What are the primary challenges that industries faces when it comes to predictive maintenance? And how will fully-automated predictive maintenance improve services to clients and a companies income?

Maintenance done at the right time reduces costs. Regular preventative maintenance can extend a machine’s life by a number of years. However, these savings can be increased with targeted predictive maintenance, based on predictive analytics. No more guesswork involved, an engineer can actually see with certainty which parts will need replacing and when.

Machines are all connected to the IIoT (Industrial Internet of Things), these symptoms of failure are scattered over millions of data points and come from various different sensors at different times. Finding the critical signals amongst millions of scattered data points is not humanly possible. While many factories now have teams of data scientists in place to analyse this data and diagnose issues, current manual methodologies are simply not getting consistent results.

Unlike generic data patterns, machine data patterns are constantly changing, so prediction models become outdated quickly and because failure occurs at different points in the process, monitoring a single sensor on a machine doesn’t give a complete picture.


Revolutionising Industry by Automating Predictive Maintenance

Machines are able to analyse thousands of data points and variations a second and are far better able to output prediction models. Through machine learning, they can accurately and consistently predict future failure.

By organizing months, even years’ worth of data, machines can also build thousands of effective data models running together in order to deliver the optimum version. By doing this, machines are able to make far more accurate predictions by successfully segmenting data, comparing and contrasting current real-world output comparing it to the historical records.

The benefit is, by using predictive maintenance and machine learning OLSPS Analytics is able to dramatically improve efficiency and lower costs in factories and industrial environments.


Other Considerations for the Manufacturing Industry

There are other more wide-ranging suggestions for the manufacturing industry:

  • Part harmonization

For example, through machine learning predictive maintenance, predictive models are able to show which parts will be the first in line to fail and what will need replacing in the next six months. Allowing teams to better manage inventories and stockpile the right replacement parts.

  • Cost-benefit analysis

Teams are able to better understand the cost and risks of not performing maintenance on time.

  • Warranty Claims

Analytics can help better define warranty boundaries by modeling usage patterns, companies can better assess their warranty offerings.

  • Risk Mitigation

Manufacturers can also avoid paying penalty fees by fixing issues when they are notified of future failures.

Analytics: The Brainpower Behind Smart Manufacturing