Artificial intelligence (AI) has numerous fascinating applications. However, using AI for predictive maintenance is arguably one of the most compelling options for the industrial sector. Many AI predictive maintenance solutions can help factory decision-makers save money by detecting problems before they become catastrophic. Plus, well-tuned algorithms for predictive maintenance lets people switch to a more practical schedule regarding upkeep. Rather than following the standardized guidance of an operating manual, predictive maintenance can enable an approach customized to real-world usage.
Algorithms for Predictive Maintenance Handle Massive Amounts of Data
One of the reasons decision-makers are so interested in AI predictive maintenance solutions is because they can process gigantic quantities of information people could not otherwise use to guide their choices.
The oil company Shell is a good example. Leaders there set and met a goal of using AI for predictive maintenance on a huge scale. They wanted to monitor 10,000 pieces of equipment. The associated algorithms for predictive maintenance are impressive, too. More than 11,000 algorithms gather 20 billion rows of data per week from more than 3 million sensors. All that data and algorithmic power deliver leaders more than 15 million predictions each day.
Pumps, compressors, and control valves are among the assets Shell executives chose to oversee with sensors and AI. The associated results allow them to operate more economically while pursuing sustainability. Operations become more streamlined and productive because managers are aware of equipment degradation and other issues before they can cause emergency equipment shutdowns.
Your facility may already collect some data, so you’re off to a good start. However, using AI for predictive maintenance lets you take things to the next level. You will see a tremendous jump in the amount of information available for you to consider when making critical decisions.
AI Predictive Maintenance Can Prevent Costly Production Stoppages
Industrialization has been a key driver in the sales of certain types of equipment. For example, nations such as China and India have been robust markets for air compressors that support automation in factories there. Air compressors are vital parts of automotive assembly, chemical production, food and beverage packaging, and more. However, factory leaders that don’t have good visibility into how their compressed air systems are running could lose money.
One company offers a five-part audit that includes continuous energy monitoring plus AI upon request. This solution can reportedly save clients up to 60% on pneumatic equipment operating costs. People that know about vulnerabilities in compressed air systems or any other industrial assets can develop and deploy algorithms for predictive maintenance that support their findings.
The associated cost savings with predictive maintenance can often be substantial. Statistics from one company that helps businesses develop and implement such solutions were undoubtedly impressive. They showed that while the total amounts saved vary depending on the company type, they were high across the board. For example, an automotive industry manufacturer could save $500,000 per year by achieving a 5% reduction in unplanned downtime. An aerospace manufacturer that boosted its overall equipment effectiveness (OEE) by 5%-10% saved $300,000 annually.
Switching to AI predictive maintenance from more traditional strategies can give decision-makers insights that make them aware of issues sooner and then take proactive steps to mitigate their effects. That capability could mean the difference between a production outage that lasts days or weeks and one prevented because people can get necessary parts and servicing before a shutdown occurs.
AI for Predictive Maintenance Improves Part Lifespan Awareness
Statistics indicate that approximately 55% of businesses implement reactive maintenance strategies. However, decision-makers that only start dealing with issues once they become apparent could take too long to remedy those problems. However, researchers found that depending on AI for predictive maintenance could lead to major improvements by providing more accurate expectations for how long certain components will last. They demonstrated how a commercially available machine learning (ML) solution could predict the remaining useful life of engines. Predictive maintenance algorithms were trained on the component’s current and past performance data. The AI could then use that information to predict how much longer someone could use the part before it causes equipment failure.
One of the surprising and useful takeaways from the researchers’ work was that average engine lifetimes could range from 128 and 362 cycles. They concluded that proceeding with a maintenance plan based on averages was inadequate due to the high variability. The results showed the machine learning models were highly accurate and allowed people to make more confident decisions about when to perform maintenance.
For example, the algorithms might say an engine should work for 50 more cycles, but there’s a 10% chance it will fail after only 30. In that case, scheduling maintenance within the next 30 cycles can give a decision-maker 90% certainty that they’ll prevent engine failure. Such predictions let people maintain their organizational budgets by not keeping too many unnecessary parts on hand.
How Can People Make the Most of AI Predictive Maintenance Solutions?
It takes time and consideration to determine the best ways to deploy AI for predictive maintenance. However, a good starting point is for decision-makers to think about which issues have severely and repeatedly hindered their production. What have the associated ramifications been inside and outside a factory environment?
People must also set a budget associated with predictive maintenance AI. It’s understandable if they want to use the technology on a pilot basis first. If the initial rollout provides the expected outcomes, they can scale up and decide how quickly they want to make the additional investments.
Some providers of predictive maintenance solutions offer plans that allow people to pay on a per-usage basis. Those companies generally take care of everything a company needs to start using the technology, including installing the sensors and training the algorithms. In such cases, the service provider has real-time data about how a piece of connected equipment performs and can alert customers when it’s time to schedule a service call.
There’s no single best way to use AI predictive maintenance to save money and enhance production. However, the tips and case studies here will get you off to a good start.