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2 Journal of Machine Engineering

2 2017

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Smart wireless sensor network and configuration of algorithms for condition monitoring applications

Uhlmann E., Laghmouchi A., Geisert C., Hohwieler E.

Journal of Machine Engineering

2017

Vol. 17, No. 2

45--55

Due to high demand on availability of production systems, condition monitoring is increasingly important. In recent years, the technical development have improved for realization of condition monitoring applications as a result of technological progress in fields such as sensor technology, computer performance and communication technology. Especially, the approaches of Industrie 4.0 and the use of the Internet of Things (IoT) technologies offer high potential to implement condition monitoring solutions. The connection of several sensor data of components to the cloud allows the identification of anomalies or defect pattern, this information can be used for predictive maintenance and new data-driven business models in production industry. This paper illustrates a concept of a smart wireless sensor network for condition monitoring application based on simple electronic components such as the single-board computer Raspberry Pi 2 modules and MEMS (Micro-Electro-Mechanical Systems) vibration sensors and communication standards MQTT (Message Queue Telemetry Transport). The communication architecture used for decentralized data analysis using machine learning algorithms and connection to the cloud is explained. Furthermore, a procedure for rapid configuration of condition monitoring algorithms to classify the current condition of the component is demonstrated.

Intelligent production systems in the era of Industrie 4.0 – changing mindsets and business models

Uhlmann E., Hohwieler E., Geisert C.

Journal of Machine Engineering

2017

Vol. 17, No. 2

5--24

Industrie 4.0 has been becoming one of the most challenging topic areas in industrial production engineering within the last decade. The increasing and comprehensive digitization of industrial production processes allows the introduction of innovative data-driven business models using cyber-physical systems (CPS) and Internet of Things (IoT). Efficient and flexible manufacturing of goods assumes that all involved production systems are capable of fulfilling all necessary machining operations in the desired quality. To ensure this, production systems must be able to communicate and interact with machines and humans in a distributed environment, to monitor the wear condition of functionally relevant components, and to self-adapt their behaviour to a given situation. This article gives an overview about the historical development of intelligent production systems in the context of value-adding business models. The focus is on condition monitoring and predictive maintenance in an availability oriented business model. Technical as well as organizational prerequisites for an implementation in the production industry are critically analysed and discussed on the basis of best practice examples. The paper concludes with a summary and an outlook on future research topics that should be addressed.