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How Artificial Intelligence can prevent Machine Failures

Despite rapid advances in technology, we’re still seeing many stories of product recalls, factory failures and other types of maintenance accidents repeated, year after year.

Just last March, Ford recalled almost 5,000 cars locally after seven burst into flames and more than 50 caught fire overseas. While mechanical and technical failures sometimes can’t be prevented, most of the time they can be avoided by being spotted earlier enabling recall before a user-facing incident occurs. Today, next-gen analytics and automation technologies exist that can help manufacturers avoid the burden – and bad publicity – of having to spend millions of dollars dealing with maintenance failure crisis.

Predictive maintenance on the rise

According to reports from McKinsey, predictive maintenance could save global businesses an incredible $630 billion a year by 2025. No wonder why more and more manufacturers around the world are investigating the potential outcomes of investing in this area. Analytics technologies, which were very inaccessible and complicated to use a few years ago, are now becoming more accessible.

Developments in (), machine , the Internet of Things (IoT) and data science mean that humans can now rely on technology to spot equipment failures and danger before they occur.

Predicting equipment failures based on smart analytics techniques can result in introducing timely maintenance which can lead to reducing operation costs and keeping downtime at minimum.Also, being able to properly analyse data from the entire manufacturing chain – including data on how the manufactured product is being used – can reveal meaningful connections and trends. It can not only save time and money, but also streamline the entire production process and boost efficiency at every level.

How it all works

Having -enabled capabilities means that advanced machines in factories – and final products used by consumers, i.e. cars – can collect and monitor various types of data 24/7, through in-built sensors. This data can then be interpreted, in combination with additional contextual information, with the help of data science. By combining data provided by sensors, contextual information coming from within and outside the business, with predictive techniques, manufacturers can capture real-time status of parts and functions, as well as learn from several scenarios’ history to predict and prevent failures. […]

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