06. October 2021
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Predictive maintenance for cost reductions, higher equipment effectiveness and reduced downtime

Increasing numbers of industrial companies are becoming interested in predictive maintenance - for good reason. After all, the potential in terms of cost reductions, higher equipment effectiveness and reduced downtime is enormous. But how do machine operators get started in this area? What conditions must be created in production in order to implement modern maintenance strategies? 

Predictive maintenance on the shop floor

Predictive Maintenance: Definition and advantages

Predictive Maintenance: The definition of this term is important to understand how the approach works and also its potential. It is a maintenance strategy for companies based on machine data. This data comes from sensors and is analyzed in real time by special AI-powered software. If the algorithm detects a deviation from normal or a pattern that has indicated an imminent need for maintenance in the past, it sounds an alarm. For example, a technician can now be assigned to check the machine and perform maintenance if necessary.

Compared to traditional maintenance strategies that follow fixed inspection intervals and a reactive principle, predictive maintenance has several advantages. Maintenance personnel are no longer deployed "on spec." Instead, they only have to intervene when there is an actual need for maintenance. Predictive maintenance not only saves resources, it also reduces machine downtime. This is because predictive maintenance identifies problems before they lead to plant shutdowns. In addition, the early measures even extend the service life of the machines.


Maintenance of a heat exchanger

During maintenance of a heat exchanger, for example, deposits in the lines can lead to clogging. The result would be production errors and downtime. Also challenging is that the flow inside heat exchanger lines cannot be measured directly for technical reasons. In such a case, one solution is to use sensors to measure the temperature difference upstream and downstream of the heat exchanger. The measured values can now be collected and visualized. This makes it possible to define certain threshold values. As soon as these are exceeded or undershot, maintenance staff can be notified via a warning system.

Predictive maintenance for spindles

The benefits of predictive maintenance can best be seen by looking at specific examples of predictive maintenance. For example, spindles in milling machines are prone to breakage during the production process. In addition, the repair of these components can be associated with high costs. These costs can be significantly reduced if the time of spindle breakage can be accurately predicted. This is possible by equipping milling machines with special ultrasonic or vibration sensors. This allows the data to be evaluated in real time and compared with patterns of a worn spindle or one that is about to break. If a corresponding pattern is detected, the software triggers a maintenance alarm.

The basic idea can be transferred to almost any type of machine. In this respect, there are hardly any limits with regard to specific use cases in machine parks.


Implementing predictive maintenance

Companies must create the following conditions

Prevention instead of reaction, lower maintenance costs, fewer breakdowns, higher equipment effectiveness, longer service life of capital goods: the potential of predictive maintenance is great. But which path do machine operators have to take in order to enjoy the benefits?

The basic prerequisite for the introduction of modern maintenance strategies is sensor technology and connectivity. If machines and systems are equipped with sensors, certain operating parameters such as temperature, pressure or number of revolutions can be measured. In newer machines, the required sensors are usually already available or can be retrofitted. What is also relevant is the networking of systems and machines to enable transmission to a higher-level maintenance system.


Achieving goals with proven solutions and best practices

Predictive maintenance is a significant building block for the success of Industry 4.0. However, until now, corresponding solutions have mostly been limited to specific machine brands. ADAMOS solves this problem by providing an interoperable ecosystem and best practices. This enables machine operators to implement a holistic predictive maintenance strategy. And only maintenance strategies that follow this approach will bring maximum benefit to manufacturing companies.

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