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The Future of Maintenance is Now: How Machine Learning is Advancing the Industry

Posted under Blog by Nick Leonard, Growth Marketer

Historically, maintenance has been viewed as a cost center. Organizations were hesitant to dedicate money to maintenance initiatives because every dollar spent on maintenance was considered a profit dollar lost. Despite this, maintenance is critical for asset-intensive companies to be viable in the market. Equipment needs to be maintained for production to continue.

The 21st century has thrown us into a world where maintenance is considered a nuisance and it is often cheaper to replace an object rather than repair it. However, the 21st century has also presented us with advances in technology that maintenance teams could only dream of in the past.

The concepts behind artificial intelligence and machine learning have existed for a long period of time, often showcased in science fiction movies about robots who eventually take over the world. While we aren’t quite at the point of intelligent robots taking over, the application of artificial intelligence and machine learning is beginning to expand across industries.

What is the difference between Machine Learning and Artificial Intelligence?

Artificial intelligence and machine learning are concepts that are often used interchangeably. While they are similar, the perception that they are the same thing can lead to confusion. Simply put, artificial intelligence is the broader concept of machines being able to carry out tasks in an intelligent manner, whereas machine learning is the current application of artificial intelligence where machines are granted access to data and automatically learn and improve from experience without being explicitly programmed.  

Why is Everyone in the Maintenance Industry talking about Machine Learning and Artificial Intelligence?

The maintenance industry poses an especially interesting prospect for machine learning applications. In recent years, asset-intensive organizations have been grasping for ways to change their maintenance processes from reactive to predictive in order to increase asset efficiency, reduce downtime and ultimately save money. Machine learning offers a new, smarter route to achieve these goals.

While the concept of artificial intelligence and its application to machine learning have been in existence for decades, it has only become a possibility in recent years. This is due to:

  • the growth in Big Data
  • the expansion of the Industrial Internet of Things (IIoT)
  • the mass collection of asset sensor data
  • the availability of processing power for analyzing large data sets
  • the increased demand from asset-intensive organizations for advanced predictive maintenance applications.

Machine Learning for Maintenance

Traditionally, we have seen machine learning concepts applied to the transportation and financial industries in the forms of automated cars and fraud monitoring respectively. In recent years, the applications for machine learning have expanded into various industries, including the maintenance industry.

In machine learning, algorithms don’t rely on explicit programming, but instead, improve their performance based on internal data analysis. This means the machine learning application actively observes what is happening and what the outcome is. From this data, the application has the power to form predictions and continually learn and improve based on success and failure tracking and analysis.

When thinking about how this applies to maintenance organizations, machine learning offers the ability to leverage predictive algorithms to optimize maintenance processes, reduce downtime and boost production. In other words, machine learning offers maintenance organizations the ability to gain a deeper understanding of the ins and outs of their physical assets. This enables businesses to adapt and respond to changing dynamics in real-time by detecting patterns and deriving alternative courses of action in the future to prevent similar problems. Machine learning helps to monitor assets, predict problems and change routines in order to help maintenance organizations optimize their maintenance processes.

The rise in machine learning capabilities for maintenance has changed the way maintenance is being perceived. For possibly the first time, organizations are getting excited about the opportunities available to their maintenance teams. Executives are quickly realizing the impact that this concept and its associated applications can have on their ability to optimize their maintenance processes, implement and maintain a predictive maintenance process and most importantly, save money.

As a result of artificial intelligence and machine learning applications, Fortune 1000 organizations are beginning to place a larger focus on, and investment in, their maintenance processes. Combined with next-generation ERP functionality, businesses can build a foundation to lead the way to new business models and highly adaptive processes. It is safe to say the future of maintenance is looking bright thanks to artificial intelligence and machine learning.

To learn more about how VIZIYA is utilizing machine learning, Request a Demo now.

Read Part 2 of the series, The Future of Maintenance is Now: How Automation, IIoT, and Auto Scheduling is driving asset management. 

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