Improve reliability by using AI

Improve reliability by using AI

Unreliability in production caused by technical errors or defects can be fatal. But still it’s quite common. Corrosion is in the middle of the most dominant causes of failure. As found by the German Federal Environment Agency, it was among the most frequent reasons for malfunction of production in Germany in 2019.

What happens when reliability is poorly evaluated?

The likelihood of such incidents increases over time as machines age. So chances of failure happening becomes more and more likely. This leads to a higher risk of injuries or property damage, and also an overall less efficient production if failure occurs. To prevent this, it’s not uncommon to use analysis of reliability.

Traditional reliability analysis methods

To stay up to date the methods used to monitor the reliability of production should be appropriate. A comparison of traditional reliability analysis methods and modern methods based on AI was performed by the Institute of Electrical and Electronics Engineers in 2020. They pointed out that AI was able to perform better than traditional methods. Thus more accurate predictions of reliability in production could be shown.

How AI can help gain a competitive advantage

The use of AI for monitoring reliability can help to understand monitored data more accurately and rapidly. Thus, it can save lots of money. On the one hand because production failure can be predicted and prevented. On the other hand, loss in efficiency because of unplanned failure can be minimized. AI can be used in predictive maintenance to work towards delivering reliable and predictable production.

Technological innovation for reliability analysis

Progress in the accuracy of monitoring is inevitable. Thus, it’s better to embrace it, understand it and use it to improve production. AI can help your company in many aspects to rise up to industry 4.0 – the monitoring of the productions also needs to be kept in mind to evolve accordingly.

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