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.

In thermometry, the displayed temperature value of an object depends on the size of the object. This behaviour, the size-of-source effect, might be a major cause of measurement uncertainty in a thermoscene. The influence of diffraction and digitization can be described advantageously with the modulation transfer function. Especially with very small objects the displayed temperatures are too low. When imaging large objects, not only the edge areas are affected, but also the entire image.

A new metrological atomic force microscope (MAFM) head design is shown. We investigated the MAFM head in the nanomeasuring machine (NMM-1) for different high-precision and large-scale traceable measurement tasks. Due to their integration, the MAFM head can benefit from the large measuring range, high-precision and traceability of the NMM-1 for efficient measurements on different samples. The presented results show the realised macroscale measurements with sub-nanometre resolution.

We propose a surface plasmon resonance (SPR) sensor based on a ZnO / Au hybrid thin-film material structure and experimentally investigate its sensitivity improvement. The Kretschmann-based SPR sensor utilizes ZnO thin films and nanostructures for performance enhancement. The advancement in SPR technology relies on a low-cost, high-sensitivity, and high-selectivity sensor. Metal oxide (MO) has been incorporated into the SPR sensor to be used for detection of biological and chemical compounds.

This paper discusses the deployment of an internet of things (IoT)-based model to monitor the fermentation of tea in a tea factory in Kenya. The model uses deep learning to detect the optimum fermentation of tea as fermentation progresses. To further improve on the results, a majority voting technique based on regions is used. The model shows promising results, and its predictions correlated well with those of the experts.

A TIR100-2 emissometer is used to measure the emissivity of thermal insulation products using the low-emissivity effect to increase the thermal resistance, but the uncertainties of measurements on low-emissivity foils were not known. The study allowed the determination of the uncertainty through the detailed analysis of the measurement principle and the determination of the influences of the parameters. The measurement results were validated by comparison to a reference measurement setup.

We have prepared an article that demonstrates one of the ways to recognize objects on the ground surface. This paper is a result of experimental data that were collected with unnamed aerial vehicles (UAVs) with synthetic aperture radar. Although UAV radar has a small monitoring area, we noted that such pictures can contain the steady features of the mutual arrangement between detected objects. We have constructed an artificial neural network that solves the tasks of group object recognition.