In manufacturing, coordinated process chains that go hand in hand are a prerequisite for keeping up with the competition. Predictive maintenance refers to the timely repair and maintenance of machines before they fail and come to a standstill.
The goal of predictive maintenance is to prevent machine downtime by connecting IoT-enabled assets. This connection should provide real-time data from the machines. The data will be used to create predictive, cost-effective as well as efficient maintenance protocols.
Repairs can thus be targeted to the problem to shorten or completely prevent machine downtimes. Above all, this saves production costs. It also promotes more effective deployment of maintenance and service staff.
Furthermore, the planning of a company’s spare parts procurement is simplified, which at the same time minimizes buffer stocks. Another advantage is the increase in productivity. By collecting machine data, not only future decisions can be made, but conclusions about previous optimization processes can be drawn, too.
Predictive maintenance technology
The digital generation of machine data in real time should give a comprehensive picture of their actual condition. This data can then be used to create possible failure scenarios. These scenarios are used to identify problems at an early stage and prevent machines in production from coming to a standstill for too long.
Machine learning and artificial intelligence are used to identify complex relationships in the data to process the massive amounts of data.
Among other things, recording the data from the machines makes it possible to detect any deviations that arise, so that escalation levels can be implemented. An example of this would be an unusual increase in the measured temperature of a machine, which in turn can then be immediately reported to an employee.
The central technology required for predictive maintenance is, in the first instance, condition monitoring. It describes the continuous measurement, documentation and analysis of physical variables and information from the production machines. By comparing the currently recorded data with previous data, initiated problems can be detected at an early stage.
The interpretation of the stored data volume is made possible with the help of artificial intelligence. Machine learning is used to compare and understand processes with the help of software to develop long-term relationships and patterns.
By comparing collected data from multiple machines, as many scenarios as possible can be tapped, which favors active countermeasures and thus predictive maintenance.
Predictive maintenance with smart wearables
The integration of smart wearables can provide support at this point. By linking these with the production systems or the analytics models, fault messages can be sent directly to the responsible service employee. The service employee, in turn, can use step-by-step instructions to correct errors and thus avoid long machine downtimes.
This significantly reduces response and coordination times, and the actual problem resolution begins earlier. Unscheduled downtime is thus further reduced.
In addition, the camera function of the smart wearables enables an immediate documentation of the maintenance measure. This function ensures an optimized quality management. At the same time, smart wearables can facilitate communication among employees.
Maintenance employees can digitally communicate with their colleagues, forward tasks, and request help from their colleagues by touching a button.
Smart wearables that make work easier in the production area include smartwatches. With the help of these, a maintenance worker has his hands free for his repair work. Therefor he can simultaneously call up required information, contact colleagues and document all work steps.
Predictive maintenance enables companies to predict failures and thus plan maintenance work when it is immediately required. Providing the information also enables machines and plants to operate at peak performance and consequently make the best possible use of production capacities.
To make maintenance processes more reliable in the long term and reduce unplanned maintenance work, it is advisable to implement predictive maintenance. This not only enables an increase in effectiveness and productivity, but above all saves company resources.
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