Feasibility and Impact of Predictive Maintenance

Published by : Industrial Automation

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Among the wealth of use cases for AI & ML in manufacturing, one rises above the rest in terms of feasibility and impact – predictive maintenance. Predictive maintenance addresses the age-old challenge of ensuring maximum availability of critical manufacturing systems.

Amidst the Industry 4.0 revolution, manufacturers are embracing cyber-physical systems and predictive analytics to drive astounding levels of innovation. In fact, across all industries, manufacturing is expected to generate 1/3 of the world’s investment in AI systems in 2019. This is mainly due to the competitive environment and the tremendous opportunity that exists across a wealth of use cases in this industry.

Competition is one reason for the rapid adoption of these technologies. In the current climate, those that don’t evolve quickly will likely find themselves outperformed by aggressive and nimble global competitors. Another reason is the tremendous amount of opportunity. Manufacturers can use AI systems to design smart products, run smart factories, forecast demand, ensure quality, reduce production downtime, and manage supply chain risk.  

Among the wealth of use cases for AI & ML in manufacturing, one rises above the rest in terms of feasibility and impact – predictive maintenance. Predictive maintenance addresses the age-old challenge of ensuring maximum availability of critical manufacturing systems, while simultaneously minimizing the cost of maintenance and repairs.

Manufacturers can use AI systems to design smart products, run smart factories, forecast demand, ensure quality, reduce production downtime, and manage supply chain risk. But one-use case rises above the rest in terms of feasibility and impact – predictive maintenance. It addresses the age-old challenge of ensuring maximum availability of critical manufacturing systems, while simultaneously minimizing the cost of maintenance and repairs.

The Integration of AI in Advance Predictive Maintenance

As connectivity and statistics accessibility grow to be cheaper and more enormous in the industry, many companies are seeking to predictive preservation, or condition-based, renovation, powered via device mastering and analytics.

Predictive Maintenance is largely driven through time-based data. For instance, on a car, protection is determined with the aid of the quantity of time handed or mileage is driven to decide when maintenance needs to be done. These facts also compare how a particular asset is performing as compared to the relaxation of your like assets. Data definitely tells you what would possibly happen. Most upkeep technologies recognition on transporting facts, not aggregating it into real-time analytics. But sending the facts is simply the primary step — what you do with that records is what surely matters. AI and machine learning can help aggregate and make use of your data, faster.