Manufacturing plants are under constant pressure to improve reliability and decrease downtime. A new generation of predictive maintenance (PdM) is being deployed in factories around the world that uses artificial intelligence (AI) to detect equipment failures before they happen. This AI-based PdM is made possible by the availability of data from sensors and other sources, as well as the development of algorithms that can learn from this data to predict when failures are likely to occur. The benefits of AI-based PdM include improved equipment reliability, reduced downtime, and cost savings. In this blog, we will discuss how AI predictive maintenance is transforming factories.

What is Predictive Maintenance?

Predictive maintenance (PdM) is a type of condition-based maintenance that uses historical data collected from sensors to predict when an equipment failure is likely to occur. The goal of PdM is to prevent equipment failures by scheduling maintenance before the failure occurs. PdM has traditionally been used in industries such as aerospace and defense, where the cost of equipment failures is high. However, with the advent of AI-based PdM, the benefits of PdM are now being realized in a wider range of industries, such as manufacturing, automotive, and healthcare.

How AI is being used in Predictive Maintenance

AI is being used in PdM in two main ways: to collect and analyze data, and to develop predictive models. Data collection and analysis: In order to predict when equipment failures are likely to occur, data must be collected from sensors and other sources. This data is then processed and analyzed to identify patterns that indicate when a failure is likely to occur. Predictive modeling: Once data has been collected and analyzed, predictive models can be developed to predict when failures are likely to occur. These models are typically developed using deep or machine learning. 

The benefits of Predictive Maintenance (PdM)

AI-based PdM offers a number of benefits over traditional PdM, including improved equipment reliability, reduced downtime, and cost savings.

Improved equipment reliability: By predicting when failures are likely to occur, AI-based PDM can help to improve equipment reliability. This is because maintenance can be scheduled before the failure occurs, preventing the equipment from failing in the first place. 

Reduced downtime: It can also help to reduce downtime by predicting when failures are likely to occur. This allows maintenance to be scheduled during times when the equipment is not in use, preventing downtime. 

Cost savings: It saves money by reducing the need for reactive maintenance. Reactive maintenance is when maintenance is performed after a failure has occurred. This type of maintenance is often more expensive than preventive maintenance, which is performed before a failure occurs.

The future of AI Predictive Maintenance

AI-based PdM is still in its early stages of development. However, it is already having a transformative effect on factories around the world. As data collection and processing technology continues to improve, and as predictive models become more accurate, the benefits of AI-based PdM will continue to increase. In the future, AI-based PdM is likely to become the standard for maintenance in factories and other industrial settings. This is because AI-based PdM has the potential to significantly improve equipment reliability and decrease downtime, while also saving money. 

If you’re looking for a Predictive Maintenance solution for your manufacturing business, Averroes.ai offers a range of IoT-enabled products and services that can help you achieve your desired outcomes. Request a demo now.