Educación e investigación en ingeniería ferroviaria: tren de mediciones CTO, una sala de clases sobre rieles

La industria ferroviaria a nivel mundial necesita de una gran cantidad de nuevosprofesionales, capaces de resolver los crecientes desafíos que este modo presenta. Siendo una industria por lo general conservadora, atraer a las y los mejores no es tarea fácil. En este resumen, se discuten algunos desafíos del mundo ferroviario y se presenta el tren CTO como una manera novedosa de atraer estudiantes. Hasta la fecha, el tren CTO hasido ocupado principalmente para labores de investigación de la universidad tecnológica de Delft y por algunas empresas. Se pretende adaptar el tren para ocuparlo como sala de clases durante el año académico 2017-2018. Siendo un proyecto en desarrollo, el objetivo principal de este resumen es discutir desafíos futuros, adelantar la discusión de la necesidad de formación de nuevos profesionales ferroviarios que se tendrá en Chile y, sobre todo, fomentar la cultura y amor por el modo ferroviario.Railway Engineerin

Intelligent Data Fusion for Anomaly Detection in Dutch Railway Catenary Condition Monitoring

Aiming to handle the increasing variety and volume of railway infrastructure monitoring data, this paper explores the use of intelligent data fusion methods for automatic anomaly detection of railway catenaries. Three classical data dimensionality reduction methods, namely the principal component analysis (PCA), the autoencoder neural network, and the t-distributed stochastic neighbor embedding (t-SNE) are adopted for the data fusion of catenary monitoring data. Then, anomaly detection can be achieved using new features that are automatically extracted from the original data, which requires no prior knowledge of the data or catenary conditions. A case study using data measured from the Dutch railway is presented to compare the performance of the three methods. Six types of catenary monitoring data, including pantograph-catenary contact force, pantograph-catenary friction force, contact wire thickness, contact wire height and stagger, are used in the presented case study. It is demonstrated that both PCA and autoencoder can detect anomalies from catenary monitoring data, while t-SNE shows little indication of such ability. Further, the autoencoder outperforms PCA in distinguishing anomalies in the case study, likely owing to its superiority in analysing data with nonlinearity. Overall, autoencoder is a promising technique for automating the anomaly detection of railway catenaries. The detection results can provide indicators for failure prediction and maintenance decision making.Railway Engineerin

A multiple spiking neural network architecture based on fuzzy intervals for anomaly detection: a case study of rail defects

In this paper, a fuzzy interval-based method is proposed for solving the problem of rail defect detection relying on an on-board measurement system and a multiple spiking neural network architecture. Instead of outputting binary values (defect or not defect), all data will belong to both classes with different spreads that are given by two fuzzy intervals. The multiple spiking neural networks are used to capture different sources of uncertainties. In this paper, we consider uncertainties in the parameters of spiking neural networks during the training phase. The proposed method comprises two steps. In the first step,multiple sets of the firing times for both classes are obtained from multiple spiking neural networks. In the second step, the obtained multiple sets of firing times are fuzzy numbers and they are used to construct fuzzy intervals. The proposed method is showcased with the problem of rail defect detection. Thenumerical analysis indicates that the fuzzy intervals are suitable to make use of the information provided by the multiple spike neural networks. Finally, with the proposed method, we improve the interpretability of the decision making regarding the detection of anomalies.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Railway Engineerin

Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy

During oncological surgery, it can be challenging to identify the tumor and establish adequate resection margins. This study proposes a new two-layer approach in which diffuse reflectance spectroscopy (DRS) is used to predict the top layer thickness and classify the layers in two-layered phantom and animal tissue. Using wavelet-based and peak-based DRS spectral features, the proposed method could predict the top layer thickness with an accuracy of up to 0.35 mm. In addition, the tissue types of the first and second layers were classified with an accuracy of 0.95 and 0.99. Distinguishing multiple tissue layers during spectral analyses results in a better understanding of more complex tissue structures encountered in surgical practice.Medical Instruments & Bio-Inspired Technolog

Comparing in vivo and ex vivo fiberoptic diffuse reflectance spectroscopy in colorectal cancer

In vivo data acquisition using fiberoptic diffuse reflectance spectroscopy (DRS) is more complicated and less controlled compared to ex vivo data acquisition. It would be of great benefit if classifiers for in vivo tissue discrimination based on DRS could be trained on data obtained ex vivo. In this study, in vivo and ex vivo DRS measurements are obtained during colorectal cancer surgery. A mixed model statistical analysis is used to examine the differences between the two datasets. Furthermore, classifiers are trained and tested using in vivo and ex vivo data. It is found that with a classifier trained on ex vivo data and tested on in vivo data, similar results are obtained compared to a classifier trained and tested on in vivo data. In conclusion, under the conditions used in this study, classifiers intended for in vivo tissue discrimination can be trained on ex vivo data.Medical Instruments & Bio-Inspired Technolog

