Predictive Maintenance at Digital Factory
Predictive maintenance with the help of digital twins – SAP and the simulations specialist CADFEM show how it works.6 Mar 2017
The service life of a relay depends on various factors: The size of the switched load, the surrounding temperature, and the frequency and duration of operations. All these things can affect power loss in the arc load, the temperature of the contacts, or wear and tear. In security-sensitive applications, special relays with positively driven contacts are frequently used to signal errors. This makes them very reliable; however, these signals are sent when the failure occurs.
At Digital Factory, simulation specialist CADFEM shows that outages can be predicted beforehand. At the Simulation House (Hall 6, Stand K52), the company demonstrates live how a relay and its digital twin work together: Detailed FEM simulations for magnetic circuits, temperature, and mechanics are used as behavioral models and combined with concentrated elements for kinematics and wiring in the system simulator. The sensor data captured during operation of the actual relay is sent to a cloud-based IoT platform. This supplies the simulation model with the data, automatically evaluates the results, and depicts the remaining cycle of operations.
"Digital twins allow us to evaluate product features relevant to the service life that real sensors can hardly measure," says CADFEM. "Using the detailed simulation results – in this case contact temperature and arc energy – the actual wear and tear and the remaining service life can be individually evaluated." According to this Munich-based company, switching from preventative to predictive maintenance can create major savings. In line with figures from the US Department of Energy, downtime can be reduced by 70 percent and costs by one quarter. In addition, improved field data and detailed service life information accumulate for new business models.
At the SAP stand in Hall 7 (B04), visitors can discover the life time cycle of a complex technical installation from the viewpoint of the manufacturer and operator. The Internet of Things, machine learning, 3D printing, and augmented/virtual reality (AR/VR) allow for new business models and processes across the entire service chain. The center of the showcase is the digital twin that mirrors the actual machine one-to-one, including usage and user data. Conclusions for improved, failsafe use can be made from operating data. In addition, design engineering can make targeted improvements and service technicians can focus entirely on exceptional circumstances. This allows products to be monitored continuously, constantly improved, and maintenance carried out in advance. Failures and anomalies can be identified at an early stage and replacement parts can be identified more easily using AR and then fabricated with 3D printers.
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