Machine Learning & Predictive Maintenance
What is machine learning?
Machine learning is math, statistics, and probability analyzed at scale by leveraging computers. A plant manager or business leader with 30+ years of experience has insight and intuition that helps them make business decisions. That’s what computers are capable of replicating at scale with machine learning.
Business and machine data is fed into an algorithm to harness the same insights of seasoned pros, but the machine learning algorithm makes decisions exponentially faster by using real-time inputs. This enables the algorithms to connect the dots that humans never could.
Machine learning supplies business leaders with deep insights into manufacturing data and how their company operates to make better decisions.
What is the biggest misconception about machine learning?
One of the biggest misconceptions about machine learning is the level of resources required to get started. In reality, it doesn’t take a team of in-house, PhD-level data scientists or a multimillion-dollar budget to get started. Many small to medium size companies assume they are too small to benefit from machine learning, but this couldn’t be further from the truth.
Data is the most important thing for machine learning, and a valid machine learning or AI strategy is achievable without in-house data scientists. With the right data collection, any company can benefit from machine learning.
How does machine learning fit into Industry 4.0?
Machine learning and AI are required to achieve Industry 4.0. It’s where the rubber meets the road. All the effort companies exert to create data management practices is applied to how machines operate on the factory floor to drive machine automation and business decisions.
Leveraging machine learning for predictive maintenance and operational intelligence are the actionable realization of Industry 4.0. Algorithms help companies with predicting downtime, optimal runtime for parts inventory data, and other data flow intelligence on the factory floor. These are real-life applications of the smart factory and Industry 4.0.
What is predictive maintenance?
Predictive maintenance is a specific implementation of machine learning that uses past data to predict machine failures, downtime, and maintenance schedules with precision. Historical data from different aspects of a machine’s operations feed machine learning algorithms to predict when parts on a machine will fail. With the right data, predictive maintenance can determine when a machine will be out of service within a margin of a few hours.
Predicting when a machine will be out of service supplies plant managers with the information needed 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. If a key factory machine goes down, the costs throughout the supply chain can be enormous. It’s not just the one machine’s production that costs money, but every machine downstream as well. Then there are the logistics costs and further impacts to the business as a whole.
Let’s use an industrial printer as an example. We aren’t talking about an inkjet under your desk, but a multimillion-dollar piece of machinery the size of a room, or even a football field.
If the industrial printer has a $100 belt rip unexpectedly, and the maintenance department doesn’t have a replacement belt in stock, it can cause the machine to go down for hours or even days. If the Industrial printer is the first step in a factory with many machines that require the output from that printer to operate, the ripple effects can be massive.
To take it a step further, if that factory supplies multiple other factories with source materials, the costs go up exponentially. Not to mention the other costs incurred by a delay in production.
What is a specific example of predictive maintenance in a factory setting?
If the industrial printer is part of a machine learning strategy for predictive maintenance, the historical data collected on the machine can enable an accurate prediction of when the $100 belt will fail. This prediction helps with stocking the correct replacement parts and having them ready before the equipment fails.
Rather than the machine going down unexpectedly, the maintenance team can schedule maintenance at an optimal time for the least downstream effects. A technician can replace the $100 belt within a few minutes, staving off a catastrophe.
Without predictive maintenance, a $100 belt has the potential to cost a company $100 million. This example may sound extreme, but the impact is still worth investigating if the costs are in the thousands of dollars.
What type of data is required for predictive maintenance?
Before starting a predictive maintenance project, it’s best to begin with the end goal in mind. It’s not about collecting data on everything and trying to make sense of it down the road. That type of information can be hard to parse and make use of in the future.
By starting with the end goal, you can work backward to find the exact type of data needed to realize that goal. For instance, if a particular industrial printing machine is causing a lot of problems and costing the company serious revenue, start with a goal to improve the uptime of that printer. With machine learning and predictive maintenance, the business team can target that industrial printer and start collecting key data (e.g temperature and vibration data) in the problem areas to mitigate the downtime costs with that key piece of equipment.
Kurvv for predictive maintenance
Jeff Croft, the Co-Founder and Chief Revenue Officer at Kurvv.ai, contributed his expertise to this article.
Kurvv is the force multiplier for your digitalization efforts. Their focus is on machine learning for predictive maintenance and operational intelligence.
Quality CAD component data is essential for enabling Industry 4.0. We help companies both offer high-fidelity CAD models to their customers, as well as help engineers ensure the data they are receiving is accurate and up-to-date.
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