Bovine mastitis is the most frequently reported disease among dairy cows worldwide. Treatment of udder disease often involves the use of antimicrobial substances, which is difficult to justify with respect to their possible effect on the development and spread of antimicrobial resistance. Prevention of udder disease is therefore always preferable to treatment. The study presented here statistically analyzed the probability of mastitis occurring during 3,049 lactation periods on 208 farms and attempted to ascertain which on-farm management factors contributed to the occurrence of this udder disease in Austria. Farm management was assessed via online surveys completed by 211 farmers (211/251; response rate = 84.1%) as well as national milk performance recorders observing milking technique and herd veterinarians evaluating farm hygiene levels. Veterinary treatment records were used as a basis for mastitis reporting. The analysis was carried out using a generalized linear mixed model. The study population was not randomized but was part of a larger observational study. More than three fourths of the study farms were run conventionally, and the remainder were organic. Freestalls (and straw yards) made up 66% of the study population, and 34% of farms had tiestalls. Herd size ranged from 8 to 94 dairy cows (mean = 26.9; median = 21), with the most common breed (74% of all cows) being dual-purpose Simmental (Austrian Fleckvieh). A mastitis risk of 14.4% was reported via veterinary treatment records. The following factors were shown to be associated with a reduction in the risk of mastitis occurring: regular access to pasture (odds ratio, OR = 0.73), automatic milking machine shut-off (OR 0.67), and access to feed immediately after milking (OR = 0.43). Detrimental effects, which were likely to increase the probability of mastitis occurring, included lactation number (OR = 1.18), farming part time (OR = 1.55), and udders on the farm being classed by herd veterinarians as medium to severely soiled (OR = 1.47). The study presented here was able to confirm several management factors recommended to reduce the probability of mastitis occurring during a cow's lactation period, with particular relevance for the small dairy herds common to Austria.
Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts become likely and thus wear can occur. Bearing temperature measurements with thermocouples serve as a reliable, fast responding, individual bearing-oriented method that is comparatively simple to apply. In combination with a corresponding reference model, such measurements could serve to monitor the bearing condition. Based on experimental data from an MAN D2676 LF51 heavy-duty diesel engine, the derivation of a data-driven model for the crankshaft main bearing temperatures under steady-state engine operation is discussed. A total of 313 temperature measurements per bearing are available for this task. Readily accessible engine operating data that represent the corresponding engine operating points serve as model inputs. Different machine learning methods are thoroughly tested in terms of their prediction error with the help of a repeated nested cross-validation. The methods include different linear regression approaches (i.e., with and without lasso regularization), gradient boosting regression and support vector regression. As the results show, support vector regression is best suited for the problem. In the final evaluation on unseen test data, this method yields a prediction error of less than 0.4 ∘C (root mean squared error). Considering the temperature range from approximately 76 to 112 ∘C, the results demonstrate that it is possible to reliably predict the bearing temperatures with the chosen approach. Therefore, the combination of a data-driven bearing temperature model and thermocouple-based temperature measurements forms a powerful tool for monitoring the condition of sliding bearings in internal combustion engines.
Radon is a noble gas that occurs naturally as a decay product of uranium. Aside from smoking, radon is considered to be one of the major causes of lung cancer. Indoor environments, where radon can accumulate and potentially reach high concentrations, are of a particular concern. A mixed effects additive model along with a data-driven cross validation model selection method is applied to model the mean indoor radon concentration of dwellings in Austria. For this model a prediction approach is introduced, which enables the mapping of indoor radon potential to identify radon areas in Austria. The data used for modeling was collected in monitoring campaigns for private dwellings in Austria from 2013 to 2019. The proposed method allows policy makers to identify regions with high indoor radon concentrations and enables them to meet regulatory requirements or prioritize radon protection measures. The currently published Austrian radon map and the delineation of radon areas in Austria is based on this proposed method.