Rail Condition Monitoring using Axle Box Acceleration Measurements: Defect detection in the Netherlands and Romania

In this paper we discuss rail condition monitoring based on axle box acceleration (ABA) measurements. We present three case studies. The first one in The Netherlands, the detection of local defects with different severity levels (squat A, squat B and squat C) is analysed. The second case from the Faurei testing ring in Romania, the detection of rail defects over the whole testing ring is presented and examples of responses at a local defect (wheel-burn) is discussed with measurements at 80km/h (conventional speed measurement) and 200km/h (high speed measurement). In the third case, ABA measurements were obtained during operation in a train with passengers in the railway line near Brasov, Bartolomeu-Zărneşti. Examples of the defects and validations are discussed.Railway Engineerin

Combining diffuse reflectance spectroscopy and ultrasound imaging for resection margin assessment during colorectal cancer surgery

Establishing adequate resection margins during colorectal cancer surgery is challenging. Currently, in up to 30% of the cases the tumor is not completely removed, which emphasizes the lack of a real-time tissue discrimination tool that can assess resection margins up to multiple millimeters in depth. Therefore, we propose to combine spectral data from diffuse reflectance spectroscopy (DRS) with spatial information from ultrasound (US) imaging to evaluate multi-layered tissue structures. First, measurements with animal tissue were performed to evaluate the feasibility of the concept. The phantoms consisted of muscle and fat layers, with a varying top layer thickness of 0-10 mm. DRS spectra of 250 locations were obtained and corresponding US images were acquired. DRS features were extracted using the wavelet transform. US features were extracted based on the graph theory and first-order gradient. Using a regression analysis and combined DRS and US features, the top layer thickness was estimated with an error of up to 0.48 mm. The tissue types of the first and second layers were classified with accuracies of 0.95 and 0.99 respectively, using a support vector machine model. Medical Instruments & Bio-Inspired Technolog

A condition-based maintenance methodology for rails in regional railway networks using evolutionary multiobjective optimization: Case study line Braşov to Zărneşti in Romania

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using Hilbert-Huang Transform, the locations where the major risks are detected and ssessed with a degradation model. Finally,evolutionary multiobjective optimization is employed to solve the maintenance decision problem, and to facilitate the visualization of the trade-offs between number of interventions and performance. Real-life measurements from the track from Braşov to Zărneşti in Romania are included to show the methodology.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Railway Engineerin

Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe

This ex-vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy tissue, with the aim to develop a technology that can assess resection margins for the presence of tumor cells during oral cavity cancer surgery. Diffuse reflectance spectra were acquired on fresh surgical specimens from 28 patients with oral cavity squamous cell carcinoma. The spectra (400 to 1600 nm) were detected after illuminating tissue with a source fiber at 0.3-, 0.7-, 1.0-, and 2.0-mm distances from a detection fiber, obtaining spectral information from different sampling depths. The spectra were correlated with histopathology. A total of 76 spectra were obtained from tumor tissue and 110 spectra from healthy muscle tissue. The first- and second-order derivatives of the spectra were calculated and a classification algorithm was developed using fivefold cross validation with a linear support vector machine. The best results were obtained by the reflectance measured with a 1-mm source-detector distance (sensitivity, specificity, and accuracy are 89%, 82%, and 86%, respectively). DRS can accurately discriminate tumor from healthy tissue in an ex-vivo setting using a 1-mm source-detector distance. Accurate validation methods are warranted for larger sampling depths to allow for guidance during oral cavity cancer excision.Medical Instruments & Bio-Inspired Technolog

Diffuse reflectance spectroscopy as a tool for real-time tissue assessment during colorectal cancer surgery

Colorectal surgery is the standard treatment for patients with colorectal cancer. To overcome two of the main challenges, the circumferential resection margin and postoperative complications, real-time tissue assessment could be of great benefit during surgery. In this ex vivo study, diffuse reflectance spectroscopy (DRS) was used to differentiate tumor tissue from healthy surrounding tissues in patients with colorectal neoplasia. DRS spectra were obtained from tumor tissue, healthy colon, or rectal wall and fat tissue, for every patient. Data were randomly divided into training (80%) and test (20%) sets. After spectral band selection, the spectra were classified using a quadratic classifier and a linear support vector machine. Of the 38 included patients, 36 had colorectal cancer and 2 had an adenoma. When the classifiers were applied to the test set, colorectal cancer could be discriminated from healthy tissue with an overall accuracy of 0.95 (±0.03). This study demonstrates the possibility to separate colorectal cancer from healthy surrounding tissue by applying DRS. High classification accuracies were obtained both in homogeneous and inhomogeneous tissues. This is a fundamental step toward the development of a tool for real-time in vivo tissue assessment during colorectal surgery.Medical Instruments & Bio-Inspired Technolog