In today’s data-driven world, industrial companies have more data to work with than ever before, and one way that businesses are harnessing the power of that data is through predictive maintenance. The rise in industrial artificial intelligence, which can be applied to predictive maintenance, along with industrial IoT has revolutionized aviation, finance, energy, and more.
Using AI, companies can analyze massive amounts of data from the manufacturing process. Applying machine learning allows for predictions of future actions, alerting companies to what may go wrong, when, and why. This process works through algorithms that learn a machine’s behavior, and use that information as a baseline to diagnose and alert deviations in real-time. By inputting historical data, such as temperature, pressure, etc., the output provides information on the desired result, such as machine failure warnings. Companies utilize supervised and unsupervised machine learning depending on the scenario, data available, and asset failure rates.
Industrial manufacturers are flocking to predictive maintenance due to its cost and productivity savings. Poor maintenance can reduce a plant’s productivity by five to 20%. Plus, studies found that unplanned downtime costs industrial manufacturers roughly $50 billion each year.
Artificial intelligence takes the guesswork out of preventative maintenance. Instead of replacing a part every six months, machine learning algorithms tied to sensors that measure the depreciation of the part can predict when a part needs replacement, which could be six months, four months, or one year.
Here are more use cases for enterprises that are harnessing the power of AI in predictive maintenance.
A computerized maintenance management system (CMMS) simplifies manufacturing maintenance. CMMS gives real-time insight into the state of machines, inventory levels, schedules, and more. The next step for CMMS, a step that many enterprises have taken, is adding technologies such as artificial intelligence.
AI allows for continuous monitoring, which includes anomaly and failure detection. The system reads the data patterns and predicts operation failure, sending alerts when necessary. These systems read and detect patterns that humans might miss or overlook, and they work around the clock.
Monitoring asset performance and predicting when a part might fail is important for many industries but vital for some. Consider aviation or food manufacturing; if a part breaks down while the airplane is the hanger, that causes annoying delays for passengers. But if a part breaks while in flight, this could have devastating consequences. Halts in food production may lead to spoiled food going on the marketplace. Or, if a part breaks, pieces of the machine may end up in the food, endangering those that consume it.
Much of inventory management is already automated, with spare parts ordered every few months, depending on the machine. This is part of preventative maintenance, or preventatively changing a part by performing maintenance checks every six months or annually. But for many manufacturers, this can result in overspending on parts, as perhaps a machine didn’t need a replacement part after six months.
By predicting asset failures instead of automatically ordering more parts, companies can optimize their inventory and stop spending on unnecessary parts. Most large scale manufacturers have a robust spare parts inventory, but AI can predict whether or not these spare parts are even needed.
Automated anomaly detection provides useful data that can predict which parts will fail, when, and why. Consider having decades of that data. Companies will understand which parts are needed, when, or they can make more significant changes to machines that fail consistently at a certain point. This process optimizes inventory management by always having the part on hand when necessary, instead of over-ordering or not having the part at the right time, causing downtime.
Holistic approach for total productive maintenance
The goal of Total Productive Maintenance (TPM) is no downtime, accidents, or delays in the manufacturing process. Using an eight-pillar approach, TPM covers all maintenance aspects of industrial manufacturing. While TPM typically relied on planned maintenance to achieve Overall Equipment Effectiveness (OEE), new technologies have begun to make TPM a reality in the manufacturing industry.
Four pillars in particular, “autonomous maintenance,” “planned maintenance,” “quality management,” and “new equipment management,” all truly benefit from artificial intelligence. This technology allows for automated and self-maintenance and converts planned maintenance to predictive maintenance. AI ensures quality control over equipment and optimizes inventory. Predictive maintenance can dramatically improve OEE, a crucial KPI in the manufacturing industry.
A safer environment
While many companies have implemented predictive maintenance in silos, this will likely become a more holistic approach in the industry. And for good reason. One benefit of predictive maintenance is improved safety for consumers and those working at the plants. This approach reduces accidents, which can harm those working with the equipment. It’s no surprise then that Market Research Future forecasts that the global predictive maintenance market will grow to $6.3 billion by 2022. And with more data than ever before, enterprises using AI in predictive maintenance stand to benefit greatly. For more information on how to accelerate AI adoption in the manufacturing process, contact Quickpath today.