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OA Förderung 2022 With a rapidly expanding global population placing an ever growing demand on freshwater resources, an increased focus on irrigation techniques tailored to the specific needs of plant appears as one solution to minimize overall freshwater consumption. Precision irrigation methods seek to realize an acceptable compromise between yield and irrigation water consumption through control of the timing and quantity of water supplied to plants. The goal is to maintain the water content of the soil, achieve specific water use efficiency with regard to yield or maintain the physiological response of the plant to water stress within predetermined limits. Reliance on soil moisture measurements to establish irrigation water demand inadequately addresses heterogenous distribution of water in soil. Growing research interest is observed detailing the determination of plant water status directly from physiological responses. This paper reviews irrigation control approaches based on different plant water status assessment techniques. A distinct focus is made on application scale of the discussed control approaches, an aspect that has not been considered intensively enough in previous discussions of irrigation control approaches. A discussion of the observed strengths and shortcomings and technological advances supporting the various methods used to quantify plant water status extends the review. Emerging trends that are likely to have an impact on plant water status determination and optimal timing and quantification of irrigation water requirements are integrated to show latest results. A peek into the future of precision irrigation foresees greater reliance on plant-based signals, both in characterization of the control variable, namely the plant water status, and in generation of controller outputs in terms of quantity and timing.

The use of ultrasonic waves in the context of SHM offers methods to analyze materials and systems. Both Acoustic Emission-based approaches (passive, active) are limited by the propagation characteristics of ultrasonic waves, especially in inhomogeneous materials like carbon fiber reinforced polymers (CFRP). The use of the piezoelectric and inverse piezoelectric effect is a very accurate method of sensing and exciting ultrasonic waves. However, the transducers resonance characteristics affect the waveforms. For illustration in this contribution different excitation signals are experimentally compared in frequency domain by fast Fourier transform (FFT) and in time-frequency domain by continuous wavelet transform (CWT). Then transducers effects along the propagation path of the wave are investigated. Frequency and fiber direction dependent damping factors of ultrasonic waves in CFRP as well as the influence of the transducers are determined. The distance between sensors in a sensor network is limited by attenuation, so fiber direction must be considered. Finally, by analyzing the frequency response of the transducer, a filtering method is developed to compensate for the resonance characteristics of the transducers. Finally, a more accurate estimate of the energy released and therefore a more accurate estimate of the severity of damages/failures is proposed.

Within human-machine systems the monitoring of human behavior contributes to the safety of the overall system. Detecting critical situations or observing human errors will allow to take over the guidance of systems by automation or to warn to affect the interaction operator/human. In the project FernBin beside others the supervision of the captain’s actions in inland shipping is addressed. The focus is to detect human errors and also non- optimal behaviors. Additionally a reliability-based analysis of the captain’s actions will also enable a safer driving behavior and the reduction of accidents and dangerous situations due to suitable warning and interaction strategies of the supervision system. In this contribution a Situation-Operator-Modeling (SOM) approach is used to describe the captain-vessel-interaction and to illustrate the captain’s behavior as a graph-based-model. As example a ‘Turn-around’ maneuver is considered. A SOM-based action space consisting of possible captain’s behaviors leading to a meaningful desired final situation is developed and applied to a ‘Turn-around’ maneuver. Using this approach a manifold of sequences can be generated describing the human interaction options. Subsequently and online a modified CREAM approach (cognitive reliability and error analysis method) is used allowing the calculation of the human reliability performance score (HPRS), resulting to static reliability measures. The HPRS can be also assigned directly to the SOM-action space. In difference to previous developments this allows beside the deterministic supervision (SOM) also the individualized definition of the safest/most reliable action sequence as well as the opposite. Therefore an event-discretized behavior model situated supervision performance of the captain’s driving behavior with human reliability score numbers is established for the first time. As example the behavior of remotely operating captains of inland vessels is used as experimental example.

Metalworking fluid (MWF) differentiating is of important significance as their additives affect metalworking process extremely so the evaluation of MWF should be suitable conducted. Acoustic Emission (AE) measurements can be easily taken as process-close measures for evaluating cutting and forming processes as well as MWFs performance. In thread forming process, AE measurements from different kinds of MWFs could be – related to the position of the tool - divided into different process phases: air, forward, and reverse phases. From physical view, forward part contains most useful AE data. Therefore, extracting forward part data from the overall signal is significant for MWFs classification. Because boundaries among different parts in time domain of AE signals are not clear, in this contribution, a new data processing method is proposed to abstract the forward part data from non-related parts of the whole measurement signal. For the first time, scalogram is applied to find the boundaries in time domain as an aid. Firstly, the intact measurement signal is transformed from time domain to time-frequency domain by continuous wavelet transform (CWT) and scalogram is acquired. As boundaries among different phases are obvious in scalogram, by reverse calculation, boundaries’ location in time domain could be defined and forward part data of each measurement are picked out. Afterwards, data in forward phase are divided into different samples and each sample contains data of one round. Finally, samples’ features are extracted and classified by convolutional neural network (CNN). By adjusting CNN structure and hyperparameters with cross validation method, features in time domain could be distinguished well. For five kinds of testing MWF, the classification accuracy is as high as 98.11 %. For reference, oil-based, and water-based MWF classification, the results can reach to 98.94 %. Accuracy for water-based MWF distinction is 97.55 % while for oil-based MWF distinction is 98.29 %. Comparing with results using the whole AE measurements, these results improve significantly. Good results show that the proposed data processing method could extract most useful information from the whole AE measurements in time domain for MWF distinction. Besides, the proposed method also provides an effective way for data analysis in the future.