Would you like to be able to look into the future and spot downtime before it occurs?

If you want to keep your manufacturing machinery running optimally, the answer to that question is probably yes! And the good news is that this is exactly what you can do with Predictive Maintenance.

 

Predictive maintenance was first introduced in highly complex industries where unexpected errors can have severe consequences. Over time, the concept has been carried to other industries. Not least because costs have decreased significantly in recent years. Sensors, data storage and digital technologies have become less expensive, making it possible for even small companies to get started.

 

Approaches to maintenance

Basically, there are four different approaches to maintenance. To give you an understanding of why Predictive Maintenance is considered a game changer in the manufacturing industry, a presentation of the different approaches is in place.

 

Reactive Maintenance is the old-fashioned way of performing maintenance. It is based on the principle “don’t fix it if it ain’t broken” meaning that equipment is allowed to run until it breaks down. There are many disadvantages to this approach. The main ones being the unexpected downtime and costs caused by long waiting times for personnel and spare parts.

 

Preventive Maintenance is based on experience with the machinery and component manufacturer guidelines. Maintenance is performed at regular intervals – e.g. after 10,000 operating hours – and often, a pre-defined number of components are replaced at each maintenance interval. As a result, you may end up replacing or servicing perfectly good parts. In addition, there is a risk that the maintenance personnel causes damage to the machinery which in turn will result in even more downtime.

 

Condition-based Maintenance combines Preventive Maintenance with collection of relatively simple production data. By combining parameters such as the time since last maintenance, production cycles per day and sensor readings, the maintenance schedule can be based on the actual condition of the machinery. The advantages are reduced downtime and costs as maintenance can be planned more precisely.

Predictive Maintenance takes Condition-based Maintenance a step further. It is based on real-time monitoring of machinery, collection and storage of big data and state-of-the-art analytical methods. Used in the right way, this will let you know exactly what needs to be fixed or replaced and when. Downtime and availability of personnel and spare parts can be planned in detail, and all unnecessary work and replacement of spare parts is avoided.

 

How does Predictive Maintenance work?
On a high level, Predictive Maintenance is comprised by three layers. And in order to implement it, you need a solution that can bring these three layers together in real-time and in an automated way.

The first thing you need is data. Data is comprised by operational data retrieved from e.g. PLCs and sensors, internal data retrieved from maintenance logs, procurement systems etc. and external data such as weather or other types of environmental data.

Secondly, your Predictive Maintenance solution must include data storage and advanced predictive models that analyze the collected data. This analysis will detect irregularities or potential failures and can deliver diagnostics of the factors causing these issues.

Finally, Predictive Maintenance is about using these insights and analytics to predict events and to get recommendations for the next best action for keeping your production running. By continuously collecting and analyzing data and evaluating the effect of failures and remedies on the entire production chain, your Predictive Maintenance tool will keep getting better. 

 

Predictive Maintenance – go or no-go?
Predictive Maintenance is truly transforming current maintenance processes. The insights gained by collecting, storing and analyzing all kinds of data and combining these insights with learnings from continuous improvements are unprecedented. And the immediate gains in the form of reduced costs for maintenance and spare parts, increased capacity and production output as well as improved production quality are immense.

However, you do not have to go all in with Predictive Maintenance from day one. If you start by defining your current approach to maintenance, you can slowly move towards a more digital approach, e.g. by starting to collect and store data from your equipment.

To do this, investing in an IIoT platform is a good starting point. Make sure to choose a solution that fits your current maintenance approach, but also one that can be extended and customized as you continue the journey towards Predictive Maintenance.

 

 

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