Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines

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 °C 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

Idebenone Prevents Oxidative Stress, Cell Death and Senescence of Retinal Pigment Epithelium Cells by Stabilizing BAX/Bcl-2 Ratio

Purpose: Age-related macular degeneration (AMD) is one of the leading causes of blindness. Degeneration of the retinal pigment epithelium (RPE) is pathognomonic for the disease, and oxidative stress plays an important role in the pathogenesis of this disease. This study investigates potential antiapoptotic and cytoprotective effects of idebenone on cultured RPE cells (ARPE-19) under conditions of oxidative stress. Methods: ARPE-19 cells were treated with 1-100 µM idebenone. Cell viability (MTT assay), induction of intracellular reactive oxygen species (ROS) and histone-associated DNA fragments in mono- and oligonucleosomes, expression of proapoptotic BAX and antiapoptotic Bcl-2 as well as senescence-associated β-galactosidase (SA-β-Gal) activity were investigated under exposure to hydrogen peroxide (H2O2). Results: Idebenone concentrations from 1 to 20 µM showed no toxic effects on ARPE-19 cells. When cells were treated with H2O2, pretreatment with 5, 7.5, 10, and 20 µM idebenone led to a significant increase in the viability of ARPE-19 cells. In addition, idebenone pretreatment significantly attenuated the induction of SA-β-Gal and intracellular ROS as well as the amount of histone-associated DNA fragments after treatment with H2O2. The reduction of proapoptotic BAX and the elevation of antiapoptotic Bcl-2 under idebenone show that this process is rather mediated by inhibiting H2O2-induced apoptosis, not necrosis. Conclusion: In this study, idebenone increased survival of ARPE-19 cells and reduced cell death, senescence, and oxidative stress by stabilizing the BAX/Bcl-2 ratio

Machine Learning for Nondestructive Wear Assessment in Large Internal Combustion Engines

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-the-art 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 nondestructive yet reliable methods for quantifying the surface condition. A deep-learning framework is proposed that allows computation of the bearing load curves from reflection RGB images of the liner surface that can be collected with a wide variety of simple imaging devices, without the need to remove and destroy the investigated liner. For this purpose, a convolutional neural network is trained to predict the bearing load curve of the corresponding depth profile from the collected RGB images, which in turn can be used for further wear evaluation. Training of the network is performed using a custom-built database containing depth profiles and reflection images of liner surfaces of large gas engines. The results of the proposed method are visually examined and quantified considering several probabilistic distance metrics and comparison of roughness indicators between ground truth and model predictions. The observed success of the proposed method suggests its great potential for quantitative wear assessment on engines during service directly on site

Comparison of radon mapping methods for the delineation of radon priority areas - an exercise

Background: Many different methods are applied for radon mapping depending on the purpose of the map and the data that are available. In addition, the definitions of radon priority areas (RPA) in EU Member States, as requested in the new European EURATOM BSS (1), are diverse. Objective: 1) Comparison of methods for mapping geogenic and indoor radon, 2) the possible transferability of a mapping method developed in one region to other regions and 3) the evaluation of the impact of different mapping methods on the delineation of RPAs. Design: Different mapping methods and several RPA definitions were applied to the same data sets from six municipalities in Austria and Cantabria, Spain. Results: Some mapping methods revealed a satisfying degree of agreement, but relevant differences were also observed. The chosen threshold for RPA classification has a major impact, depending on the level of radon concentration in the area. The resulting maps were compared regarding the spatial estimates and the delineation of RPAs. Conclusions: Not every mapping method is suitable for every available data set. Data robustness and harmonisation are the main requirements, especially if the used data set is not designed for a specific technique. Different mapping methods often deliver similar results in RPA classification. The definition of thresholds for the classification and delineation of RPAs is a guidance factor in the mapping process and is as relevant as harmonising mapping methods depending on the radon levels in the area.Funding: This work is supported by the European Metrology Programme for Innovation and Research (EMPIR), JRPContract 16ENV10 MetroRADON (www.euramet.com). The EMPIR initiative is co-funded by the European Union’s Horizon 2020 research and innovation programme and the EMPIR Participating States

