Road surface wetness is a contributing factor in traffic accidents. As the amount of friction reduction correlates with the water film height covering the road surface, a quantification is of high relevance in order to improve traffic safety. Both drivers and autonomous vehicles would benefit from additional information. This paper presents a novel concept for road wetness quantification. It is based on a $2\times 4$ -planar capacitive transducer array, capable to detect water spray ejected by the tires and its wetness-related dependencies. The reliable assessment of these dependencies by a proposed capacitive sensor system is shown in an experimental study on an asphalt circuit for various wheel speeds. Besides the spray’s correlation with speed, the results reveal significant differences in transducer positions and designs confirming the array’s relevance regarding wetness quantification. In addition, a 1-nearest neighbor classifier capable of automatically distinguishing between eight wetness levels is proposed. The classifier is optimized by an extended version of balanced accuracy and reaches similar performance as binary classifiers from related research. A balanced ratio between capacitance increase-, standard deviation- and speed-related feature types is one key aspect of classifier performance. Furthermore, up to a certain extent, the array’s individual transducers can significantly contribute to classifier performance with design- and position-related advantages.

Due to the correlation between friction reduction and the road-covering water film height, knowledge about the current wetness level is of relevance for drivers and autonomous systems. A promising approach for wetness quantification is based on capacitive transducer arrays that enable to detect water spray ejected by the tires. Even though previous studies on this approach have shown the feasibility of wetness classification, optimization opportunities exist. While these previous investigations were limited to one feature selection algorithm, we study various algorithms and demonstrate the potential for optimization. Besides an application-specific algorithm that is capable of determining class-dependent features resulting in a performance improvement of more than 0.03 for the considered evaluation metrics, sequential forward floating selection in particular yields the most significant performance increase of more than 0.06. In addition, prior studies were limited to a test track with constrained conditions. Thus, in order to study the transferability of the preceding results, we present investigations on new experimental data acquired on public roads. The unknown and varying environmental conditions on public roads as well as a larger wheel speed range and steering angle effects are shown to significantly decrease classifier performance. We demonstrate that fusing two transducer arrays of different wheel arch liners increases performance by around 0.02. Here, a considerable information benefit can be attributed to the different transducer positions with design-related advantages. Furthermore, we show that the fusion with additional sensor data available in the vehicle results in a further performance improvement of more than 0.02.

Tapered roller bearings are used partly in very rough and highly stressful environmental conditions. Therefore, the need for condition monitoring is increasing. This study is intended to provide an approach for monitoring bearings in a two-stage planetary gearbox based on vibration analysis. In total, the data of six damage phenomena and one healthy bearing are collected. A convolutional neural network (CNN) is trained and evaluated by using the balanced accuracy. Mainly, it is investigated how many damage severities can be detected. In addition, the robustness of the model regarding unknown speeds and damage phenomena should be proven. The results show a very good differentiation up to all of the presented damage phenomena. The classifier reaches an averaged balanced accuracy of 0.96. Also, samples collected at unknown speeds can be classified well for speed values within the known range. For unknown damage phenomena, the classifier shows limits so that a reliable classification is only applicable with a binary classifier, which differentiates between healthy and damaged. The investigations therefore show that a reliable detection of bearing damage is possible in a two-stage planetary gear. Furthermore, the transferability of the model is successfully tested and implemented for the binary classifier.

Carbon fibre reinforced composites are being used in more and more areas of application, for example in hydrogen storage tanks. In addition to structural-mechanical advantages, these materials also have disadvantages, such as susceptibility to mechanical damage. This makes structural monitoring advisable in safety-critical applications. In addition to the detection of damage, the localization of the source of the acoustic signal, respectively the damage, is a central component. Signal localization on fibre composites is a major challenge, due to the high direction dependency of the speed of acoustic signals. Measurement inaccuracies and low signal strengths, especially in noisy environments, lead to high localization errors when locating the source based on the difference in signal arrival times in a sensor network. When using triangulation-based methods, it is even possible that several plausible signal sources are identified. This article therefore analyses the extent to which localization accuracy is affected by a reduction in the signal-to-noise ratio. Two different localization methods are used for the investigations and their accuracy is evaluated when the signal-to-noise ratio is reduced. These are, on the one hand, classic triangulation, adapted to an anisotropic material. On the other hand, localization via a convolutional neural network is investigated. For the investigations, acoustic signals with different signal-to-noise ratios are applied to a carbon fibre prepreg plate in three series of measurements and localized using a sensor grid. In addition to the effect of the signal-to-noise ratio, the effect of the source position relative to the sensor grid is investigated.