Abstracted/indexed

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.

The publication contributes to a uniform procedure for in-process measurement data acquisition in additive manufacturing, which is essential for a controlled correction of detected manufacturing deviations. The developed methodology is based on an analysis of the melt pool contours relative to a referencing system integrated in the powder bed. By recording the referenced melt pool and the shimmering contours after powder application, manufacturing deviations can be evaluated more accurately.

During the geometric calibration of infrared cameras, the parameters that describe where object points are mapped onto thermal images are determined. The required reference data is obtained by capturing so-called calibration targets. In established approaches, it is difficult to estimate the lens distortions precisely. A newly developed target and its evaluation algorithm make it possible to increase the sensitivity of the calibration by finding reference points close to image borders.

A novel experience replay particle swarm optimization algorithm is presented and successfully deployed to improve the optimization performance for reconfigurable analog integrated circuits of industry 4.0. An optimization approach is introduced that relied on THD-based indirect measurement method that varies from the traditional calibration approach. Instead, the proposed calibration methodology optimizes all characteristics of the reconfigurable amplifier at once.

Detection of flammable gases is necessary to avoid explosive atmospheres. Commercial pellistors require an operation temperature above 450 °C for the detection of methane. We present a novel wireless low-power catalytic gas sensor system based on non-precious metal catalyst for the detection of methane and propane operated at 350 °C. The combination of a MEMS-based sensor with a low-power radio system provides the opportunity to monitor complex infrastructures without using a power grid.