Machine learning
What is machine learning?
Machine learning is mathematics, statistics and probability analyzed on a large scale using computers. An operations manager or executive with 30 years of experience has insights and intuition that help them make business decisions. This is exactly what computers can replicate on a large scale using machine learning.
Business and machine data is fed into an algorithm to utilize the same insights as seasoned professionals, but the machine learning algorithm makes decisions exponentially faster by using real-time inputs. This allows the algorithms to make connections that humans never could.
With the help of machine learning, executives gain deep insights into their company's production data and operations to make better decisions.
What is the biggest misconception about machine learning?
One of the biggest misconceptions about machine learning is the amount of resources required to get started. In reality, you don't need a team of PhD data scientists or a million-dollar budget to get started. Many small to medium sized businesses assume they are too small to benefit from machine learning, but this is far from the case.
The opposite is true. Data is the most important thing in machine learning, and an effective machine learning or artificial intelligence strategy is achievable even without in-house data scientists. With the right data collection, every company can benefit from machine learning.
How does machine learning fit into Industry 4.0?
Machine learning and AI are necessary for the realization of Industry 4.0. This is where the rubber meets the road. All the efforts companies are making to develop data management practices are being applied to the operation of machines on the factory floor to drive machine automation and business decisions.
The use of machine learning for predictive maintenance and operational intelligence is the practical realization of Industry 4.0, with algorithms helping companies predict downtime, optimal runtime for spare parts inventory data and other smart data flows on the factory floor. These are real-world applications of the smart factory and Industry 4.0.
What is predictive maintenance?
Predictive maintenance is a specialized implementation of machine learning that uses past data to accurately predict machine failures, downtime and maintenance schedules. Historical data from various aspects of a machine's operation is fed into machine learning algorithms to predict when parts of a machine will fail. With the right data, predictive maintenance can determine within a few hours when a machine will be out of service.
Predicting when a machine will fail provides plant managers with the information they need to limit the cost of machine downtime. They can pre-order parts and schedule maintenance to limit downtime for key machines on the factory floor.
How much money can predictive maintenance save?
Predictive maintenance can save companies tens of millions of dollars. When a key machine on the factory floor breaks down, the cost to the entire supply chain can be enormous. It's not just the production of that one machine that costs money, but also every machine downstream. Then there are the logistics costs and other impacts on the company as a whole.
Let's take an industrial printer as an example. We're not talking about an inkjet printer under your desk, but a multi-million dollar machine the size of a room or even a soccer pitch.
If an industrial printer unexpectedly breaks a $100 belt and the maintenance department doesn't have a replacement belt in stock, it can cause the machine to be down for hours or even days. If the industrial printer is the first step in a factory with many machines that need the output of that printer to operate, the impact can be huge.
To take it a step further: If that factory supplies several other factories with output materials, the cost increases exponentially. Not to mention the other costs associated with a delay in production.
What is a concrete example of predictive maintenance in a factory environment?
If the industrial printer is part of a machine learning strategy for predictive maintenance, the historical data collected on the machine can provide an accurate prediction of when the $100 belt will fail. This prediction helps to have the right spare parts in stock and ready before the equipment fails.
Instead of the machine failing unexpectedly, the maintenance team can schedule maintenance at an optimal time that has the least downstream impact. A technician can replace the $100 belt in a matter of minutes, averting disaster.
Without predictive maintenance, a $100 belt can cost a company $100 million. This example may sound extreme, but the implications are worth exploring when the cost is in the thousands.
What kind of data is needed for predictive maintenance?
Before you start a predictive maintenance project, you should first be clear about the goal. It's not about collecting data on everything and trying to make sense of it after the fact. This type of information can be difficult to analyze and use in the future.
By starting with the end goal, you can work backwards to find exactly the type of data needed to realize that goal. For example, if a particular industrial printing machine is causing a lot of problems and costing the company a lot of money, you should start with the goal of improving the uptime of that printer. Using machine learning and predictive maintenance, the business team can target this industrial printer and start collecting key data (e.g. temperature and vibration data) in the problem areas to reduce the cost of downtime on this important piece of equipment.
Kurvv for predictive maintenance
Jeff Croft, the co-founder and Chief Revenue Officer of Kurvv.ai, contributed his expertise to this article.
Kurvv is the force multiplier for your digitization efforts. The company focuses on machine learning for predictive maintenance and operational intelligence.
Machine learning is revolutionizing the manufacturing industry by enabling companies to gain deep insights from vast amounts of data, leading to smarter, faster decisions and more efficient operations. This technology is particularly important for predictive maintenance, where it can significantly reduce downtime and save companies millions by predicting machine failures before they occur.
For engineers using platforms such as 3Dfindit, access to accurate and high quality CAD data is crucial for adapting to Industry 4.0 standards. Accurate component data not only increases the effectiveness of machine learning algorithms, but also ensures that your designs meet the demands of the modern smart factory.
