Decentralized data analytics for maintenance in industrie 4.0

Due to the increased digital networking of machines and systems in the production area, large datasets are generated. In addition, more external sensors are installed at production systems to acquire data for production and maintenance optimization purposes. Therefore, data analytics and interpretation is one of the challenges in Industrie 4.0 applications. Reliable analysis of data (e.g. internal and external sensors), such as system-internal alarms and messages produced during the operation, can be used to optimize production and maintenance processes. Furthermore, information and knowledge can be extracted from raw data and used to develop data-driven business models and services, e.g. offer new availability contracts for production systems. This paper illustrates an approach for decentralized data analytics based on smart sensor networks

Condition monitoring in the cloud

Due to the very high demands on availability and efficiency of production systems and industrial systems, condition-based maintenance is becoming increasingly important. The use of condition monitoring approaches to increase the machine availability and reduce the maintenance costs, as well as to enhance the process quality, has increased over the last years. The installation of industrial sensors for condition monitoring reasons is complex and cost-intensive. Moreover, the condition monitoring systems available on the market are application specific and expensive. The aim of this paper is to present the concept of a wireless sensor network using Micro-Electro-Mechanical Systems – MEMS sensors and Raspberry Pi 2 for data acquisition and signal processing and classification. Moreover, its use for condition monitoring applications and the selected and implemented algorithm will be introduced. This concept realized by Fraunhofer Institute for Production Systems and Design Technology IPK, can be used to detect faults in wear-susceptible rotating components in production systems. It can be easily adapted to different specific applications because of decentralized data preprocessing on the sensor nodes and pool of data and services in the cloud. A concrete example for an industrial application of this concept will be represented. This will include the visualization of results which were achieved. Finally, the evaluation and testing of this concept including. implemented algorithms on an axis test rig at different operation parameters will be illustrated

Smart life cycle monitoring for sustainable maintenance and production: An example for selective laser melting machine

Smart linking, evaluation and provision of information over the life cycle of a product are becoming growingly important. The use of information extracted from combination of monitoring data, product data, maintenance information, and from product utilisation data can increase the availability of production machines and reduce the costs and resources cause by machine downtime. Especially for new manufacturing technologies such as Selective Laser Melting, the storage and management of such information are crucially important to develop knowledge and improve the quality of the machines and their products. By acquiring data from the machine, processing them and calculating proper key performance indicators, the critical region where the failures are most commonly found and the critical subsystems responsible for the failures are identified. Moreover, using the historical data, the tolerances for those subsystems can be defined