As digitalization advances, improvements in technology have made it easier to conduct condition assessment of key components of large internal combustion engines such as cylinder liners. Due to their movement relative to the piston, the inner surfaces of the liners are subject to constant wear, and it is critical that the engine operator is informed in advance of any imminent damage. This study deals with wear assessment for cylinder liners from Type 6 gas engines from INNIO Jenbacher GmbH & Co OG with a cylinder displacement of approximately 6 dm3. Currently, wear quantification with this type of liner requires high-resolution microscopic surface depth measurements. The depth maps of the surface are then used for further analysis of the liner surface topography. To perform these microscopic measurements, the liners must be disassembled, cleaned and cut into segments, which is a major drawback of the current measurement process. Since the cylinder liners examined can no longer be used even if no major wear is detected, the main goal of the research presented here is to develop a method that is able to predict the depth map of a liner from a single RGB reflection image, i.e., a color image with no direct depth information. In recent years, depth map prediction from RGB images has become a vital part of image analysis in various fields such as the automotive industry, gaming and robotics. However, only a few studies deal with depth map predictions on a microscopic scale. For this study, both RGB and depth images of the cylinder liner surface with pixel-wise alignment are obtained with the help of the same confocal microscope. This data set contains 740 pairs of high-resolution microscopic depth and RGB reflection images capturing a roughly 2 × 2 mm area. As there are no landmarks, the depth of the surface is measured relative to the core of the profile. This is a main difference to most other studies, which mainly focus on absolute depth measurements. First, the physical connection between the depth and the reflection images is investigated and described mathematically. This theoretical model provides good insight into how the information about the structure of the surface contained in the RGB image can be separated from other influencing factors such as lighting condition or color. Next, a deep learning framework is proposed to estimate the liner depth profiles from the RGB reflection images. A convolutional neural network is trained in a supervised manner to learn the correspondence between RGB and depth images. Using the physical model obtained in the first step, an RGB image is reconstructed from the predicted depth map. To ensure the physical plausibility of the model’s predictions, the similarity between the RGB input and the corresponding reconstruction is enforced by a reconstruction term. The proposed machine learning approach is comprehensively evaluated using meaningful distance measures between depth predictions and corresponding ground truth profiles. The results show that the proposed method is able to predict the depth profiles of the cylinder liners very accurately, indicating the great potential for engine liner wear assessment using microscopic RGB images.
Real-time estimation of actual object depth is a module that is essential to performing various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment of machinery parts. During the last decade of machine learning, extensive deployment of deep learning methods to computer vision tasks has yielded approaches that succeed in achieving realistic depth synthesis out of a simple RGB modality. While most of these models are based on paired depth data or availability of video sequences and stereo images, methods for single-view depth synthesis in a fully unsupervised setting have hardly been explored. This study presents the most recent advances in the field of generative neural networks, leveraging them to perform fully unsupervised single-shot depth synthesis. Two generators for RGB-to-depth and depth-to-RGB transfer are implemented and simultaneously optimized using the Wasserstein-1 distance and a novel perceptual reconstruction term. To ensure that the proposed method is plausible, we comprehensively evaluate the models using industrial surface depth data as well as the Texas 3D Face Recognition Database and the SURREAL dataset that records body depth. The success observed in this study suggests the great potential for unsupervised single-shot depth estimation in real-world applications.
<div class="section abstract"><div class="htmlview paragraph">Digital technologies are capable of making a significant contribution to improving large internal combustion engine technology. In particular, methods from the field of artificial intelligence are opening up new avenues. So-called “intelligent” engine components rely on advanced instrumentation and data analytics to create value-added data, which in turn can serve as the basis for applications such as condition monitoring, predictive maintenance and controls. For related components and systems, these data may also allow for novel condition monitoring approaches. This paper describes the use of value-added data from an intelligent diesel fuel injection valve that give detailed information about the injection process for real-time prediction of key combustion parameters such as indicated mean effective pressure, maximum cylinder pressure and combustion phasing. These parameters are usually involved in combustion controls and power unit condition monitoring and normally acquired using in-cylinder pressure indication systems, which are costly and prone to wear. On the one hand, a data-driven model for key combustion parameters based on an intelligent fuel injection valve could replace an indication system. On the other hand, such a model may enable backup functionality and mutual condition monitoring of the fuel injection valve and the indication system. The data required for model building were acquired from a medium-speed four-stroke single-cylinder research engine with a displacement of approximately 15.7 dm<sup>3</sup>. Different machine learning methods are compared to obtain an accurate yet reliable model for each of the desired combustion parameters. In addition to the value-added injection data, readily available parameters on production engines serve as model inputs (e.g., engine speed, charge air and exhaust gas pressures). Based on the results, the quality of the model predictions is evaluated, and it is assessed whether the approach might be useful for series engine applications.</div></div>
Digitalization offers a large number of promising tools for large internal combustion engines such as condition monitoring or condition-based maintenance. This includes the status evaluation of key engine components such as cylinder liners, whose inner surfaces are subject to constant wear due to their movement relative to the pistons. Existing state-of-theart methods for quantifying wear require disassembly and cutting of the examined liner followed by a high-resolution microscopic surface depth measurement that quantitatively evaluates wear based on bearing load curves (also known as Abbott-Firestone curves). Such reference methods are destructive, time-consuming and costly. The goal of the research presented here is to develop simpler and nondestructive yet reliable and meaningful methods for evaluating wear condition. A deep-learning framework is proposed that allows computation of the surface-representing bearing load curves from reflection RGB images of the liner surface that can be collected with a simple handheld device, without the need to remove and destroy the investigated liner. For this purpose, a convolutional neural network is trained to estimate the bearing load curve of the corresponding depth profile, which in turn can be used for further wear evaluation.
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