Synchronization problems within distributed sensor networks are a major challenge in the field of Industry 4.0. In this paper, artificially generated time shifts between sensor data and their influence on remaining useful lifetime prediction of electromechanical cylinders are investigated. It is shown that time shifts within sensor data lead to poor remaining useful lifetime predictions. However, this prediction can be significantly improved using various methods as shown in this contribution.

Wireless sensor systems gain more and more importance for modern industry 4.0 and internet of things applications. This paper introduces a new wireless communication system that enables real-time and highly robust wireless communication. Using this system makes it possible to transfer any digital bus system transparently. Based on a prototype, measurements and evaluations of the new systems are demonstrated.

We focus on the implementation of monitoring algorithms for the induction machine in WSNs. As there are restrictions on the sensor node, such as low cost, low power, weak calculation and small memory size, the algorithm should be simple and efficient. The model-based method is used for the temperature estimation algorithm development. The experiments prove that the KF algorithm implementation is suitable for real-time temperature estimation on a wireless sensor node.

Many applications in high-voltage environments require electrically isolated sensors. We demonstrate a purely optical sensor link using only low-cost high-brightness LEDs and only one polymer optical fiber (POF) for remote powering of isolated electronic sensor nodes of as well as for data transmission of sensor data to a central unit. No optical couplers or special photo diodes are required, to save costs and reduce size. This enables us to install sensor networks in the field with low effort.

The paper introduces a novel method for 2-D localization of a target in the indoor environment using the RSSI approach with only two sensors. The proposed method reduces the number of sensors by one compared to the trilateration method, which uses at least three sensors for 2-D localization. The achieved accuracy under the white Gaussian noise condition is about 3.4 cm as an average of all positions inside the considered simulated environment.