Predictive Maintenance Analytics In Real World

Today when the competition is tough and innovations are frequent, it is indeed a challenge for the companies to keep ahead of its rivals. All these years companies and manufacturing units have been relying upon customer feedbacks and their own market researchers for providing the solutions to their customers. Many companies are still not geared up to deal with big data as they are not conversant with real-time analytics that has now become almost mandatory in the production and manufacturing across industries.

Indeed the big data has emerged as the make or break factor in any industry. If the company is unable to make use of the big data that is now being generated online and on other machine platforms such as sensors, satellite feeds, GPS trackers and so on, they would find it hard to survive in the future as the businesses that are able to successfully marry their production lines with big data real-time analytics and make use of predictive maintenance analytics would be the ones who would rule the world.

The sprouts of this new business environment are everywhere. A number of Companies which are worth their salt are already switching to real-time analytics and making use of predictive technologies. Germany is at the forefront of this new transformation which is now moving toward fourth generation industrial revolution or as it is now often called industry 4.0.

It is the new paradigm in which Cyber-Physical system enabled environment would be the norm and as more software and embedded intelligence go into the making of industrial products and complex systems, the predictive technologies would become even more important as they would make big data work in tandem with the electronic equipment by providing intelligent algorithm for intelligent and superior products.

There is increasing trend toward building smart factories that are provided with control centric optimization and intelligence. This intelligence can be many times efficient if it can interact with different systems in its vicinity that have a direct bearing on the machine performance. This transformation of regular machines into self-aware and self-learning machines by making them talk to their environment improves productivity and overall performance and maintenance management.

Although the autonomous computing methodology has been implemented successfully in computer science, self-learning machines are still far from implementation in current industries. The transformation from today’s status into more intelligent machines requires further advancement in the science by tackling several fundamental issues.

Though the transformation has already begun in a big way but to make it a revolution, the self-learning machines which work with predictive technologies would have to be achieved. This has already been achieved in computer science where several machines become self-aware and are able to take feedback from their surrounding but in the real world, it may take some time though the process is already begun and thanks to real-time analytics and predictive maintenance analytics, are changing the industry and transforming it into industry 4.0!

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