Predictive maintenance technologies have applications in a range of sectors, including in manufacturing. It makes manufacturers more productive, improves the service they offer to customers, and increases profits.
Plus, as the technologies improve, particularly in relation to machine learning algorithms, the benefits to manufacturers will increase.
The Traditional Maintenance Approach
Manufacturers in all sectors seek to optimise OEE – overall equipment effectiveness. It is a metric that takes into account three main factors – availability, performance, and quality. To optimise OEE, manufacturers work to improve all these factors. Predictive maintenance has a considerable impact on the first – availability.
Availability is the amount of time a machine on a production line is available for manufacturing operations. There are a number of reasons why a machine may become unavailable, including for cleaning, batch changeover processes, and quality inspections.
However, two of the most important and costly reasons for machine unavailability are planned maintenance and unplanned stops due to breakdowns and equipment failures.
Manufacturers have traditionally worked to minimise the impact of planned and unplanned downtime due to maintenance and equipment failures through planned maintenance schedules. By planning maintenance work, manufacturers can help to prevent breakdowns. They can also schedule the work when it will have the least impact on production output.
Moving Beyond Planned Maintenance
While planned maintenance does make machines available for longer, improving OEE, it is far from perfect. One of the main reasons for this is the generalised nature of the schedules that manufacturers follow.
Maintenance schedules typically follow the advice provided by the manufacturer of the equipment. However, that manufacturer cannot possibly take into account the many variables that exist in every location where their equipment is in operation.
From facility to facility and company to company, the products being manufactured will be different, the tooling will be different, and the materials being used will be different. Output rates will differ too, as will things like temperature and humidity, all of which can impact the performance of a machine.
So, even with rigorously adhered to maintenance schedules, breakdowns still occur.
Unplanned downtime like this is costly because of the loss of production time, but it can also result in more costly repairs as the breakdown can cause further damage. For example, the breakdown could occur because a single component fails, but the knock-on effect of the machine breaking down could result in other components also getting damaged.
On the other side of the coin, following the OEM’s guidelines will result in some manufacturers scheduling maintenance more often than necessary. This situation also has a negative impact on availability and the all-important OEE calculation.
Predictive maintenance technologies provide a solution that can deliver tangible returns on investment in short periods of time.
How Predictive Maintenance Works
When predictive maintenance is used in a manufacturing facility, sensors monitor the performance of machines on the production line. This monitoring includes overall performance as well as monitoring components.
The sensors provide this information to the predictive maintenance algorithm in real-time. It then predicts when a machine or component is likely to fail, enabling the scheduling of maintenance before that happens. The result is maintenance that occurs at exactly the right time – not too late when the component has already failed and not too early when the component has sufficient cycles left to continue in operation.
Predictive Maintenance + Digital Twins + AI
The description above is just the starting point for the possibilities of predictive maintenance in the manufacturing sector. Further significant strides can be made with the use of digital twin technologies combined with machine learning.
A digital twin is a virtual replica of a physical entity. That entity could be a product, component, process, or machine. In manufacturing, it is possible to create digital twins of factory floor equipment as well as entire production lines.
At a basic level, a digital twin makes it possible for engineers to analyse production line equipment and run simulations without impacting output. They can do this using real-time data provided by sensors.
AI technologies have the potential to take this further, as machine learning algorithms can run multiple potential scenarios to understand the impact of those scenarios on maintenance and machine performance. The AI algorithm itself could then make adjustments on the production while also scheduling maintenance in a way that minimises the potential for breakdowns.
The Benefits of Predictive Maintenance in Manufacturing
- Schedule maintenance at the optimum time.
- Prevent unplanned downtime due to equipment failure.
- Increase production output.
- Reduce the cost of repairs.
- Extend the lifecycle of manufacturing equipment.
- Prevent products and raw materials from being wasted due to equipment failures.
In other words, predictive maintenance can have a tangible impact on the bottom line, where manufacturing facilities can become more profitable as a result of the benefits above. This return on investment makes predictive maintenance an exciting technology in the manufacturing sector.
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