduled machine failures to a minimum, increasing equipment reliability and total asset uptime. It should also optimise the amount of time allocated to maintenance tasks, and thereby reduce overall operational costs. In the long run, predictive maintenance aims to maximise production hours and improve a company's bottom line, through a substantial reduction in maintenance costs.

Predictive maintenance is a dream come true for manufacturers, as new tools and technologies continually improve machine performance statistics. Long term savings will come at the cost of some initial outlay, as CMMS and sensory equipment doesn't come cheap. All equipment will need wireless-enabled sensors to produce the data on which the predictive capabilities rely, and there must be a network capable of sharing it. Some form of AI will be necessary to analyse the large amounts of data being generated, as well as the means of storing and retrieving information when necessary. CMMS is increasingly being used for these tasks, setting up and maintaining an information database of all of a company's maintenance operations.




Get More From Rowse Straight To Your Inbox