Defining genes: a computational framework

The precise elucidation of the gene concept has become the subject of intense discussion in light of results from several, large high-throughput surveys of transcriptomes and proteomes. In previous work, we proposed an approach for constructing gene concepts that combines genomic heritability with elements of function. Here, we introduce a definition of the gene within a computational framework of cellular interactions. The definition seeks to satisfy the practical requirements imposed by annotation, capture logical aspects of regulation, and encompass the evolutionary property of homology

Residual Iris Retraction Syndrome After Artificial Iris Implantation

PURPOSE: To evaluate the effect of an artificial iris implant on the remnant iris. DESIGN: Interventional case series. METHODS: SETTING: Single center. PATIENT POPULATION: Forty-two consecutive patients. Observation Procedures: Morphologic evaluation over 24 +/- 14 months. MAIN OUTCOME MEASURES: Remnant pupillary aperture, iris color, visual acuity, intraocular pressure, and endothelial cell count. RESULTS: In 7 of 42 cases (16.7%), the residual iris aperture dilated from 36.6 +/- 15.4 mm(2) preoperatively to 61.1 +/- 12.5 mm(2) 1 year postoperatively (66.9% increase). In 5 of 7 affected eyes the artificial iris had been implanted into the ciliary sulcus; in 2 eyes it had been sutured to the sclera. Four of the 7 patients presented with remarkable complications: 2 eyes needed glaucoma shunt surgeries owing to pigment dispersion; 1 suffered from recurrent bleedings; and in 1 case artificial iris explantation was performed owing to chronic inflammation. Anterior chamber depth and angle, endothelial cell count, and visual acuity did not change in this cohort. Changes in color were not observed in the remnant iris. CONCLUSIONS: The implantation of an artificial iris prosthesis can lead to a residual iris retraction syndrome. It is likely that residual iris is trapped in the fissure between the artificial iris and the anterior chamber angle, preventing further pupil constriction. Another possibility could be a constriction or atrophy of the residual iris. A scleral sutured implant and an implantation in the capsular bag were both found to prevent the iris retraction. The study group number is inadequate to allow statistical comparison of these different implantation methods. As the use of artificial irises increases, we may expect more patients with iris retraction syndrome in the future. (C) 2018 Elsevier Inc. All rights reserved

Challenges and Complication Management in Novel Artificial Iris Implantation

Purpose. Evaluation of postoperative artificial iris prosthesis-related complications. Design. Retrospective cohort study. Methods. Fifty-one consecutive patients underwent pupillary reconstruction using an artificial iris implant made from silicone between 2011 and 2015. Quantity and quality of complications were subclassified into three groups including mild, moderate, and severe complications. Their management and the learning curve were evaluated. Results. In total, 13 (25.5%) of 51 included artificial iris implantations showed unexpected events in various degrees: mild complications: recurrent bleeding (n=1, 2.0%), slight but stable iris deviation (n=1, 2.0%), capsular fibrosis (n=2, 3.9%); moderate complications: suture cutting through the residual iris (n=1, 2.0%), glaucoma (n=3, 5.9%), and corneal decompensation (n=3, 5.9%); severe complications: artificial iris suture loosening (n=2, 3.9%) and dislocation (n=3, 5.9%), synechiae (n=2, 3.9%), glaucoma (n=2, 3.9%), and corneal decompensation (n=5, 9.8%) with the need for surgery, cystoid macular edema (n=3, 5.9%) and retinal detachment (n=1, 2.0%). The complication rate decreased from 83.3% (5 of 6 implantations) in the first year to 13.3% (2 of 15 implantations) in the 4th year. Nineteen of 45 evaluated patients showed a significant gain in best-corrected visual acuity (BCVA) from 1.09 ± 0.56 logMAR to 0.54 ± 0.48 logMAR (p<0.001), and 13 of 45 eyes had a significant BCVA loss from 0.48 ± 0.39 logMAR to 0.93 ± 0.41 logMAR after surgery (p<0.001). Conclusions. The artificial iris is a feasible option in the treatment of iris defects with a wide spectrum of postoperative complications. The significant reduction of complications after twelve implantations implicates that the procedure is not to be recommended in low volume settings